Overview

Scenarios let you model price changes in Flexprice before you make them, with none of the billing risk that comes with real-world experimentation. Define the price change you’re considering, set your parameters, and Flexprice outputs forecasted revenue, customer impact, and churn guidance using your real historical data, so you can make an informed decision before going live. Because Flexprice sits at the intersection of your product usage and revenue data, Scenarios are useful for:

Backtesting to de-risk price changes before rolling them out
Forecasting for new product pricing
Churn prediction to identify which customers are most at risk of churning if prices change

Before you start

To use Scenarios, make sure you’re up and running with Flexprice. Specifically, you’ll need to:

Ingest events: gives Flexprice visibility into your product usage to power the scenario
Define a meter: models the specific usage metric the price change applies to
Have a plan with subscriptions: defines the customer cohort your scenario will run over

Creating a scenario

Define your parameters

Name your scenario for easy reference, set a historical time frame, and select a plan to run the scenario over. This determines the historical data used in your analysis.

Aim for a window that provides sufficient, representative data. At least 30 to 90 days is recommended for meaningful results. The longer the window, the more visibility you have into customer behavior and seasonal patterns.

Add one or more pricing hypotheses

A hypothesis represents a hypothetical price change you’re considering. Flexprice supports two pricing types:

Usage-based prices: best for modeling prices that scale with variable events: tokens consumed, API calls made, minutes of audio processed.
Fixed fee prices: best for capturing upfront repeatable revenue, such as a platform fee or minimum spend.

A scenario can include one or multiple hypotheses to compare against your baseline. Common setups include:

Optimize the price point: create a hypothesis, duplicate it, and assign different rates to compare
Find your value metric: create one hypothesis per metric candidate to compare them side by side
Compare pricing strategies: model standalone usage-based pricing vs. hybrid usage + fixed fee
Compare pricing models: evaluate unit pricing vs. tiered pricing on the same data

We recommend up to 5 hypotheses per scenario. Beyond that, the results become harder to compare at a glance.

Run the scenario

Flexprice runs your scenario against real historical data. You’ll receive a notification when results are ready to review.

Acting on insights

Scenario results are designed to help you decide whether and how to implement your price change. Key revenue and customer risk impacts are surfaced for consideration, replacing the manual spreadsheet alternative.

Top-line insights

The summary cards highlight the upside potential and downside risk associated with your price change across the customers in your scenario. Flexprice highlights:

The hypothesis with the largest absolute revenue increase
The hypothesis with the lowest relative % change to what customers currently pay

When evaluating a price change, don’t optimize purely for revenue uplift. Weigh the risk of price increases on your existing customers, especially your largest ones.

Revenue over time

Monitoring how incremental revenue changes over time across hypotheses helps you:

Diagnose unexpected revenue variability
Identify seasonality impacts
Surface underlying product usage patterns worth considering

Aim for a smooth, consistent revenue curve. Inconsistent spikes may suggest the price change is not aligned with your optimal monetization strategy.

Impact concentration

Understand where the impact of your price change is concentrated. A

5 increase affects a customer paying

1 very differently from one paying $100.

Aim for outcomes concentrated in the lower-right of the impact chart: the least relative % change to baseline revenue, with the greatest absolute revenue upside.

Customer impact table

Proactively identify which customers will be affected by your change. Get ahead of notifying your customer-facing teams before the change goes live. From this table, you can:

Review historical subscriptions and invoices to understand customer LTV
Create a price override to accommodate exceptions
Migrate affected customers to a new plan version while grandfathering marquee accounts on existing pricing

Before you start

Creating a scenario