Step 1
Research Agent reviews the data
AI Agent
Agents used
AI Agent Recipe
See what drove the loss, what signals appeared early, and where future win-back opportunities may exist.
What this recipe gets you
Solves for scattered customer signals that make it hard to understand why revenue was lost and where recovery opportunities still exist.
CLARITY
Know exactly why a customer left
Every breakdown is grounded in reality and sorted into clear sections, so you see the real drivers behind the loss.
CONSISTENCY
Turn loss into a trend you can fix
When churn reasons are structured the same way every time, patterns surface fast, helping your team spot preventative issues.
REVENUE
Win the accounts worth reviving
Each summary flags whether the door is still open and who to reach out to, so re-engagement becomes a targeted play.
How it works
Outreach connects to the CRM and tools your team already uses, then layers on an AI agent that pulls together the context a rep would otherwise dig for manually. Here's the full path, from disconnected insights to a structured analysis your team can use to rebuild the relationship.
1
Research Agent reviews the data
AI Agent
2
A structured breakdown is delivered
Action
It reviews history and activity data to identify what drove the loss, which warning signs appeared, and how the relationship changed over time.
The agent returns the primary churn drivers and any win-back indicators in a format your team can use for analysis and action.
See the recipe in action
Book time to see your AI agent recipes in action and explore how they can help your team drive predictable revenue with Outreach.
Configuring your agents is easy
In Outreach, you set up an agent once using plain-language instructions. No code required. You tell it what to look for and what to produce, and it turns scattered account history into a clear, executive-level summary.
FAQ
How does the agent determine why a customer churned?
The Research Agent pulls data from: exit interviews (if conducted), support tickets and sentiment analysis, conversation notes, product usage trends (what dropped off?), and market signals (did they buy from a competitor?). It synthesizes this into a diagnosis: price, product-market fit, customer success, competitive, or timing-based churn.
What's the difference between preventable and unpreventable churn?
Preventable: product gaps, poor onboarding, competitor pressure, pricing (you could have fixed these). Unpreventable: they were acquired, went out of business, product no longer fits their business model. The recipe surfaces which bucket each churn falls into so you can learn what to improve.
How do I use this insight to improve retention going forward?
The recipe identifies patterns across churned accounts—e.g., "All 5 churned customers were 0-6 months old and showed low API adoption." That tells you to improve onboarding or API documentation. Or "They all churned 6 months before contract end"—so you need better engagement at the 4-month mark.
Should I try to win back churned customers?
Sometimes. If the churn was preventable and you've fixed the issue, a win-back conversation makes sense. If the churn was due to a permanent fit issue, win-back efforts are likely wasted. The recipe recommends which customers are worth the outreach.
How do I talk to a churned customer about their decision?
Approach it with curiosity, not defensiveness. Ask open-ended questions: "What could we have done differently?" "If we fixed X, would you consider us again?" The goal is learning, not guilt-tripping. The recipe suggests talking points for these conversations.
What's a typical churn diagnosis breakdown?
Across B2B SaaS, typical churn reasons are: 35% product fit (they outgrew or moved away from your category), 25% customer success/support issues, 20% competitive pressure, 15% pricing/budget, 5% other. Your breakdown will be different—that's exactly what the recipe helps you discover.
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