As we approach the halfway point of 2017, we thought we’d take a look at some of the most-read content we have produced so far this year on an area of enormous strategic importance not just to Salesforce Commerce Cloud, but also to our customers: AI-powered personalization.

Salesforce Einstein, which is embedded in Commerce Cloud, empowers retailers and brands to create true one-to-one personalized shopping experiences – beginning with a shoppers’ very first click. Einstein enables data-based decisions and grows smarter over time, so you no longer need to rely on broad, demographic data that is largely assumption-based and provides a very limited view of your customer’s true shopping behavior.

Introducing Einstein Commerce Insights: Get Smarter About Your Shoppers

This blog post was not only the most popular Einstein-related post of 2017 thus far, it was the third most popular overall, underscoring the high level of interest in AI capabilities. Commerce Insights is a shopping cart analysis dashboard that empowers brands to understand which products are most commonly purchased together, and gives merchandisers a granular view of shopping activity, including product-specific sales and top co-purchase categories. Each day, the Einstein engine analyzes order history to calculate the frequency with which items are basketed together. This information is used to group products together, thereby increasing the likelihood of a higher average order value.

Huge Momentum for Machine Learning with Salesforce Commerce Cloud

When this blog post was published in January, we wrote that more than 95 retailers and brands powering over 250 sites globally were using Product Recommendations powered by Salesforce Einstein. Since then, we’ve nearly doubled the number of sites using Einstein to deliver personalized recommendations. It’s easy to see why. Commerce Cloud has AI embedded into the fabric of the platform, which makes implementing AI a truly seamless experience for retailers.

Black Diamond Sees 15% Revenue Boost With Salesforce Einstein

This case study is one of many Einstein success stories. Black Diamond, purveyor of climbing, hiking and skiing gear, ran an A/B test of Einstein-powered Product Recommendations against a third party. “Typically with A/B tests we see a small lift from one side, which lets us know the better path,” said Adam Smart, Senior Web Merchant. “We don’t usually see a test where we have 100% confidence in one side, but that’s what happened.” With Product Recommendations, Black Diamond saw a 9.6% increase in conversion, and a 15.5% Increase in revenue per visitor.

Best Practices for Implementing Product Recommendations

This post was written by a strategist in our Retail Practice group, who partners with global retailers and has seen what works and what doesn’t when it comes to intelligent recommendations. Some tips: don’t distract, interact. In other words, recommendations should enrich and expedite (not overwhelm and distract from) the shopping experience; move slowly. Start with the basic rule setup for each recommendation type: homepage, category page, cart, etc. Test the next experience against the current one to determine business benefit. Then create new versions of each rule set separately with tweaked configurations. Finally, test, deploy the “winner,” and repeat. Our strategist recommends conducting these tests page by page, with a new test starting every few months.

We’ve recently launched other Salesforce Einstein content, including a video highlighting a personalized shopping experience, and a best practice guide for implementing Einstein. We believe AI-powered personalization is a game-changer in the industry, but don’t take our word for it. As Brian Hoven, Global Head of eCommerce at Icebreaker (and Einstein user) put it, “If you’re not using this you’re missing out on quite an opportunity.”