The most common alternatives to Net Promoter Score℠ include CSAT, Customer Effort Score, broader Voice of Customer programs, and custom experience indexes. But these are still largely based on broad sentiment scores. Another option is to measure what actually happened during the experience and connect that evidence to outcomes such as spend or repeat purchase.
That distinction matters when comparing NPS alternatives. Replacing one recommendation or satisfaction question with another may give you a measure that fits a particular job better. But it does not necessarily give you a different kind of evidence.
According to Bain’s guide to measuring Net Promoter Score, NPS® is calculated from a 0 to 10 likelihood-to-recommend question. Customers scoring 9 or 10 are promoters, 7 or 8 are passives, and 0 to 6 are detractors. The percentage of detractors is subtracted from the percentage of promoters to calculate the score.
There is a reason the measure has endured. It gives leaders a consistent language for customer loyalty, and many businesses have years of trend data behind it. But a recommendation score still has limits. It can tell you that loyalty moved without showing which part of an experience changed, whether a particular service behavior happened, or what that difference might mean commercially.
So the useful question is not simply, “What should replace NPS?” It is: what evidence is missing from the measurement program you already have?
Why teams look for an NPS® alternative
Teams usually start looking for an NPS alternative when a recommendation score is no longer answering the questions the business needs to answer. The issue might be financial linkage, confidence in the measure, or simply that the recommendation question does not fit the customer relationship particularly well.
Forrester’s NPS Q&A takes a pragmatic view. It reported that 67% of VoC and CX measurement leaders surveyed said their executives use NPS as one of their primary measures of CX success. That creates what Forrester describes as a “wicked gravity”: moving away from a familiar executive measure carries a real burden of proof.
Its fit test is useful too. Forrester suggests questioning whether NPS is the right beacon metric if it is inversely correlated with financial success in your business, employees strongly dislike it because of previous implementations, or customers respond badly to the recommendation question.
That is more useful than declaring the score good or bad in the abstract. If it still gives leadership a useful view of loyalty, there may be little benefit in removing it. If teams cannot explain what drives the number or connect it to decisions, that creates a stronger case for adding other measures alongside it.
Does NPS® predict growth? What the research says
One of the recurring problems with NPS is not necessarily the score itself. It is the strength of the growth claim sometimes attached to it. Much of the academic NPS criticism focuses on whether recommendation intention should be treated as a uniquely strong predictor of company growth.
Fred Reichheld’s 2003 Harvard Business Review article, The One Number You Need to Grow, made the original case for recommendation intention as a particularly powerful growth measure. That was an influential claim, but it was also one researchers could test.
In 2007, Timothy Keiningham, Bruce Cooil, Tor Wallin Andreassen, and Lerzan Aksoy published A Longitudinal Examination of Net Promoter and Firm Revenue Growth in the Journal of Marketing. The study used longitudinal data from 21 firms and more than 15,500 interviews from the Norwegian Customer Satisfaction Barometer and attempted to replicate analyses used to support Net Promoter.
The researchers did not replicate the claimed clear superiority of Net Promoter over other measures in the industries they studied. The finding was not that a recommendation measure can never relate to growth. It was that its superiority over other measures should not simply be assumed. The paper also received the 2007 Marketing Science Institute/H. Paul Root Award, recognizing the Journal of Marketing paper judged to have made the most significant contribution to marketing practice. Ipsos published the award announcement.
However, there are limits to what you can conclude from the research. The study used one national dataset, covered 21 firms, and tested the industries represented by that data. It would be an overreach to turn the result into “NPS does not work.” The more useful conclusion is that businesses should validate what a recommendation score predicts within their own organization rather than assume a universal relationship with growth.
The survey-based Net Promoter Score alternatives
Most Net Promoter Score alternatives still take the form of another customer-reported measure. CSAT, CES, broader Voice of Customer programs, and custom indexes can all be valuable. The important thing is understanding the job each measure is designed to do.
- Customer Satisfaction Score (CSAT) asks how satisfied someone was with an experience, product, or interaction. It works well when you need a direct read on something specific, such as checkout, delivery, service, or support.
- Customer Effort Score (CES) focuses on how easy or difficult something was to complete. It can be particularly useful when friction is the concern, such as returns, customer support, digital checkout, or onboarding.
- Voice of Customer programs are broader. They can combine surveys, verbatim feedback, operational information, and other customer signals. Their value depends less on one headline score and more on the quality of the evidence and how effectively it supports decisions.
- Custom experience indexes give businesses the option to measure something closer to their own customer proposition. Forrester gives Virgin Money’s Smile score as one example, with NPS retained as a supplementary measure.
These are legitimate alternatives. But they do not all answer the same question.
NPS® vs CSAT
The NPS vs CSAT comparison comes down to what you need to measure. NPS is generally used as a relationship or loyalty outcome measure. CSAT is usually closer to a particular interaction and asks whether the customer was satisfied with what happened.
That can make CSAT more useful for a defined touchpoint. If satisfaction falls following a checkout change, delivery experience, or service interaction, the question is close to the thing the team needs to investigate. NPS works differently. It gives you a broader recommendation signal that can be monitored over time.
Neither automatically tells you what caused the result. A satisfaction score might tell you checkout experience has deteriorated without showing whether the problem was wait time, staff availability, payment friction, or something else. A recommendation score can also move without revealing which operational change caused it. So CSAT vs NPS is not really a contest over which score is universally better. It is a question of which measure fits the decision.
NPS® vs Customer Effort Score
The NPS vs CES comparison is between advocacy and effort. Customer Effort Score is designed to capture how easy or difficult an experience felt to the customer. That makes CES useful when reducing friction is the goal. A retailer might use it around checkout or returns. A service business might use it following a support interaction. Its narrower focus can be an advantage because it brings the measure closer to a particular journey.
But CES is still a reported perception. A customer tells you how difficult something felt. The score alone does not show whether they spent more or less, returned, abandoned a purchase, or encountered the specific process or behavior that caused the difficulty. That requires another layer of evidence.
What the usual NPS alternatives have in common
Going beyond NPS starts with recognizing that most familiar alternatives still represent the same broad category of evidence. One may be better than NPS for a particular question, but recommendation, satisfaction, and effort measures all depend on customers reporting a perception, intention, or evaluation.
How and when those answers are collected also matters. Traditional post-visit surveys depend on someone seeing an invitation after an experience and choosing to respond. That can make it difficult to generate enough signal for decisions at store, shift, product, or daypart level.
Our guide to customer survey response rates looks at why collection method and timing make such a large difference to participation. Our guide to survey fatigue also looks at what happens when feedback requests become too frequent, too long, or require too much effort.
None of this makes survey research invalid. Strategic studies, relationship tracking, customer interviews, and targeted surveys all have important jobs. But when the decision is operational or financial, another evidence layer can make that existing research more useful.
What ~450,000 responses show about recommendation scores and basket size
A useful alternative to a recommendation score does not necessarily need to be another headline metric. Sometimes the better question is a more specific one. There are three useful levels to distinguish:
- Relationship metrics give one score for the overall relationship or visit. NPS®, overall CSAT, and recommendation questions sit here. If the number moves, the business still needs to work out which touchpoint changed, what happened there, where the problem sits, and what to do about it.
- Attribute ratings narrow the focus. Questions about service, value, experience, cleanliness, or product choice tell you which part of the experience the customer is evaluating, but the answer is still a judgment.
- Behavioral questions ask whether something happened. Was assistance offered? Did the customer find everything they needed? Were they greeted? Did somebody suggest an additional item?
These are still customer-reported measures. The difference is that the customer is recalling a specific fact moments after the experience rather than forming a broader judgment about it. An operational standard is what the business has decided should happen. A behavioral question is one way to ask whether it did. Execution is how consistently that standard is delivered across the estate. So behavioral questions can be used to measure whether operational standards are actually being executed.
First, an important caveat
TruRating analyzed transaction-linked feedback from four retail estates during August 2026. The analysis compares the average transaction value of customers who answered each question positively with the ATV of customers who answered negatively, within the same estate and month.
Each vertical represents one retail estate, not the wider industry. Response volumes vary by question, and the results cover one month only, so they should not be described as a trend. Currencies also differ between the estates, which is why the analysis uses percentage differences rather than combining the underlying transaction values.
Most importantly: this is correlation, not causation.
Customers with larger baskets may simply be more likely to answer positively. A +19% association does not mean improving a measure will cause transaction value to increase by 19%. What the analysis does show is which questions carried the strongest commercial signal in each estate, and how much that signal varied depending on what customers were asked.
A recommendation question ranged from +1.5% to +18.8%
The first finding is directly relevant to anyone considering alternatives to NPS. The relationship between a recommendation question and basket size varied dramatically across the four estates.
| Retail estate | Recommendation question | High-volume behavioral comparison |
|---|---|---|
| Grocery estate | +16.2% | Checkout quick and easy +12.9% |
| Travel retail estate | +18.8% | Found everything needed +11.5% |
| Apparel and footwear retailer | +3.2% | Offered assistance +27.1% |
| General retail estate | +1.5% | Found everything +19.2% |
The recommendation question led the comparison in two estates. In the other two, it sat close to the bottom. In the grocery estate, customers answering positively to the recommendation question had an ATV 16.2% higher than customers answering negatively. In the travel retail estate, the difference was 18.8%.
But the picture changed completely in the other formats. In the apparel and footwear retailer, the recommendation question was associated with just +3.2% ATV, compared with +27.1% for whether assistance had been offered.
In the general retail estate, the recommendation question was associated with only +1.5%, while customers who reported finding everything they were looking for had an ATV 19.2% higher. That general retail comparison is backed by large samples on both measures. The recommendation result came from 156,331 positive and 3,261 negative responses, while the “found everything” result came from 178,474 positive and 5,053 negative responses.
So neither difference can easily be dismissed as a small-sample artifact. The recommendation question’s association with ATV ranged roughly twelvefold across the four estates.
That means saying “recommendation scores do not relate to spend” would be wrong. But saying “recommendation scores predict spend” would also go further than the data allows. The more defensible conclusion is that the commercial signal varies materially by retail environment and should be tested rather than assumed.
August 2026 transaction-linked analysis
How different customer questions relate to basket size
Percentage difference in average transaction value between customers answering positively and negatively to each question, within the same retail estate and month.
Grocery estate
Up to 195,000 responses per question
Travel retail estate
Up to 52,000 responses per question
Apparel and footwear retailer
Up to 14,600 responses per question
General retail estate
Up to 178,000 responses per question
How to read this: Figures show association, not causation. They compare average transaction value for positive and negative responses within the same retail estate during August 2026. Each vertical represents one retail estate, not the wider industry. Response volumes vary by question.
The +24.9% sales-associate recommendation result in travel retail is directional because it is based on 1,617 responses, substantially fewer than the other measures in that estate.
The most useful question was different in every estate
There was no universal question that rose to the top everywhere. In the apparel and footwear retailer, the strongest association came from what happened on the shop floor.
Customers who reported being offered assistance had an ATV 27.1% higher than customers who said they were not. In the general retail estate, the strongest signal was availability. Customers who said they found everything they were looking for had an ATV 19.2% higher.
In the grocery estate, the recommendation question led overall, but checkout measures sat close behind. Checkout quick and easy was associated with +12.9% ATV, while Checkout wait reasonable reached +11.9%, both on close to 200,000 responses.
The travel retail estate adds an important qualification. A question about receiving useful sales-associate recommendations showed the largest ATV difference in that estate at +24.9%. But that finding came from only 1,617 responses, far fewer than the tens of thousands behind most other questions in the estate, so it should be treated as directional rather than as the headline result.
On the larger samples, the recommendation question was associated with +18.8%, while whether customers found everything they needed was associated with +11.5%. The implication is not that every retailer should switch to behavioral questions. It is that a single group-wide question set can miss the specific thing that matters in a particular format.
Assistance mattered most in one estate. Availability mattered in another. Checkout carried substantial signal in another. You cannot reliably infer the right question from outside the business, that’s why we work with retailers to identify the metrics that are important to their business.
Specificity matters more than question type
The data does not support a simple claim that behavioral questions outperform attitudinal questions. Grocery makes that obvious. Service led several specific checkout questions. Travel retail does too. Good Experience was associated with +22.7% ATV, while one behavioral question, whether a promotion caused the customer to buy more, was associated with just +0.1%.
So the useful distinction is not behavioral versus attitudinal. It is how far the question sits from something the business can actually change.
- A relationship score tells you something about the whole relationship.
- An attribute rating gets you closer by identifying the dimension.
- A behavioral question can get closer again by asking whether a specific operational standard was delivered.
If “Would you recommend us?” falls, there may be several layers of diagnosis before a store manager knows what to do. If “Were you offered assistance?” falls in a group of stores, the gap is much clearer.
That makes specificity valuable even when the broad relationship metric has the stronger statistical relationship with spend. The aim is not simply to find the question with the biggest percentage beside it. It is to find a measure that provides useful evidence and points the business toward something it can investigate or change.
From customer measurement to retail execution
This is where the distinction becomes useful outside the Insights or CX team. Behavioral questions measure whether operational standards are being executed. Those standards can cover more than service.
- Being offered assistance is a service standard.
- Finding everything you came for can expose an availability issue.
- Moving around the store easily relates to layout.
- Merchandise being organized and easy to shop relates to merchandising execution.
The measurement question changes, but the operational principle is the same: the business has decided something should happen, and customer feedback provides evidence about whether it actually did. That also creates a direct connection with mystery shopping.
Mystery shopping has traditionally been used to verify operational standards through scheduled observations. Behavioral questions can ask real customers about many of those same standards continuously across a much larger volume of transactions. Different instrument, but the same underlying question:
Did the experience the business designed actually happen?
Our guide to transaction-linked feedback explains how linking the answer directly to the purchase adds another layer by allowing teams to see not only where execution varies, but how different customer-reported experiences are associated with commercial outcomes.
Value is another reason to test assumptions
Value produced another counterintuitive result, although the four-estate data makes the pattern less universal than it first appeared. Customers answering positively about Value had:
- 6.7% lower ATV in the grocery estate
- 6.2% lower ATV in the general retail estate
- 0.4% higher ATV in the apparel and footwear retailer
- 14.5% higher ATV in the travel retail estate
So the inverse relationship appeared in three of the four estates, but reversed substantially in travel retail. General retail also produced another unusual result: Service was associated with −1.0% ATV.
There are plausible explanations for both findings. A larger basket might make a customer more sensitive when deciding whether a purchase represented good value. Travel retail may behave differently because the buying environment, available alternatives, and price expectations are different.
But those are hypotheses. This analysis does not prove either explanation. The more useful lesson is to check the assumption before turning a customer metric into a target. If a team is being asked to improve Value, Service, recommendation, or any other score because leadership expects higher spend to follow, first test whether that relationship actually exists in the business.
What this means when looking beyond NPS®
The case for going beyond NPS is therefore not that recommendation questions lack value. In two of these four estates, the recommendation question had one of the strongest relationships with basket size.
The problem is assuming that the same relationship will hold everywhere, or asking a high-level relationship score to identify what needs to change operationally. The four-estate analysis points toward a more useful measurement architecture.
- Use a relationship metric when you need to understand the overall customer relationship.
- Use attribute ratings when you need to understand which part of the experience customers judge positively or negatively.
- Use behavioral questions when you need to understand whether a specific operational standard was delivered.
And where commercial prioritization matters, connect those answers with transaction outcomes and test what the relationship actually looks like in your own business. That gives teams more than another score. It gives them a way to move from what changed, to where to look, to what might actually be worth fixing.
What behavioral measurement looks like in practice
The above analysis shows the relationship structurally. Individual retailer examples show how that kind of evidence can then be used operationally. At Paradies Lagardère, customers were asked at the point of payment:
“Did a team member suggest an item for you today?”
That is a very different question from asking for an overall recommendation or satisfaction rating. It asks the customer whether a specific behavior happened during that visit. Because the answer was connected with transaction data, Paradies Lagardère found that customers who reported receiving a suggestion spent 10% more per transaction.
The same analysis showed an execution gap: half of stores were delivering the behavior less than 60% of the time. Paradies Lagardère then modeled a $1.8 million recoverable revenue opportunity from improving execution in the lower-performing half of stores by 25 percentage points.
Our Hanes Australasia fitting room analysis followed the same principle.
It showed that customers who were asked whether fitting room assistance had been offered. Across 41,000 ratings analyzed, Hanes found that ATV was 18% higher when fitting room help was offered. The same analysis identified a 1% revenue growth opportunity from improving fitting room support for an additional 5% of customers.
Both case studies provide something concrete to investigate. A recommendation score might tell you that loyalty has changed. An action question can tell you whether a specific event happened, show how consistently it happened across the estate, and reveal whether different transaction outcomes appear alongside it. That gives teams something they can test, coach, and measure again.
Should you replace NPS® or supplement it?
For most organizations looking at alternatives to NPS, supplementing the existing measure is the more sensible starting point. Forrester also argues for considering supplementation rather than automatic replacement, highlighting organizations that use other experience or ease measures alongside NPS.
There is practical value in that approach. A long-running recommendation measure may have years of trend history and considerable executive recognition. Removing it can break continuity without solving the actual measurement gap.
Replacement makes more sense when the measure clearly fails a fit test. Perhaps the question does not make sense for the customer relationship. Perhaps internal teams have stopped trusting it. Or perhaps the organization has tested it against commercial outcomes and found another measure consistently provides a more useful signal. Otherwise, different measures can do different jobs:
- A recommendation score can provide a relationship-level outcome.
- CSAT can assess satisfaction with a selected experience.
- CES can capture perceived effort.
- Behavioral and transaction-linked evidence can show whether specific events happened and how those events relate to commercial outcomes.
That is a stronger measurement architecture than expecting one score to answer every customer question. And that is how TruRating should be considered in this context.
How to choose an NPS alternative
Choosing an NPS alternative starts with the decision you need the metric to support, rather than finding another headline score to replace the one you already have.
- Start with the decision. Use a loyalty measure when you need relationship health, CSAT when you need satisfaction with a specific experience, CES when you need to understand effort, and behavioral evidence when you need to know whether something actually happened.
- Decide where you need to act. A brand-level KPI has different data requirements from a measure intended to guide a specific location, shift, product category, or journey.
- Think about timing. Quarterly relationship tracking may be sufficient for one decision. A store team trying to understand an execution problem this week needs a much faster signal.
- Decide whether financial linkage matters. If the metric will determine where you invest, coach, or scale an initiative, connecting customer evidence to spend, retention, conversion, or another outcome makes prioritization easier.
If the wider question is which technology should collect and analyze the data, our Qualtrics alternatives comparison looks at different approaches across enterprise experience platforms, research tools, and transaction-linked feedback.
The useful shift is to stop asking one customer metric to do every job. You can keep the long-term measures that provide continuity while adding evidence that helps explain what is happening underneath them.
For customer and insights teams, that creates a stronger connection between research and commercial decisions. For teams closer to operations, it provides enough granularity to see where an experience or behavior is changing and where further investigation is worthwhile.
That is where behavioral, transaction-linked feedback earns its place. It does not need to replace the customer measures already trusted by the business. It fills the gap between knowing what customers say about the relationship and understanding what happened during the experience, where it happened, and how it relates to commercial outcomes.
See what is actually driving performance in your stores
TruRating connects customer feedback with transaction data so you can see which experiences and operational standards are associated with spend, store by store. Book a demo to see how TruRating can add a high-volume, transaction-linked layer to your existing CX and insights program.
Frequently asked questions
Answers to common questions about NPS alternatives, customer experience metrics, and behavioral measurement.
What are the alternatives to Net Promoter Score℠?
Is NPS® still relevant?
Does NPS® predict revenue growth?
What is the difference between NPS® and CSAT?
Should we replace NPS® or add something alongside it?
What is a behavioral alternative to a recommendation score?
Net Promoter®, NPS®, NPS Prism®, and the NPS-related emoticons are registered trademarks of Bain & Company, Inc., NICE Systems, Inc., and Fred Reichheld. Net Promoter Score℠ and Net Promoter System℠ are service marks of Bain & Company, Inc., NICE Systems, Inc., and Fred Reichheld.
