A product recommendation engine tracks your website visitors’ behavior to suggest goods they may be interested in. E-commerce giants like Amazon and Alibaba have built their success on proprietary reco engines that highlight the most relevant items for each person. Recommendations can happen within websites themselves, in email campaigns or online ads. When used in the right way, this technology can greatly improve conversion rates, reduce cart abandonment and bounce rates, increase average order value and, generally speaking, boost e-commerce revenues. Just to give you an idea, McKinsey reports that 35% of Amazon’s revenue can be attributed to their recommender engine.
How do product recommendation engines work?
Product recommendation engines typically rely on machine learning algorithms to dynamically serve up the most relevant products for each individual. These algorithms can be fairly basic. For instance, showing “trending products” by leveraging data on the most purchased items for a given time period, or recommending “recently browsed” products based on what others have clicked on. But algorithms can get significantly more nuanced these days. So called “contextual bandit” algorithms use known visitor data to recommend the best converting offers and promotions before the visitor has made their interests explicit. Text analysis algorithms are equally fancy. These use Natural Language Processing (NLP) help you determine which keywords you should use in your content to offer a personalized experience for each visitor.
Enough tech-speak – can you use product recommendation engines too?
Yes. Product recommendation engines are here to stay and are setting the stage for the future of online shopping. So, how do you implement one in your e-commerce site? If you have a large development team, you may want to try developing recommender algorithms yourself. If you don’t, there are several software vendors that offer solutions out of the box. Who offers the best?
Choosing a product recommendation engine
We made a list of top product recommendation engines to help you review your options.
We looked at:
- Implementation time and requirements
- Available features
- Level of support