Measuring Feature Stickiness Beyond DAU/MAU Ratios

For over a decade, the ratio of Daily Active Users to Monthly Active Users (DAU/MAU) has been hailed as the golden metric of product “stickiness.”

However, in modern application telemetry, DAU/MAU is frequently misleading. A high volume of paid acquisition or one-off transactional users can artificially distort your active user denominator, while masking the fact that your core power users are engaging at an entirely different cadence.

The Flaw of the Single Stickiness Metric

Consider two applications, both reporting a DAU/MAU ratio of 20%:

• App A: 100% of users log in exactly 6 days per month (steady, distributed utility).
• App B: 20% of users log in all 30 days per month (dedicated power users), while 80% log in once and never return.

From a top-level aggregate dashboard, App A and App B look identical. In reality, their user dynamics, monetization potential, and retention vulnerabilities are completely opposite.

Constructing the Power User Curve (L28 Histogram)

To see true engagement depth, we plot the Power User Curve, also known as an L28 Distribution.

An L28 histogram plots how many days a user was active in the previous 28-day window on the X-axis (from 1 to 28 days), and the percentage of your total monthly active users on the Y-axis.

Percentage of MAU
▲
│ █ 
│ █                                              █
│ █ █                                          █ █
│ █ █ █                                      █ █ █
│ █ █ █ █ █ ▄ ▄ ▄ ▄ ▄ ▄ ▄ ▄ ▄ ▄ ▄ ▄ ▄ ▄ ▄ █ █ █ █ █
└─────────────────────────────────────────────────────►
  1   3   5   7   9  11  13  15  17  19  21  23  25 28  Active Days / Month

Interpreting the Curve:

1. Left-Skewed (Decay Curve): If 70%+ of users are clustered between 1 and 3 days, your product is either an infrequent utility or suffers from high early churn.
2. Smile Curve (Bimodal): A high spike at 1–2 days (new/evaluating users) and another spike at 20–28 days (core power users). This is the hallmark of healthy engagement with a clear subset of dedicated champions.
3. Right-Skewed: Strong daily habit formation, typical of messaging, core workflow engines, or enterprise operating hubs.

Calculating Feature Affinity Scores

Beyond overall active days, you must evaluate which specific sub-features generate repeatable engagement. We calculate the Feature Affinity Index:

Feature Affinity = (Active Days Utilizing Feature X) / (Total Active Days in Application)

If users who utilize your advanced reporting tool have a feature affinity score of 0.85 (engaging with it on 85% of their active days), that feature is a primary retention anchor, even if only 15% of your total user base has discovered it.

Practical Takeaways for Product Teams

1. Retire aggregate DAU/MAU as a single decision metric. Segment it by user tenure (D1–D30 vs. D90+).
2. Plot monthly L28 histograms for every major user persona or subscription tier.
3. Direct marketing and onboarding focus toward the features with the highest affinity scores, helping new signups reach power-user status faster.