The platform’s audience keeps growing—monthly active users are at record highs. Yet beneath that impressive expansion, a quieter problem unfolds: each user is doing less. Sessions shorten, interaction rates fall, and revenue per user declines. Growth in breadth is being offset by erosion in depth. This is the engagement paradox—when user growth hides the gradual loss of attention, frequency, and value.
Hilbert’s AI Growth Engine provides a systematic method to confront this challenge. It transforms raw data into clarity, structures solutions into projects, and continuously tracks KPIs to break the cycle.
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- If you prefer a deeper understanding of the dynamics, continue reading for the full article, including definitions, traditional vs. AI approaches, and detailed implications.
When More Isn’t Better: The Mechanics of Shallow Growth
Engagement decline during user expansion often signals that growth quality has deteriorated. The business is acquiring broader, less interested audiences—users drawn by discounts, virality, or convenience rather than intrinsic product value. These new cohorts inflate MAU counts but contribute less per capita in revenue, activity, or time spent. The result: topline user growth masks bottom-line engagement decay.
This pattern tends to emerge when acquisition strategies shift from precision to scale. Early adopters—users with strong product fit—interact deeply. But as campaigns target wider audiences, relevance drops. Conversion rates may remain healthy, but post-acquisition behavior becomes shallow. A 2023 Amplitude engagement benchmark report found that companies growing MAUs faster than 30% year-over-year typically saw a 12–18% decline in median session depth within the same period, particularly in consumer apps and e-commerce.
Competitive pressure accelerates this erosion. As alternatives proliferate, users fragment attention across multiple apps or sites—what behavioral economists call “multi-homing.” Instead of consolidating loyalty, brands share users who divide time, spend, and focus among several platforms. This makes retention harder and ARPU more volatile.
A second driver lies in content and offer saturation. When the feed, catalog, or experience becomes repetitive, perceived novelty declines. Customers log in less frequently, spend less time exploring, and convert less often. Each new user adds less incremental value, creating diminishing returns to scale.
Operationally, declining engagement per user distorts forecasting and ROI models. Average revenue per user (ARPU) declines even as total revenue rises, making future projections unreliable. Marketing teams spend more to acquire users who generate less, stretching CAC payback. Product teams struggle to isolate what’s failing—relevance, usability, or fatigue.
The psychological root is straightforward: attention is finite. Growth shifts user composition from loyalists to casuals, and unless the product continuously renews its perceived value, attention wanes. In this context, growth without engagement is hollow—it inflates metrics but weakens the foundation.
Reversing this pattern begins with segmentation clarity. Measure engagement distribution rather than averages: who drives usage, who fades, and when. Identify the “power users” sustaining core metrics, and study how they behave differently. The goal isn’t just to grow MAUs but to expand the proportion of high-engagement users.
Next, address value decay. If engagement declines because users feel the experience is static, introduce freshness through dynamic recommendations, limited-time features, or personalized content. If the issue stems from competitive overlap, build switching barriers via loyalty programs or integrated ecosystems that reward repeated use.
Finally, integrate engagement-adjusted growth KPIs into dashboards—metrics like “Effective Active Users” (weighted by time or transactions) or “Revenue-Engaged Users.” These expose whether new growth strengthens or weakens the system.
In short, user count is volume. Engagement is conviction. When volume rises and conviction falls, the growth story needs rewriting.
Traditional Approach vs. Hilbert’s AI Growth Engine
Traditionally, companies track engagement metrics in isolation: DAU/MAU ratios, session time, or churn. These snapshots fail to reveal why engagement is declining or how much it’s tied to user mix changes. Growth teams often celebrate record user acquisition while missing that new cohorts are less engaged than older ones.
Hilbert’s AI Growth Engine combines behavioral analytics, cohort profitability, and usage intensity metrics to uncover the root causes of engagement decline. It distinguishes between “healthy” user growth (adding active, retained users) and “shallow” growth (adding passive or low-value users). This clarity enables strategic reprioritization—investing in retention and feature depth instead of volume alone.
Some examples of questions the system is able to answer:
- How has average session frequency per user changed over the past 12 months?
- Are engagement declines driven primarily by new users or existing ones?
- Which cohorts show the steepest drop in interactions, session length, or order frequency?
- How does engagement vary across acquisition channels, regions, or device types?
- What is the elasticity between engagement depth and ARPU?
- Which product categories or features are losing interaction share?
- How does engagement decay correlate with retention and repeat purchase rates?
- What role do competitive app launches or pricing changes play in engagement loss?
- Which UX or content changes coincide with the steepest engagement declines?
- How much incremental ARPU would be recovered by restoring last year’s average engagement per user?
Citations
- Amplitude (2023). Engagement Benchmark Report: Depth, Frequency, and Retention in Growth Contexts.
- McKinsey & Company (2022). When Scale Dilutes Value: Managing User Quality in Growth Strategy.
- Forrester Research (2023). Attention Economics: Why User Growth Without Depth Fails.