What can it do
We challenge our customers and partners to boil down their issues into recommendation problems and we are growing the selection of use cases day by day. Here are some of the most popular ones;
- eCommerce optimisation (recommending the next product to buy)
- AI augmented customer interactions (recommending the right information to the customer)
- Next best action nudges for customers (e.g. recommending where a customer could improve their personal finances)
- Mining for latent customer insights (recommending what is the truly interesting information to know about a custom
- Process optimisation (recommending to a customer or employee the next step they should take to complete their action)
- Proactive churn management (recommending which customers might churn and importantly what to do about it)
Predictive, self-learning conversations
Powered by artificial intelligence, questions are interpreted and relevant knowledge base articles, online resources or subject matter experts within the organisation are recommended.
Tailored check-out journeys
This video shows our engined creating tailored check-out journeys for real customers based on the products our recommender systems suggest they are most likely to buy.
While this example is base on 1m real flight records there is no reason it could as easily work on financial products or eCommerce transactions.
At the end of the day, data is data
RecommenderX build hybrid models
Hybrid models are effectively multiple different models being run in parallel and then comparing the output of those models against one another and choosing the most accurate of them or combining them to maximise efficiency.
Most of the inaccuracies that arise from non-hybrid recommender systems are due to the underlying model hitting an edge case that it is not equipped to deal with and therefore failing, by running the models in parallel as one model hits an edge case the other models are able to compensate.
Why hybrid models matter
The main reasons that some companies are reluctant to adopt automated systems is because they break too often; a customer will present an edge case to the model and the entire journey will break down. The company has now frustrated the customer and destroyed much of the satisfaction they were working to build, in fact a poorly thought out approach to automation can actually reduce customer satisfaction rather than improve it.
That is why hybrid systems are so important, they reduce the number failures an individual model would encounter by an many orders of magnitude.