How to Increase Average Order Value (AOV): The Behavior-Based AI Approach

In the world of ecommerce, traffic and conversion claim an outsized share of attention. Of course, getting lots of relevant visitors onto a website or app where they can make purchases—and then convincing them to go through with that purchase—is key to generating revenue. But as our friends at Optimizely wisely remind us: Sometimes marketers focus much of their energy on increasing traffic to a website when it would more impactful – and profitable – to increase their AOV. Increasing traffic typically costs money, while increasing AOV does not.

 

Average order value (AOV) is a measurement of performance for an ecommerce platform that tracks the average dollar amount spent in each customer purchase from that platform. AOV is calculated by dividing total revenue by the number of orders—and of course, regardless of what product or service is being sold, increasing the amount of revenue generated by each order is a meaningful goal for all ecommerce companies.

 

For many years, figuring out how to get a customer to spend more money when making a purchase online was a matter of A/B testing. The tactics for increasing AOV thus looked a lot like the tactics for increasing the conversion rate of a landing page: creating two versions of a given step on the ecommerce user journey, and then using Google Analytics and similar tools to assess which customer experience (CX) design worked the best at delivering the most revenue per order. 

 

In addition to A/B testing different CX, ecommerce companies have worked to increase average order value through tactics like cross-selling (“Would you like to add a brush to your shampoo purchase?”) and upselling (“This best-selling brush is only $5 more!”). These and similar strategies are essentially this-then-that “rules” for customer engagement that can be set up in a content management system (CMS) or product information management (PIM) system. 

 

Here’s where it gets interesting. It used to be that the product rules for tactics like cross-selling had to be created and updated manually in the ecommerce platform, a resource-draining task. But these days, ecommerce tools powered by artificial intelligence (AI) and machine learning (ML) like Optimizely’s Optimization as a Service (OaaS) make the creation and optimization of such responsive actions faster, simpler and more effective. This both creates internal efficiencies and improves the customer experience, as AI suggests new tactics based on trends in user behavior and ML makes those tactics more effective over time.

 

There’s much more that AI and ML-powered tools can do. For one: content recommendations. Product suggestions informed by AI and ML are what made Amazon into the behemoth that it is today. The more relevant the content that a shopper sees, the more likely they are to make a purchase—and the more products they are likely to buy. Doesn’t get much simpler than that formula for increasing AOV.

 

If there’s one recommendation that readers take away from this blog post, let it be this: to succeed in today’s highly competitive e-commerce environment, companies need to make the capture and analysis of data a top priority in terms of resource allocation.

 

We are in the very early stages of the next great leap that AI and ML promise to make for ecommerce platforms (and for average order value): personalization. If you’ve ever been greeted by name on a website or an app, that’s a basic example of personalization. With the right AI and ML tools, companies can leverage personalization to increase AOV with tactics like unique offers for certain customer segments. At Optimizely, this type of personalization tactic helped one retail client achieve a 33% increase in relative revenue.

 

There is much more to come from the AI and ML-powered ecommerce optimization, including a step-up in the types and sophistication of the recommendations that customers see. As these smart tools continue to leverage historical data analysis to predict what a user might do or want next based on similar users’ past behavior, the ability of ecommerce companies to persuade their customers to buy more items or choose pricier options will be vastly enhanced.

 

At Nansen, we specialize in helping companies improve key performance indicators like AOV by creating powerful, scalable digital platforms that engage and delight their customers. We’re proud to be a Silver Optimizely Partner, and to have worked with the Optimizely team for more than a decade to put transformational strategies into action for ecommerce clients.


If you’re wondering how to take your ecommerce optimization to the next level, we can help. Let’s start a conversation.