Turn on Analytics for Target and a small cluster of dimensions shows up in your component list. Most practitioners drag “Target Activity > Experience” into a freeform table, get the numbers they need, and never look at the rest. That works right up until a report looks wrong, and then the answer is almost always…
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 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….
Adobe’s Model Context Protocol (MCP) tooling represents a meaningful shift in how practitioners can interact with Adobe Experience Cloud products, enabling them to query data, surface insights, and drive workflows through natural language and AI-native interfaces. At MiaProva, we’ve been tracking this closely, and we’re excited to share that we’ve now built native support for…
MiaProva Is Adding a Built-In, Context-Aware Prompt Library for Adobe Target, Analytics, and CJA There’s a version of Adobe tool training that most teams are living with right now: a mix of documentation tabs, YouTube tutorials, internal Confluence pages that are six months out of date, and the occasional tribal knowledge from the one person…
Optimization teams have never had more data, more tools, or more pressure to drive meaningful business impact. But as the ecosystem has grown more powerful, it has also grown more fragmented. Teams are now expected to connect experimentation, analytics, audience management, activation, and governance across multiple platforms — all while keeping programs scalable, observable, and…
Adobe Target teams are sitting on an incredible amount of signal – activities, offers, audiences, A4T data, and years of experimentation history. The hard part isn’t having data; it’s knowing what to do next. In a new early-peek demo, we walked through the Adobe Intelligence Services we’re building inside MiaProva to make that “what next?”…
