Which customer feedback metric to track: star ratings, CSAT, effort score or NPS?
Choose the metric by the question you need answered. Star ratings show how products are received, CSAT measures satisfaction with one interaction such as a support ticket, a customer effort score measures how easy something was, and NPS measures whether customers would recommend you. For a small online store, the most useful pair is usually your star average plus a count of what reviews are about, because a number tells you how many and only the text tells you why.
What this comes down to
- Each metric answers one question: stars rate products, CSAT rates one interaction, effort scores rate ease, NPS rates loyalty.
- NPS was introduced by Fred Reichheld in Harvard Business Review in 2003 and asks how likely a customer is to recommend you.
- Every score needs the text behind it; a number tells you how many customers are unhappy, never why.
- For a small store, a star average plus counts of what reviews are about covers most decisions without a new survey.
Four metrics, four questions
| Metric | The question it answers | Best for |
|---|---|---|
| Star rating average | How do buyers rate this product? | Comparing products; spotting a drop |
| CSAT | Were you satisfied with this interaction? | Support tickets, deliveries, returns |
| Customer effort score | How easy was it to get this done? | Checkout, returns, finding answers |
| NPS | Would you recommend us? | The relationship with the brand over time |
A score tells you how many. Only the words tell you why.
NPS, briefly and correctly
Net Promoter Score was introduced by Fred Reichheld in The One Number You Need to Grow, published in Harvard Business Review in December 2003. It asks how likely a customer is to recommend the company to a friend or colleague.
The common calculation uses a scale of 0 to 10: 9 and 10 are promoters, 0 to 6 are detractors, and the score is the percentage of promoters minus the percentage of detractors, so it runs from minus 100 to 100. It measures the relationship, not a product: a customer can love your brand and still dislike one item.
Why a star average is not enough on its own
You already have a star rating for free. The trouble is what it hides: ratings tend to cluster at five and one, so an average of 4.2 might be many delighted buyers and a steady group of very unhappy ones with a shared complaint. The average moves; it does not say what moved it.
The guide on rating drops shows how to trace a change in the average back to its cause.
Every number needs its text
Whichever score you pick, collect the free-text answer alongside it. Most survey tools let you add a why?
box after the score. Without it, a falling NPS or CSAT becomes a meeting where everyone guesses.
Then read that text the same way you read reviews: tag it by topic, count the topics, and track the counts next to the score. The Review Analyzer is built for reviews, but survey comments are short customer text too, and you can paste up to 30 at a time free.
A setup for a small store
- Track the star average per product, monthly. You already have it.
- Count review topics monthly, so you know why the average moves.
- Add CSAT to support once you have enough tickets to be worth measuring.
- Consider NPS later, when repeat customers matter enough to track the relationship.
Questions
What is the difference between NPS and CSAT?
NPS asks whether a customer would recommend you, which measures the overall relationship. CSAT asks whether they were satisfied with a specific interaction, such as a support ticket or a delivery.
How is NPS calculated?
On the common 0 to 10 scale, 9 and 10 are promoters and 0 to 6 are detractors. NPS is the percentage of promoters minus the percentage of detractors, giving a score from minus 100 to 100.
Which customer feedback metric should a small ecommerce store use?
Start with what you already have: the star rating per product, plus a monthly count of what reviews are about. Add CSAT for support and NPS later, and always collect the free-text reason with any score.
Read next
What is customer review analysis? What it finds that star ratings hide
Review analysis turns reviews into counts: topics, sentiment, severity and which need a reply. What it produces, and what a star average cannot tell you.
Where to find customer feedback beyond reviews: tickets, returns and questions
Support tickets, return reasons, pre-purchase questions and surveys hold more feedback than reviews do. Where each source lives and how to read them together.
Why did my product rating drop? Four causes and how to tell them apart
Small numbers, a real change, a new way of asking, or removed reviews. How to tell which one lowered your product rating, starting with the date it began.