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Data & Analytics

Model the decision before you make it.

Ask 'what happened last time we raised prices?' ZeroTwo 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.

Integrates with:

What changes

Before
With ZeroTwo
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

Model inputs

Before

Assumptions typed into yellow cells from stakeholder conversations

With ZeroTwo

Historical patterns from your actual customer data

Segmentation

Before

One model for the whole customer base

With ZeroTwo

Impact segmented by customer type, because different segments respond differently

Auditability

Before

A spreadsheet where nobody can trace how the numbers were derived

With ZeroTwo

Model code, source queries, and historical data all visible

Time to answer

Before

Days to weeks building and iterating on the spreadsheet

With ZeroTwo

Hours. Ask the question, review the model, iterate on assumptions

The pricing meeting is Thursday. The model is a spreadsheet held together with guesses.

The CEO needs to evaluate a 15% price increase impact on churn, net revenue, and expansion rates. Finance builds three scenarios using guesses in yellow cells rather than actual customer data. The modeling takes multiple days with low confidence.

Your organization has complete historical pricing changes and churn events in your database. The data exists to build a grounded model. But converting raw billing history into a forward-looking scenario takes SQL, Python, and more time than anyone has before Thursday.

How ZeroTwo models business scenarios

1

Parses the question and identifies relevant historical data

Google

ZeroTwo maps the question to past pricing changes, churn events, segment attributes, and contract values in your database.

2

Pulls historical pricing and churn patterns

PlanetScale

Which customers saw increases, by how much, and what happened next -- renewed, churned, downgraded, or expanded. Segmented by customer type.

3

Builds a forward model from historical rates

Google

ZeroTwo 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.

4

Delivers the projected impact to your channel

Slack

ZeroTwo posts the segment-by-segment projection with the historical basis shown. Source queries and model code attached.

On demand · Projected impact delivered to Slack

Get started in under 10 minutes

1

Connect your tools

One-click OAuth for each integration. No API keys, no engineering.

2

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.

3

It runs on schedule

On demand. Ask a new question whenever a decision needs modeling and the scenario lands in minutes.

Frequently asked questions

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.

Yes. The model runs as Python code in a sandbox. Every line is visible: how historical rates were computed, what assumptions were applied, how the projection was built. You can edit the code, override assumptions, and rerun.

ZeroTwo is honest about it. If there's no relevant history in your data, the model says so rather than fabricating projections. It can still show you the current state (who would be affected, by how much) even without historical analogues.

They're only as good as the historical data they're based on. If you've changed pricing three times with clear churn data after each, the model has a reasonable basis. If you've done it once, it's one data point and the model will say that. ZeroTwo shows you the data, not magic.

'Rerun but assume mid-market churn is higher than historical.' ZeroTwo updates the model and delivers revised projections in minutes. You can iterate as many times as you need.

Related workflows

Stop doing the work your tools should do for you.

Set it up once. ZeroTwo runs it every time.