How to Increase Sales With AI Recommendations

Making an enjoyable customer experience is proven to increase sales. Users love the “do it for me” (difm). Companies like Netflix, YouTube and Amazon have based their growth strategies on recommendation engines. Users don’t need to think too much about what to consume next, they just need to click a button from a carousel of personalized recommendations that fit each and every client individually. The benefits for companies using good recommendation engines are many, from the increase in sales, to engagement, and increasing the brand value.

Product recommendations

Based on what other people bought, or on past purchasing history, or even on how products look and match together, recommendation models are able to offer the right product to the right person. Amazon has a history of your purchases, and other people's purchases and can make correlations and offer their clients products that other people bought. Netflix is able to group similar content by not only using the tags of the video content (“series”, “drama”, “actors'') but they also analyze the video to search for similar graphic patterns and provide movie recommendations of movies that have a similar “look and feel”.?

User profiling and personalization

Datasets containing some kind of information about customers or leads can reach very large sizes containing a lot of useful information, but it’s actually very hard to extract meaningful information from a certain volume of data. Unsupervised learning algorithms analyze these big datasets and find patterns helping companies understand much better their customers, from basic profiling to more complex patterns hidden in the data. If combined with recommendation engines, a huge fuzzy dataset can be turned into a valuable user profiling tool that is able to provide personalized recommendations to even hundreds of thousands of users. These are used in many businesses, for example in brick and mortar retail where there are a large amount of people shopping and leaving some data footprints, but without AI models it’s impossible to make sense of that data.

Visual recommendations

A problem with standard recommendations is that we need big and nicely labeled datasets. In huge datasets, for example a database of 2 million customers, with 300 000 products, and each product has like 10 attributes (brand, product name, color, product category, etc).... You can imagine that maintaining those datasets can get tricky. And in some cases, the most important is how products look, and how products fit together. For example, toilet equipment, where you need to match different products (faucet, bathroom, shower) in a similar style. Or shoes matching with accessories (belt, hat, bag…). Users want super easy navigation and want a very simple shopping experience. If they have to browse and navigate the eshop looking for different items… their attention span is really limited, they will either settle with one product (when they could have very easily bought 2-3 items if it was easy to buy it)