Shopper Insights
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Audience segmentation is a crucial aspect of retail media networks, as it allows retailers to tailor their marketing and advertising efforts to specific groups of consumers. By dividing their audience into smaller segments based on various characteristics, retailers can create more targeted and effective campaigns that are more likely to
In today’s Retail Media Landscape, digital marketing might seem the way to go. However, offline campaigns are the most effective way to reach and engage with customers in the ready-to-buy mode. Whether it’s through methods like print advertising, digital signage in-store, or experiential marketing events, offline campaign analytics are crucial for better targeting and engagement offline and can help retailers build brand awareness and drive sales. The key to success with offline campaigns is understanding how to optimize them for maximum impact. This is where analytics comes in. By gathering and analyzing data on your offline campaigns, you can better understand how they’re performing and make informed decisions on how to improve them. This article will explore the role of in-store analytics in offline campaigns and how it can help retailers get better targeting and engagement with their audience offline. Gathering data for offline campaign analysis Traditionally, retailers could collect data on offline campaigns through surveys and focus groups. While these methods can still be useful, they have their limitations. They can be time-consuming and costly. Furthermore, the data they provide may not always be representative of the broader audience. Fortunately, technology has made it easier to track the performance of offline campaigns. For example, retailers can use QR codes or unique URLs to track the effectiveness of print advertisements. Additionally, track foot traffic at experiential marketing events using beacons or other location-tracking technology. The benefits of using in-store tracking technology to measure offline campaigns include: Analyzing offline campaign data for better targeting and engagement Once you’ve gathered data on your offline campaigns, the next step is to analyze it to understand how your campaigns are performing and identify areas for improvement. There are several key metrics you should track when analyzing your offline campaign data, including: To make sense of this data, it’s helpful to use data visualization tools like charts and graphs. These can help you see patterns and trends in the data and make it easier to understand the results of your campaigns. In addition to tracking these key metrics, it’s also important to consider the overall context in which your campaigns are being run. Also, identify how offline attribution can help get better targeting and engagement. This includes factors such as the competitive landscape, economic conditions, and any relevant industry trends. In summary, by taking a holistic approach to analyzing your campaign data, you can get a more complete picture of how your campaigns are performing and identify opportunities for improvement. The role of Retail Analytics platforms By using a Retail Analytics platform, retailers can measure the performance of their offline campaigns. With Shoppermotion, retailers can track in-store traffic, measure the effectiveness of marketing campaigns, and optimize their store layouts and merchandising to improve sales. Here are a few ways Shoppermotion can help retailers measure the performance of their offline campaigns: Overall, Shoppermotion’s technology provides retailers with valuable insights into the performance of their offline campaigns. It helps them optimize their in-store strategies for maximum impact. Strategies for better targeting and engagement Once you’ve analyzed your offline campaign data, you can use the insights you’ve gained to improve targeting and engagement strategies. One way to do this is by segmenting your in-store audience based on the data you’ve collected. For example, if you’ve run an experiential marketing event and tracked the foot traffic using beacons, you can analyze the data to see which demographics were most likely to attend. You can then use this information to tailor future campaigns to these demographics. Another way to improve targeting and engagement is by personalizing campaigns based on the data you’ve collected. For example, if you’ve run a print advertising campaign and used QR codes to track the effectiveness, you can analyze the data to see which ads had the highest conversion rates. You can then use this information to create more personalized ads for individuals who are most likely to convert. In addition to targeting and personalization, there are a few other strategies you can use to improve the engagement of your offline campaigns: Offer incentives to obtain better targeting and engagement People are more likely to engage with your campaign if there’s something in it for them. Consider offering incentives such as discounts, freebies, or sweepstakes to encourage people to take action. Use storytelling for better targeting and engagement People are more likely to remember and be engaged by a campaign that tells a compelling story. Consider incorporating storytelling elements into your campaigns to make them more memorable and impactful. Make it interactive and get better targeting and engagement Interactive campaigns are more likely to engage people and get them to take action. Consider incorporating digital signage or other interactive elements such as quizzes, polls, or games into your campaigns to increase engagement. Conclusion In conclusion, in-store analytics can play a critical role in improving the performance of offline campaigns. By gathering and analyzing data on your campaigns, you can better understand how they’re performing and make informed decisions on how to implement better targeting and engagement strategies. However, it’s important to remember that offline campaign optimization is an ongoing process. To get the most out of your campaigns, it’s crucial to continuously gather and analyze data to identify areas for improvement and make adjustments as needed. By following these best practices, you can ensure that your offline campaigns are as effective as possible in reaching and engaging with your audience.
Understanding changing consumer behavior is essential for retailers looking to stay competitive in today’s marketplace. By tracking and analyzing consumer behavior, retailers can make informed decisions about their products, services, and marketing strategies. In-store analytics is a powerful tool for gathering data on consumer behavior. It allows retailers to get a deeper understanding of how customers interact with their products in a physical retail setting. This article aims to provide a comprehensive overview of how retailers can use in-store analytics to understand changing consumer behavior. It will explore the types of data and the tools available for gathering and analyzing this data. Ultimately, will explore the key metrics that retailers should consider when tracking consumer behavior. Finally, it will explore the importance of ongoing analysis and the role of technology in understanding changing consumer behavior. Gathering data on consumer behavior There are several types of data to gain insights into consumer behavior in a physical retail setting. This can include data on foot traffic, sales data, customer demographics, and customer interactions with products and staff. Foot traffic data for understanding changing consumer behavior With in-store technology installed throughout the store, retailers can collect foot traffic data. By tracking the movement of customers within the store, retailers can get insights into which areas of the store are most popular. Also, which are the products that are drawing the most attention. Sales Data for understanding changing consumer behavior Using point-of-sale systems to collect Sales Data. POS systems can track information about each purchase made in the store. Amongst others, including data about the items purchased, the total amount spent, and the payment method used. Customer Demographics Using customer surveys and focus groups to gather Customer demographics. This allows retailers to collect more qualitative data on their customers’ preferences. Additionally, consumer behaviors, and motivations. Customer interactions for understanding changing consumer behavior Finally, retailers can track customer interactions with products through customer feedback and reviews. Also, by observations made by the store’s staff. Regarding reviews, some studies show that reviews impact 80% of purchases, hence consumers see them as relevant when looking to purchase a product. In summary, tools for collecting in-store data can include all kinds of in-store tracking technology to obtain: Overall, it’s important to choose the right tools and methods for collecting data that align with the specific goals and needs of the business. Understanding changing consumer behavior data It’s important to analyze data in order to gain insights into consumer behavior. There are several key metrics to consider when analyzing consumer behavior data, including conversion rate, average order value, and customer lifetime value. Best practices for data analysis include setting clear goals and objectives, creating a plan for data collection and analysis, and regularly reviewing and updating the analysis process. It’s also important to consider the context in which retailers collect data, as it can impact the results and conclusions that are drawn. Using consumer behavior insights to drive business decisions Insights gained from analyzing consumer behavior data help inform a wide variety of business decisions. For example, product development, pricing strategy, and marketing efforts. If data shows that a particular product is popular among a certain demographic, a retailer may decide to focus marketing efforts on targeting that demographic. It’s important to regularly review and update consumer behavior insights, as consumer preferences and behaviors can change over time. By continuously tracking and analyzing consumer behavior data, retailers can stay ahead of trends and make informed decisions that drive growth and success. One way to stay on top of changing consumer behavior is to regularly conduct market research. This can include surveys and focus groups with customers, as well as competitive analysis to see what other businesses in the industry are doing. By staying up-to-date on market trends and consumer preferences, retailers can adapt and pivot their strategies as needed. Another key factor to consider when analyzing consumer behavior is the impact of technology. With the proliferation of online shopping and mobile devices, consumer behavior is constantly evolving. Retailers need to be aware of how these technological changes are impacting consumer behavior. Be prepared to adapt their strategies accordingly. Understanding changing consumer behavior with in-store analytics In-store analytics can be an invaluable tool for understanding changing consumer behavior. By collecting data on customer interactions with products, foot traffic patterns, and sales data, retailers get a comprehensive view of how consumers are interacting with their products in a physical retail setting. Shoppermotion has developed innovative technology for collecting in-store data. Shoppermotion is able to track shoppers’ paths throughout the store. Provides insights into which products/areas of the store are most popular. With this information, retailers can optimize the store layout. Additionally, improve their product placement and marketing efforts to meet the needs and preferences of customers. In addition to tracking foot traffic patterns, Shoppermotion’s technology can gather data on customer interactions with products. This includes information on: This data can provide valuable insights into consumer preferences and behaviors. Shoppermotion helps retailers to make informed decisions about their products and marketing strategies. Overall, in-store analytics is a powerful tool for understanding changing consumer behavior. Ultimately, adapting to the needs and preferences of customers. By leveraging the latest technology and data analysis techniques, retailers can get insights into consumer behavior. Moreover, make informed decisions that drive growth and success. Conclusion In conclusion, understanding consumer behavior is crucial for retailers looking to stay competitive in today’s marketplace. In-store analytics is a powerful tool for gathering data on consumer behavior. It allows retailers to make informed decisions about their products, services, and marketing strategies. By regularly reviewing and updating consumer behavior insights, businesses can stay ahead of trends and ultimately drive growth and success. It’s important for
Touchpoints are the various ways in which a customer interacts with a brand, both online and offline. In the world of modern retail, these touchpoints play a crucial role in optimizing the in-store experience and shaping the customer’s journey and perception of a Retail Media Network. This article will explore the various types of touchpoints in a retail setting, as well as strategies for optimizing these touchpoints to create a seamless and enjoyable experience for customers. Types of touchpoints in a retail setting There are two main categories of touchpoints in a Retail Media Network: physical touchpoints and digital touchpoints. Physical touchpoints for optimizing the in-store experience Physical touchpoints refer to the tangible elements of the customer experience, such as the store layout, product displays, and in-store signage. These touchpoints can have a significant impact on the customer’s perception of the store. Also on the products being sold. For example, a cluttered and disorganized store layout may create a negative impression and deter customers from purchasing. Contrarily, a well-designed and visually appealing store layout can help to create a positive and inviting atmosphere. Digital touchpoints for optimizing the in-store experience Digital touchpoints, on the other hand, refer to the online channels through which a customer interacts with a brand. This can include the company’s website, social media accounts, mobile app, etc. These touchpoints allow customers to access information about the company and its products. Additionally, they allow customers to make purchases and engage with the brand in a virtual setting. Effective touchpoint strategies in Retail Media Networks One of the key ways in which retailers can optimize their touchpoints is by leveraging technology for optimizing the in-store experience. This can include the use of kiosks and interactive displays. This allows customers to access product information. Furthermore, make purchases without the need for assistance from store staff. Mobile apps can also provide customers with personalized recommendations and offers, as well as facilitate the in-store payment process. Another effective strategy for optimizing touchpoints in retail is the integration of online and offline touchpoints. This can involve using online channels to drive foot traffic to physical stores, or vice versa. For example, a retailer might offer online customers the option to reserve products for in-store pickup or use in-store signage and displays to promote online sales and special offers. This helps to create a seamless experience for customers and encourages them to engage with the brand across multiple channels. Challenges and considerations in managing touchpoints One of the main challenges in managing touchpoints in retail is ensuring a consistent brand experience across all channels. This means that both the physical store and online channels reflect the same values, messaging, and aesthetics. It’s also important to consider the various devices and platforms that customers may use to interact with a brand, optimizing the experience for each one. Another key consideration in managing touchpoints is the handling of customer data and privacy concerns. Retailers should be transparent about how they collect and use customer data, and ensure that they have the appropriate consent and security measures in place to protect this data. Optimizing the in-store experience with touchpoints There are several ways in which retailers can optimize the in-store experience with touchpoints: Use technology to enhance the in-store experience As mentioned in the previous section on effective touchpoint strategies, technology can be a powerful tool for optimizing the in-store experience. This can include the use of kiosks and interactive displays to provide customers with product information and facilitate the payment process, as well as the use of mobile apps to provide personalized recommendations and offers. Consider the customer journey Retailers should take a holistic view of the customer journey. Consider how touchpoints can be used to create a seamless experience from start to finish. This might involve using in-store signage and product displays to guide customers through the store. Alternatively, provide additional services such as free Wi-Fi or seating areas to make the shopping experience more comfortable. Foster a sense of community In-store events and experiential marketing campaigns can be effective to create a sense of community and engage customers. This might involve hosting workshops, classes, or product demonstrations. Furthermore, offering special discounts or promotions to customers who participate in these events. Train staff to be knowledgeable and friendly The interactions that customers have with store staff can have a big impact on their overall experience. Retailers must optimize these interactions. First of all, ensure the staff is knowledgeable about the products and services they offer. Secondly, they are friendly and approachable. Last, staff should also be trained to handle customer inquiries and complaints effectively. Personalize the in-store experience By collecting customer data both online and offline, retailers can create a more personalized in-store experience for their customers. This might involve implementing and measuring targeted offline campaigns. Moreover, offering personalized product recommendations based on a customer’s previous purchases or browsing history. Overall, optimizing the in-store experience with touchpoints involves a combination of: By considering all of these factors, retailers can create a truly memorable and enjoyable in-store experience for their customers. Shoppermotion’s in-store tracking technology for optimizing the in-store experience Shoppermotion is a company that offers in-store tracking technology to help retailers optimize the in-store experience. This technology tracks customer behavior in real-time, providing retailers with valuable insights. For example, how customers interact with products, displays, and other elements of the store. Shoppermotion’s technology can provide retailers with data on which products and displays are attracting the most attention from customers. Retailers can identify what is and isn’t working in terms of product placement and display design, hence making adjustments accordingly. Additionally, it provides retailers with insights into customer traffic patterns and flows. Retailers can identify areas of the store that may be causing congestion. Ultimately, make changes to the store layout or signage to improve the overall customer experience. Overall, Shoppermotion’s in-store tracking technology can be a powerful tool for optimizing the in-store experience. By providing valuable insights into customer behavior and enabling retailers to deliver
Coupons are a popular and effective way for retailers to attract and retain customers, however, the success of a coupon campaign depends on its redemption rate. In-store tracking technology can boost coupon redemption rates by providing retailers with data on customer behavior and preferences. This article will explore the various ways in which in-store tracking can be used to increase coupon redemption rates, including personalized offers, targeted marketing, and real-time analysis of customer data. By leveraging in-store tracking, retailers can create more effective and efficient coupon campaigns that drive sales and customer loyalty. The role of coupons in retail marketing Coupons are a common marketing tool used by retailers to attract and retain customers. They help promote new products, encourage repeat purchases, and drive traffic to stores or websites. There’s a wide variety of channels to distribute coupons. Basically in-store, online, through email or social media, or through partnerships with other businesses. Read more about Coupon Marketing here. There are several benefits to using coupons in retail marketing. Coupons are a powerful motivator for customers. Particularly when they offer a discount on a high-demand item or significant savings on a purchase. Additionally, coupons can help retailers gather valuable data on customer preferences and behavior. This can tailor future marketing efforts and improve the effectiveness of future coupon campaigns. Finally, coupons can help retailers build customer loyalty and foster long-term relationships with their customers. Despite their potential benefits, coupon campaigns can be challenging to execute effectively. Equally important, retailers must monitor their coupon effectiveness. Factors such as the type of coupon offered, the target audience, and the distribution channels used. In-store tracking technology is a valuable tool for retailers looking to optimize their coupon campaigns and boost redemption rates. The benefits of in-store tracking to Boost Coupon Redemption Rates This section will delve into the benefits of using in-store tracking technology to boost coupon redemption rates: Overall, in-store tracking technology is a valuable tool for retailers looking to optimize their coupon campaigns and drive customer loyalty. By leveraging data on customer behavior, retailers can create more effective coupon campaigns that drive sales and customer loyalty. Best practices for in-store tracking to boost coupon redemption Rates Here are some best practices for implementing in-store tracking for coupons: Use a combination of tracking technologies In-store tracking offers multiple technologies for its implementation such as Wi-Fi, Bluetooth, beacons, and cameras. Retailers can use a combination of these technologies to gather data on customer behavior and preferences. Consider privacy concerns It is important for retailers to be transparent about their data collection practices. On some occasions obtain customer consent before collecting any personal data. Retailers should also take steps to protect customer data and ensure that it is only used for legitimate business purposes. Integrate tracking data with other customer data In-store tracking data can be combined with other sources of customer data. For example, email marketing data or online purchase history, to create a more comprehensive view of customer behavior and preferences. This helps retailers create more targeted and effective coupon campaigns. Test and optimize Use in-store tracking to gather data on the effectiveness of your coupon campaigns. Analyze this data to identify what is working well and what must improve, and make adjustments as needed. Segment your audience Use in-store tracking to segment your audience based on factors such as demographics, purchase history, and location. This will allow you to create more targeted and effective coupon campaigns. By implementing in-store tracking strategically, retailers can use this technology to boost coupon redemption rates and drive customer loyalty. Equally important, retailers need to use coupons in a thoughtful and responsible manner: Overall retailers can effectively use in-store tracking to boost coupon redemption rates and drive customer loyalty. Examples of in-store tracking to Boost Coupon Redemption Rates Here are four examples of successful in-store tracking for coupons: Altogether, these case studies demonstrate the potential of in-store tracking to boost coupon redemption rates and drive customer loyalty. By using data on customer preferences, retailers can create targeted coupon campaigns that drive sales and customer loyalty. Conclusion In conclusion, in-store tracking technology can be a valuable tool for retailers trying to optimize their coupon campaigns and boost redemption rates. Retailers can create personalized and targeted coupon offers that drive sales and customer loyalty. In-store tracking can also help to analyze customer data in real-time and make adjustments to coupon campaigns as needed. Shoppermotion is a provider of in-store tracking technology that helps retailers gather data on customer behavior and preferences. By using Shoppermotion’s technology, retailers can create more targeted and effective coupon campaigns that drive sales and customer loyalty. Overall, in-store tracking technology is a powerful tool for retailers looking to boost coupon redemption rates and drive customer loyalty.
The retail industry is constantly evolving, and with the rise of e-commerce, it’s more important than ever for brick-and-mortar stores to find ways to compete and remain relevant. One way for businesses to step ahead is by leveraging in-store analytics in Retail to gather insights about customer behavior and preferences. In-store analytics refers to the collection and analysis of data about customer behavior and interactions within physical retail spaces. By using sensors, cameras, and other technologies, retailers can gather data on things like foot traffic, dwell time, product interactions, and more. This data can provide valuable insights into customer behavior and help retailers optimize the in-store shopping experience. This article will explore the role of in-store analytics in the future of retail. Also, how it can be used to improve the customer experience, enhance operations and productivity, and drive sales and revenue. Lastly, delve into the ways in which in-store analytics is likely to evolve in the future and the impact on the retail industry. The Benefits of In-Store Analytics There are numerous benefits to using in-store analytics in the retail industry. Retailers can gain a deeper understanding of their customers and use this information to optimize the in-store shopping experience. Some of the key benefits of in-store analytics include: Altogether, in-store analytics can provide valuable insights into customer behavior. Additionally, it helps retailers optimize their operations, improve the customer experience, and drive sales and revenue. Real-Time Insights and Personalization In-store analytics has the ability to provide real-time insights and personalization. By using in-store tracking technologies, retailers can gather data on customer behavior in real time. Ultimately, use this information to tailor the shopping experience to individual customers. For example, retailers can identify customers who are spending more time in certain areas of the store. As a result, make personalized product recommendations based on their interests and past purchases. Furthermore, using in-store analytics helps trigger personalized offers and promotions based on customer behavior, increasing the likelihood of a sale. In addition to providing real-time insights and personalization, retailers can identify trends and patterns in customer behavior. By analyzing data over time, retailers can also detect emerging trends. Consequently, use this information to inform their product development and marketing strategies. Overall, in-store analytics allows retailers to provide a more personalized and relevant shopping experience for their customers. This can lead to increased customer satisfaction and loyalty. It can also help retailers stay ahead of emerging trends and adapt to changing customer preferences. Enhanced Customer Experience and Engagement Another key benefit of in-store analytics is the ability to enhance customer experience and engagement. By collecting and analyzing data on customer behavior, retailers can identify areas for improvement. Once these areas have been detected, make changes to enhance the in-store shopping experience. Retail Media Networks using in-store analytics can identify bottlenecks in the shopping process and make changes to streamline the experience. Retailers can also inform store layout and product placement, making it easier for customers to find what they’re looking for. In addition to streamlining the shopping process, in-store analytics can also help enhance customer engagement. Providing personalized and relevant content the interactions will boost. Furthermore, tailoring in-store displays and promotions to individual customers increases the likelihood of a sale. By using in-store analytics, Retailers can provide a seamless and engaging shopping experience for their customers. This leads once again to increased customer satisfaction and loyalty. The result of providing personalized and relevant content and offers is to drive sales and revenue. Improved Operations and Productivity In addition to enhancing customer experience and engagement, in-store analytics can also help retailers improve their operations and productivity. By collecting and analyzing data on customer behavior and interactions within physical retail spaces, retailers can identify inefficiencies in their operations and make changes to improve efficiency. By using in-store analytics retailers can optimize their store layout and product placement based on foot traffic and dwell time data. This can help reduce the time it takes for customers to find what they’re looking for and improve overall productivity. In-store analytics can also help retailers optimize their staffing levels and scheduling based on data on foot traffic and sales volume. This can help retailers ensure that they have the right number of staff on hand to meet customer demand and improve overall efficiency. In conclusion, retailers can identify and address inefficiencies in their operations, leading to increased productivity and cost savings. Additionally, they can optimize their staffing levels and scheduling to better meet customer demand and drive sales. The Future of In-Store Analytics In-store analytics is likely to continue evolving in the future, with new technologies and approaches emerging to help retailers better understand and serve their customers. Some of the key trends and developments in the field of in-store analytics include: The future of in-store analytics looks bright, with new technologies and approaches emerging to help retailers better understand and serve their customers. By leveraging in-store analytics, retailers can stay ahead of the curve and remain competitive in today’s rapidly-changing retail landscape. Conclusion In conclusion, in-store analytics plays a crucial role in the future of retail, helping retailers understand and serve their customers and optimize their operations. By collecting and analyzing data on customer behavior and interactions within physical retail spaces, retailers can gain valuable insights into customer preferences and make informed decisions about how to optimize the shopping experience. The use of in-store analytics is likely to continue evolving in the future, with new technologies and approaches emerging to help retailers better understand and serve their customers. This includes the increased use of artificial intelligence and machine learning, greater integration with online data, and the rise of omnichannel retail. In-store analytics offers a range of benefits for retailers, including improved customer experience and engagement, enhanced operations and productivity, and increased sales and revenue. By leveraging in-store analytics, retailers can stay ahead of the curve and remain competitive in today’s rapidly-changing retail landscape. Shoppermotion’s Retail Media solutions provide in-store analytics to retailers. Using Shoppermotion’s in-store analytics technology,
In today’s digital age, it’s no secret that online shopping has become a major part of the retail industry. With the convenience and accessibility of shopping from home, so many consumers turn to e-commerce platforms for their purchasing needs. However, traditional brick-and-mortar stores remain a very important part of the shopping experience for most consumers. As such, it’s important for retailers to find ways to bridge the gap between online and offline shopping. One way to do this is by connecting online and offline shopping data, which can offer a range of benefits for both retailers and consumers. This article will delve into the various ways that connecting online and offline shopping data can be beneficial and how it can help retailers improve the overall shopping experience for their customers. The Importance of Connecting Online and Offline Shopping Data Retailers do need to have a strong online presence in addition to their brick-and-mortar stores. With the rise of e-commerce, consumers expect to be able to shop online and have access to a wide range of products and information. Nevertheless, consumers value their in-store shopping experiences and the opportunity to see and touch products before making a purchase. Connecting online and offline shopping data allows retailers to better understand and cater to the needs and preferences of their customers, regardless of whether they’re shopping online or in-store. Retailers can get a more comprehensive view of their customers’ shopping habits and preferences. Understand what products and services are most popular, which channels and marketing efforts are most effective. Identify potential gaps in their product offerings and make informed decisions about which products to stock and promote. In addition to helping retailers better understand and serve their customers, connecting online and offline data leads to increased efficiency and cost savings. For example, retailers can use data on online and in-store sales to optimize inventory management and reduce waste. They can also use data to streamline their marketing and advertising efforts. Target their efforts more effectively and lead to increased ROI. Overall, this merger is crucial for retailers who want to stay competitive in today’s retail landscape. It allows them to better understand and serve their customers, optimizes their operations, and increases their overall sales and revenue. Let’s explore the key benefits of merging online and offline shopping data more closely: Improve Customer Experience The most important benefit of connecting online and offline shopping data is the ability to improve the overall customer experience. Combining data from both channels, retailers gain a better understanding of their customers’ needs and preferences. They use this information to tailor the shopping experience to their individual needs. For example, retailers can make personalized product recommendations to customers based on their past purchases and browsing history. This can help retailers build stronger relationships with their customers and increase the likelihood of repeat purchases. Merging online and offline data can also help retailers improve the in-store shopping experience. They can modify their store layout and product placement, making it easier for customers to find what they’re looking for. Retailers can also optimize inventory management, ensuring that popular products are always in stock and readily available for customers. In summary, it allows retailers to offer a more personalized and seamless shopping experience. Regardless of whether they’re shopping online or in-store. This leads to increased customer satisfaction and loyalty, ultimately driving sales and revenue. Enhance Marketing Efforts connecting online and offline data Another advantage of merging online and offline shopping data is the ability to enhance marketing and advertising efforts. Retailers can gain a more complete picture of their customers’ shopping habits, which can inform targeted marketing and advertising campaigns. Retailers can use data on online search and purchase patterns. Tailor their email marketing campaigns and targeted ads to individual customers based on their interests and past purchases. Retailers can also use data on in-store purchases to inform their loyalty programs and personalized offers to drive repeat business. Connecting online and offline data also helps retailers optimize their advertising spend. Analyzing data on the effectiveness of different marketing channels and campaigns, retailers can allocate their advertising budget for maximum ROI. Retailers can target their marketing and advertising efforts. Build stronger relationships with their customers by providing personalized and relevant content and offers. Increased Sales and Revenue Another key benefit is the potential for increased sales and revenue. By combining data from both channels, retailers can gain a more comprehensive view of their customers’ shopping habits and preferences. This can improve product development and pricing strategies. Retailers can use data on online and in-store sales to identify and focus on their most popular products and categories. Retailers can also identify opportunities for upselling and cross-selling. Lastly, optimize pricing strategies based on demand and competition. Retailers can better understand the customer journey and identify potential barriers to purchase. By analyzing data on customer behavior and feedback, they identify pain points in the shopping experience. Hence retailers can make informed decisions about their product offerings, pricing, and marketing efforts. Ultimately this will lead to increased sales and revenue. Lastly, connecting online and offline data can enhance a better understanding of the Retail Media Network’s customers increasing customer loyalty and repeat purchases. Better Inventory Management connecting online and offline Data Lastly, an important advantage of connecting online and offline shopping data is improved inventory management. By combining data from both channels, retailers can get a comprehensive view of product demand and adjust their inventory levels accordingly. Retailers can use data on online and in-store sales to identify which products are most popular and ensure that they’re well-stocked at all times. Retailers can also use data to identify slow-moving products and adjust their inventory levels accordingly to avoid excess stock and waste. Connecting online and offline data helps retailers optimize their
Retail marketing strategies are constantly evolving as retailers strive to meet the needs and preferences of their customers. Leveraging shopper data to improve retail Marketing strategies is one of the most effective ways for retailers to step ahead. By collecting and analyzing data on customer behavior, retailers can gain valuable insights into what drives their customers’ purchasing decisions and how to effectively target them with marketing campaigns. This article explores the various ways in which retailers can use shopper data and the different types to improve their marketing efforts and drive sales. Understanding Shoppers data Shopper data refers to the information that retailers collect about their customers’ behaviors and preferences. This data can be collected through a variety of methods, including online browsing and search activity, in-store behavior, purchase history, and demographic information. Retailers can use this data to gain insights into what drives their customers’ purchasing decisions. Also, learn how to effectively target them with marketing campaigns. Basically, by collecting and analyzing shopper data, retailers can improve their marketing strategies. Furthermore, drive sales by targeting their marketing efforts to specific segments of their target market and creating personalized recommendations and offers for individual shoppers. Types of shopper data There are several types of shopper data that retailers can collect and analyze to improve their marketing strategies. Some examples include: Demographic data This type of data includes information about a shopper’s age, gender, income level, and location. Retailers can use this data to segment their target market and tailor their marketing efforts to specific demographics. Purchase history data / Transaction data This type of data includes information about a shopper’s past purchases, including the products they have bought, the frequency of their purchases, and the amount they have spent. Retailers can create personalized recommendations and offers for individual shoppers based on their purchasing habits. Online browsing and search data This type of data includes information about a shopper’s online activity, such as the websites they visit, the products they search for, and the items they add to their online shopping cart. Retailers can use this data to understand a shopper’s interests and preferences and to target them with relevant marketing messages. In-store behavior data This type of data includes information about a shopper’s behavior while they are in a physical store, such as the products they engage with, the time they spend in the store, and the interactions they have with store staff. Retailers can optimize the in-store experience for shoppers and identify opportunities for upselling and cross-selling. Social media data This type of data includes information about a shopper’s activity on social media platforms, such as the brands they follow, the posts they engage with, and the reviews they leave. Ultimately, retailers can understand shoppers preferences and interests and target them with relevant marketing messages on social media. In summary, by collecting and analyzing these various types of shopper data, retailers can gain a comprehensive understanding of their customers. Also, use this knowledge to improve their marketing strategies and drive sales. Benefits of using shopper data in retail marketing There are several benefits to using shopper data and Improve Retail Marketing Strategies: How to leverage shopper data to Improve Retail Marketing Strategies Retailers can leverage shopper data effectively by: Using a combination of data sources to Improve Retail Marketing Strategies Firstly, Shopper data can come from a variety of sources. Online and offline purchases, website activity, customer service interactions, and surveys. Using a combination of data sources can provide a more comprehensive view of shopper behavior and preferences. Use data analysis tools and techniques There are various tools and techniques that can be used to analyze shopper data and improve Retail Marketing Strategies. For instance, statistical analysis, machine learning algorithms, and data visualization tools. Retailers can gain insights into shopper behavior and preferences. Ultimately, identify trends and patterns and make informed decisions based on data-driven insights. Segmentation to improve Retail Marketing Strategies By dividing shoppers into different groups based on shared characteristics, retailers can create targeted marketing campaigns and personalized recommendations. Personalization Retailers can create personalized experiences for individual shoppers, such as customized product recommendations or personalized emails. Retargeting to improve Retail Marketing Strategies By tracking shoppers’ online activity, businesses can serve targeted ads. For example, to shoppers who have visited their site or shown interest in a specific product. Customer feedback Shopper data can be used to gather customer feedback and insights, which can help businesses improve their products and services. Trend analysis to improve Retail Marketing Strategies Lastly, over time, retailers can identify trends and patterns that can inform their strategy and decision-making. Overall, it’s important to approach shopper data with a strategic and ethical mindset. Use it in a way that respects shoppers’ privacy and builds trust with customers. Altogether it’s important to note that when collecting shopper data, retailers should be transparent about their practice. Correspondingly, ensure they are complying with the GDPR in the European Union. Here are some best practices for leveraging shopper data effectively: By following these best practices, retailers can effectively leverage shopper data while respecting shoppers’ privacy and building trust. The challenges of leveraging Shoppers Data to Improve Retail Marketing Strategies Altogether, retailers need to be ready to face the following challenges when leveraging shopper data to improve Retail Marketing Strategies: How to face the challenges with Shoppermotion To overcome these challenges, retailers must adopt a strategic and ethical approach. Consequently, invest in tools and resources to collect, manage, and analyze data effectively. Basically, Shoppermotion provides data-driven solutions to help retailers optimize their operations and improve the customer experience. Some of the ways that Shoppermotion can help retailers leverage shopper data effectively include: Overall, Shoppermotion’s solutions can help retailers effectively leverage shopper data to improve the customer experience. Consequently, drive business success. Conclusion In conclusion, leveraging shopper data can be a powerful tool for improving retail marketing strategies.
In today’s highly competitive retail landscape, understanding and catering to the needs and preferences of shoppers has become essential for businesses to thrive. Maximizing the benefits of in-store shopper tracking is hence crucial to gain valuable insights into shopper behavior. By collecting data on how shoppers move through the store, what products they interact with, and how long they spend in different areas, retailers can gain a deeper understanding of what drives sales and make informed decisions about store layout, product placement, and marketing efforts. This article will explore the various benefits of in-store shopper tracking and how retailers can maximize their potential to drive sales and improve the shopping experience. The rise of in-store shopper tracking The use of in-store tracking technologies has been on the rise in recent years, as retailers look for ways to collect shopper behavior. These technologies can take various forms. For instance, sensors and cameras that track the movements of shoppers throughout the store. Or mobile apps and loyalty programs that collect data on the products that shoppers purchase. One of the primary drivers behind the rise of in-store tracking is the increasing availability and affordability of the necessary technologies. The below factors have made it easier and cost-effective for retailers to collect data on shopper behavior. In addition, the growing focus on customer experience and personalization. This has led retailers to seek out ways to gather more detailed information about their shoppers. By tracking in-store behavior, retailers can gain insights into what drives sales. Ultimately, tailor the shopping experience to meet the needs and preferences of individual shoppers. Overall, the rise of in-store shopper tracking reflects a shift towards a more data-driven approach to retail. Businesses seek to use data to inform their decision-making and better understand and serve their customers. Understanding Shopper tracking in the store In-store shopper tracking refers to the collection of data on shopper behavior within a retail store or other physical location. This data can be collected through a variety of technologies. There are several key benefits to understanding in-store shopper tracking. First, it allows retailers to optimize the layout and design of their stores to better meet the needs and preferences of shoppers. For example, data on how shoppers move through the store can be used to identify bottlenecks or areas that are underutilized, and changes can be made to address these issues. In addition, in-store shopper tracking can help retailers to better understand the factors that influence purchasing decisions and to tailor their marketing efforts accordingly. For example, data on what products shoppers interact with or spend the most time looking at can be used to inform product placement and promotional strategies. Finally, in-store shopper tracking can also help retailers to improve the overall shopping experience by identifying areas for improvement and addressing any issues that may be causing frustration or inconvenience for shoppers. By gathering and analyzing data on shopper behavior, retailers can identify opportunities to enhance the in-store experience and drive sales. Types of in-store tracking There are several types of in-store shopper tracking technologies that retailers can use to gather data on customer behavior and preferences, as well as to optimize store layouts and improve the shopping experience for customers. Some common types of in-store shopper tracking technologies include: Benefits of in-store tracking In-store shopper tracking can provide several benefits to retailers, including: Best practices for implementing in-store shopper tracking Here are the best practices retailers should follow when implementing in-store shopper tracking: Additionally, it is important for retailers to be transparent about their use of in-store tracking technologies. In other words, ensure that they respect the privacy of their customers. Conclusion In conclusion, in-store shopper tracking provides numerous benefits to retailers. These include improved customer experience, increased sales, enhanced inventory management, security, and cost savings. By tracking the movements of customers within a store, retailers can determine what products and services are most popular. Also, identify bottlenecks or areas of the store that may be causing customer frustration. This information helps optimize store layouts and improve the overall shopping experience for customers. Retailers should consider using a combination of different tracking technologies and integrating in-store tracking data with other customer data to create a more complete picture of customer behavior and preferences. By following these best practices, retailers can effectively use in-store shopper tracking to improve the shopping experience for their customers and drive business success. Shoppermotion’s in-store shopper tracking services can be tailored to meet the specific needs and goals of each retailer. For example, a retailer might use Shoppermotion’s services to track foot traffic patterns within a store, identify popular products and services, or measure the effectiveness of in-store TVs, or in-store promotions. By using Shoppermotion’s in-store shopper tracking services, retailers can gain a deeper understanding of their customers and use this information to drive business success.
Personalization is a key trend in the retail industry, with consumers increasingly expecting a shopping experience that is tailored to their individual needs. The role of in-store analytics in the rise of personalized retail experiences is becoming relevant because retailers can create personalized shopping experiences by using in-store data on customer behavior and interactions within physical retail spaces. In-store analytics allows retailers to collect and analyze data on things like foot traffic, dwell time, product interactions, and more. This data can provide valuable insights into customer behavior and preferences, which can be used to tailor the shopping experience to individual customers. This article explores the role of in-store analytics in the rise of personalized retail experiences and how it can be used to improve the customer experience, drive sales and revenue, and enhance operations and productivity. We’ll also look at the ways in which personalized retail experiences are likely to evolve in the future and the impact they will have on the retail industry. The Benefits of In Store Analytics In-store analytics offers numerous benefits for retailers looking to create personalized shopping experiences for their customers. By collecting and analyzing data on customer behavior and interactions within physical retail spaces, retailers can gain a deeper understanding of their customers and use this information to tailor the shopping experience to individual customers. Some of the key benefits of in-store analytics include: Personalization. In-store analytics allows retailers to tailor the shopping experience to individual customers based on their interests and past purchases. Retailers can provide personalized recommendations and offers, increasing the likelihood of a sale. Improved customer experience. In-store analytics can help retailers understand what attracts customers to their stores and what keeps them coming back. Retailers can identify areas for improvement and make changes to enhance the customer experience. Analyzing data on foot traffic, dwell time, product interactions, etc. Enhanced operations and productivity. Retailers can identify bottlenecks in their operations and make changes to improve efficiency. For example, using data on foot traffic and dwell time to optimize store layout and product placement. Furthermore, reduce the time it takes for customers to find what they’re looking for. Increased sales and revenue. By providing personalized recommendations and offers, in-store analytics helps to drive sales and revenue. Retailers can also use data on customer behavior and preferences to inform their product development and pricing strategies. Overall, in-store data can provide valuable insights into customer behavior and preferences. Additionally, it helps retailers create personalized shopping experiences that drive sales and revenue. In Store analytics in Personalization and Customer Experience Personalization is a key trend in the retail industry. Consumers increasingly expect a shopping experience that is tailored to their individual preferences. In-store analytics can be a powerful tool for retailers looking to create personalized shopping experiences for their customers. By collecting/analyzing data on customer behavior/interactions within physical retail spaces, retailers can gain a deeper understanding of their customers. Also, they can use this information to tailor the shopping experience to individual customers. This can include: Personalization can also extend to the customer service experience. In-store analytics allows retailers to identify and address individual customer needs in real-time. For example, using in-store data to identify customers spending more time in certain areas of the store. Consequently, retailers can offer assistance or make product recommendations based on their interests and past purchases. In summary, in-store analytics is a powerful tool for retailers looking to create personalized shopping experiences. By leveraging in-store data, retailers can stay ahead of the curve and remain competitive in today’s rapidly-changing retail landscape. The Future of Personalized Retail Experiences The future of personalized retail experiences looks bright. New technologies and approaches emerge to help retailers better understand and serve their customers. Some of the key trends and developments in the field of personalized retail experiences include: Overall, the future of personalized retail experiences looks bright, with new technologies and approaches emerging to help retailers better understand and serve their customers. By leveraging in-store analytics and other technologies, retailers can stay ahead of the curve and remain competitive in today’s rapidly-changing retail landscape. Conclusion In conclusion, in-store analytics is a powerful tool for retailers looking to create personalized shopping experiences for their customers. By collecting and analyzing data on customer behavior and interactions within physical retail spaces, retailers can gain a deeper understanding of their customers and use this information to tailor the shopping experience to individual customers. Overall, the use of in-store data is likely to continue evolving in the future. New technologies and approaches emerge to help retailers better understand and serve their customers. This includes the increased use of artificial intelligence and machine learning, greater integration with online data, and the rise of omnichannel retail. Shoppermotion solutions provide in-store analytics to retailers. By using sensors, cameras, and other technologies, Shoppermotion gathers data on customer behavior and interactions within physical retail spaces in real time. This data provides retailers with valuable insights into customer behavior and preferences. Lastly, whether you’re a small retailer just starting out or a large Retail Media Network looking to stay ahead of the curve, Shoppermotion’s in-store analytics solutions can help you create personalized shopping experiences that drive customer satisfaction and loyalty.
Touchpoints are the various points of interaction that a customer has with a brand or company during their journey from awareness to purchase. These interactions can occur through physical, digital, or traditional media channels, and can include actions such as visiting a store, viewing an advertisement, or interacting with a brand on social media. In the modern landscape of marketing and advertising, it is crucial for businesses to understand the impact of touchpoints on campaign performance and to optimize these interactions in order to drive desired outcomes. This article will delve into the concept of touchpoints, their role in the customer journey, and the ways in which they can be measured and optimized for maximum impact on campaign performance. What are touchpoints and why are they important? Touchpoints are any points of contact between a customer and a brand or company. This can include both physical and digital interactions, such as visiting a store, viewing an advertisement, or engaging with a brand on social media. In marketing and advertising campaigns, touchpoints are an important way for businesses to reach and connect with their target audience. Some examples of touchpoints in marketing and advertising campaigns include: Touchpoints play a crucial role in the customer journey, as they help to guide the customer towards a purchase decision. For example, a customer sees an advertisement for a product on TV and visits the brand’s website to learn more. This initial interaction with the brand (the TV ad) is a touchpoint and helps move the customer further along in their journey toward a purchase. In-store touchpoints, such as interacting with a salesperson or trying out a product, can also be an important part of the customer journey and can help to influence the final purchase decision. Types of touchpoints There are 4 main types of touchpoints: Measuring the impact of touchpoints on campaign performance Measuring the impact of touchpoints on campaign performance is essential for understanding how interactions with customers are driving outcomes. Touchpoints KPIs There are a number of KPIs that businesses can use to track the effectiveness of their touchpoints, including: There are many tools and techniques that businesses can use to measure the performance of their touchpoints. These can include website analytics platforms, customer relationship management (CRM) systems, and marketing automation tools, among others. Some businesses may also use A/B testing to compare the performance of different touchpoints and to optimize their campaigns. Touchpoint KPIs with in-store tracking technology If retailers install in-store tracking technology, they can measure a number of KPIs related to physical touchpoints. Some potential KPIs for physical touchpoints could include: These are just a few examples of the KPIs retailers can use with in-store tracking technology. It is important for businesses to choose the right KPIs to track based on their specific goals and objectives. Other techniques for measuring touchpoint performance may include surveys, focus groups, and customer interviews. Overall, it is important for retailers to have a clear understanding of their key performance indicators. This way retailers can use the right tools and technologies to measure the effectiveness of their touchpoints. Best practices for optimizing the impact of touchpoints Optimizing touchpoints in a marketing campaign can help businesses to connect with their target audience. Here are some best practices for optimizing touchpoints in a campaign: Altogether, by following these best practices retailers can optimize their touchpoints to drive sales. Ultimately, retailers can reach and connect with their target audience. Optimizing the impact of physical touchpoints Shoppermotion offers in-store tracking solutions that can help retailers to identify touchpoints in their stores. After, they can analyze their performance and optimize them. For example, popular product displays or high-traffic areas. Using Shoppermotion’s in-store tracking solutions, retailers can gain insights into how customers are interacting with different touchpoints in their stores. Afterward, retailers can use this information to optimize the performance of the touchpoints. Conclusion In conclusion, touchpoints are an important part of marketing campaigns. They provide retailers with opportunities to reach and connect with their target audience. Furthermore, by understanding the impact of touchpoints on campaign performance, retailers can make better-informed decisions. For example, touchpoints to focus to optimize them in order to drive revenue and sales. There are many types of touchpoints, including physical, digital, traditional media, and emerging touchpoints. Touchpoints can be tracked using a variety of key performance indicators (KPIs). Ultimately touchpoints can be optimized through best practices. Identifying and prioritizing key touchpoints, integrating touchpoints across channels, and testing and optimizing touchpoints. Shoppermotion’s in-store tracking solutions are a valuable tool for retailers looking to optimize their touchpoints. In the near future, touchpoints will continue evolving in marketing. Retailers continue to find innovative ways to reach and connect with their customers. As such, it will be important for businesses to stay up-to-date with the latest developments in touchpoint technology. Furthermore, be willing to experiment and try out new approaches in order to drive better results from their campaigns.
In-store analytics is the process of collecting, analyzing, and using data about customer behavior and interactions in physical retail spaces in order to improve business performance and drive results. The role of in-store analytics in the age of e-commerce is more important than ever, as it allows retailers to understand and optimize the in-store customer experience in a way that can help to drive better results. The importance of in-store analytics in the age of e-commerce cannot be overstated. With the rise of online shopping and the increasing competition from e-commerce companies, it is more important than ever for brick-and-mortar retailers to understand and optimize the in-store customer experience in order to drive results. By collecting and analyzing data about customer behavior and interactions in physical retail spaces, businesses can gain insights into what works and what doesn’t and can use this information to make informed decisions about how to improve the in-store customer experience. This article will explore the importance of in-store analytics in the age of e-commerce, and will discuss the various types of in-store analytics that retailers can use. We will also cover techniques for collecting and analyzing in-store data, as well as best practices for using in-store analytics to drive revenue and sales. The importance of in-store analytics in the age of e-commerce In the age of e-commerce, in-store analytics is becoming more important as retailers look for ways to stay competitive and attract customers in a digital landscape. One of the main ways in-store analytics can drive results is by helping retailers better understand and optimize their in-store customer experience. By analyzing data on customer foot traffic, browsing and purchase patterns, and other metrics, retailers can identify areas for improvement and make informed decisions about store layout, product placement, and other factors that can impact the customer experience. In-store data can also help retailers optimize their inventory management and supply chain operations. By analyzing data on sales, returns, and other key metrics, retailers can better predict demand and adjust their inventory levels accordingly, leading to reduced waste and cost savings. Examples of successful in-store analytics in the age of e-commerce include retailers using data to personalize the in-store experience for customers, such as by using customer data to recommend products or personalized discounts. Other retailers have used in-store data to optimize their store layouts and product placements to drive sales, and to improve their supply chain operations by analyzing data on sales, returns, and other key metrics. Overall, in-store analytics plays a critical role in helping retailers stay competitive and drive results in the age of e-commerce. By collecting and analyzing data from their physical retail locations, retailers can gain valuable insights that can help them optimize their operations and improve the customer experience. Types of in-store analytics In-store data is becoming an increasingly important tool for retailers looking to optimize their operations and drive results. There are several types of in-store analytics that retailers can use to gain insights into their business and make informed decisions. Customer traffic and footfall analytics One type of in-store analytics is customer traffic and footfall analytics. This involves collecting and analyzing data on the number of customers visiting a store, as well as their movements and behaviors within the store. This data can help retailers understand which areas of the store are most popular, identify trends in customer behavior, and optimize the store layout and product placements to drive sales. Customer behavior analytics Customer behavior data is another important type of in-store analytics. This involves collecting and analyzing data on how customers interact with products, browse the store, and make purchases. This data can help retailers understand what drives customer behavior and make informed decisions about the types of products and marketing efforts that are most effective at driving sales. Sales and revenue analytics Sales and revenue data is another key type of in-store analytics. This involves collecting and analyzing data on sales and revenue, as well as key metrics such as average transaction size and customer lifetime value. This data can help retailers understand what drives sales and identify opportunities for growth. In-store Marketing Analytics In-store marketing analytics is another important type of in-store analytics. This involves collecting and analyzing data on the effectiveness of in-store marketing efforts. For example, signage, promotional materials, and in-store events. This data can help retailers understand what marketing efforts are effective at driving sales. Ultimately, make informed decisions about future marketing efforts. By collecting and analyzing data from their physical retail locations, retailers can gain valuable insights into revenue trends. Additionally, learn the effectiveness of marketing efforts, enabling them to make informed decisions and drive success. Techniques for collecting and analyzing in-store analytics There are several techniques that retailers can use to collect and analyze in-store analytics. These techniques can help retailers gain valuable insights into the effectiveness of marketing efforts, enabling them to optimize their operations and drive results. Use of in-store tracking technology One technique for collecting and analyzing in-store data is the use of in-store tracking technology. This can include sensors, beacons, and other types of technology that collect data on customer foot traffic, behavior, and purchases. This data can be analyzed to understand customer behavior and identify trends and patterns that can inform business decisions. Customer Data analysis Customer data analysis is another technique for collecting and analyzing in-store data. This involves collecting and analyzing data on customer demographics, preferences, and behaviors, as well as data on sales and revenue. This data can help retailers understand their customer base and identify opportunities for growth. Market research Market research is another relevant technique for collecting in-store analytics. This can include surveys, focus groups, and other types of research to gather insights into customer attitudes and behaviors. This data can be combined with data from in-store tracking technology and customer data analysis. The result is to gain a more complete understanding of customer behavior and business decisions. For more information about Market Research, read this complete guide for
Audience segmentation is the process of dividing a large group of potential customers into smaller, more targeted groups based on shared characteristics or behaviors. In the context of in-store marketing, learning how to effectively segment your in-store audience is essential to reach and connect with specific groups of customers in a more personalized and relevant way. The importance of segmentation in in-store marketing cannot be overstated. By dividing customers into targeted groups, businesses can create marketing efforts that are more tailored to the needs and interests of specific segments, which can lead to better results. For example, a retailer targeting young, fashion-conscious customers may use different marketing techniques and tactics than a retailer targeting older, more practical-minded customers. By segmenting their audience, retailers can create relevant marketing campaigns. This article will explore the importance of audience segmentation in in-store marketing and the various types of in-store audience segments that retailers can target. Furthermore, it will also cover techniques for identifying and targeting these segments, as well as best practices for segmenting your in-store audience. The importance of audience segmentation in in-store marketing Audience segmentation is a powerful tool for driving results in in-store marketing. Retailers can create marketing strategies tailored to the needs and interests of specific segments, which leads to better ROI. These are the key benefits of segmentation in driving results: A department store might use segmentation to create different marketing campaigns for different groups of customers, such as young families, college students, and seniors. Each of these groups may have different needs and interests. By creating targeted marketing campaigns for each group, the store can increase the relevance and effectiveness of its marketing efforts. Similarly, a fashion retailer, for example, might use segmentation to create different marketing campaigns for different types of fashion-conscious customers, such as casual vs. formal, trendy vs. classic, and so on. By segmenting its audience in this way, the retailer can create targeted campaigns that are likely to drive outcomes. Types of in-store audience segments There are many different types of in-store audience segments that retailers can target. Some of the most common types of in-store audience segments include: Altogether, these are the most common types of in-store audience segments that retailers can target. By understanding the different characteristics of different segments, retailers can create targeted campaigns that are likely to drive sales. Techniques for identifying and targeting in-store audience segments These are the most effective ways for retailers to identify and target in-store audience segments. Customer data analysis By analyzing customer data, businesses can gain insights into the characteristics and behaviors of different segments of their audience. This can include data such as purchase history, website activity, and other types of customer interactions. Markedly, by using data analysis tools and techniques, retailers can identify key segments and create targeted marketing campaigns. Market research Market research can be a valuable tool for understanding the needs and preferences of different segments of the in-store audience. This can include surveys, focus groups, and other research techniques that allow businesses to gather insights from customers directly. In-store tracking technology In-store tracking technology, such as cameras, sensors, and beacons, can be used to track customer movements and behaviors in real-time. By analyzing this data, retailers can gain insights into the characteristics and behaviors of different in-store audience segments. Lastly use this information to create more targeted, relevant offline marketing campaigns. Overall, by using a combination of these techniques, businesses can gain a better understanding of their audience. Ultimately create marketing campaigns that are more likely to drive revenue and sales. Best practices for segmenting your in-store audience Basically, to get the most out of audience segmentation in in-store marketing there are practices to follow. Altogether, segment your in-store audience by: In summary, by following best practices retailers can segment their in-store audience to boost sales. Ultimately, reach and connect with their target audience. The future of audience segmentation Looking to the future, is expected to see a continued evolution of audience segmentation in in-store marketing. Retailers continue to find new and innovative ways to reach and connect with their customers. As such, is crucial to stay up-to-date with the latest developments in segmentation technology and to be willing to experiment with new approaches. Shoppermotion offers in-store tracking solutions that can help retailers to segment their in-store audiences. Basically, Shoppermotion’s solutions can track customer movements and behaviors in real-time. Retailers can obtain all data they need to identify different segments of their in-store audience. Conclusion In conclusion, audience segmentation is an important part of in-store marketing. It allows retailers to reach and connect with specific groups of customers in a more personalized and relevant way. By dividing customers into targeted groups, retailers can tailor marketing campaigns to the needs of specific segments. Consequently, this leads to better results. There are many types of in-store audience segments that retailers can target. Demographic, behavioral, psychological, and location-based segments. Afterward, there are techniques that retailers can use to identify these segments. Customer data analysis, market research, and in-store tracking technology. Consequently, by following best practices retailers can segment their in-store audience in a way that helps to drive better results. Overall, by using Shoppermotion’s solutions retailers gain a better understanding of their audience and can create and measure offline campaigns.
In the world of Retail Media and Digital Marketing, it’s easy to get caught up in the hype of online advertising and the endless stream of data it provides. However, it’s important not to forget the importance of Offline Attribution in Retail Media and the role it plays in driving sales and customer loyalty. In the retail industry, offline attribution is a crucial aspect of understanding how different marketing channels contribute to in-store sales. By analyzing the impact of offline marketing efforts, retailers can make informed decisions about their marketing strategy and allocate resources more effectively. This article will delve into the importance of offline attribution in Retail Media Networks and how helps retailers understand the effectiveness of their marketing efforts. What is offline Attribution in Retail Media? Offline attribution refers to the process of measuring the impact of offline marketing efforts on in-store sales or other desired outcomes. This can include traditional marketing channels such as print advertising, radio and television ads, and in-store signage, as well as more modern tactics like email marketing and social media. Offline attribution helps retailers understand which marketing channels are driving the most sales and how they contribute to the overall customer journey. By analyzing data on customer behavior and purchase patterns, retailers can determine which marketing efforts are most effective in driving foot traffic and sales. Furthermore, Offline attribution in Retail Media Networks is important because it allows retailers to optimize their marketing spend and allocate resources more effectively. By understanding which marketing channels are performing the best, retailers can make informed decisions about where to focus their efforts and allocate budget. In addition, offline attribution in Retail Media helps retailers understand the full customer journey and how different marketing channels work together to influence purchase decisions. This can be especially useful for retailers who rely on both online and offline marketing efforts to reach their target audience. Types of offline attribution in Retail Media There are several different types of offline attribution methods that retailers can use to measure the impact of their marketing efforts on in-store sales. The most common methods include: Linear Attribution / Multi-touch attribution This method assigns equal credit to all marketing touchpoints that a customer encountered before making a purchase. For example, if a customer sees a television ad, receives an email promotion, and sees an in-store sign before making a purchase in-store, each of these touchpoints would be credited for the sale. In other words, linear attribution assigns credit to multiple marketing channels that contributed to a sale. First-touch attribution Also known as First-click attribution. This method assigns credit to the first marketing touchpoint that a customer encountered before making a purchase. For example, if a customer sees a print ad and then receives an email promotion before making a purchase, the print ad would be credited for the sale. Last touch attribution Also known as last-click attribution. This method assigns credit to the last marketing touchpoint that a customer encountered before making a purchase. For example, if a customer receives an email promotion and then sees an in-store sign before making a purchase, the in-store sign would be credited for the sale. Single touch attribution This method assigns credit to a single marketing channel that directly leads to a sale. For example, if a customer sees a digital signage ad and then immediately makes a purchase in-store, the in-store ad would be credited for the sale. Time decay attribution This method assigns more credit to marketing touchpoints that occurred closer in time to the purchase. For example, if a customer receives an email promotion and then sees an in-store sign before making a purchase, the in-store sign would be credited with a greater weight than the email promotion. Sales lift analysis This involves comparing sales data from a control group (who were not exposed to the marketing campaign) to a test group (who were exposed to the campaign). This allows retailers to measure the campaign’s impact on sales and determine the overall return on investment (ROI). Overall, it’s important to note that each of these methods has its own strengths and limitations, and retailers should choose the one that best fits their needs and goals. Benefits of using offline attribution in Retail Media Networks Offline attribution is perfect for retailers relying on both online and offline marketing efforts to reach their target audience. There are several benefits to using offline attribution in the retail industry: Improved marketing efficiency By analyzing the impact of different marketing channels on in-store sales, retailers can allocate resources more effectively. Enhanced customer understanding Offline attribution helps retailers understand the entire customer journey and how different marketing channels work together to influence purchase decisions. This can help retailers tailor their marketing efforts to better meet the needs and preferences of their target audience. Greater ROI with Offline Attribution in Retail Media Networks Retailers can identify the campaigns with the best ROI and focus on those. Better targeting By analyzing customer behavior data, retailers can identify trends and patterns that can inform their targeting efforts. This can help retailers reach the right customers with the right marketing messages at the right time. Increased sales By understanding how marketing channels contribute to the customer journey, retailers can improve and drive more in-store sales. Enhanced ability to measure the impact of offline campaigns Offline attribution in Retail Media enhances the ability to measure the impact of offline campaigns on in-store sales. By
During the last few years and together with the rise of Retail Media, Digital Signage has gained popularity among Offline Marketing strategies inside the store. Digital Signage wins in Offline Retail Media: it can boost sales and revenue and it plays a relevant role in enhancing the customer experience in the store. Brands and retailers can impact prospective customers offline by using the in-store TVs available to display ads. This is actually one of the best moments to promote a product or a brand, stimulate impulse buying and increase sales of specific products at the point of purchase (POP). Furthermore, Digital signage allows retailers to replicate all their fresh online displays in their physical stores and deliver specific calls to action at specific times (i.e. buy now, use this discount or promotional code, etc.). In other words, bring their online efforts in-store. Additionally, Digital Signage offers a superior ROAS when compared to other offline channels, hence it is becoming vital for retailers that want to develop their offline channels. Implementing Digital Signage strategies can help maximize offline conversions on their Retail Media Networks and improve their offer to all brands interested in this form of advertising. This article will first define Digital Signage and highlight its benefits in Retail Media. Secondly, it will list the essentials of a successful Digital Signage solution and describe the main types of digital signage displays. Lastly, it’ll cover how to implement the best Digital Signage solution. Digital Signage definition Digital Signage is an electronic out-of-home advertising (OOH) medium that displays dynamic digital media such as text, images, video, and interactive content, usually on a TV screen. A sub-segment of the broader signage industry, Digital Signage involves the use of electronic displays to show digital content. Basically, Digital Signage refers to display technologies like LED walls (or video walls), projection, and LCD monitors to vividly display webpages, videos, directions, customer reviews, product ratings, marketing messages, or digital images. Also known as In-store media, Digital Signage’s main purpose is to show displays on computer monitors or in-store TVs to customers while they are browsing a retail store. Certainly, this kind of in-store advertising is gaining relevance in department stores, supermarkets, specialty stores, and any other kind of retail store. The Benefits of Digital Signage in Retail Media There are many benefits of using digital signage in offline retail settings. Digital Signage is a very effective way for brands to build brand awareness, create product/service engagement and consumer interaction, and ultimately maximize sales and revenue. Consequently, it’s becoming vital for Retail Media Networks to include this form of advertising in their offer. Additionally, Digital Signage can increase foot traffic and improve customer experience in the store by making customers feel more welcome, delivering unique and engaging content, and informing customers about special deals, new products, or promotions. Below are some of its main advantages: Increase brand awareness and visibility Digital Signage allows brands to share their story and engage potential customers. Today’s customers are interested in what brands stand for, not just their products or the quality they offer. With Digital Signage brands have the option to bring their online marketing efforts and storytelling in-store. Drive sales Impactful video messages have a major impact on customers’ buying decisions. Businesses can boost revenue by up to 30% by using Digital Signage and promoting impulse purchases. Displaying messages and offers at high-traffic sites in the store and POS have the power of encouraging customers to spontaneously purchase products that were not on their shopping lists. Improve customer experience Brands can engage customers at the point of sale and provide them with helpful and relevant information. Overall, retailers can update customers about ongoing in-store promotions, attracting customers’ attention and helping them to get the best deals in the store. Communicate targeted messages to specific audiences Target displays with personalized messages can be updated in real-time implementing the right technology. Increase the chances of upselling and cross-selling opportunities. Digital Signage allows Retailers to use their in-store TVs to recommend related items, promote items essential to a product’s performance, offer discounts on product bundles, and add complementary products. Facilitate customer engagement By delivering the right ad at the right time, messages are likely to stay accurate and relevant. With digital signage, retailers can change their messages in real time. Enhance interaction Digital Signage allows customers to interact in new ways with the brand’s content. It helps to create an immersive experience. Brands can use in-store TVs to create phygital experiences for customers to interact with their product range and displays. Generate real-time data and insights Obtaining Digital signage analytics and generating real-time data requires specific technology to determine the number of people who walks by the in-store screen, stops and watch, interact, and are interested in the message being displayed. Reduce operational costs Digital signage is perceived as a cost-effective method for advertising and providing information. Retailers can update in-store TVs through a central content management platform, boosting work efficiency significantly. Overall Digital signage ensures branding message is consistent and reduces human error. With a cloud-based content management system retailers can oversee and manage their display content across multiple locations, ensuring their branding messages are consistent across all areas. Enhance security and safety In-store TVs can also be used for displaying Safety Signs. Ensuring staff and visitors are well aware of the possible dangers and hazards ahead in certain situations and/or environments. Improve sustainability Last but not least, Digital signage reduces the need to print posters, flyers, etc. it saves paper and ink, hence it’s eco-friendlier than traditional print signage, cutting down carbon footprint. The essentials of Digital Signage in Retail Media Digital signage can be used in various offline retail settings, such as in-store, at the point of sale, in
Retail Media has become essential in the current advertising environment. Retail Media Networks allow brands and retailers to connect with their customers through the power of data and personalization, customizing their message and impacting customers when they are in the mindset of shopping. There isn’t a better time to promote a brand than when the customer is on a retail site, ready to buy a product. But the power of Retail Media goes further and beyond online networks. Brands and retailers can impact prospective customers when they are physically in the store by implementing and measuring performance of offline Campaigns. In order to maximize the impact of Retail Media investment, it is important for brands and retailers to measure the performance of all their Retail Media campaigns, both online and offline. Retail Media Networks capture all the information collected on a shopper’s click path. Hence studying the shoppers’ behavior online and measuring online campaigns has become an easy task. However, brands and retailers must not forget the potential of combining the different channels available in Retail Media, in particular the offline. In the Groceries industry, an important percentage of shoppers still prefer to shop in the store. This is why brands and retailers must not miss out on the opportunity of measuring the performance of offline campaigns in Retail Media in order to impact ready-to-buy shoppers and increase sales. This article will focus on Offline Campaigns and the different performance indicators that can be used to measure their success. Offline Campaigns definition As the offline term indicates, offline campaigns utilize offline media channels to create product awareness. An offline campaign is defined as any form of advertising implemented away from a brand’s owned online channels. Throughout the years, the list of offline channels has become longer as new technologies emerged in the market. We can now find a great variety of offline channels that brands and retailers can use to implement offline campaigns. Below are a few examples: Traditional media: TV, Radio, Print Outdoor: Billboards, Transit Event Marketing: Sponsorship, Trade Shows, Conferences Publicity: PR, News However, the best time for retailers and brands to promote their products is when prospective customers are in that “ready-to-buy” mode, and when it comes to offline, this will happen in the store. The importance of offline campaigns Is no doubt that the online channel has given retailers and brands the opportunity to capture so much information about customer behavior and real-time shopping journeys. Click paths have been crucial for segmenting audiences and targeting ads effectively. However, this should not lead to the conclusion that offline campaigns are no longer important. In fact, many studies have shown that even if shoppers are informed and have done their research online, they still prefer to buy in the store. This is usually explained by the need for the shopper to touch and see the product before they buy. For these reasons, a significant amount of purchasing decisions are still made in the store and not online. Therefore, offline campaigns are game changers for brands and retailers that want to reach all potential customers, not only those that are online. There are two types of shoppers: digital shoppers and in-store shoppers. Both of them are equally relevant to any Retailer or Brand. In-store shoppers go directly to the store to buy the product or service they are looking for. Creating ads to impact in-store shoppers is as important as creating ads for online shoppers in an online retail media network. The best place to advertise a product to an in-store shopper is with In-store TVs, Shelf-talkers, Endcaps, or any other form of in-store advertisement. Types of offline campaigns There are different types of offline campaigns in Retail Media Networks. In-store Displays or Point of Purchase (POP Displays) In-store Events: Sampling, demonstrations, etc. This article will focus on POP Displays / In-Store Displays. In-store displays can be printed or digital. Printed in-store Displays In-store displays refer to those ads placed near the product that is being advertised. Include signage, standalone displays, aisle fixtures, and banners. A Point of Purchase or POP campaign is a type of campaign that intends to stimulate impulse buying and increase sales of a product at the point of purchase. Displays are designed to catch the eye of shoppers and convince them to purchase the product. In-store displays feature offers in the shopping journey and can be free-standing or fixed installations in the middle of the aisles, throughout the store, at the entrance/exit of the store, in the shopping tools, etc. Here are some examples of retail displays forms that can be used in an offline campaign: Free-standing displays: Floor Standing Display Unit Isle Unit Display cases or cabinets Gondola Display Unit Information POP Display Glorifiers (designed by brands to offer a highly visual experience) Countertop Display Unit Store Window Display Shop in Shop (larger shops) Store Entrance/ Exit Displays Banners located at the store’s entrance/exit Shopping tool Displays Displays in the Shopping Cart Fixed installations: Shelving Units Endcap Units Checkout Displays Shelf Display Units Shelf stoppers Sidecap Display Units Digital Signage / Digital POP Displays Digital Signage includes all forms of digital signs, screens, and recordings displaying ads in a retail store. Ads can be shown in dynamic banners, videos, and recordings / audio clips. In-store TVs / Digital Signage: This form of advertising effectively shows a mix of product announcements and promotions. Audio ads / In-store audio clips: Audio clips are growing fast as a form of advertising and brands are immersing them in their offline campaigns. How to measure the performance of offline campaigns After listing the numerous options and places to display in-store ads, it’s time to talk about measurement. How do marketers measure the performance of offline campaigns in Retail Media? How do they calculate their ROI and measure the campaign’s effectiveness? Measurement is a top priority for any marketing campaign,
In 2020, Google announced the end of third-party cookies over the course of two years. The same year, Apple declared limiting the use of IDFA (Identifier for Advertisers), which enables advertisers to hunt down users across different apps. Digital marketers and advertisers have long depended on third-party cookies and tags to deliver personalized and targeted ads. Furthermore, this technology also aids them in learning more about their web visitors. Anyhow, things have changed in the past decade. There’s a growing concern for greater consumer control and consumer privacy online. By the mid of 2023, the three most popular internet browsers will stop backing for third-party cookies. Safari and Firefox were the first to do it, and Google is planning to join them next year. Their settlement will have a significant crash on the online advertising industry. Preparing to put the brand in a cookie-less world is essential if marketers want to tune into what will soon be the new normal. Understanding how cookies work Websites rely on first-party cookies to improve user affairs. First-party cookies recall the action of a website’s visitor. Performing the same task repeatedly is not a requisite. They give users a personalized experience and a perception that their chosen brand knows them. Third-party cookies are laid down by other websites. For instance, an advertising service (Google Ads) develops and places a third-party cookie on a website to monitor user actions and behavior. The consequences are personalized advertisements that mirror what consumers search for and like. Advertisers and marketers rely on third-party cookies because they collect the following data using them: User preferences Service and product preferences Information showing a user’s previous searches Demographic information like gender, age, and location of website visitors The data is then used and stored to target customers with video ads and online displays tailored to their cookie profile. However, by 2023, the way advertisers and publishers use Google ad-tracking tools and cookies is going to change. The tech giant plans to close out google third-party cookies on Chrome browsers. The whole digital ad industry will need to calibrate and find innovative ways to deliver personalized ads to users. The phase-out of Third Party Cookies Google is planning to stop using third-party cookies in their most popular browsers, including Chrome. Simultaneously, many advertisers will learn this has huge consequences, and many are musing about what digital marketing will look like in the future. The removal of old and now out-of-date technology like third-party cookies will give advertisers a hard time tracking the web activity of potential customers, which will have a huge impact on things such as remarketing. Third-party cookies have assisted advertisers big time in targeting ads depending on the data they collect, ranging from gender and age to historic behavior on search history and websites. In principle, Google’s purpose is to satisfy the concerns of their audience, consumers who have learned about the capture and use of their data, and more and more perceive third-party cookies as a pattern of privacy-invading technology. Moreover, with no more third-party cookies to depend upon, advertisers will drive their interest toward first-party cookies and Retail Media for targeting information. First-party cookies are stored on the territory and can be utilized for things like recommendation systems and on-site search optimization. What is Google’s plan B? As an alternative to dependence on third-party cookies, Google has been developing the Privacy Sandbox, which is set to be a less intrusive blend of targeted advertising. The Privacy Sandbox is a cluster of technologies that aim to shield the privacy of users online, while still equipping businesses with tools and technologies to advertise efficiently. As part of the Sandbox, Google has developed the new Topics API – a new proposal for cookie tracking. The algorithm runs within the browsers of a user and classifies them within a bunch of high-level interest groups like food, travel, or fashion. These broad sets can then be used for targeting the right audience. When Google announced the decision to end third-party cookies, various advertising agencies criticized the action. The main concern was the impact this decision would have on the ad industry. Due to their confusion, GetApp surveyed to find the marketing impact of the phase-out of third-party cookies. The survey concluded that: 44% of marketers estimate they require to level up their spending by 5%-25% to reach the same goals as 2021 41% of marketing experts are of the view that the biggest challenge will be the impotence to track user data in detail 23% of marketers predict they will look up to email marketing software because of Google’s new policy. What advertisers need to know about Google’s Phase-Out As third-party cookies are so crucial to advertising technology, their elimination will significantly affect Digital Advertising. Again, Marketing experts that rely thoroughly on data from google third-party cookies for ad targeting and different campaigns will need to amend their strategies. Administering frequency and reach, evaluating and measuring third-party data, advertiser’s business models, DMP, and biddable technologies are affected. This may need an amendment in channel investments, marketing budgets, and advertising campaign strategies. In a world full of marketing and advertising campaigns, there are two groups that are most affected by the end of third-party cookies: Those who sell values and ideas Those who sell services and products The first group, referred to as purpose-driven marketing, will be severely affected by the third-party cookies‘ phase out. It is difficult to intercept how a consumer feels about certain problems. It is harder to determine how to motivate consumers to take a specific action than intercepting consumers who might buy a specific product. How to replace Third-Party Cookies? It is a fact, Google Third Party Cookies are vanishing away in 2023. What is going to replace them in digital advertising? Here are the best alternatives to third-party cookies that can aid in keeping websites running smoothly. Tracking consumers in alternative ways – Privacy Sandbox A new technology called the Federated Learning of Cohorts (FLoC) will collect user data and
A correlation matrix is a powerful tool for understanding relationships between the categories visited in our stores. In brick-and-mortars, it can be used to identify opportunities for cross-selling products and optimize the location of our secondary touchpoints. The time variable is an important part of the correlation matrix. It can help you to understand how different stops interact over time and to identify trends that may be useful for your store.
One of our tools became pretty popular during this calendar year. Almost 70 Clients have already installed Digital Flow. This is our latest tool which is tracking customer journeys, similar to our automated version. However, with the digital flow, the coworker is drawing lines (which equals to the customer pathway) directly on the tablet. The basis is the rough layout of the story or touchpoint, which shows all the aisles, end caps, customer ways, etc. The tool is so easy to use and yet delivers great results. Only now we are celebrating 60.000 flows, being each flow a different customer journey, which our clients have performed. This data helps the retail managers to take appropriate commercial decisions. Automatic Reports As we are constantly receiving feedback where we are continuing to improve our tools to make them even more attractive for our clients. We are introducing 1-click PDF/PPTX reports which can be downloaded directly from the Digital Flow tool which we believe will become very popular. Focus area Reports One example of a future report would be a detailed report about dedicated areas of the store/touchpoint. Imagine you are a Leader in a grocery store and your area of responsibility is all non-alcoholic drinks. You would simply push a button (or we would send it out weekly/monthly) and you would receive a detailed report about your category. This could include as follows: Number of total flows (flow = customer journey) inside your department Demographics, % of male, female, families etc Share of customer shopping behavior (weekdays vs. weekends, mornings vs. evenings etc.) Evolution of traffic incl. comparison vs. previous periods to indicate a trend Visit time and duration time All events (passed by, touch, engage, buy, ask etc.) Conversion funnels (from traffic to passed by to touch to buy) per product/ Range group etc. Hotspots vs. Coldspots inkl. Ranking Benchmarking against Country/ Global etc. And much more This is in fact a great business intelligence tool and the commercial team or the decision-makers will get instant results. Of course, the more flows the team is performing over a certain period, the more representative the results are. We recommend a minimum of 100 flows per week per section/category. The results would be outstanding and that would improve the customer shopping experience and at the end of the day, Sales and Profit for the organization. Reconstructions Reports Another potential report we will be creating is for reconstructions. Retailers are constantly remodeling their departments, either fully or partially. This report would be created after a rebuild has been completed and it would show a comparison of before and after. Imagine you are a leader of the sofa department in a furniture store. Together with your partners from interior design, logistics, maintenance, and others you have successfully rebuilt the area over the weekend in order to welcome customers in the new area on a Monday morning. You have performed 200 flows the week before the rebuild and another 200 flows the week after. Then you push the bottom and you will receive this report. The report will include certain data and help the leaders from the mentioned departments, when meeting for the handover, to evaluate the rebuild. Why are we doing these reports? We’ve built these reports for many reasons, being the most important ones: It will save a lot of time: Managers and leaders are very busy so by offering these reports we simply help them. We’ve seen that coworkers spend almost 3000 hours every year preparing the PDFs with the data collected from the customers’ flows Insightful results: as we get the info digitally, we can dive much deeper into the results (compare periods, see progressions, correlations, etc.) Standardize the information: we’ve seen how hard it is for the managers to understand the information they receive in these studies. Normalizing the studies will simply make their lives easier and they can focus (together with their peers) on taking actions and decisions This detailed report for a dedicated area is only one example of reports we are going to create and make available to our clients. KPIs are essential for Retailers The KPIs which will be included in the report could be as follows: Total duration of time customers spend in the area Hotspots vs. Coldspots Conversion funnels for the range Most popular products and take up rate for shopping tools demographics , showing who is interested in which part of the area, e.g. % of female being engaged in leather sofas or % of families buying armchairs Shopping tool trends and changes Correlation with other areas Benchmarking with previous re-builds Summary Digital Flows as one solution to track customer journeys throughout the store/ touchpoint are very insightful and deliver amazing results for the leader. The new reports we are going to make available soon will help the leader to save a lot of time and be much more efficient. They will receive detailed reports for dedicated topics which they could take as a basis for decision making.
성공적인 프로모션 캠페인을 진행하기 위해서는 올바른 정보를 아는 것보다 더 중요한 것은 없습니다. 이는 우리가 왜 매장 내 분석을 해야 하는지를 보여줍니다. 이제 우리는 한 단계 더 나아가 브로셔에서 제공하는 일부 프로모션 상품에 대한 식료품 소매업체의 전환 유입경로를 계산하는 방법을 알아보겠습니다 우리 측정항목의 가장 큰 장점은 현재 고객에 의해 관심을 받는 제품이 무언인지, 기대 보다 관심을 받지 못하고 있는 제품이 무엇인지 식별 할 수 있다는 것입니다. 이는 마케터가 더 나은 전환 유입 경로 전략을 수립하는데 도움이 됩니다. 전자 상거래 플랫폼의 경우 사용할 측정항목이 더 많기 때문에 들어오는 고객의 정보를 분석하는 것이 훨씬 간단합니다. 그러나 실제 매장 내에서도 다양한 측정항목을 적용하여 원하는 결과를 얻을 수 있습니 다. 전환 유입경로를 계산하는 방법 먼저 프로모션에서 수집한 데이터를 자세히 살펴보겠습니다. 여기에서 마케팅 관리자의 경우 전환 유입 경로가 매장 내 프로모션의 효과를 확인하기 위해 고려해야 할 흥미로운 분석이라는 점에 유의해야 합니다 각 섹션이 나타내는 측정항목을 이해하기위해 각 측정 항목을 다음과 같이 분류합니다: Penetration.- 섹션이 수신한 트래픽을 나타냅니다. 이는 프로모션 기간 동안 50,000명의 고객이 있었다면 9,950명이 CSD 섹션을 방문했음을 의미합니다. 이 데이터는 매장 우리 회사의 기술로 수집됩니다. Bounce.- 섹션에서 10초 미만의 시간을 보낸 사람들의 비율을 보여줍니다. Browsers.- 섹션에서 10초에서 15초 사이를 보낸 사람들의 비율을 보여줍니다. Engaged.- 섹션에서 15초 이상을 보낸 사람들의 비율을 보여줍니다. Conversion rate.- 특정 제품을 구매한 식료품점 방문자의 비율을 보여줍니다. 예를 들어 시리얼의 전환율이 3.7%라는 것은 프로모션 기간 동안 방문한 총 방문자 중 1,850명이 이 제품을 구매했음을 의미합니다. 전체적으로 볼 때 이런 정보들은 매장 방문자가 쇼핑 중에 특정 제품의 구매자가 되는 과정을 보여주며 이는 식료품 소매업체에서 전환 유입경로를 계산하는 최적의 방법입니다. 전단지 판촉 분석 전환 유입 경로를 분석하기 위해 계란과 연어를 예로 살펴보겠습니다. 계란.- 보다시피 Penetration rate이 높은 제품입니다. 이는 대부분의 사람들이 슈퍼마켓에 있는 동안 이 섹션을 방문한다는 것을 나타냅니다. 또한Conversion rate역시 높습니다. 이는 계란이 기본 상품이기 때문에 대부분의 사람들이 구매를 하고 있음을 알 수 있다. 유입경로를 살펴보면 프로모션 기간 동안 50,000명의 방문자 중 7,200명이 결국 계란을 구매했다는 결론에 도달합니다. Bounce및 Browsers에 대한 분석에서 우리는 몇 가지 중요한 정보를 얻습니다. 계란은 Bounce에서 높은 비율을 보여주며, 이는 대부분의 사람들이 계란 섹션에서 10초 미만을 보낸다는 것을 의미합니다. 이것은 사람들이 이미 자신이 좋아하는 브랜드를 알고 있고 이 섹션에 올 때 단순히 계란을 바로 집어 가는 것을 의미하기 때문에 이것은 나쁜 지표가 아닙니다. 반대로, Browsers에서 많은 사람들이 10~15초 만큼의 시간을 어떤 계란을 살지 결정하는 데 소비하지 않는다는 것을 보여줍니다 연어.- 얼핏 보면 계란에 비해 Penetration 퍼센트가 상당히 낮은 것을 알 수 있다. 이 정보 덕분에 사람들이 정기적으로 연어를 사지 않는다고 가정할 수 있습니다. 또한 Conversion rate은 연어를 최종적으로 구매한 사람들의 비율이 상당히 낮다는 것을 보여줍니다. 이는 연어가 재구매 율이 높은 제품이 아님을 보여주는 지표입니다. 전체 방문자 중 프로모션 기간 동안 연어를 구입한 사람은 1,100명에 불과했는데, 이는 계란에 비해 현저히 낮은 수치입니다. Browsers을 자세히 살펴보면 구매 여부를 결정하는데 10초에서 15초 이상을 소비하는 사람들의 비율이 높다는 것을 알 수 있습니다. 일반적으로 신선제품에서 이런 양상을 많이 띄며 사람들은 유통 기한이나 제품 상태와 같은 다른 측면에 따라 결정을 내리는 경향이 있습니다. 비율은 제품에 따라 달라집니다. 이는 기본 제품이 일반적으로 Penetration rates이 높기 때문에 높은 비율이 예상 되는 반면 신선 제품 또는 특정 상품의 경우 (예: 아이스크림) 비율이 낮습니다. 두 경우 모두 상황에 따라 Conversion rate이 변합니다 수행작업 일반적으로 전환 유입경로를 계산할 때 구매자가 된 방문자 수를 식별합니다. 계속해서 유입 경로의 각 단계를 발전시킬 수 있는 다양한 작업을 살펴보겠습니다. 각 단계를 분석한 후 사람들이 우리 제품에 관심을 잃는 단계를 식별하는 것이 중요합니다. 유사한 매장을 벤치마킹하여 유입 경로와 가격이 다른 카테고리와 일치하는지 확인 하는 것을 권장합니다. 분석결과 평균수치와 차이가 크다는 것을 알게 되면 이전에 설명한 내용에 따라 해당 조치를 구현해야 합니다. 결론 데이터 그 자체는 데이터가 어떻게 사용되고 어떻게 해석되는지 만큼이나 중요합니다. 첫번째로 가장 중요한 것은 올바른 데이터를 확보하는 것입니다. 여기에서 분석 도구가 가치를 발휘합니다. 각 판촉이 어떻게 진행되고 있는지에 대한 데이터를 얻을 뿐만 아니라 각 판촉이 그와 같은 방식으로 수행되는 이유에 대한 통찰력을 얻을 수 있습니다. 이는 마케팅 관리자가 정보에 입각한 결정을 내리기 위해 매우 중요합니다. 어떤 제품이 잘 작동하고 어떤 제품이 기대에 미치지 못하는지 쉽게 식별할 수 있기 때문입니다. 후자의 경우 결과를 개선하기 위해 구현할 수 있는 조치가 있습니다. 예를 들어 고객이 특정 제품을 이해하지 못한다고 생각하면 다양한 요리 방법을 선보이는 것이 솔루션일 수 있습니다. 이를 통해 마케팅 관리자는 고객을 제품에 더 가깝게 만들고 판매 증가로 이어질 수 있습니다. 이 기사에서는 식료품 소매업체의 전환 유입경로를 계산하는 방법을 보여줍니다. 매장 내 분석을 제어하고 제품 프로모션 및 일반 매장 관리에 대한 현명한 결정을 내릴 준비가 되었다면 저희에게 연락해주세요.
Items within a store do not exist within a vacuum. There are correlations both subtle and overt between various products within a section but also between their placement, reach, and visibility. Understanding these dynamics is essential for any section manager to make effective decisions. But in order to capture both the subtle and overt correlation between products in each section, we must first be able to track them. After which we can convert this raw data into meaningful information that gives a breakdown of what exactly is happening within a given section. Products correlations This is where we come in. Thanks to our technology we can extract valuable information about the performance of the different categories. We simplify all that raw data into different subsets. Let’s take a grocery store that utilizes our system as a simple case study. For convenience let’s call it “Jay’s Grocery store”. You have your fresh fruits and vegetable sections, your meat and poultry sections, and the various other things you would expect to find in a normal grocery store. If we analyze the Fruit and Vegetable section, we will find the following information: Visitors. 49.83% of total visitors have passed by this section within a given time frame (usually a business day). Dwell time. This is the average time visitors spend in the section, starting from the moment they enter the section until they exit it. In the case of the Fruit and Vegetable section, customers spend 1 min. 45s. interacting with the category. Bounce rate. This is the percentage of visitors who have spent less than a certain time (normally 10 seconds) in the section. The image above tells us that 6.44% of customers have had no interaction with the Fruit and Vegetable section. Engagement. Percentage of visitors who have stayed more than a certain time in the section. This, however, comes with a caveat. We must bear in mind that this duration is highly dependent on the type of business and customers. For example, the engagement for customers at a grocery store would be lower than that for a furniture store. This is because shoppers spend less time deciding what food to buy, whereas furniture shopping requires a long decision-making process with customers usually coming back repeatedly to view a particular piece before making a choice. Given that in the example we are talking about the Fruit and Vegetable section of a grocery store, the engagement rate shows that 81.17% of the customers have spent more than 10s interacting with the section. Engagement dwell time. This is the average time visitors spend in the section once they have been engaged. In simple terms, the time starts when a visitor interacts with a product within the section until they move on. This can be triggered by them pausing at certain points or picking up items. For our example, the engagement dwell time is 2 min. 07s. In addition to this data, we can obtain information about the top correlated sections, which are those that are usually visited during the same shopping journey. Thanks to this, we will be able to see the bigger picture of the section. As you would expect from a grocery store, some items are more popular than others depending on the season, but to find out how each section interact and relate to each other is where the analytics comes in. Following our previous example, we have selected the Fruit and Vegetable category to obtain the metrics related to this section. If we take a look at the top correlated sections, we will see that 69.9% of customers who visited the Fruit and Vegetable section took a trip over to the meat category also, all within the same shopping journey. In the case of beer, 44.5% of customers visited this section as well, and 39.9% of total visitors spent a considerable time at the yogurt/cream section. For any category manager, this is critical information. Not only will they be able to launch effective cross-promotions, but also manage their categories according to shoppers’ insights. It’s a simple concept. If I know that the more time visitors spend in my category, the more money they will spend, I will try to attract their attention and make them spend as much time as possible. All this information is gathered thanks to the correlation matrix. This is important because it measures the probability of a shopper stopping at a certain category assuming that they already stopped at any other section of the store. If you can predict where next a shopper will go based on where they have been, you can make adequate preparations and increase sales. Application of products correlations to facilitate in-store targeting activities Now let’s take this a step further and talk about how to take advantage of this. In e-commerce, there is a strategy called retargeting, by which retailers try to attract the attention of their bounced traffic. Knowing where your customer is likely to go next is critical for this to work. A way to adapt retargeting to brick and mortar could be to launch cross-selling strategies. Let’s take an example of a product such as beer. In order to get cross-selling to work a few simple steps might include: Placement of beers in strategic points throughout the store. If I want people to buy beer, I need to attract the customer’s attention in other points rather than the beer category. Let’s say that thanks to the correlation matrix I know for sure that people who visit the beer category visit as well the snacks section → cross-selling: in the beer section I can place an endcap or a chips promotion panel. Conclusions Any competent category manager understands the importance of having the right information at the right time. The same goes for store managers and category managers too. But in order to make proper decisions, this information must be presented in such a way that it’s easy to understand and allows for quick implementation of desired
It is undeniable that the job of a Category Manager is essential for the correct functioning of a grocery store. Our goal in this post is to support Category Management with fresh ideas and trends so that they have all the information needed when designing strategies for 2021 with the ultimate target of increasing their sales. Category Managers design product placement so that everything seems appealing during our visit to the store, but their duties do not end up here. They also contribute to the profitability of the company by fostering sales via marketing and in-store promotions and they manage everything related to product inventory, either they order new items or try to improve the speed of inventory turnover. Today, we will cover the most successful trend to boost sales this year, including: Analysis of the sections’ traffic Performance of local flyers and promotions Shopping missions Measurement of the power hours The study of the correlation matrix ROI Analysis Conversion funnel The importance of understanding our customers’ behavior Let’s say that the report is addressed to the Category Manager of the dairy and cheese products section of a well-known supermarket. The category has not achieved the expected results during the last semester of the year after the COVID-19 breakthrough, so changes need to be made to face a better 2021. The first thing here is to analyze the information about the category to understand what is happening and how the shopper is interacting with it. Thanks to our token-based technology, we collect complete shopping journeys in a passive and anonymous way. Then this transforms into metrics that let us know how customers behave around the different categories. By crossing all this information we can prepare a thorough report that includes different analytics in order to help Category Managers with the decision-making process. In the following sections, we will review all the analytics they can take advantage of. Section’s traffic The first thing a member of the Category Management team should consider is the traffic related to their sales received by the category. They have information about the tickets, so they can know how many customers have purchased a product from the category. On the other hand, we can obtain the percentage of visitors who have interacted with the section, so all this information lets them know the exact traffic their section is receiving. Taking into account that Category Managers have endless decisions to make, they rely on different sources to obtain information about market trends (consumer panels, manufacturer’s numbers, internal data, etc.). This is essential for them in order to do benchmarking since they can compare the situation of their category in relation to their competitors and assess if their performance is low on average. Another figure related to traffic is the average time spent. But careful! We will have to do benchmarking once again to see if dwell times are within the normal range. And keep in mind that different products have different dwell times (find out more about it in this post). Local Promotions Thanks to all the information examined in this post, Category Managers are able to know what is going on in their categories in order to make informed decisions. But this is not the only application of these metrics. When mixing all of them, they let us analyze specific actions or tasks of our job as a whole. For instance, if a member of the Category Management team would like to measure the effectiveness of a local promotion (i.e. promotional flyers), we can develop a report analyzing the performance of each product by means of these metrics and relate them to the sales. In a previous post, we showed what an analysis like this would look like. This image shows the recap of the products analyzed in the aforementioned post. Shopping missions All shoppers have an objective when visiting a grocery store, and thanks to shopping missions we can cluster shopping journeys in order to identify which is the main purpose of customers’ visits. By analyzing different aspects such as the categories visited, the hour of the day, or the shopping tool used, we can classify shopping journeys into four types of shopping missions: fill-in, stock-up, urgent item, and daily shop. For a Category Manager, this information can become very useful when trying to understand which is the predominant type of customer that visits their category in order to adapt strategies to their specific needs. For instance, if we identify that most of the customers that visit our category are conducting daily shops, in other words, that they come to the store to visit certain categories with a focus on fresh and perishable items, it would be a good idea to place other fresh products in my category to foster their sales. On the contrary, if I see that my customers visit mainly the store because they need something urgent, I could place small format products that they can grab quickly and effortlessly. Another interesting thing we can extract from this metric is the distribution of shopping missions during a week for the whole store. If we have previously identified the predominant shopping mission for our category, knowing when this type of customers visit the store can help us recognize the best days to launch promotions. According to the previous example, I know that my category is mainly visited by people who go shopping for stock-up missions. When taking a look at the distribution during a week, we can identify that people who do regular stock-ups visit stores mainly on Mondays and Sundays. Therefore, it would be interesting to develop some kind of in-store activation during these days to attract the attention of my target audience. Stock-up missions take place when customers visit a wide variety of sections in bulk to be able to fill the refrigerator for the upcoming weeks. Therefore, customers will be more willing to take advantage of a 3×2 promotion or a pack that comes with an extra free item
Having the right information is essential when it comes to planning and decision-making in our stores. Relying on sales figures can be a starting point when making important decisions in our stores, but analyzing our customers from another angle will give us more valuable information. If we put them at the center of our strategy, we will be satisfying their needs in a more precise way, therefore improving customer loyalty. Today we will focus on traffic analysis and how to take advantage of two important metrics: power hours and best windows. If you have read some of our posts, you will be familiar with the technology we use in Shoppermotion. On the contrary, if you are joining us for the first time, you should know that we work with a token-based technology that lets us obtain the complete shopping journeys of customers in a passive and anonymous way (if you want more information about this, you should read this post). Once we obtain this information, we analyze it in order to identify trends within stores. In other words, thanks to traffic we can forecast behavioral patterns within stores so that we can adapt our strategies to customers’ needs. In order to obtain enriched data when it comes to traffic analysis, we have developed two important metrics: power hours and best windows. Power hours, a good indicator for traffic Thanks to this metric it is possible to measure the number of devices at the store at a specific moment. By measuring this, we are able to know how traffic distributes throughout the day and week. For store managers, it is essential to make an analysis of traffic distribution since their mission is to supervise the correct functioning of the store. Hence, forecasting busy hours lets them make effective decisions in terms of: Stock replenishment: if I identify that customers mainly visit my store between 1 and 2 pm, I would want to make sure that all categories are replenished before customers arrive at my store. Personnel management: once again, if I identify peak hours, I would make sure that there are enough associates to assist customers during their shopping journeys, as well as enough open checkouts. Store management: it is important as well to identify off-peak hours so that associates can develop other tasks (i.e. cleaning) when traffic is low. In-store activations management: identifying how traffic distributes all over the week will give us a hint on how to design promotional calendars. Let’s analyze an example of a supermarket’s power hours metric in order to understand its potential for the decision-making process. This example shows that the store receives most of its visits on weekends. On the contrary, the most unpopular days are Wednesdays, followed by Tuesdays and Thursdays. Knowing this, store managers will never launch a promotion on any of these unpopular days since it won’t obtain good results. When it comes to busy hours, it must be noted that customers mainly visit the store as of 2 pm, so categories must be replenished before customers arrive and cleaning tasks must take place at noon. Best windows If we want to take a step further when analyzing in-store traffic, another metric that can come in handy is what we call the best windows. This metric combines traffic with the average time spent in-store. The y-axis represents the visitors per hour and the x-axis refers to Shoppermotion’s Window Index™, which takes into account the number of people, the length of the visit, and customers’ mobility throughout the store. Hence, big circles indicate those moments during a week that accumulate more traffic and longer visits. So, the bigger the circle, the better. This metric is intended for big store formats, as the average time spent is more relevant for them (in convenience stores, the average dwell time is low since customers usually buy a low amount of products). Store managers tend to analyze the impact of a campaign by measuring only the traffic received, but it is important to take into account the time spent as well. It is true that the more traffic, the more chances of increasing our conversion rates, but the more time spent, the more chances of shoppers visiting all categories. Therefore, at the end of the day, both ways can lead to an increase in sales. Let’s see this with an example comparing the orange and blue circles. Orange circle: Sunday 1-2 PM, average time 10.75, 36 visitors Blue circle: Saturday 1-2 PM, average time 11.10, 30 visitors As we can see, the blue circle is bigger but the number of visitors is lower. What really makes a difference, in this case, is the average time, which is 3.3% higher than that of the orange circle. Therefore, this indicates that at some points the impact can be higher taking into account the time spent and not the number of customers. Regarding the advantages of analyzing this metric, we can affirm that they are pretty similar to those explained in the power hours metric. Both metrics are significantly useful when deciding when to launch a promotion. Thanks to them we get to identify busy moments of the day and the week, so they let us identify the perfect moment to launch a campaign or to develop an in-store activation. Conclusion If we want to adapt our strategies to customers’ needs, it is important to analyze different types of information rather than only focusing on sales. For instance, traffic analysis tells us a lot about what is going on in our store and how our customers are behaving. Hence, it is essential to identify busy hours and off-peak moments to make effective decisions. Thanks to metrics such as power hours and best windows, store managers can design store strategies based on enriched criteria that place customers at the center of their plan of action. Even though there are differences between both metrics, they tell us valuable information to take into account when deciding the best moments to launch
As we described in our previous post, in-store analytics can be applied in a wide variety of businesses, including shopping malls. It allows us to analyze the behavior of customers to understand their shopping trends and habits, but the analytical capacity isn’t limited to this alone. We can measure other activities that take place within malls using shopping mall traffic analysis. One such activity would be various events organized within a shopping center. We will be taking a closer look at these events and how it is possible to measure their impacts within a shopping complex. Shopping malls attract a lot of people, this stems from the wide variety of products and services offered, so managers take advantage of the situation and organize targeted activities for the different demographic groups. It is not unusual to see entertainers playing games with children or maybe companies showing new products they just launched. And, predictably, these activities aim at fostering sales, so thanks to tracking technologies we can measure their effectiveness. Performance of Activities in Malls Let’s take a look at a case study of a Spanish mall. Thanks to its 218 commercial areas distributed on two floors, it attracts approximately 7 million visitors every year. Hence, various activities and events are held throughout the year to entertain customers. For instance, over a given time period, there was a spike in sports-related activities and since they were able to identify an increase in customers interested in sports stores, they decided to organize related events, resulting in the celebration of the CrossFit games during a weekend. Using in-store analytics we are able to track customer activities before the various events and those that occur during and after the games. What we are trying to piece together is the effectiveness of the staged event and see if it leads to a notable increase in sports-related sales. Shopping mall traffic analysis To start off with, let’s compare the traffic received in the sports stores on the first floor of the shopping center before and after the event happened. Before and after foot traffic measurement using Wi-Fi tracking in a Shopping Mall As we can see, traffic increased up to 100% in some cases just because of this event. In many cases, customers need a boost to encourage them to go shopping. Since participants used a wide variety of accessories and sports-related items during the event, we could assume that this was a strategy to create a need that was previously non-existent. And it worked! Driving customers’ attention through events This example demonstrates that it is very important to attract customers’ attention with appealing actions that are out of the ordinary. And, more importantly, is to measure their impact in order to see how customers are responding to them, something that can be easily done thanks to tracking technologies. Having this data, managers are able to quickly tell what works and what doesn’t. If the reverse was the case in this example and customers did not have a notable increase in interest for sports-related goods showcased in the live event, then managers can use that information to either tweak future events or change things all together and try other ideas. For mall managers, this information is important to see the changes in traffic patterns, but this is relevant as well for tenants. At the end of the day, tenants are the ones who need to measure the effectiveness of their campaigns and investments in the shopping center. Hence, if mall managers share information about customer behavior within their stores, they will be helping tenants as well as strengthening their relationship and commercial connection with them. Pop-up stores in shopping centers Let’s say that a cosmetics brand launches a pop-up store in one of the main aisles of the shopping center. Let’s assume that the intention of this campaign is to launch a new product (maybe a lipstick line) and promote it, as well as to improve brand awareness (maybe by giving samples or something else to attract customers). In order to attract customers, they hire a professional make-up artist or Youtuber and make the stand more appealing to shoppers. Benefits of shopping mall traffic analysis Thanks to tracking technologies, we can help both the center and the retailer by: Measuring the traffic before and after the event Measuring the engagement rate – people who stop to see what is going on Measuring the conversion rate – people who have bought something vs people who have passed by Measuring where are people coming from in order to contextualize visit and shopping missions (these are the ways we categorize shopping journeys) Measuring the frequency of visit in order to identify which are the best days to launch future promotions Benchmark with previous campaigns or similar centers in order to measure the impact of the promotion and see whether it has been a success or not Having this information at hand allows organizers to understand the outcome of their event. Not only are they able to know the direct impact of such a campaign but are also able to gauge the effectiveness of various components within the event itself. For example, the engagement rate with the Youtuber or makeup artist, what products within the new product line gathered the most interest – this is not always the most item sold and this information can be used to tweak things like pricing and other features. Conclusions Events are a big part of the shopping mall experience. Most customers can recall, at some point, seeing or taking part in such a shopping complex they frequent. Some have turned out to be very fun and entertaining, some reminded us of a desire to make certain purchases and others have left us uninspired and only illicit a casual glance. To an organizer, these varied reactions are important as they tell the success or failure of an event. Shopping mall traffic analysis is a great way to understand our customers’ needs. While there are a
We have spoken at length in previous articles about the importance of analytical data to decision making for retailers. From how it can boost sales to how it enables the streamlining of promotional content placement and optimization of existing categories. All this remains true when applied to a larger space. Regardless of the scale of your business, it is a critical factor to have in-store analytics available to you on-demand. Hence, in this post, we will explain which shopping mall analytics can we extract and how to interpret them in order to understand our customers. Wi-Fi Analytics However, unlike the case of a grocery store where each shopping tool can have its own tracker, things get more complicated for places like shopping malls. The question becomes, how do you track a large volume of customers as they make their way around a shopping center without being intrusive and allow them to maintain both privacy and anonymity. An added bonus would be to do it in such a way as to now require complex additional hardware. One such solution we employ is the use of Wi-Fi tracking. Since most shopping malls already have an existing Wi-Fi infrastructure, it can be used as the bases for this system. Most modern Wi-Fi access points have the ability to geolocate the signals they receive. The first thought that comes to mind when you hear such a statement would be that most shoppers do not connect to the Wi-Fi network in stores, so it is not possible to use that for tracking. However, there is a workaround for this. Most modern phones have inbuilt Wi-Fi and as long as it is not turned off or in Airplane Mode, their device will ping out to your access points on a regular basis even when they don’t connect to your Wi-Fi. This method is both possible and acceptable to use that for anonymous measurement. Another advantage is that this can be done in real-time. There are, however, a few considerations to be taken into account when it comes to shopping mall analytics. Whereas in a smaller setting such as the previously mentioned grocery stores, where the whole shopping journey is analyzed, in a larger setting, such as a shopping center, due to its size, it is only possible to analyze how customers move around the main aisles. Thanks to heat maps generated by our solution, we can know the areas with more customer engagement throughout the shopping mall. Shopping malls visit analysis Besides, since large shopping centers are made up of individual shops of varying categories, we can use this method to gain a better understanding of what’s happening within them. Our solution makes it possible to know where customers are spending most of their time. Plus where they are most likely to head to after leaving a particular store (more on this in a bit). With this information, managers will be able to do benchmarking with similar malls and optimize operational and strategic decisions consequently. This ranges from the placement of in-store advertising, store arrangements, and distribution to many more. If we combine this information with the correlation matrix, as hinted to earlier, we will be able to identify the connections between categories. This metric lets us know the probability of customers visiting a category assuming they have already visited another category during their visit to the mall. For instance, we can assume that a high percentage of visitors who have visited restaurants have also spent a relevant time in fashion stores or vice versa. Besides, it is also possible to identify the main purpose of customers’ visits to the mall. Depending on the commercial stores visited, there are four types of visit missions: Regular Stock-up: when customers visit a wide variety of stores because they need to buy a high amount of products. Daily shop: when customers visit some stores to buy products required for daily use. Target shopping: when customers visit a few stores because they need products that have got over. Urgent item: when customers visit one or two stores and ignore the rest because they need something very specific. For example, this could happen when a girl has a party and needs to go to the shopping mall to find a pair of heels. If you have already read our shopping mission’s post – if not, you might want to take a look as we take a deeper dive into shopping missions, you can have a read here – you will notice that the categorization is the same. The only thing that changes, in this case, is the areas visited in shopping malls. Thanks to this information, managers will be able to adapt their strategies to what customers really need. Conclusions Data is always critical and information is king. Without which it is difficult to know what the right tools for any given task should be and hampers the decision-making process. In-store analytics change this. This process is done in a non-intrusive way so as not to intrude or interfere with the customer’s experience. By extracting shopping mall analytics, managers will be able to optimize operational and strategic decisions.
Almost two months after the outbreak started, it is now undeniable that the world is facing a health crisis unprecedented in our era. What started as a localized virus in China has spread overnight to affect most of the countries in the international arena, thus changing the way we live and interact with one another. Although business activity is slowly restarting in some countries, it is unquestionable that returning to normalcy will take time. Small businesses are slowly restoring commercial activity in order to overcome the damages caused by COVID-19, but going back to the routine means implementing some measures to ensure it is safe for customers to go shopping again. Since health authorities have declared that social distance plays a critical role in fighting against the spread of the virus in-store, all businesses will have to guarantee their customers respect a minimum distance. Our team has just launched a tool to help retailers comply with this measure. Find more details on our website. At Shoppermotion we work with token-based analytics, that is, we attach little electronic devices to shopping carts and baskets that broadcast their position in real-time, so we can collect the complete journey of customers in stores passively and anonymously. Besides, we combine it with Wi-Fi and cameras in order to monitor behavior tendencies and obtain ground truth about in-store traffic. Therefore, this new application helps retailers control traffic per section thanks to our real-time broadcasting tokens. It includes Real-time alerts Reports every 5 minutes to security, associates, and store management team. Hence, if social distancing is not being respected or the maximum number of people has been reached, it will automatically send an email and SMS notification. Capacity-oriented KPIs New metrics and results have been released such as average distance between shopping tools, the density of foot traffic per square foot in order to comply with any social distance requirements in-store. Crowds prevention Our machine learning engine forecasts unexpected increases in traffic and notifies associates in real-time before the limits are reached to avoid bottlenecks in the layouts. All these measures reflect our willingness to help retailers when it comes to ensuring in-store social distance. It cannot be denied that in-store behavior will change due to COVID-19, so retailers will have to be prepared to offer their customers the strongest guarantees in terms of safety. At Shoppermotion we would like to be by your side in these difficult moments. If you are interested in knowing more about our tool, you can always get in touch with our team, we’d love to help.
