As consumers have more channel choices than ever before, the role of the physical store has become more important than ever for providing customers with effortless experiences and earning their loyalty.

This helps explain why a growing number of retailers are increasingly analyzing data about customer behaviors, customer traffic patterns, inventory placement, and other information to gain a deeper understanding about customer preferences, merchandising placement, and employee performance to identify opportunities to strengthen the in-store customer experience – and bolster business performance.

In-store technologies are helping to deliver more personalized experiences to customers. For instance, an apparel retailer is able to use surveillance cameras and facial recognition technologies to help identify individual customers as they enter a store.

The retailer can cross-reference that information with CRM (customer relationship management) or transactional data about that customer including past purchases, average amount spent per visit, etc.

Applying these types of customer data with analytics can enable sales associates to offer each customer the type of relevant and personalized assistance that can boost the in-store shopping experience.

Using the same this scenario, a retail manager can also drill deeper into customer service interactions. For instance, the store manager can measure how long it takes a sales associate to greet a customer or the number of customers who browse merchandise before receiving sales assistance.

The retail manager can use these insights to determine whether sales associates are delivering consistent customer service or determine whether customer support targets are being met.

From there, the store manager can take appropriate steps for employee coaching or adjust staff scheduling to ensure that the right number of sales associates are on the floor at the right times.

Retail leaders can also use data regarding customer foot traffic, product placement, and point-of-sale information to improve the in-store customer experience in other ways. For instance, a regional manager for a convenience store chain can analyze these data sets to help determine the most effective product placement strategies that benefit the customer experience while boosting sales.

Predictive analytics and data discovery may inform the regional manager that its most frequent customers – men – are most likely to purchase prepared/fast food when buying gas. The regional manager uses these insights to place prepared foods in close proximity to the checkout registers where male shoppers are most likely to see these items.

Not only does this product placement lead to an increase in prepared food sales, it also generates increases in customer satisfaction and return trips per month from this customer segment.

Next Steps:

  • We invite you to watch the first part of a three-part complimentary, on-demand webcast series, “Optimizing Store Performance with Analytics.” In the first webcast with Spotfire partner and CPG&R expert, InfomatiX, you will discover why having more insight through ad hoc analytics is something your business shouldn’t go without.
  • Subscribe to our blog to stay up to date on the latest insights and trends in big data, and big data analytics.

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