It is a fair question, and it is harder to answer than it should be. The information exists, but it is scattered across a documentation page, a legal product description, and a lot of half-right blog posts that were accurate three release cycles ago. One of those customers had been told by a third party…
There is real buzz around the Model Context Protocol right now. The question is not whether to use it with Adobe Target, but how. Here is an honest look at the two good answers. There is a lot of energy in the community right now around the Model Context Protocol, and for good reason. MCP…
If you run Adobe Target, you have probably heard that the Web SDK is where Adobe is taking everything. That is true. But “Adobe wants you to” is a weak reason to touch a stable personalization stack, and a bad reason to touch it badly. This post lays out what actually changes when you move…
Ask a room full of AEP practitioners to explain what a merge policy does and you will get a decent answer. Ask the same room how many merge policies their audiences currently touch, and which ones, and you will get silence. That silence is the problem. Merge policies are one of the few objects in…
The best discovery calls are the ones the prospect runs for you. A large financial organization evaluating MiaProva recently sent us a spreadsheet. No slideware, no vendor questionnaire. Just an honest inventory of how their digital experimentation program actually operates: 23 pain points, each with a problem statement and the business impact behind it. It…
Yesterday I had the pleasure of co-hosting a webinar with Drew Burns of Adobe on a topic that has consumed a good chunk of my professional life over the past year: AI-powered optimization with Adobe Target’s MCP tooling. Turnout was incredible. We may have set a record for Adobe webinar participation, and the opening poll…
Introducing Audience Complexity Scoring in MiaProva: a 0–100 cost and risk score read directly from the audience definition. It started as a way to protect the nightly batch job. It turned into a much bigger signal — because the same characteristics that make an Adobe Experience Platform audience expensive to evaluate (long lookbacks, aggregations, joins,…
A follow-up on our batch segmentation investigation — and the architectural pattern that eliminates the problem at its source. In our last post, we walked through how a single newly published audience caused a large retailer’s nightly AEP batch segmentation job to run for 7.3 hours instead of 3.5. The likely culprit was a segment…
A real-world investigation into AEP batch segmentation performance, segment complexity, and the observability gap in between. Recently a client’s nightly batch job took 7.3 hours instead of 3.5. Profile count was actually down slightly from the night before. Here’s what we found. Many enterprise RT-CDP deployments that rely on scheduled audience activation depend on a…
One of the harder parts of experimentation reporting is not the decisioning itself. It is getting the decisioning data into the reporting layer in a way that survives real world complexity. A MiaProva customer recently ran into this exact issue. They had recently added a third-party content decisioning and feature flagging platform to their stack….
