Ever spent way too much time scrolling through movies on Netflix? How about driving to the store after browsing to exhaustion online?
Choice overload is a very real problem, especially for online retailers that carry an ever-widening catalog of products. Amazon listed more than 75 million individual items for sale in March of 2021 (which makes me feel slightly better about spending the better part of two hours trying to find new bike tires).
Personalization to the rescue
Given the overwhelming options, it’s not surprising that more than a third of Amazon purchases come from product recommendations. And other businesses rely even more heavily on AI-based recommendation engines. For example, 80% of Netflix subscribers’ watch time comes from personalization algorithms.
These high-level numbers hide a critical fact: tailoring your product catalogue to customers’ needs improves the shopping experience. This drives better conversion rates, larger order sizes, stronger brand loyalty, and reduced acquisition costs.
What’s the Catch?
Implementing recommendation systems typically takes a substantial investment in people, software, or both. I helped lead the development of a recommender system at my previous employer that, while extremely lucrative once mature, took a team of ten more than six months to get running. And research suggests that’s a relatively short timeline!
Having seen both the benefits of and obstacles to AI-driven recommendations made it easy to pick Slick Predict’s first product, which takes a whole lot of the cost out of adding personalized recommendations to your Shopify store.
