What is the difference between Retail Analytics and In-Store Analytics?

To put it simply, in-store analytics is a part of retail analytics. Retail analytics includes all components of retail operations and decision making while in-store analytics focuses on customer traffic and behaviour in a physical store outlet.

What is Retail Analytics?

Retail Analytics primarily serves four purposes which are, interpreting data, determining what information is needed, defining how to best gather the information and lastly, analysing how that information will be used in a retailing environment. To retailers, these analytics provide meaningful insights which help in the decision-making process thus aiding them in improving and reinforcing what is needed for their businesses.

For instance, retail analytics is proven beneficial for sales, inventory and procurement. With the acquired data, retailers can better manage inventory by understanding customer needs, as well as lower space wastage and overhead costs.

What is In-store Analytics?

In-store Analytics is part of Retail Analytics, however it focuses on analysing and obtaining data of customer traffic and behaviour upon their visit to the store. In-store Analytics is used to ensure better store performance by knowing customer preferences/interests thus increasing sales. With the help of in-store analytics, retailers can comprehend customers’ needs better and then have better product placement, guide purposeful in-store footfall as well as increase street capture. Not only that, retailers are also able to create a unique, personalised in-store experience for customers by sending a personalised coupon, suggesting an abandoned cart from online shopping sessions and more. Apart from that, retailers can also identify the marketing attribution that works best for their stores.

Apart from helping store managers to run their businesses, in-store analytics also provides an advantage in retail competition. If a retailer is able to offer the right products for the right customers at the best time and in their preferred shopping environment, he or she will have an advantage compared to its competitors. This is due to the fact that the more useful data is collected, the more chances a retailer can optimise a customer’s shopping experience.

When a retailer has data on in-store traffic, then you can calculate a conversion rate. For example, if a store has 500 visitors and 50 sales transactions per day, then the store has a conversion rate of 10%. Now the retailer changes and fine-tunes things like product placement, product presentation, store layout, cashier operations, etc. If the store’s conversion improves, let’s say it increases to 12% over 4 weeks, then the changes which the store manager has done were good changes, and this becomes a continuous process of improvement. However, this is only possible if you have metrics to compare it to (in this case the conversion rate).

This shows why In-Store Analytics is so critical. Without such store data you are unable to compare and unable to set KPIs. Without In-Store Analytics you can only review and compare sales data but you ignore what influences customers inside the store.

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