Why is retail analytics important?

Retail analytics is the most effective way that traditional retail can compete with e-commerce.  It gives a retailer the same kind of information that e-commerce retailers collect.  This allows retailers to build their marketing strategies, both in and out of store, in a way that maximizes their revenue.

A major area in which retailers are lagging behind e-commerce is the collection of data.  E-commerce companies collect information on every person visiting their site through the use of cookies.  They then utilize that data to give their customers a targeted marketing experience.  Retailers have the ability to do something very similar, and collect retail analytics data.  They can use this data to increase their revenue.

What does a retailer do with their analytics data?

Once a retailer has their analytics data, they can proceed in a number of ways.  Their goal should be to use this data in a way to drive retail effectiveness.  One of the biggest issues for them is the foot traffic conversion.  While most online stores expect a traffic conversion of 2-3%, retailers should aim for a much higher conversion rate: approximately 15-20%.  By leveraging data, retailers can elevate their conversion rate, and increase their revenue.

Another issue retailers face is whether or not they are hitting target demographics.  This is particularly important because retailers need to know their audience, as this information is crucial to building a marketing strategy.  Additionally, once a retailer knows their audience, they can leverage marketing skills to expand it.

The most immediate information a retailer can cull from their data is optimization of staffing.  If a retailer knows how many people are going to be in a store (or a particular area of their store) at a given time, they know how to best staff their store.  This is significant because it reduces overhead, and prevents staff from being left idle on the sales floor.

How to measure effectiveness

Once a retailer implements the changes they made due to their data, they need to measure how effective their changes were by seeing the difference the changes made in revenue.  As these changes take shape, the retailer will feasibly see an increase in their revenue.  If this is the case, they can look to make more changes based on the continuously incoming data.  If it is not the case, there are a number of courses of action.  They can look to see where the discrepancies come from.  For example, calibration issues can cause large disparities in information.  Also, they can check whether the information was implemented in the most effective way.  Further insight into a customer base will always serve to aid the retailer.