by Mattieu Blandineau
Building a recommendation engine from scratch can be complex, while relying on off-the-shelf, packaged solutions makes it almost impossible to develop a differentiated customer experience or gain a competitive advantage. Now, there is another way — today, retailers have an ability to build recommendations using an API-driven machine intelligence approach that is simple to integrate and easy to use.
The Machine Learning Approach
The existing systems and software stack in any organization can be elevated to the advantages on offer here — and the functionality far outstrips off-the-shelf packaged solutions. With Machine Learning models, brands can automatically deliver tailored recommendations for online shoppers. Those models enable retailers to increase conversions and orders by analyzing the items a shopper interacts with (e.g. clicks, adds to a cart, and/or purchases) and suggesting similar or complementary products during the same session.
With those models being packaged into a simple and flexible API, developers can build machine learning powered recommendations on your company’s digital storefronts by using as little as six lines of code. This will help to improve conversions and increase the average order value. The machine learning approach surfaces the most relevant recommendations, offers, or suggestions for a shopper in milliseconds. Using these models enables brands to leverage data from two sources: shopper behavior (the shoppers’ actions across a website or app, including previous purchases) and product data (all product attributes contained in the product catalog, including product, description, availability, and price).
Positive Use Cases
For The Vegan Kind, a provider of subscription boxes of vegan goodies, it is all about helping customers make the right choices by providing them with great alternatives and suggestions as they browse. After deploying an API-first recommendations solution, The Vegan Kind was able to surface recommended products associated with the items visualized, ultimately increasing the opportunity for shopping cart expansion.
API-first approach, front-end frameworks, and advanced documentation also ensures that it is simple to integrate and highly flexible. HiCart, the creators of a user-friendly, Lebanese marketplace where members can enjoy a seamless experience, was struggling to offer recommendations with their current implementation. With as little as six lines of code, HiCart was able to implement recommendations into their online experience and go into production in four days. Now, when a shopper searches for a specific item, additional alternatives are surfaced as well. This means the customer has more choices, a more satisfying experience, and less of a chance of abandoning their shopping cart.
Today, many companies are figuring out how to bring the in-person shopping experience online. Product recommendations are no different than a sales associate suggesting a different product to go along with an item that a customer is thinking about purchasing. With the ability to search smarter with API-driven machine intelligence, retailers can create better online experiences for their customers. If you are interested in trying an API-first approach for your recommendations, try Algolia Recommend for free at www.algolia.com/users/sign_up.