Technical Product Manager
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It should come as no surprise that Algolia believes in the power of a great search & discovery experience. In fact, we’ve built our entire company around it. Our goal has always been to improve speed and relevance for users so that they can find what they’re looking for and get on with their day.
We’ve come to realize that search is not the only way to find or discover. Search needs to be combined with other means of discovery to offer a more far-reaching and satisfying experience.
Think about your users’ day-to-day experiences online. They’re not consuming content and making purchases only after entering a query into a search bar. They’re also clicking on links that their friends and family sent them. They’re pressing “play” on one of the “recommended for you” titles on Netflix. They’re adding another item to your Amazon cart because it’s being suggested as something “people often buy with” their initial products of interest.
In other words, they’re acting on recommendations. They do this because recommendations make their online experience easier and more enjoyable. A relevant recommendation changes the typical dynamic of being online: rather than needing to search to find content, recommendations finds the content for you. This matters because product quality is the chief reason most customers stick with brands. In a world where it’s more difficult than ever to retain customers, that means a lot: “Product quality is the chief reason 74% of loyal consumers resist switching brands.”
This is why we’ve built Algolia’s Recommend product, and why we’re telling you now that we recommend using Recommend to make recommendations (we know — meta). Used strategically, in conjunction with a great search experience, Recommend can create an online experience that’s satisfying and engaging for end users. Let’s take some time to explore why it complements search so well, how you can use it to increase relevance for customers, and where it can be most useful.
There’s more information and content online now than ever before. Over the course of a day, most of us spend time sifting through entertainment (Netflix has over 7,000 titles to choose from), products (Amazon’s catalog is made up of 12 million products and counting), and media (some news sites post thousands of news stories a day).
While it’s fantastic to have so much information at our fingertips, the sheer volume of it means most of us spend more time trying to find what we’re looking for than actually enjoying it. At Algolia, we solved the search aspect of this problem years ago by using the power of data and various search algorithms to make the search experience faster and more intuitive and relevant. We wanted to bring that expertise into the realm of recommendations.
In order to transform the user experience, we first had to focus on the developer experience. We knew that we’d need to make the Recommend suite of tools easy to implement and super fast, just like search. That’s why we took a building-block approach that developers can integrate into their own experience to enhance it.
We also wanted to build a solution that would work in a number of settings: homepages, category pages, product details pages, and article pages. Depending on the scenario (and we’ll get into a few use cases shortly), recommendations can be leveraged effectively at different parts of the user’s journey; we wanted to accommodate each potential use case.
One of the most obvious use cases for Recommend is in an online retail setting. Whether you choose to integrate it into your homepage, product detail pages, or checkout experience (or all three), it can serve as a tool to inspire users and guide them towards the products they need. Integrating Recommend into the homepage, for example, transforms it into a tool for inspiration. Right away, the customer is shown that they’re getting a custom, tailored experience via a collection of thoughtful suggestions, rather than having to start a search of their own.
The Recommend journey can be continued beyond the homepage, however. Used on product pages, it can help users refine their needs. Perhaps the product the user has landed on isn’t exactly what they were looking for. Using Recommend to surface similar products with different colors and features allows people to continue to find relevant information and ultimately make the best purchase without having to toggle back to the homepage to redo their search. With Algolia, this leads to a 32% increase in products added to cart.
Recommend can even be integrated into the checkout experience to increase average order value (AOV) with product recommendations that are frequently purchased along with the items in the user’s shopping cart. Used strategically, Recommend keeps customers engaged at every touchpoint, in a way that feels supportive and helpful. It also leads to a 150% increase in order rate and a +13% conversion over your entire website. We’ve also measured +20% increases in add to basket and -24% lower bounce rates.
Recommend serves a different function in the context of media. For one, the user isn’t looking to make a purchase. Additionally, people typically land on content and article pages from Google search pages or via a link sent to them by friends or family. In this scenario, the article page is the first—and potentially only—touchpoint the user has with your website.
For this reason, Recommend is an excellent tool to employ on article pages. You could think of Recommend as a sort of mini-homepage here, highlighting big and trending articles elsewhere on your website. You could also use it to link to articles on similar topics. Either way, Recommend can do a great deal to keep users engaged and serve them the content they would have otherwise gone searching for on their own. Recommendations get +60% clicks with Algolia compared to other providers.
Finally, Recommend can be integrated into any website’s search journey to make search results even more helpful and relevant. Often, this looks like adding a carousel of recommended products or results after the first five or so search results.
Again, you can go one of a number of ways here. In a retail setting, you could use Recommend to serve up a set of “frequently bought together” results, or you could display a number of similar SKUs to boost alternate products that the user might want to buy or explore instead. In a media setting, you could show other articles with similar keywords to keep users on your website.
It’s within this scenario that we can really see how Recommend can complement the search experience. When businesses have the right tools, they can find their own secret sauce in terms of building an experience that’s the most engaging and helpful for their users. When those tools are easy to configure, experiment with, and tweak, the possibilities are endless.
And for a change that makes such a big impact, it’s shockingly easy to implement. Just three steps and six lines of code and you’re ready to Recommend.