How Predictive Analytics Can Revive the CX at Retail Stores
In the era of new retail, customer experience (CX) is the new frontier of competitive advantage. Due to advances in digital mobile technologies, consumer spending has undergone a marked shift in the last few years. Much of what consumers want is driven by social media. The desire to share pictures and stories on Facebook and Instagram is making consumers prioritize experiential value of products over the products themselves. All these changes in consumer behavior is exerting pressure on retailers to reconsider the value they are currently offering in-store.
To adapt to the rising expectations of these digitally savvy customers, most retailers understand the need to collect customer data. However, collecting data is not the same as generating actionable insights. In fact, there exists a big gap between them. By merely knowing “Lucy visited your store 2 times last month and bought a pair of denims at each of those visits” is not enough. One also wants to know – “who are the other customers that are likely to visit your store and purchase a pair of denims?” Such level of actionable intelligence can be the difference between a winning retailer and the one who gets left behind.
Building a 3D and 360 degree View of Every Consumer
Retailers who want to emerge as champions of customer experience are investing in emerging location-based and predictive analytics technologies to anticipate customer needs and create an element of serendipity in stores. By using a WiFi based analytics platform, they collect data on who their customers are, how they move and interact with their stores and how long they linger in a certain zone.
Once enough data has been collected on a certain customer, they can look at historical trends and use that information to predict what that particular customer could possibly want in the future. Integrating data from other internal databases such as CRM, POS and loyalty with data captured through a location insights platform can reveal a 360 degree view of the customer, allowing them to create a highly relevant and a personalized experience for their customers.
Predictive Analytics Helps you Change the Conversation
Predictive analytics solutions can totally uplift the in-store experience by inspiring people to make discoveries in the most natural way; by firing their imagination and creating a demand for things they didn’t even expect to purchase.
Instead of having sales staff asking customers for what they want, these systems can intelligently glean insights about them in a non-intrusive manner and then use that information to serve them with personalized recommendations that look natural.
Use Case of Predictive Analytics in Retail
Predicting Demand – Not being able to find appropriate sizes in apparel stores is a leading cause of customer churn. By tracking which clothing items, sizes and styles are fast moving in certain store locations, retailers can predict demand and accordingly stock those types of merchandise in their stores. This not only helps avoid customer churn but also drives cost cutting by maintaining optimal inventory. Moreover, by identifying the styles which are most popular, the store associates can understand top trends and make recommendations to customers instead of merely sharing their personal opinions.
At a fundamental level, all these efficiencies lead to the overall improvement of customer experience. When a customer is able to find his/her size or is directed to the store which stocks that size and also receives personalized shopping advice from store associates, it just makes them feel more valued and elevates their in-store experience. At a time when ecommerce operators can lure a customer with the promise of convenience, it’s experiences like these that make shopping at retail stores a therapeutic experience.
Predicting Lifetime Value of a Customer – A typical customer acquisition strategy entails minimizing the cost of acquisition (CAC) per customer. However, customer analytics can enable him to optimize his strategy by predicting the lifetime value of a customer. If a retail marketer studies the behavior and the spending pattern of each his existing or past customers, he will be able to group them in different data sets. And then, by identifying which attributes correspond to high value customers and which align with customers who are likely to churn, he can estimate the CLV of these customers. Based on these inferences, he may be able to infer which new customers are likely to have a higher CLV and then adjust the CAC accordingly.
Simply put, by understanding how your most valuable customers engage with you, you will not only be able to optimize their in-store experience at every touchpoint but also replicate the same experience for new customers who exhibit similar patterns.
Predicting Customer Sentiment – While pricing, service and selection are important, they are not the only factors that drive the in-store experience. The store layout, the ambience and even weather can have a huge impact on shoppers. But how do you measure which factors are affecting sales and when? By measuring the change in footfall traffic, dwell time, and sales before and after adjusting the store layout can indicate whether or not it was successful.
Dillard’s Inc. department stores used predictive analytics to determine if it should pull Calvin Klein dresses into their own boutique away from the general sales floor. Tests in 11 stores last year revealed that it should not because sales of other labels’ dresses fell when the Calvin Klein dresses were removed from the mix on the floor.
A lot of guesswork goes into everyday decisions taken at retail outlets. Such methods of trial and error are not sustainable for companies who want to differentiate on customer experience. Retailers who can leverage predictive analytics and data science can positively impact their bottom lines and create delightful experiences for their customers. In the future, the mark of success will be in predicting what your customers want even before they know it themselves.
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