Replacing Marketing Basket Analysis with AI Product Recommendations in Email

As eCommerce marketers, we are constantly searching for contextual communications that leverage various data points to optimize conversions and lift incremental revenue. Consider your businesses shopping behavior data. If you’re only using this for revenue reporting and inventory analysis, then it’s likely you’re, literally, leaving money in the shopping cart. According to Barilliance– personalized product recommendations increase conversion rate 5.5 times!

Analyzing baskets requires the creation of associations rules, statistically significant trials through a large amount of transactions and the analysis of very large data sets. So what’s the quickest path to data driven, personalized recommendations that deliver the promised increase in incremental revenue and conversion rates? Artificial Intelligence (AI) allows you to leverage the power of data science in constructing stronger personalized recommendations by automating the calculation of patterns within your existing and incoming commerce data. That’s a fancy way of saying – it does all the work for you. If you’re on the fence of automating your merchandising, take a look at some of the smarter recommendations you can make with the help of automated machine learning. That means – no data scientist needed.

“You May Also Like”

You May Also Like, is an implicit product recommendation that leverages past purchasing data, live data and even what you know about a customer to merchandise products in an email to merchandise products that are similar to products they’ve engaged with or may be interested in based on what you know about them.

Where should it live?

This kind of recommendation is best used in the context of extending a customer journey and wallet.

“People Like You Buy”

In a previous post, I discuss Cialdini’s principle of persuasion liking as it relates to product recommendations. By stitching together a person’s viewing history and comparing it with similar shoppers behaviors’, you can create a relevant recommendation that persuades customers to purchase. Consuming and processing this much data in a way that confirms it’s significance requires the assistance of automated machine learning. Consider, Salesforce’s Einstein for easy to use AI capabilities.

Where should it live?

People Like You Buy is a great way to increase engagement and nudge a shopper towards a most likely eventual purchase.

“Frequently Bought Together”

Frequently Bought Together is a great way to increase order value by identifying purchasing patterns within your commerce data linking product purchases together. This cross-selling technique can also leverage your commerce data with your demographic data to create multiple tiers of recommendations that is truly personalized to the customer.

Where should it live?

Try using this towards the end of purchase and even in post purchase communication for maximum impact. This creates a narrative between the two as well as reaches them when they are most ready for action.

Leveraging purchasing behaviors to create contextual content that’s engaging is a great way to improve your customer journey and maximize conversions. While these are just three examples, it’s evident that complicated basket analysis is no longer the only way to increase your email conversion rates. Skip the data scientist – and consider A.I.

Published by Diandrah Lamarche

Creative eCommerce manager with a passion for leveraging data and digital capabilities to build online branded experiences that expand audiences, deliver innovation and exceed business goals. Ask me about eCommerce, being a mom, cooking (or eating) and travel.

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