From Data to «Sensemaking»: Why Your Loyalty KPIs Are Lying to You

In the world of User Research, We know that a number without context is a dangerous hallucination. In Mexican organizations, it's common to see dashboards gleaming with positive metrics while the customer base quietly erodes out the back door. That old story about a high NPS guaranteeing growth is the reason why many companies don't see their own obsolescence coming.

The real problem is not the lack of data, but the lack of meaning. Metrics tell us what the symptom is happening, but only strategic research tells us Why is happening (the cause). In Gerund, we understand that interpreting loyalty is not a statistical exercise, but one of Sense-makingThe art of connecting the dots between business, context, and people to make decisions based on evidence, not assumptions.

The Risk of the Wrong Metric: The Blindness of the Average

The biggest mistake in loyalty analysis is averaging the experience. An average hides the extremes where friction truly lives. Stop celebrating decimal variations in CSAT when you don't know if that score comes from a customer who loves you or one who simply didn't have time to complain.

• The NPS Promoter TrapA customer might rate you a 9 because they like your communication, but they might be about to leave you because your delivery process is slow.
• CSAT as a False PositiveA successful interaction (tactical satisfaction) does not build a long-term relationship (relational loyalty).
• Ghost Churn«Many users stop using the service months before canceling their subscription. If you only measure formal cancellation, you're late to the conversation.

We interpret metrics as a living ecosystem: if Customer Effort Score (CES) rises, emotional loyalty decreases, and Customer Lifetime Value (CLV) is eventually destroyed.

Metrics as a starting point for research

In Gerundio, we are not passive Excel analysts. We are researchers who transform KPIs into Behavioral Archetypes. Our methodology Deciphering allows data to stop being static and become design criteria

1. Data Translation to Pain PointsWe don't stop at «NPS dropped 2 points.» We investigate what specific friction in the Service Blueprint causes that decline.
2. Identifying «Hostage Customers»We use data cross-referencing to identify users who are retained due to lack of options, not preference. We design to unblock them towards genuine loyalty before the competition does it for us.
3. Field ValidationWe leave the office to understand the user's real context. The data tells you that usage dropped; research reveals that it dropped because the user changed their habits and your product no longer fits into their new routine.

5 Research Principles for Making Informed Decisions

To make your metrics drive design actions and not just reports, apply these pillars:

1. Listen to the silence, not just the complaintThe most dangerous customer is the one who stops using your service without saying anything. Use usage behavior as your most honest loyalty metric.
2. Segment by behavior, not demographicsDon't make decisions based on «women aged 30-40.» Make decisions based on «users experiencing checkout friction.» Behavioral segmentation is the only one that impacts the product.
3. CLV is the ultimate judgeAny improvement in design must be reflected in the customer's lifetime value. If your loyalty strategy doesn't increase profitability per user, it's cosmetic, not strategic.
4. Use CSAT to detect fatigueA sudden drop in satisfaction is a sensor of Cognitive Friction. Something in your interface or process became difficult to use. Fix it immediately.

   5. Add the «Why» to each metricNever present quantitative data without a qualitative hypothesis. True interpretation occurs when you talk to people to understand the story behind the number.