Model the decision before you make it.
Ask "what happened last time we raised prices?" Doe pulls historical pricing and churn data from your database, segments the impact by customer type, and builds a forward model you can inspect and adjust. You make the decision with actual data instead of guesses in a spreadsheet.
Doe helps you model business decisions by querying your database for historical patterns. Ask what happened last time you raised prices, expanded into a segment, or deprecated a feature. Doe finds the historical data, segments the impact by customer type, and builds a forward model you can inspect and adjust. Connects to Neon, Supabase, and PlanetScale. All queries and model code visible.
What changes
| Dimension | Before | With Doe |
|---|---|---|
| Model inputs | Assumptions typed into yellow cells from stakeholder conversations | Historical patterns from your actual customer data |
| Segmentation | One model for the whole customer base | Impact segmented by customer type, because different segments respond differently |
| Auditability | A spreadsheet where nobody can trace how the numbers were derived | Model code, source queries, and historical data all visible |
| Time to answer | Days to weeks building and iterating on the spreadsheet | Hours. Ask the question, review the model, iterate on assumptions |
How Doe models business scenarios
Doe maps the question to past pricing changes, churn events, segment attributes, and contract values in your database.
PlanetScaleWhich customers saw increases, by how much, and what happened next — renewed, churned, downgraded, or expanded. Segmented by customer type.
Doe applies historical churn rates by segment to your current customer base at the proposed 15% increase. If multiple past changes exist, it shows the range of outcomes. Model code fully visible and editable.
SlackDoe posts the segment-by-segment projection with the historical basis shown. Example: "Mid-market churn increased X points after the last 10% raise — applying that rate at 15% gives this range." Source queries and model code attached.
The pricing meeting is Thursday. The model is a spreadsheet held together with guesses.
The CEO wants to raise prices 15%. Before the decision, she needs to know: how will this affect churn? Net revenue? Expansion? Finance builds a spreadsheet with three scenarios, each based on assumptions typed into yellow-highlighted cells. Churn estimates come from a conversation with the CS lead, not from actual data. The model takes three days and nobody trusts it.
Your database has every pricing change you've made, every churn event, every expansion with exact timing. The data to answer "what actually happened last time" is there. The problem is turning that into a forward-looking analysis within the decision timeline.
Get started in under 10 minutes
Connect your tools
One-click OAuth for each integration. No API keys, no engineering.
Describe what you need
“We are considering raising prices 15% on the Pro plan. Model the impact on MRR, churn, and upgrade rate using our last 12 months of billing data. Show best-case, base-case, and worst-case.”
It runs on schedule
On demand. Ask a new question whenever a decision needs modeling and the scenario lands in minutes.
Scenario Modeling FAQ
Any question where your database contains relevant history. Pricing changes (what happened last time?), feature deprecation (which customers use this and what's their churn risk?), market expansion (how do customers in adjacent segments behave?). The quality of the model depends on how much relevant history you have.
Stop doing the work your tools should do for you.
Set it up once. Doe runs it every time.
