Personalization Glossary

A/B testing? Recommendation models? The personalization engine space uses several acronyms that may seem daunting, so we're here to help. You can find definitions to the most common e-commerce customer experience optimization terms here!

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Average Revenue Per User (ARPU)

What is Average Revenue Per User (ARPU)? 

Average Revenue Per User (ARPU) is the amount an eCommerce retailer makes, on an average over a given time frame, from visitors that enter their online store. It’s calculated by dividing the total revenue by the total number of users, in that time period. 

Why is it important to measure ARPU? 

ARPU is a common metric used to understand the lifetime value of customers. For marketers, it is increasingly important as costs of acquisition (CAC) of new customers have increased significantly over the last few years. The CAC numbers should be a fraction of the Average Revenue Per User for a given time period. For example, if retailers make a revenue of $ 800,000 in a quarter, and let’s say they got 8,000 users in that time, their ARPU would be $100. 

It is important to note that ARPU is calculated with unique users and not visits. One user may have visited a store multiple times. 

What factors affect ARPU?

Things that affect Average Revenue Per Visitor (ARPU) include the number of returning visitors,  the average cost of an item, conversion rates and Average Order Value (AOV). Returning visitors spend 67% more than new ones according to CMO by Adobe.

What can retailers do to improve ARPU?

There are many ways to improve ARPU. We have seen that personalized recommendations result in a significant lift in all three metrics that impact ARPU- conversion (number of users who make a purchase), AOV (the average value of an invoice)  as well as % of return visitors. Online stores with higher returning visitors show sustained growth. 

As important as it is to choose the right type of product recommendation, it is equally important to place the recommendation at the right place on the shopper journey, keeping in mind shopper intent. Therefore, Visually Similar product recommendations work best on the PDP pages. Recommended for You, which takes into consideration individual shopper preferences for a wide range of attributes, improves conversion rate when placed on pages where the maximum drop off occurs. 

Personalized recommendations must take into consideration every individual shopper’s preferences for a wide range of attributes. Furthermore, retailers must be able to alter content, real-time based on a shopper’s behavior. With, retailers can use the power of AI to build rich customer profiles for every individual shopper that walks into their online store. then uses this understanding of customer profiles in combination with real-time behavior data, altering content real-time to help a shopper find a product they love, in the shortest possible time. 

How to use ARPU for business planning

  • Identify campaigns that have the greatest impact– While marketing campaigns are often measured on CTRs and traffic, ARPU adds a qualitative layer to those metrics. By calculating ARPU, it helps marketers identify which campaigns had the greatest impact on revenue growth. 
  • Identify channels- As an extension of the above logic, ARPU can also be used to identity acquisition channels that have the greatest impact on revenues. 
  • Identify high-value customers. ARPU is often used as a way to segment high-value customers who should be treated especially from the other segments. 
  • Forecasting– mapping the change in ARPU can be a good indicator of organizational health and act as an important metric in forecasting future growth. 

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