Personalization Glossary

At PSYKHE, we're obsessed with the why: Why do we like the things we like? Why does one person like a pair of shoes, a sofa, a travel destination, or a song, while someone else does not? Understanding all the factors that drive individual taste, including personality and psychographic data, enables us to create the world's most sophisticated recommendation engine. After all, you can't personalize without personality. Shaping a stellar recommendation strategy is a big job, so to help you grasp what a complete AI personalization blueprint for your business entails, we've collated definitions related to personalization here.

Personalization Engine

Providing deeper context about individual users and consumers, personalization engines employ data science to deduce patterns that allow businesses to edit, buy, select, tailor and deliver personalized messaging, content, product selections, and other interactions. Personalization engine software solutions typically use machine learning algorithms such as collaborative filtering, which depends on the choices of similar individuals. A variety of variables can be used to better understand the consumer and provide accurate recommendations when creating a personalization engine, including past purchase history, site interactions, and psychographic data. Using an effective personalization engine increases conversion, ad effectiveness and consumer satisfaction, hence improving business outcomes.