Retail and Data Analytics: Analyzing Customer Journey

Understanding the customer journeys within a retail store has always been a focal point for decision-makers to design the layout of their store accordingly and place the products which complement the customer journey, hence achieving greater sales through an increase in basket size. With the influx of data usage in the retail industry, it has become more interesting and accurate.

In this article, we will be discussing one of the methods of analyzing customer journeys and re-planning store layouts with the use of data analytics.

There are multiple ways to understand the customer journey marketers have been using in past, such as observing customers and how they move within a store and make purchases. One of them is utilizing basket reports to understand this pattern.

Basket analysis or basket report consist of retail sales structured in transactional data, having unique order IDs, barcodes, and product bought in a single purchase. Each product bought in a single purchase has the same order ID but is structured as a separate transaction.

To understand customer purchase patterns through this structured data, a product is cross analyzed with other products purchased in the same basket multiple times. If snacks product are bought with drinks, chocolate, and tobacco, and this combination is found in multiple orders, it gives different types of information.

Firstly, these products/categories represent cross-selling categories, and it can be said that tobacco is being sold with snacks, chocolate, and drinks, and vice versa. Secondly, the customer is moving along these categories within a store, which can be said that in a store a customer took either drink, snack, chocolate, or tobacco, and this movement can be verified from the placement of these drinks within a store. 

Changes in layout can be made if a marketer intends to change customer behavior and add more products in this combination, which will eventually increase the basket size hence increasing the sales per order as well. It can be concluded that an increase in the use of data in the retail market will prove to be more effective in getting desired results rather than experimenting with intuition-based knowledge.