Run an End-to-End AI Retention Framework
A closed-loop retention engine: research, blend with your data, recommend, create, report, and improve — increasingly on autopilot.
The most advanced pattern uses the Optimove MCP as the core of a full retention loop rather than a single task. Claude conducts market research on a retention topic, blends it with your Optimove instance data and best-practice mechanics from the Academy, recommends an approach, assists with campaign creation, runs post-campaign reporting and analysis, and loops the recommendations back for ongoing improvement.
As the framework matures, more of it moves to scheduled tasks. For example, a morning task can pull the previous day's registration and conversion data as soon as the overnight batch completes, with further daily pulls (such as campaign results) coming online over time — so most of the activity becomes scheduled-task based and the loop runs largely on its own.
What you need to run this on the Claude branch.
| Ingredient | What to use |
|---|---|
| AI assistant | Claude (Cowork, with scheduled tasks) |
| Connector | Optimove MCP |
| Knowledge | Optimove Academy best-practice articles + market research |
| Optimove data | Instance data, lifecycle stages, campaign results |
| Optimove products | Optimove Loyalty (loyalty missions) |
Copy this into your assistant with the Optimove connector on. Replace anything in [brackets].
Act as my retention strategist. Using the Optimove MCP plus current best practices from the Optimove Academy: 1. Research retention approaches for [topic] 2. Blend that with my instance data — lifecycle stages, at-risk segments, recent campaign results 3. Recommend a best-practice approach 4. Draft the campaigns and, where relevant, a loyalty mission 5. After it runs, pull the results and tell me what to change next time
A demo of the closed loop — research, blend with instance data, recommend, then a scheduled morning data pull.
The flow, step by step:
Frame the loop. Set Claude up as your retention strategist and point it at the Optimove MCP and the Academy. Give it the retention topic you want to work on.
Research and blend. Claude researches best practices, then blends them with your instance data — lifecycle stages, at-risk segments, recent results.
Recommend and create. It recommends an approach and drafts the campaigns, including a loyalty mission where it fits the strategy.
Report after the run. Once the campaign runs, Claude pulls the results, analyzes performance, and recommends the next adjustment.
Automate the pulls. Move recurring steps to scheduled tasks — a morning pull of the prior day's registration and conversion data after the overnight batch, then daily campaign-results pulls — so the loop runs largely on autopilot.
At an online lottery operator, the CEO built an end-to-end AI retention framework with the Optimove MCP at its core: it researches retention topics, blends that with instance data and Academy mechanics, recommends best-practice approaches, assists with campaign creation, and runs post-campaign reporting that feeds back into the next cycle. It is increasingly wired into scheduled tasks — one already pulls the prior day's registration and conversion data every morning after the overnight batch — so most of the activity is becoming scheduled-task based. The operator is also a flagship beta tester for loyalty-mission creation via the MCP, letting a six-person team operate like a much larger one.
research → create → report → improve
prior-day data pulled every morning
operating like a much larger one
Is this realistic for a small team?
Yes — the framework is specifically what lets small teams punch above their weight, because the loop handles research, drafting, and reporting.
What moves to scheduled tasks first?
Usually the recurring data pulls — prior-day registration and conversion after the overnight batch — followed by daily campaign-results pulls.
Do I need every step to start?
No. Most teams start with research-plus-recommend or a single scheduled pull, then extend the loop as they build trust.