Currently, there is a transition from traditional retail experiences, characterized by generic product placements, impersonal offers and often frustrating checkout lines, to individually tailored customer experiences with a special focus on customer interests. In this new phase of retail, customer data is the key to customized interaction. Imagine a supermarket that not only recognizes your presence, but considers your individual preferences and needs. Intelligent sensors capture your shopping behavior, your customer profile allows the supermarket to offer you personalized product recommendations. Loyalty points and discount coupons are replaced by digital apps. Pick&go concepts eliminate waiting in line at the register. Innovative technologies like augmented reality and contactless payment options optimize customer satisfaction. Sustainability initiatives give customers the feeling of actively participating in environmental protection measures. At the same time, transparent data protection guarantees customers control over their own data.
But how can this be implemented on an architectural level? In this blog post I want to explore how a centralized management of customer data can enable a personalized customer experience in retail. At the center of this architecture is a Customer Data Platform (CDP) that creates a comprehensive customer profile from all relevant touchpoints and sorts customers according to their individual needs. The centralized management of customer data enables retailers to develop a deep understanding of their customers and provide tailored offers that meet their demands and interests
Data Sources for Retailers
Customer data is key to individualizing the service offering. There are several ways for the retail company to collect this data.
Customer registration: By setting up a registration system, customers can voluntarily provide their data. This can happen when creating a customer account on a website or filling out a registration form in the store.
Customer cards and loyalty programs: Retailers can introduce customer cards or loyalty programs where customers have to provide their data to benefit from discounts, points or other advantages. This data can then be used for personalized offers and marketing campaigns.
Point of sale systems: A customer activity repository and POS systems contribute to improving the customer experience by storing customer receipts and enabling personalized offers, real-time interactions, seamless omnichannel experience, and efficient inventory management when identifying the customer.
Website visits: User behavior, page views, and time spent provide valuable information about the interests and preferences of both anonymous and registered users.
Online purchases: When shopping online, customers typically provide their data to place orders and process payments. Retailers can use this data to build customer profiles and make personalized recommendations or offers.
Customer surveys and feedback: Through surveys or requests for feedback, retailers can obtain valuable information from customers. This data can help better understand customer needs and preferences and adjust the service accordingly.
Social media interactions: Retailers can gain customer data from interactions on social media platforms. For example, they can use information about likes, comments, or shared content to identify customer preferences and interests.
Data partnerships: Retailers can also enter data partnerships where they gain access to third-party customer data. This can happen through purchasing data from marketing agencies or loyalty programs that collect and analyze customer data.
The key point in customer profiling is data protection and the customer's consent to collecting and processing their data. It is important that the customer is informed at all times about the timing and type of data processing and has the confidence that their data is used exclusively to improve their customer experience. A central customer data platform is responsible for the purposeful processing of this data.
What role does the CDP play in this?
The various sources of customer data are connected to a central Customer Data Platform (CDP). The CDP contains not only the contact details of customers, but also collects and analyzes their activities such as purchasing behavior, website visits, complaints, online orders, support tickets, and more. In addition, the CDP can also manage a product catalog so that each customer can be evaluated and categorized down to the specific SKU of their purchase. This enables not only marketing campaigns and personalized recommendations, but also targeted recall actions for customers whose purchased product is defective by directly addressing them.
Product recommendations, recipe suggestions, offers, and nutritional information can be mirrored directly to the mobile phone via augmented reality
Such use cases demonstrate how an improved customer experience increases the benefits and added value and contributes significantly to optimizing communication, offerings, and the service portfolio.
Use-Cases for Improving Customer Experience in Retail
A Customer Data Platform (CDP) can have various use cases in retail. Here are seven examples of CDP use cases in retail:
Customer Segmentation: The CDP can analyze and segment customer data to identify different target groups. This allows the retailer to create personalized marketing campaigns and offers for specific customer segments.
Example: Customers who regularly buy running apparel of a particular brand from the sports segment "Running" are informed about incoming autumn initiatives and offers. They receive targeted notifications online or via email, offering them exclusive deals and reservations for products available either online or at a store near them. This allows them to benefit early from the new autumn collections and ensure that desired items are reserved for them.
Analyzing Customer Behavior: By tracking and analyzing customer behavior, the CDP can provide insights into customer preferences, shopping habits, and preferences. This information can be used to offer personalized recommendations or targeted cross-selling and upselling actions.
Example: A photography equipment retailer uses knowledge about its customers' purchases to optimize offers for accessories and novelties. By analyzing the purchased products, the store can specifically address the individual needs and interests of customers. Additionally, the interactions of customers on the website allow the camera store to be informed about their changing interests early on. Based on this information, it can offer its customers targeted and optimized offers and alternatives, considering historical data.
Customer loyalty and Retention: By analyzing customer data, the CDP can assist the retailer in providing customized offers, rewards, and discounts to strengthen customer loyalty and enhance customer retention.
Example: A grocery store analyzes the shopping behavior of a long-standing customer to understand their favorite coffee brand and preference for organic products. Upon entering the store, the customer receives a list of organic products on offer through the app. Additionally, they are notified that they will receive a discount on a pound of their favorite coffee once they reach a certain spending threshold on their organic purchases.
Omnichannel Marketing: With a CDP, customer data from various channels such as an online store, physical store, mobile apps, etc., can be merged. This allows the retailer to deliver consistent and personalized omnichannel marketing and strengthen customer loyalty across various channels.
Example: A customer who has specifically informed himself about a TV set on the website of an electronics store is informed via the company's mobile app about the branch nearby. This encourages the customer to transition from an online sale to a local purchase.
Optimizing Inventory: The CDP can provide real-time data on the demand for specific products. This allows the retailer to optimize his stocks to avoid overstocking or shortfalls, ensuring customer satisfaction at the same time.
Example: A company that specializes in weekly changing theme offers in the categories of clothing, household goods, and seasonal products, alongside its core business of selling coffee, records increasing demand for children's shoes of a specific brand in its online purchases. As a result, analyzing the customer profiles and their interests helps the company organize prioritized deliveries of merchandise to specific regions to cater to the increased demand.
Purchase Prediction: Based on past transaction data and customer behavior, the CDP can make predictions about future purchases and product preferences. This allows the retailer to create targeted marketing campaigns and adjust the inventory accordingly. Example: A DIY store has a group of regular customers who regularly purchase tools of a particular brand. Based on the purchase data, the DIY store realizes that these customers often sell their tools on eBay when a new model or an innovation hits the market. The DIY store offers these regular customers special conditions for their forecast purchases, and marketing campaigns and inventory are adjusted accordingly.
Improving Customer Service: The CDP can provide customer data and histories to improve customer service. Customer service representatives have access to comprehensive information about the customer, his purchase history, and any problems, leading to personalized and effective customer service.
Example: A car dealership realizes that a customer with a specific vehicle has rising repair costs and a strong brand loyalty. Through discussions with the service department, the dealership learns that the customer is considering purchasing a new vehicle. To act proactively, the dealership's sales department offers the customer a test drive with a new model from its inventory and presents financing options that also include the trade-in of the old car.
All these simple use cases demonstrate that by consolidating customer information, a variety of improvements can be achieved in customer communication, the diversity of offerings, and the service portfolio. It is foreseeable that in the future, innovations such as Pick&Go, beacon tracking, augmented reality, and AI-supported product recommendations will play a larger role in personalized customer care. These innovations also contribute to enriching customer profiles with additional data to enable even more precise personalization of customers. On the other hand, these innovations also benefit from the central customer data repository and enable customized service for the customer.