Resource /
Intelligent Feature

Recommendations

Recommendations

Recommendations are awesome and feel magical for your customers if done right.

Recommendations can come in a few flavours that can really enhance user experience through personalisation. Starting from the simplest to the most complex:

  • Profile based: Show actions or information based on segments of the user profile. "Recommended for users in your industry"
  • Similarity based: Suggest content or actions based on the activities of similar users. "People like you liked......"
  • Activity based: Intelligent recommendations based on the predictive pattern of previous activity. "Because you watched...."
  • Discovery based: A combination of user activity and profile similarity. "We think you might like..."                                                                                            

Netflix is an obvious example of recommendations forming the basis of differentiated user experience. We don't just stay for the content, we stay because it feels like it is being curated just for us. The same goes for Spotify and should feel the same for social platforms - however advertising gets in the way.

Whilst the mega tech companies have $billions to spend on perfecting massively scaled recommendation engines for the world leading personalisation, you can get started easily and cost effectively with the Arcanum platform.


IDEA: How about increasing user retention and revenue growth by recommending new things to do or buy?