I run a mid-sized SaaS product that helps small teams manage client projects. We have about 42k active users and the amount of behavioral data we collect is growing fast. The problem is that all this data lives in different places, Stripe for billing, Intercom for support, our own app analytics, and email engagement in Klaviyo. It’s becoming impossible to see the full picture of any single customer. A few months ago we implemented Blueconic as our customer data platform. It finally unified everything into clean, real-time profiles. Now we’re trying to layer AI on top of that unified data to predict churn, identify upsell opportunities, and personalize onboarding automatically. I’m finding it powerful but also overwhelming. We’re experimenting with feeding the unified profiles into GPT-based agents and some custom models, but the results are still hit-or-miss. How are other founders or product people actually using a CDP + AI together in practice? What kind of use cases gave you the biggest wins (churn prediction, personalization, segmentation, etc.)? And what mistakes did you make early on that I should avoid? submitted by /u/1acina
Originally posted by u/1acina on r/ArtificialInteligence