Why the “average score” can be misleading
Most companies open a dashboard and see a single number: the average score. It feels reassuring because it is simple. The problem is that an average:
- blends very different situations into one number,
- hides extreme experiences,
- does not explain where or why things go wrong.
It is like measuring the temperature of an entire building with one value and trying to fix the heating based on that.
Example: one average, two very different realities
Imagine two locations of the same brand.
- Location A: half of customers give a 5, half give a 1
- Location B: everyone gives a 3
In both cases, the average may look similar (around 3). But the conclusions are completely different:
- in A, you have a consistency problem (results depend on the day, person, or situation),
- in B, you have stable mediocrity (a systemic lack of quality).
If you look only at the average, you cannot tell a crisis from “boring normal”.
What segmentation is and why it matters
Segmentation means dividing data into meaningful groups to reveal differences in experience.
Segmentation helps answer questions like:
- when is the experience worse and when is it better?
- where is it worse and where is it better?
- for whom is it worse and for whom is it better?
- at which touchpoint is the problem the biggest?
Without segmentation, you have a “result”. With segmentation, you have a diagnosis.
Which segments deliver the most value?
Not all segments are equally useful. The highest value usually comes from context-based segments.
Examples of high-value segments:
- location (branch, city, service point),
- time (day of the week, peak hours, season),
- channel (online vs offline, form vs terminal),
- touchpoint (Customer Journey stage),
- employee or team (if ethical and operationally justified),
- event (a specific change, campaign, or incident).
In practice, these segments show whether a problem is systemic or tied to a specific place and moment.
Why segments reveal real problems
Segmentation works because you stop mixing different realities together. This allows you to see, for example, that:
- the overall average is good, but one location drags results down,
- ratings are high, but drop during peak hours,
- the terminal shows “okay” results while forms show negatives – because you collect feedback from different groups,
- the problem occurs at only one stage of the Customer Journey, not everywhere.
These are insights that lead to action. An average leads at best to vague concern.
How segmentation changes decision-making
With segmentation, instead of acting blindly, you can:
- set priorities: where intervention will have the biggest impact,
- distinguish a recurring problem from a one-off incident,
- test changes (before/after) in specific conditions,
- avoid punishing the whole organization for a single location’s mistake.
Segmentation is the foundation of execution: without it, assigning responsibility and verifying corrective actions is extremely difficult.
Segmentation in Data Responder – how it works in practice
In Data Responder, segmentation is not an add-on. It is the mechanism that enables meaningful conclusions.
The application allows you to segment results by, among others:
- time of feedback submission,
- place or location,
- touchpoint in the Customer Journey,
- collection channel (form, QR, terminal),
- additional parameters (e.g. dynamic URL or QR linked to an event).
This makes it possible to see not only “what the average is”, but where the problem actually originates.
Conclusions
An average is convenient, but it often lies because it merges very different situations into one number.
If you want insights that lead to action:
- segment your data before drawing conclusions,
- start with contextual segments (time, location, touchpoint),
- look for differences, not just an “overall score”.
Segmentation turns feedback from “a number on a dashboard” into a map of real, fixable problems.
