Which customers are ready to upgrade? Product data shows usage surging. Support data shows they are asking about enterprise features. Billing shows they are hitting plan limits. Each signal lives in a different tool. askotter connects them into an expansion score so CS and sales reach out at the right moment.
A customer's usage grew 340% over 6 months. They are exceeding plan limits. Their support tickets mention features only available on the enterprise tier. Every signal points to upgrade readiness. But the CS rep last checked the account 90 days ago, and the usage data lives in a product analytics tool they do not use.
Most SaaS companies only identify expansion opportunities during annual reviews or when a customer self-serves an upgrade. The proactive window, where a CS rep can guide the conversation and demonstrate value, is missed because the signals are spread across product, billing, and support tools that nobody checks together.
askotter connects product usage, billing thresholds, support ticket content, and CRM activity to score accounts for expansion readiness. When Acme Corp's usage jumps 340% and they hit plan limits, the agent flags it as an expansion signal with the context CS needs: what changed, what features they are using, and what the next tier offers. CS reaches out with a specific conversation, not a generic check-in.
Understanding these metrics helps you measure the problem and track improvement. Each links to our full glossary definition with formulas, benchmarks, and role-specific context.
askotter detects pre-churn patterns 18 days before cancellation by connecting product usage, billing, and support data. Flag at-risk accounts while there is time to act.
askotter maps every touchpoint across 90+ day B2B sales cycles. Multi-touch attribution that gives accurate credit to content, ads, and sales together.
askotter tracks feature adoption by segment, plan, and cohort. See which users adopted, which did not, and whether adoption reduces churn. Connected to billing and support data.
We will connect your data, deploy agents that watch for this specific problem, and surface what matters. Your team stays in control. AI suggests, humans decide.