Customers expect a great deal, so companies need to deploy data to optimize interactions. Those without a trove of historical data can build a better recommendation engine using affinity profiles and artificial intelligence.
Few businesses achieve the scale of Amazon, but there are benefits to thinking like the online behemoth. It is about bridging the gap between methodology and technology by tapping into vital data to personalize the customer experience.
Capgemini’s recommendation engine, which employs Natural Language Processing (NLP), provides an effective way to install a similar system within an existing ecosystem. The approach is flexible enough to accommodate minimal existing client data and leverage important external information sources at scale.
It improves on all aspects of the marketing cycle, including customer acquisition, conversion, engagement, and retention. From the moment a customer starts interacting with the company, the system collects information to improve the experience. By allowing for a more personalized approach, companies can move away from traditional marketing methods and create the right experience for customers.
This flexible and autonomous approach allows businesses to address a lack of relevant internal historical data by relying on available first-party data and third-party data sources to build an in-depth view of a consumer and focus on attributes that relate to individual affinities.
Learn how Capgemini’s recommendation engine can give you a competitive edge by bringing the intelligence of AI and affinity profiles to your customer experience.