- H2O Driverless AI builds segmentation and propensity-to-respond models from transactions, product holdings, digital activity and past campaign results.
- Each targeting decision carries reason codes, so your marketing, compliance and privacy teams can see why a customer received an offer.
- The same models work without third-party cookies: they run on first-party behavior and on the context of the page, app or channel.
- Scores feed the campaign tools you already run, in the deployment option you choose.
The same offer goes to everyone
Most campaigns still target by broad demographic segments, and the result is familiar: low response, rising cost per engagement, and customers who stop opening your messages.
Third-party cookies and device identifiers are disappearing, so audience data you used to buy is harder to get. Your first-party data is richer, but it sits across card, deposit, digital and branch systems, and every targeting choice has to hold up to a privacy and fair treatment review.
How behavioral targeting works
Your marketing analytics team joins the data you already hold, builds behavior-based segments and response models, and sends the scores to your campaign tools. People still decide the offers and the rules.
Step 1: pool your first-party data
Combine transactions, product holdings, digital sessions, service contacts and past campaign responses into one customer view. Automatic feature engineering turns raw activity into behavior signals such as spend shifts and channel preference.
Step 2: build behavior-based segments
Clustering groups customers by behavior rather than by age or postcode, and your team names the segments that matter to the brand.
Step 3: predict who will respond, and when
A classification model scores each customer's likelihood to open, click or take up an offer, and a recommendation model ranks the offers that fit. Timing models suggest when and on which channel to reach each person.
Step 4: explain, deploy and measure
Reason codes show why each customer was selected. H2O MLOps deploys the scoring pipeline and tracks model drift, and campaign results feed the next model.
Targeting without third-party cookies
Because these models run on first-party and context data, such as the app screen or the product page a customer is on, they keep working as third-party cookies and device identifiers go away.
Where this fits your campaign tools
H2O is the scoring layer underneath. Segments and scores feed the campaign and marketing tools you already run, in any deployment option your policy requires.
Proof
H2O can be used for behavior-based segmentation, response prediction and contextual targeting. No customer result for behavioral targeting is published, so this page describes the pattern.