{"id":2857,"date":"2026-11-10T09:17:06","date_gmt":"2026-11-10T15:17:06","guid":{"rendered":"https:\/\/www.miaprova.com\/blog\/?p=2857"},"modified":"2026-11-10T09:17:07","modified_gmt":"2026-11-10T15:17:07","slug":"we-analyzed-nearly-40000-adobe-target-activities-heres-what-we-learned","status":"publish","type":"post","link":"https:\/\/www.miaprova.com\/blog\/we-analyzed-nearly-40000-adobe-target-activities-heres-what-we-learned\/","title":{"rendered":"We Analyzed Nearly 40,000 Adobe Target Activities. Here’s What We Learned."},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

Enterprise optimization programs have scaled their execution far faster than their ability to manage, measure, and learn from it.<\/strong><\/p>\n\n\n\n

That’s the central finding from our analysis of 39,596 Adobe Target activities across ten enterprise programs in retail, financial services, nonprofit, and B2B technology, with histories reaching back to 2009. All customer names, personal identifiers, and activity IDs were removed.<\/p>\n\n\n\n

We didn’t examine test results or business outcomes. We looked at the operational structure of the programs: what teams create, which activity types they use, how long activities stay live, and how programs change over time. Seven patterns stood out.<\/p>\n\n\n\n

This is the same audit we run at the start of every MiaProva engagement. Before we configure anything, we pull the organization’s full Target history and look at how the program actually operates. Here, we’ve aggregated that analysis across ten programs.<\/p>\n\n\n\n

1. Optimization has become an operational machine<\/h2>\n\n\n\n

In 2015, the ten organizations created 1,183 Adobe Target activities combined. In 2025, they created 6,676: a 5.6\u00d7 increase<\/strong> in a decade.<\/p>\n\n\n\n

The growth is concentrated. Two retailers created 5,406 of those 2025 activities, nearly three times the whole group’s annual volume in any year before 2020.<\/p>\n\n\n\n

\n