Feature Adoption Matrix

Segment feature adoption by reach and usage depth using caller-defined cutoffs.

Description

Feature adoption separates eligible-user reach from usage depth.

Feature adoption separates eligible-user reach from usage depth.^[1]

Segment feature adoption by reach and usage depth using caller-defined cutoffs.

When to use Feature Adoption Matrix

Use this tool to segment feature adoption by reach and usage depth using caller-defined cutoffs.

Prepare consistently defined customer counts, durations, costs or forecast assumptions. Keep aligned records and monetary amounts on a common period and currency basis.

How to use Feature Adoption Matrix and understand its results

Prepare consistently defined customer counts, durations, costs or forecast assumptions. Keep aligned records and monetary amounts on a common period and currency basis.
Enter featureIds, activeUsers, eventCounts, eligibleUsers, minimumReachFraction, minimumEventsPerActive using the field table and calculation rules, then run the tool.
Read reachPercent, eventsPerActive, segments. The explanation below defines the units, signs, ordering and any flags or placeholder values.
If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.

Input field	Meaning	Value type; see rules below	Requirement
featureIds	Feature ids	list of string	Required
activeUsers	Active users	list of number	Required
eventCounts	Event counts	list of number	Required
eligibleUsers	Eligible users	number	Required
minimumReachFraction	Minimum reach fraction	number	Required
minimumEventsPerActive	Minimum events per active	number	Required

Enter aligned nonempty unique raw feature IDs, positive eligible counts, active within eligible and integer events≥active. Apply supplied reach/depth thresholds inclusively to core, broad-shallow, niche-deep or underused segments.

reach%=100active/eligible; depth=events/active

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
reachPercent	Reach percent	list of number
eventsPerActive	Events per active	list of number
segments	Segments	list of string

json

{
  "input": {
    "featureIds": [
      "A",
      "B"
    ],
    "activeUsers": [
      5,
      2
    ],
    "eventCounts": [
      20,
      2
    ],
    "eligibleUsers": 10,
    "minimumReachFraction": 0.4,
    "minimumEventsPerActive": 2
  },
  "output": {
    "reachPercent": [
      50,
      20
    ],
    "eventsPerActive": [
      4,
      1
    ],
    "segments": [
      "core",
      "underused"
    ]
  }
}

Limitations and assumptions

Zero active yields depth zero even with positive events. IDs are not trimmed; no telemetry definitions are inferred.

Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Lists are limited to 100,000 entries; the rules above may require smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use Product Market Fit Survey: Score the Sean Ellis very-disappointed survey against a supplied PMF cutoff. For a complementary task, use First Value Time Benchmark: Measure time-to-first-value distribution against a caller benchmark.

References

Feature Adoption Matrix — tool.io

Similar or alternative tools

Product Market Fit Survey
Score the Sean Ellis very-disappointed survey against a supplied PMF cutoff.
First Value Time Benchmark
Measure time-to-first-value distribution against a caller benchmark.