From Engagement Metrics to Alignment Metrics: The Ethical (and Profitable) Shift

TL;DR

• Engagement metrics (DAU, session time, clicks) measure attention capture, not value delivery, which is why Zynga lost 80% of market cap despite 80M DAU.
• Alignment metrics measure whether a product helps users achieve their stated goals, and they predict long-term retention better than engagement metrics.
• The shift from engagement to alignment is not just ethical: alignment-driven revenue compounds through trust, expansion, and word-of-mouth.

Alignment metrics measure whether a product helps users achieve their goals, replacing engagement metrics that only measure how much attention a product captures. Zynga’s FarmVille had 80 million DAU [1] yet the company still lost over 80% of its market cap [2], because high engagement without value delivery is indistinguishable from addiction. This post covers the engagement trap, what alignment metrics look like in practice, and the business case for making the shift.

The Engagement Trap

Here’s the dirty secret of the metrics we worship: engagement metrics measure how much of a user’s life you captured. Not how much of it you improved.

DAU tells you people opened your app. Not that they got value from it. Session time tells you they stayed. Not that they accomplished anything. Click-through rate tells you they interacted. Not that the interaction mattered. As Eric Ries argued in The Lean Startup [3], these are vanity metrics [4], numbers that make you feel good but don’t inform decisions.

We’ve built an entire industry around metrics that optimize for attention extraction: what Tim Wu calls The Attention Merchants [5] and Tristan Harris at the Center for Humane Technology [6] has spent years warning us about. We’ve confused attention with value so thoroughly that we celebrate when users spend more time in our products, even when that time is wasted, anxious, or compulsive.

(Sound familiar? It should. This is literally the same critique leveled at social media. We just don’t like hearing it applied to B2B SaaS.)

Addiction vs. Value: A Framework

I started mapping every product interaction across two axes: engagement (how much attention it captures) and alignment (how much it moves the user toward their stated goals).

The first column describes a product that’s great at capturing attention and terrible at delivering value. The second describes a product that users choose to return to because it actually helps them.

Guess which one has better 12-month retention? (It’s not the one with 80 million DAU.)

What Alignment Metrics Look Like

So what do you measure instead? Alignment metrics answer a fundamentally different question : one closer to what Cass Sunstein and Richard Thaler call good choice architecture [7]: is this product helping the user become who they want to be?

1// Engagement metrics (attention-based)← measures capture
2const engagement = {
3  dau: 0.89,             // 89% open the app daily
4  sessionTime: 12.3,     // minutes per session
5  interactions: 4.2,     // clicks per visit
6  retention7d: 0.78,     // 7-day retention
7};
8
9// Alignment metrics (value-based)← measures progress
10const alignment = {
11  goalProgress: 0.72,    // % of user-stated goals advancing
12  beliefCoherence: 0.84, // do actions match stated preferences?
13  trajectorySlope: 0.15, // is understanding improving over time?
14  selfReportedValue: 4.1,// out of 5: 'this product helps me'
15  outcomeAttribution: 0.68, // % of outcomes user credits to product
16};

Engagement metrics are backward-looking: did the user spend time here? Alignment metrics are forward-looking: is the user making progress?

The research supports this distinction. Bain & Company found [8] that companies focusing on customer value metrics rather than pure activity metrics see significantly higher long-term retention. And Harvard Business Review research [9] on “Jobs to Be Done” shows that products aligned with what users are actually trying to accomplish retain at fundamentally different rates than products that merely capture attention.

The hypothesis is straightforward: metrics that measure whether users are making progress toward their goals should outperform metrics that merely count interactions. Engagement is necessary but not sufficient, alignment is what predicts whether users will still be here in 90 days.

The Business Case (Which Is Stronger Than the Ethical Case)

I know what you’re thinking: “This sounds nice, Robert, but my board doesn’t care about alignment. They care about DAU and MRR.”

Here’s the thing: alignment drives MRR. Not directly, but through a causal chain that’s more durable than anything engagement can offer.

Engagement-driven revenue: User opens app → sees notifications → clicks around → maybe derives value → retention depends on habit, not satisfaction → churn when habit breaks or competitor offers novelty

Alignment-driven revenue: User has goal → product helps achieve goal → user experiences real progress → retention driven by value, not habit → expansion because user trusts the product with more of their work → word-of-mouth because genuine satisfaction → compounding revenue from trust

This is exactly what happened to Zynga, and to Peloton [10], and to every product built on habit-based retention. When the external conditions change (an office reopens, a novelty wears off, a competitor offers something new), habit-based engagement collapses overnight. There’s no underlying value to sustain it.

How to Make the Shift

You don’t need to throw away your engagement metrics. You need to subordinate them to alignment metrics. Engagement becomes a means, not an end. Here’s the practical shift:

Step 1: Define alignment. Ask users what they’re trying to achieve. Not once during onboarding, continuously. Build a self-model that tracks their evolving goals.

Step 2: Measure progress. For each user, is the gap between where they are and where they want to be shrinking? That’s your alignment metric. Track it like you track DAU.

Step 3: Reframe engagement. Engagement is only good when it correlates with alignment. High engagement + high alignment = healthy product. High engagement + low alignment = addiction. Low engagement + high alignment = efficient product (arguably the best outcome).

Step 4: Predict retention from alignment. Build the model. I promise you, alignment will outperform engagement as a predictor. And once your board sees the correlation between alignment and revenue, the conversation changes.

The shift from engagement to alignment isn’t a moral crusade. It’s a better business model. It’s more predictive, more durable, and more defensible. It just happens to also be the right thing to do.

And honestly? After watching company after company lose billions despite record engagement, I’ll take the metrics that actually predict what matters.

Stop measuring attention. Start measuring alignment. Self-models make it possible.

References

1. 80 million DAU
2. lost over 80% of its market cap
3. The Lean Startup
4. vanity metrics
5. The Attention Merchants
6. Center for Humane Technology
7. good choice architecture
8. Bain & Company found
9. Harvard Business Review research
10. Peloton
11. not a reliable predictor of customer retention
12. sampling bias, non-response bias, cultural bias, and questionnaire bias
13. NPS does not correlate with renewal or churn
14. Nielsen Norman Group has noted
15. Research confirms

Related

• From Engagement to Alignment: The Ethical Shift
• Two Kinds of AI Alignment You Need
• The Alignment Score, Explained: Why It Matters More Than Engagement