Some of the biggest businesses on this planet are using recommendation engines for hacking their growth, customer flow and -experience. That’s one of the key reasons for their success and not the other way around. Spotify is recommending music, Netflix movies, YouTube videos, Amazon products from household items to fashion and books. And guess, what? It works.
In the best case scenario, they offer personalized recommendations all while users allow them to target with personalized recommendations across a multitude of channels, and while the information is stored on one’s profile making further machine learning and customization possible.
The user inevitably spends time and money and builds an emotional connection to the experience provider.
For the recommendation engine to do it's work properly it must be underlined that the selection should be narrowed to as small as possible in order to make the choice easy and convenient for the consumer. Offering a customized selection of 1000 movies versus 3 movies, which one do you think will convert better? You decide.
Using an algorithm based recommendations to personalize and customize an individuals feed and recommendations increase the time and money spend on the site plus ultimately increases the emotional connection to the site which looking at Amazon, YouTube, Netflix and Spotify seem to be working pretty neatly for them.
When companies can personalize and create a more customized experience with digital tools, they are able to provide more impactful, lasting ways for employees to communicate (Read more).
The recommendation engine learns and understands users intent and preferences and shows the most relevant products. The match is done in real time, the conversion follows. Usually it can be seen as these matches are being made. Targeted matches equal more usage and conversion.
Customizing the most personal of all retail
What these companies are experts in is segmenting and algorithm. However, the individual needs, in this case, don’t have to be segmented and individualized on the very ground level.
So if what you are offering isn’t as mainstream and easily segmented and personalized as movies, books, and music for example? Take beauty, skin care, and makeup, for instance. The factors that play in are countless. Every skin is literally different, preferences, allergies, and weathers vary across the board massively. How do you even start?
Revieve does to beauty what Netflix does for movies and Spotify for music, however, the recommendation engine, algorithms, and logic behind the system must be very different in order to actually customize the most personal of all retail: beauty. Segmenting won’t simply do the trick.
What Revieve does is gather information such as age, gender, preferences, price, location, weather, skin type, skin concerns and more from the user. That already narrows the selection of 4000 beauty products usually down to some hundreds. That’s simply not good enough from the perspective of conversion. The point is to narrow the selection to a bare minimum.
By allowing the user to analyse their skin from their video or selfie using computer vision Revieve is able to analyse more than 70 individual factors such as wrinkles, redness, eye bags, undertone, skin colour, eye colour etc allowing to narrow the selection of 4000 beauty products all the way down to 3 price groups and beauty routines even less with beauty brands.
The system is usually fairly flexible and allows the provider to override automated recommendation rules and create rules manually based on the sales strategies. What’s more, the beauty recommendation engine is completely white-labeled meaning every beauty retailer and brand can make the beauty advisor look and feel like the solution is made by them to their customers when in fact the whole engine behind is provided by Revieve.
Whats a common factor with all these companies is providing a recommendation engine exclusively suitable for their field, products and customers. The second is probably lack of competition in their field. What do you think?
-Vanessa
