How Do Personalization Engines and Recommendation Systems Work?

Recommendations are a key feature of any personalized customer experience these days. Personalization engines are systems that aggregate massive amounts of data and offer up a customized experience for every employee, user, or shopper.

By tracking what one group of individuals likes or dislike or does or doesn’t do, the system can make recommendations for an individual with similar traits. A personalization engine plots these behaviors or sentiments and tries to make it’s best prediction for what similar users would want to see next or do next.
Watch now:

2aGVC2cCSBQvKKMTNQjhtK

You Might Also Like

Top 5 Use Cases for ACP in B2B Commerce

The rise of agentic commerce opens compelling new frontiers for B2B businesses.

Read More

The Role of Open Standards in MCP and ACP — Why Interoperability Matters

Open standards are what make MCP (Model Context Protocol) and ACP (Agentic...

Read More

How Agentic Commerce Protocol Could Transform Digital Commerce

The introduction of Agentic Commerce Protocol (ACP) by OpenAI and Stripe signals...

Read More

Quick Links