Machine learning has become the crux of personalizing the recommendation experience for visitors, assisting in making generalizations from observed data to provide the best products and/or offers to each specific user.
What you might be surprised to find out, though, is that high-yielding product recommendations take more than just the work of the algorithms. In fact, over the past six years that we’ve been working with brands to optimize their recommendations, we’ve discovered that the most efficient way to merchandise is through the use of real-time data about who the user is, and then dynamically changing the experiences according to the context in which customers interact - not necessarily just what the actual products are.
It’s true that the machine learning algorithms that power recommendations become smarter with more data sources, so tying in your CRM, first, along with third-party data, enables businesses to more effectively merchandise different sets of products to customers, generating greatly improved results.
But when it comes to factoring in contextual data - including user activity, affinities, geography, etc. - product recommendations need to take it a step further, tailoring the look and feel of the recommendation widgets to best suit each individual’s needs at any given moment in time.
This can be done through what we call, layout personalization, which we believe is one the most overlooked aspects of the product recommendation world.
Adapting Strategies to Context
The fact is, there's no uniform site structure that can provide an optimal user experience to each of your visitors.
No two visitors are the same, and while your existing layout may be attuned to the average user, it simply can’t be aligned to everyone’s. And if you don’t personalize your site’s layout you're limiting possibilities for engagement and missing out on a tremendous revenue opportunity.
For example, one interesting observation we've found, is that in fashion eCommerce, if the gender of the shopper is female, the images in the product recommendations widgets should be larger. I
It's much more about the overall experience for women, but when analyzing male shoppers, we've found that they're much more transactionally-natured.
The guys want to see more products per page, exploring every possible item - and as such, guiding them through a catalog with infinite-scroll to keep the products flowing produces higher-yielding results for this group of visitors.
In mobile experiences, things like product image size, filtering options, and product feed type must all be considered to increase engagement, clicks, and add-to-carts to ensure the layout properly suits the user’s device type.
Pinpointing the Purchase Funnel
Another important contextual clue to leverage is user intent, dynamically implementing recommendations based on whether the visitor’s activity is signaling that they're in discovery or transactional mode.
For someone who has arrived at a site via a broad search query, and is slowly browsing category pages, a brand should change the layout of a page to widen the assortment of items, and maybe include a Pinterest-like feed of products.
On the other hand, an individual who's ready to transact - maybe they've added an item to their cart but have decided to continue shopping - tends to respond better to the displayed content being re-ordered to present other relevant products next to the one they’ve shown an interest in. This enables brands to strike with the right upsell offer while the iron is hot and increase AOV.
The Power of a Great Recommendation Engine
The difference between an average recommendation engine and a great one depends a lot on its ability to adapt the layout, not just the merchandise.
If businesses want customers to transact more, they'll need to employ more advanced recommendation experiences - recommendations widgets should look and feel different based on the actual context of the customer, personalizing the experience by incorporating real-time user activity and context-focused optimization in a machine learning-based algorithm.
These will be key concerns for eCommerce businesses moving forward.