For personalization to perform at the highest level, large amounts of data are vital. The more data, the more precise the recommendations!
At Raptor we use various data-ingests, eg. a real-time data stream of behavioral data. To be precise, we stream data from several digital touchpoints such as web, email, social media, banner, and display.
However, some items or pieces of content have very little online user data attached to them. An example could be a product on a webshop that is not sold or viewed very frequently.
Read more: Recommend products in your content to increase sales
For a recommendation engine, sparse data means that it will be unable to recognize relevant patterns from behavioral data on such long-tail items.
Therefore, personalization providers have to develop strategies to overcome sparse data issues:
Personalization engines need data to form meaningful patterns and calculate relevant recommendations.