Here at Magento Business Intelligence, our clients often ask for help identifying which products increase customer loyalty. While there are numerous analyses one could set up, and the definition of loyalty might differ from business to business, this blog post details a simple, yet effective analysis to pinpoint these products.

Defining loyalty

For this analysis, we are defining a loyal customer as a member of your commerce dataset who comes back and orders again after a first purchase. That’s it. We don’t care if they order within a certain time range; we don’t care if their orders are of a certain value. We merely want the customer to place at least one order after their first.

Additionally, this analysis is also helpful for identifying product-based loyalty, which we are defining as a customer who places a follow-up order containing the same product or products as in their previous order.

The analysis…

This analysis, which can be set up with just a few calculated columns within Magento BI, highlights, by product:

• The number of orders that contain each given product,
• The number and percentage of those orders that led to a follow-up order, and
• The number and percentage of those follow-up orders that contain the same product. 

With these simple measures, you can begin to gain insight into what products cause your customers to purchase again from your business. Additionally, this can also help identify products that customers are more loyal to, by highlighting the products that customers purchase in back-to-back orders.

The following screenshot is an example of this analysis in Magento BI, courtesy of our favorite fake-data company, Vandelay Industries:

The first metric is a count of orders that contain each given product. The second and third metrics are a count and percentage of those orders that then led to the customer placing a follow-up order. The fourth and fifth measures are a count and percentage of the follow-up orders that contained the same product as the previous order.

Additional metrics

One quick measure that can added in, if desired, is the average or median time to the next order, by product. This could identify products whose follow-up percentage appears attractive, but whose time to reach that follow-up is quite long.

A second additional metric you might consider reporting on is the number of distinct customers purchasing each product. This measure may identify products that only a few customers are purchasing over and over, or products that many customers purchase only one time.

Iterations

The analysis explained above works for all orders, identifying whether a given product leads to a follow-up order in any case. In my mind, I think of this as an “order n to order n+1” analysis. It includes customers 1st orders leading to 2nd orders, customers 2nd orders leading to 3rd orders, …, and customer’s nth orders leading to customer’s nth +1 orders.

However, if you don’t want to include all orders, you can customize this report by defining what the n variable is. For example, if your business is much more concerned with converting a new time customer (with one order) into a repeat customer, then we can customize the report to only consider 1st orders. By doing so, we would then be analyzing how popular each product is within 1st orders, and how successful those products are at causing a customer to want to purchase from your business a 2nd time.

If you are a Magento Business Intelligence customer and you would like to add this report to your repertoire of actionable analyses, reach out to us at support@rjmetrics.com and reference this blog post. We’ll know exactly how to help!