Pricing experiments

Price affects conversions, audience composition, expectations, and long-term revenue at the same time, so a typical short A/B test often gives an incomplete picture. Consider current customers’ rights, seasonality, buying cycle and metrics after the first payment – returns, retention and expansion.

What are pricing experiments and why do they need them?

Definition and substance

Pricing experiments are system tests of product price hypotheses. You change the price or related mechanics (e.g. discounts, subscription model, trial period) and watch how users and revenue react.

Application in practice

A typical scenario is to raise the price of a subscription in one region to see if users are willing to pay more. Or you’re testing a new free period to increase conversions from a trial to paying users.

Key types of pricing experiments

A/B price testing

You take a part of users, offer a new price or other structure (for example, monthly versus annual subscription), compare metrics.

** Example:**
Product X has a high loyalty user segment. Raise the price for this group by 10%. Compare retention and revenue before and after. Net money growth is a clear signal that the segment has been undervalued.

Testing of price models

Transfer the product from subscription to freemium or usage-based, analyze the response.

** Example:**
The analytics service transfers some of the new customers to the tariff on the volume of data, and leaves the control group with a fixed price. Before the start, the team determines how to compare revenue, conversion, predictability and early churn. One month may show a reaction to a purchase, but a full renewal cycle is required to make a withdrawal.

Localization of prices

Set different prices by region, reveal the sensitivity of local markets.

** Example:**
The audiobook service checks the individual price in the market with different purchasing power. The experiment is limited to new users of the selected country and takes into account taxes, currency, local payment methods and resale risk. The decision is made on total revenue and retention, not only on the increase in conversion to the first payment.

Metrics that are important when experimenting with price

Revenue and its components

Look not only at total revenue (MRR/ARR), but also at LTV, ARPU. Sometimes, it is not the product with the highest price that gets the most money, but the right pricing structure.

Retention and Churn

Income growth means nothing if churn accelerates. When analyzing, always check the retention cohorts where new prices have been tested.

Conversion to payment

It is important not only how much they pay, but also how much they decide to switch to a paid tariff.

Typical errors and anti-patterns

Simultaneous change of several conditions

If you change the functionality and design along with the price, then you do not understand what really worked. Always test one hypothesis.

** Example:**
The platform raised the price and added new options without separating cohorts. After churn’s rise, it’s not clear if the reason was new options or price.

Ignoring segments

Different segments have different elasticity. It is impossible to assess the effect on average on the basis, even if it seems - on average it has become better.

** Example:**
In the price experiment, SaaS service for small and large businesses changed the price for everyone. Small business left, enterprise remained, the final revenue did not change – but the user base has narrowed.

Work without supervision

Experiments should be statistically significant, rather than relying on the first 20-30 sales. A small cohort is an unstable signal. It is better to wait or expand the sample.

How to start and analyze pricing experiments: a practical checklist

Before launch

1. Determine the goal: revenue growth, conversion increase, search for a price ceiling for the segment.
2. Select segments: for whom you test the hypothesis.
3. Risk assessment: possible churn, brand reputation, technical limitations of billing.

During the test.

1. Keep the experiment clean: do not change anything except the price or tariff scheme.
2. Keep an eye on key metrics: revenue, churn, conversion, LTV, retention.
3. It takes weeks, not days, to see a trend on churn or retention.

After the experiment

1. Group comparisons: how revenue has changed, churn, dynamics of new payments.
2. Segment analysis: What has changed for key groups?
3. Decide whether to scale or refuse: Sometimes a new price works for a niche, but not at scale.

** Case example:**
The SaaS platform tests the new price of the Pro-tariff only on new customers of one market. The team pre-sets the minimum significant change in total revenue, monitors conversions from the trial, and does not infer LTV before a sufficient number of renewals. Other countries check separately due to differences in currency, taxes, purchasing power and competitive environment.

Best Practices and Where to Look for Benchmarks

Where to Take Examples and Analytics

Answers to questions about benchmarks, trends and pricing strategies can be found in ProfitWell Pricing Strategies, the study OpenView SaaS Benchmarks, as well as cases of large IT companies.

What to pay attention to

• Accounting for local elasticity of demand
• Consistency of price strategy with product positioning
• Record all changes and effects on metrics

FAQ: Questions and answers

**How do you know if it’s time to do a pricing experiment? When revenue growth has slowed, churn is stable and conversion from trial users to paid users is below the market.

How long does it take to test the new price? Usually a few weeks - the duration of the average purchase cycle. B2B can take a month or more to complete.

*What are the most common mistakes? Testing on too small a sample, changing several conditions, ignoring the relevance and performance segments.

How can you avoid negative feedback from users? Clearly separate cohorts, do not change the price for current customers, inform about the reasons for the changes, do not make sudden jumps.

**Where to look for benchmarks and analytics? In the materials ProfitWell, OpenView Partners, SaaS Mag, CB Insights - see the links above.

*When should I scale the results? When there is a stable positive effect on the target metric and the trend is confirmed for several segments.