Amplitude had the product data. The challenge was making its benchmarks specific enough to answer a product leader’s real question: How does our product compare with similar products?
Amplitude was building a public benchmarking experience for product managers and product leaders. A single blended average would have combined unlike companies and product motions, making the result descriptive but not decision-ready.
HPI worked backward from the questions the audience would ask, cleaned and organized the product data, and created peer groups using company stage, product motion, and engagement depth. Aggregation and
k-anonymity thresholds were applied to protect customer privacy.
The final benchmark covered activation, retention, depth of use, and time to value. HPI also delivered the segmented dataset, reusable query patterns, and UX and messaging guidance needed for launch. In the first three weeks, the experience generated 1,120 qualified leads.
Amplitude builds product analytics software that helps teams understand how people use digital products. The company planned a public benchmarking experience as a flagship lead-generation asset for two audiences: product managers and product leaders.
The concept sounded simple: show teams how activation, retention, depth of use, and time to value compared with the market. But a benchmark is only useful if the comparison is relevant. A blended average can be mathematically correct and still mislead when it combines products with different stages, motions, and engagement patterns.
That made the core problem one of segmentation, not visualization. Amplitude needed to decide which products belonged in the same peer group, which measures would answer useful product questions, and how to make those comparisons public without exposing customer data.
The default cuts combined unlike companies and product motions. They could describe the dataset, but they could not reliably answer the question a visitor cared about: what does good performance look like for a product like ours? Product leaders needed comparisons tied to activation, retention, depth of use, and time to value within a relevant peer group.
The peer groups had to be narrow enough to feel relevant but large enough to protect individual customers. That meant privacy rules had to be part of the segmentation logic rather than a final publishing check.
The team needed a segmented dataset, benchmark logic, and a clear launch narrative without replacing the data platform or asking engineering to rebuild the experience.
HPI treated the benchmark as a decision system rather than a reporting exercise. Instead of starting with every field in the dataset, the work started with what product managers and product leaders needed to understand and worked backward to the peer groups, measures, and privacy rules required to answer those questions.
The central decision was to stop treating the entire dataset as one population. Product leaders did not need the overall average; they needed a reference point that reflected the context of a product more like their own.
The result was a fundamental shift in the question the benchmark could answer: from “Are we above or below the overall average?” to “How are we performing relative to products with a relevant context?”
The benchmark became a decision aid rather than a collection of descriptive charts.
Amplitude builds product analytics software that helps teams understand how people use digital products. The company planned a public benchmarking experience as a flagship lead-generation asset for two audiences: product managers and product leaders.
The concept sounded simple: show teams how activation, retention, depth of use, and time to value compared with the market. But a benchmark is only useful if the comparison is relevant. A blended average can be mathematically correct and still mislead when it combines products with different stages, motions, and engagement patterns.
That made the core problem one of segmentation, not visualization. Amplitude needed to decide which products belonged in the same peer group, which measures would answer useful product questions, and how to make those comparisons public without exposing customer data.
Amplitude launched the benchmarking experience with audience-ready segments and no engineering rebuild. In the first three weeks, the benchmark generated 1,120 qualified leads across its two priority audiences: product managers and product leaders.
| Measure | Result | Why it mattered |
|---|---|---|
| Qualified demand | 1,120 qualified leads in three weeks | Turned a data asset into a pipeline-generating experience. |
| Audience focus | 2 priority audiences | Kept the benchmark and launch narrative centered on product managers and product leaders. |
| Engineering impact | 0 rebuilds required | Allowed the team to launch using a segmented dataset, query patterns, and implementation guidance. |
| Benchmark design | 4 decision-ready measure groups | Covered activation, retention, depth of use, and time to value. |
| Privacy design | Aggregation rules plus k-anonymity thresholds | Protected customer confidentiality while preserving useful peer comparisons. |
The benchmark did not ask visitors to interpret one generic average. It helped them compare their product with a relevant peer group and answer questions tied to real product decisions. That made the data more useful to the audience and gave Marketing a stronger reason for visitors to engage.
The same principle applies to a broader Customer Segmentation Study: segments become valuable when they change the decision, the experience, or the action that follows.
Customer evidence. Market clarity. Revenue decisions you can defend.
