Many algorithms are being used for Product Information to sell extra items to customers, however, the technology to develop personalization in merchandising and ecommerce is growing even more rapidly.
For instance, an algorithm functioning pretty straight forward is the ‘most popular’ filter. This filter is continuously changing depending on which products are the most popular, and this thanks to AI. 5 main settings of this algorithm can be controlled, optimized and also automated.
1. Deciding on popular products over a defined time period
2. What is the unit used to define popularity? Is it number of sales to customers, number of sold units, number of views?
3. Type of filter applied to determine product set
4. Visitors’ behavior on website and on ‘most popular’ filter
5. What types of product information to promote when making the ‘most popular’ list
Each parameter will affect the final list that is created under ‘most popular products’ and this can be done thanks to A.I. If a human interaction had to be added to this multi-step optimization, this would probably result in a slower and error prone process.
The fact that this setting is using A.I. allows it to be continuously updated according to these 5 parameters.
Facets and sorts on a webshop are adjusted on user behaviors, doesn’t this remove an important part of the predictability from the user experience?
The industry standards as for displaying products are:
- Price range
- Language (etc)
Moreover, AI can be used in choosing what facets and filters to expose to the user. To do this, you have to give up a bit of predictability that you will gain back in usefulness and user experience. The criteria examined is the aggregate sales performance for the underlying product set in each filter or facet. Finally, machine learning and AI are applied to choose which facets and products are the most relevant to be shown to the user and also in what order.