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The first leg of the stool – product tagging – is based on a particular AI technology called Natural Language Processing (NLP). NLP automatically extracts each product's attributes - even for retailers with unstructured product data.
These could include single or multi-word attributes, such as ‘party shirt’, ‘slim fit jeans’, ‘Tommy Hilfiger’, ‘work to evening’ – there can be millions of attributes extracted across a retailer’s entire product catalogue. The goal is to look beyond the standard product data to understand each product’s most important features and attributes.
As consumers interact with products, these interactions are married with each product’s attributes to construct a ‘taste profile’ for each consumer. This is the second leg of the three-legged stool. The taste profile is a collection of everything a consumer interacts with – akin to a word cloud of each person's tastes and interests.
Taste profiles are the foundation of personalisation: they aid retailers in recommending new products as they’re added to the catalogue. And if a consumer happens to like the most quirky and unusual product in your catalogue, something that only 20 people in your database would dig, you can find those 20 people and connect the dots between the product and the consumers.
At the end of the day, it’s about understanding each consumer’s personal tastes, and making recommendations accordingly.
3. Algorithms bring it all together
The connection between data from the retailer’s product catalogue and each consumer’s unique taste profile is the third and final leg of the stool:
- One one side, we know all of the product attributes from the Natural Language Processing.
- On the other side, we have a taste profile for each consumer.
We continuously take these two things, do a little magic filtering, and rank products to match products to each consumer’s taste profile, while also incorporating new and popular products, together with an element of diversity.