Amperity

What Stevin gets out of Amperity

Amperity uses AI for identity resolution, and that is exactly where it sets itself apart: tying data from different sources to the same customer, even without hard identifiers. For organizations struggling with duplicate profiles and fragmented customer data, it solves a real problem that deterministic matching leaves behind. The flip side of probabilistic matching is that it is not infallible: the AI guesses well, but a guess stays a guess, and you have to be able to trust the matches before you steer on them. Where teams get stuck is the assumption that clean profiles automatically become good marketing. An accurate profile is a starting point, not an action. Stevin connects to Amperity, reads the resolved profiles and the segments, and turns them into signals with follow-up underneath, with a person seeing every change that carries weight first.

What you pair it with

Choosing Amperity solves an identity problem that many CDPs sidestep, and that is the reason to pay the premium here. If you do not have that problem, a simpler CDP is enough. Count on a phase where you validate the matching quality before you build on it. The practical approach: do not trust the resolved profiles blindly, check the matches, and make sure there is a layer on top that turns the clean profiles into action instead of just storing them neatly.

Common mistakes

• →Trusting probabilistic matches blindly without validating the quality.
• →Choosing Amperity without a real identity problem: you pay for resolution you do not need.
• →Confusing an accurate profile with a marketing action that follows from it on its own.
• →Assuming deduplicated profiles automatically deliver better segmentation without follow-up.