AI-Backed Product Recommendations: How to Increase Average Order Value

As an ecommerce business, one of your primary goals is likely to increase your average order value (AOV). One effective strategy for achieving this is to implement product recommendations for your customers. But why settle for basic recommendations when you can leverage the power of data and AI to create personalized recommendations that drive up customer satisfaction and AOV even more?

For example, let's say you have a customer named Maria who has made several purchases from your online store in the past. There are software solutions that can build a comprehensive profile of her preferences and interests by gathering data on her purchase history, including the items she has bought, the quantities she purchased, and the time between each purchase. These solutions can also gather data on her browsing behavior, such as the pages she has visited and the products she has viewed on your website. Additionally, you have demographic data on Maria, including her age and location.

By gathering all of this data about Maria, you can build a comprehensive profile of her preferences and interests. Or you use a SaaS solution like Churned to do this for you. You might discover, for example, that she has a preference for seasonal items, tends to make purchases every few months, and is in the 35-44 age range. Machine learning algorithms can then analyze this data to identify patterns and make informed recommendations. For example, the algorithms might recommend that you send Maria an email campaign featuring seasonal items a few weeks before the start of each new season. Or, they might suggest that you send her a personalized product recommendation email every few months, based on the time since her last purchase.

But simply having the recommendations isn't enough - you need to effectively integrate them into the customer journey. This could mean featuring them prominently on product pages, including them in email campaigns, or displaying them as "related items" at checkout. With the help of marketing automation tools, it's possible to automate these recommendations and personalize them at scale, ensuring that each customer receives tailored recommendations based on their individual data profile.

In conclusion, AI-backed product recommendations are a powerful tool for ecommerce businesses looking to increase their average order value. By gathering and analyzing customer data, you can create personalized recommendations that improve the customer experience and drive up sales. And with the help of marketing automation, it's possible to personalize these recommendations at scale. If you're not already using AI-backed recommendations, now is the time to start leveraging this valuable resource for your business.

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