Turning Insights into Actions: Building a Best-in-Class and Highly Actionable NPS Program

Understanding the customers’ sentiment towards your company and its solutions is critical to your company’s ability to optimize its actions with customers. It is therefore one of the most important tasks of the Customer Success team. How to best understand your customers (through the lens of NPS) is the topic of this 3-part blog series.

1. The first article in the series presented The Case for NPS: Should You Use NPS? We discussed the pros and cons of NPS and concluded that when implemented well, NPS is a powerful tool for CS teams and companies in general to gain deep and actionable insights into their customers.     

2. The second article was focused on The Best Methodology for NPS: how to structure an NPS feedback mechanism best to enable you the high response rate and specificity of data you need to not only fully understand, but also effectively act on the data.   

3. This third and final post is dedicated to the Taking Action on Your NPS Data: a practical set of playbooks and actions you can deploy in your organization putting to good use the rich data you got from your NPS survey (aka: Feedback Form).

Throughout this series we asked surveyed the Customer Success community on the importance, response rate, and use of NPS in their companies. We’ve received close to 150 responses and so throughout this article you will also find nuggets of data and insights to help support why a thoughtful framework makes all the difference when leveraging NPS.

Before we dig in, let’s recap.

1. Should You Use NPS?

NPS is a survey methodology that assesses customers’ sentiment towards a vendor. It uses a single very specifically worded question with a very rigid and specific scale to collect responses. 

Pros and Cons of NPS: The power of NPS comes from its simplicity - a single very rigidly formatted question - that produces a high response rate and enables easy and credible benchmarking of the results. The key drawback of NPS is the challenge of converting the single numeric response from customers to real insights that can be attributed to specific drivers and enable practical actions as a result. 

Our suggested methodology builds on the strengths of NPS, while addressing its drawback. Deploying it, we achieved a consistent response rate of 22-25% (that is 10x normal surveys!!!), a wealth of data to fully understand customers and strong feedback from a multitude of stakeholders on the practicality and value of the methodology.

Based on the above, we concluded that:

NPS, when implemented well, is a powerful tool and most companies and Customer Success teams should use it.  

An interesting observation from our survey is that NPS is most often used at mid-size companies. We suspect that smaller companies do not have the volume of data points (few customer) for a program like this to be viable and in very large ones established departments may push for a multitude of other KPIs.

2. The Best-in-Class NPS Methodology

The Suggested Methodology: 

• 1 Question: Use the standard-language single NPS question. 

• 1 Optional Text Box: Add an optional comment field after collecting the NPS score. 

• 3 Times: Send the feedback form to the customer in 3 specific moments along the customer lifecycle or where the data indicates a potential drop in experience. (Ex: immediately after go-live, after 90 days of usage, and then annually on the go-live anniversary).

• In-App: Distribute the feedback form to customers in-app if at all possible.

• Explain Why: Precede the standard NPS question with a brief (one-line) explanation of the reason for the feedback form being sent.

• Act of the Data: Establish a plan to analyze the data and develop a set of playbooks to act on it. 

This post is all about that action plan. 

3. Turning Data into Actions

According to the survey we conducted among Customer Success professionals in December 2024 - January 2025, 82% of respondents leverage NPS, however only 34% find it either "valuable" or "very valuable". Clearly, NPS is not “dead”. But, just as clearly, extracting meaningful insights from the NPS responses and converting them to actionable tasks is a tall order for many companies. This is totally understandable as NPS reflects the customer sentiment towards the solution as a whole, and so gaining specificity on relevant insights for the different teams involved in the customer experience, namely: Sales, Implementation, Product, Support, etc. is non-trivial.  

This is why we strongly suggest thinking of the Action Plan portion of your NPS Program in four categories. Think of it as “segmenting your audience”, just like you segment your customers, if you want to be specific and relevant to their diverse needs. 

The four areas are: 

1. Centralized Source of Truth: Create a centralized “source of truth” for all the data in easy-to-consume dashboards and visualizations. Consider enabling (view/read) access to it to people as a means to help them do more analysis on their own while ensuring they use the same data.   

2. Customers: The set of playbooks for and actions targeted at customers. Customers expect you to acknowledge and act on their survey responses. Make sure you do it well. 

3. Key Stakeholders: Lots of stakeholders across the company are interested in your insights and you are interested in their actions to aid your customers’ experience. Make sure you are intentional with your playbooks toward them.

4. Executives: Executives’ interests are often slightly different from those of functional leaders. Many executives are keen on the NPS score and competitive benchmarking. Educating and informing them on your insights should be done in a careful and intentional way to ensure you gain their continued support for the program.

A. One Source of Truth: Raw Data, Dashboards, and Visualizations

Few things are more damaging to the credibility of an analysis in a presentation than challenges to data integrity. Since businesses are increasingly complex, the company interacts with customers in an increasing variety of channels and collects data from an increasing number of mechanisms. These often lead to different teams looking at different data when trying to understand customers‘ behaviors and/or sentiments. Centralizing the data is key not only to your ability to have a more comprehensive view of the customer, but also to ensuring consistency in the data used by different teams. Do not underestimate the importance of this effort!

Start Small, Iterate and Improve Over Time (aka: a Roadmap to Success)

We developed a very comprehensive plan for what we wanted in our data. Then, we mapped it over time and created a roadmap for the evolution of those needs. Some elements were readily available, others easy to add and some required significant investment. Our roadmap enabled us to provide some value quickly, collect feedback from stakeholders, and improve the program over time. This strategy not only balanced our resources, but also built our credibility as we showed improvement over time and listened to our stakeholders.

At a high level, our roadmap for the NPS data included:

1. Collect multiple NPS datapoints: We first launched our initial NPS upon completion of initial onboarding, then add asking at 90-days post-go-live, then annually. Further, initially, we showed the NPS from each of those milestones separately, then developed a mechanism to show them on the same timeline highlighting the differences as a source for next-level insights. 

2. Build out metadata over time. We had access to basic customer data (in our CRM adn in our In-App solution) that helped us splice our data by industry and product type, but information like tenure, onboarding experience, sales channel, etc. were things we needed to tackle over time. Further, adding retention data turned out to be a bit challenging as it required some calculations, and differentiating among persona was quite hard in our environment. We tackled those over time.  

3. Enable data access to stakeholders. We provide initial access to stakeholders into our NPS data on Pendo, which is how we collected the feedback from customers. But then, we build a data model in PowerBI where we can add vastly more data and enable much more analysis.

4. Quantify qualitative data: We collected and provided to our stakeholders the comments from customers from the get-go. But, initially, this was just a list of the text. We followed quickly by adding a quantification of those comments into categories, which added a whole new dimesion to the data analysis.

Centralize the Data and Democratize Access to It

Previously we mentioned that we used Pendo.io as our vehicle to distribute our in-app NPS feedback forms to our customers. Pendo has a lot of out-of-the-box functionality when you use their survey tools, and, as a result, this was the perfect starting point to provide the raw data to our teams. At any time our product leaders and different customer-facing teams could log into Pendo, filter to a specific date, look at a specific product, see real-time scores and review customer sentiment. 

We chose this as our starting point because we did not want to be a gatekeeper of this information nor did we want people to worry we were “altering the narrative” in any way. It helped get the raw data into the hands of those who needed it while also building internal rapport and trust across teams. The drawback of sharing raw data is that it does not provide a story, a narrative or a detailed cross-datapoint analysis needed for our different stakeholders. It does not highlight correlations, illustrate trends, or show scores along the customer journey. 

So, we developed dashboards to enable visualizations of the data in different formats to address the very different needs of the different stakeholders and consumers of the data. To maximize the value of the dashboards, we used both the quantitative data (the 1 number provided by customers) and a quantification we have done to the qualitative data (the comments).

Quantify Qualitative Data: Analyzing Comments

Quantifying qualitative data is semi-art semi-science, but is very worth doing as it adds a whole additional dimension to the analysis. We created about 10 categories to group the comments and invested time by some of our people to carefully assign the comments to their right categories. Think about such categories as: Ease of Use, Availability (or lack) of Features, Too Many Issues (bugs), Sales Process, Customer Service, Software Stability, Knowledge and Training (or lack of), and more. This labeling/classification allowed us to conduct an analysis of the categories and contrast them with the promoter-neutral-detractor scores. This gave us insights into the main reasons customers made their NPS score selections.   

Visualizations: How to Turn Data into Insights

We then created an overview of all data, alongside a multitude of reports showing the data broken by key customer characteristics such as: customer type, solution type, industry, customer size, customer tenure, customer onboarding journey (self vs aided), etc. This enabled us to gain very specific insights on what works and what does not, and based on that what can we do to improve the customer experience the most. Further, each product manager could see their product analytics and compare them to other products. Each sales team could see their region and compare to others. Each Implementation team their segment and compare with others, and so on.  

The most interesting part of the dashboards was the visualization that showed the data over time.

As we noted before, NPS is just a number and as such is not, in and on itself, that useful. Conversely, it is supremely useful when one looks at it over time and layers on actions the company has taken (changes in pricing, major product releases, outages, alterations to the onboarding process, etc.). 

The driver is the number of responses as too few negate your ability to have a meaningful (statistically viable) analysis. In a high-touch mode, where you may have few very large customers, you may want to think of collecting more responses from more personas across each customer and still, you might need to review your data less frequently. Still, any frequency lower than quarterly is way too limited in providing meaningful and actionable insights in a timely manner. Conversely, if you are working in a low-touch/tech-touch mode with high number of customers, monthly meetings are a great way to begin creating baseline measures for your teams. 

Now that we had our data organized, shared, and aided with visualizations we were ready to use it. After all, data with no action plan is waste, not value.

B. Creating Action Plans for Customers

There is no better way to demotivate customers from filling your surveys in the future than not responding to those who do fill it now. The second best way to achieve the same is to respond with a generic respond that indicates to the customer you have not really listened to them... Don't fall for it!

Developing the playbooks for acting on the data with customers was one of the first tasks we did and was, to be perfectly honest, relatively simple and straightforward. The key reason is that we did NOT aim our NPS program to drive our actions with individual customers, but rather to learn about customers’ sentiment as a whole. Understanding and acting on individual customer needs we did more through CSAT and their interactions with the Support team, insights from product usage (or lack of) and feedback from other stakeholders (sales, partners, CSM) who interacted with customers 1:1. 

That said, it was critical for us to respond quickly and effectively to customers who provided us with feedback. This was our playbook:  

1. Automated Responses: a well-accepted and common best practice we followed is to send customers automated responses as soon as they provide us with their feedback. In all of them, we thanked the customer for providing us with the feedback. However, we developed slight variations of the automated responses based on the customer feedback (promoter, passive, or detractor). 

=> Detractors and Passives: We asked if they would like a follow-up call with someone from our team to see if we can address any of their concerns or questions. A number of them took us up on that, which was great for our research and product teams. 

=> Promoters: we prompted them to leave us a review on Google or in the app store. You can do the same with whatever review site your company leverages. 

2. Red-Hot Channel: We created a channel in Teams (the same can be done in Slack or other tools) and pasted exceptionally problematic comments in it. We then immediately connected with detractors who left such feedback through the use of an internal “red hot” thread. Many people have NPS scores/feedback sent directly to an internal channel on Teams or Slack. Just as survey fatigue is real for customers, NPS feedback can also be a distraction and as a result, become a null point for internal teams.

To address this, we were intentional about creating a channel with select team members to address “Red Hot” extra negative feedback. The channel consisted of a group of stakeholders (key point people from Sales, Support, Implementation, Product and CSM) and enabled us to quickly troubleshoot and tackle the feedback in a cross-functional level. Service can be managed, pricing concerns can be addressed, and product frustrations can sometimes be prioritized before the customer decides to leave. We chose to keep this process manual so our internal teams did not become “numb” to the noise the channel created and ensure immediate action. 

3. Enhanced Training and Education: In some cases where customers gave low scores because they did not know our product could do a certain thing, we leveraged a playbook that provided a warm link to our Academy (an LMS site with our customer-facing training material) or a direct link to the education resource that would best help them. When we identified a trend of such issues (e.g. a certain feature that is frequently missed), we used the data from our reports to alert our Training and Education team to make changes to the customer-facing training with those insights. This oftentimes impacted our customer journey and engagement model so addressing them together is better than working in a silo.

We measured improvement over time by ensuring that the number of “mentions” was decreasing. Was the number of training and education mentions for a specific product decreasing, constant or increasing? Was the number of feature mentions for a specific feature decreasing? Were we reducing friction? 

C. Empowering Other Internal Teams

NPS provides the customer sentiment towards the vendor as a whole. Finding ways to isolate the relevant insights to specific teams is at the heart of making NPS useful.

"NPS is not Actionable" is the main drawback of NPS according to our survey. The Reason is the inability to associates the NPS single-question feedback to specific teams.

Further, if you read all the way to here, you probably agree with us that a) you should have an NPS program and b) you should be the team driving it. Your next challenge is to make sure the multitude of stakeholders who are engaged with the customer (Sales, Implementation, Support, Training, Product, Customer Experience, Marketing, Partner Mgt, etc.) support you on those two questions (that NPS should be used AND that you should run it). 

The way to achieve this is by engaging them closely, listen to their feedback, make them feel heard and influential and provide them with value from the analysis, while reducing their level of effort in getting these insights. Here is what we did:

1. Monthly Calls: we provided frequent periodic (monthly) reports on our NPS findings. We invited all the key stakeholders (per the teams noted above), and shared with them the findings (scores, insights, trends) as well as the program evolution (action taken since the last meeting, and our planned enhancements to the program). The agenda was crisp, the data was shared openly, time was allotted to feedback and discussion, and notes were taken, published, and acted on. The consistency and transparency in providing the insights and  plans gained us credibility that we do right by the different teams.

2. Collect Feedback: we invested energy is specifically asking each of the stakeholders for feedback on what else they would like to see, what would help them more. Some of that energy came in the monthly meeting with all stakeholders, others in meetings with specific stakeholders we wanted a further deep-dive with.  Most requests came in the forms of additional metadata data and additional visualizations to slice the data in different ways. We did our best to incorporate the requests into the next iteration (or at least our roadmap) of the program.

3. Publish Action Items: over time, we were able to identify specific areas whose impact on our customers was significant. We took notes on those and discussed with the relevant stakeholder team what actions can be taken to address the issue. We then published the decision (Action, ETA, Owner), shared it with the broader team, and followed up on the completion of the tasks. Stakeholders increased their trust is our coordination as hey witnessed our followups with other teams. It supported our positioning iof ourselves as not “owning” NPS, but rather “coordinating” the NPS program for the company.   

4. Promote Deep Data Analysis: as you recall from the previous section, we enabled access to our data models to many stakeholders. We also promoted them to conduct additional analysis beyond what we did. This further strengthened the centrality of the data source we created and the role NPS played in the minds of people as helpful. Further, we promoted the use of our NPS results in other forums and in support of other actions. For example: we engaged specifically with the Product team to have the NPS data support Product Roadmap decisions, with the PS team to enhance the initial implementation process of new customers, and with Sales to enhance value statements and success stories to new customers.   

By effectively measuring and presenting the facts of our survey along with other customer-facing metrics our NPS program became a valuable source for how other teams trained and up-leveled over time. Feedback fueled growth.

D. Educating and Informing your Executive Team

Of those who completed our survey, 48% stated that their CEO is either interested or very interested in the company’s NPS score. Interestingly, and somewhat disappointing, a slightly smaller portion (41%) stated such interest by their Head of Product. If you’re going to revamp or introduce NPS into your voice of customer programs, you need to have a plan for what and how you share its results with your executive leadership team (ELT). 

In our experience, ELT is interested in knowing the following: 

1. What is our NPS score and how is it trending? 

2. How does our NPS score compare to our competitors?

3. Can NPS help us forecast other metrics (mainly usage, retention and expansion)? 

4. What are recurring themes/sentiments from customers and are we acting on them? 

5. Can NPS be used to accelerate company culture, morale and/or focus on quarterly themes?

Here are a few ways we worked to keep our ELT educated, informed and supportive of our efforts.

1. Monthly Operating Reviews: While customer-facing teams always had access to the NPS data (and other forms of the VOC program) via our dashboards outlined above and specific functional efforts, We used NPS to provide an umbrella view of our customers’ sentiment towards us. It was a good way to level-set where we are, what we do, how things evolve and how it aligns with competitors. We embedded this info into our Monthly Operating Reviews and provided a summary of the monthly reviews with the broader stakeholder meeting. The topics that got the most attention were: 

• The trend of the NPS score: executives found it reassuring to see the trend of the score going up, especially as we tied it to specific actions we have taken.

• Key themes: Our ability to show data-driven analysis of customer sentiment was praised probably as most often such sentiment is presented in ad-hoc anecdotes, which may or may not correlate with the sentiment of the broader customer base. 

• Key Products: especially around new product introductions, our NPS methodology provides a leading indicator to issues and their resolutions. 

• Competitive Benchmarking: We found the benchmarking data against competitors had considerable weight with executives. That in turn amplified our ability to influence the direction we wanted to take our product and services.

• The cross-functional alignment: on data and action items. Since we were able to establish a rhythm of the cross-functional reviews of the NPS results and use them as a base for decision-making, executives expressed satisfaction with the cross-functional work it supported.

2. Company Meetings: We made sure to include NPS data and trends in our monthly company meetings, highlighting positive and negative sentiment, improvements made to metrics, and experience on specific actions we have taken to follow up on customer feedback. We tried to expand on the overall data with specific customer testaments or case studies and tie them to the scores we got. Sharing this data and actions with the broader company had the incremental benefit of fostering a more customer-centric mindset amongst the teams and fostered a strong customer-first culture. It also stressed the cross-functional collaboration. Since we used data and slides mostly already created for the operating reviews, the incremental cost (effort, time) here was minimal.  

3. Voice of Customer Executive Briefings: We made sure to stress that NPS is just one of the feedback channels we have. We also received CSAT scores from Customer Care and Off-boarding sentiment from churned customers. Once a quarter or every 6 months (depending on the amount of data collected) we would compile all of the customer feedback and present big rocks to the executive team. Such exercises highlighted pricing concerns that needed to be addressed, product flaws that needed a blitz, and even offboarding experiences that needed overhauling. NPS in a vacuum is only one data point, when combined with others it can be a very powerful tool to leverage with your leadership team. 

3. Conclusion

Is Your Company Customer-Centric?

Almost every company we ever worked at or engaged with claimed they are “customer focused”. Some are true to this claim, others less so. Investment in collecting customer feedback is a foundational component for any company aspiring to be customer-focused. How can you be customer-centric if you don’t know - very well - what your customers do, think, and feel about you and your products?

A Voice of Customer (VoC) program is therefore paramount. Such a program can be as minimal as a few standard surveys or as deep and multi-facet as including Customer Advisory Boards (CAB), Referral Programs and Intensive live customer interviews. The tactics can and should be different for different companies based on their characteristics. But, a program should exist, and most likely, the Customer Success team will administer, coordinate, or “own” it. If you work in large companies, you may have such “ownership” reside within the Marketing or Customer Experience teams. The ownership is not what matters. What does matter are the KPI you set, collection methodology, data analysis, and consistency in sharing information and following up with actions.  If you consider yourself or aspire to be customer-focused, you must have such a program.

NPS - Implemented Well - is a Most Powerful Tool - Use it!

As we discussed, there are many metrics to assess customer sentiment. We hope we were able to convince you that NPS - implemented well - is a powerful one. We believe that measuring NPS in multiple points along the customer lifecycle and then slice the data to assess the sentiment across products, user types and any other demographic data, gives the company an powerful and actionable insights into how best to service and support the customer. The simplicity and rigidity of the NPS question are powerful means to ensure consistency and the ability to benchmark the data - internally (across different products, regions, solutions, service levels, etc.) and against competitors.   

Has Anyone Said AI?

We deployed our NPS program with a hybrid approach, mixing manual and automated actions. This was partially due to the available technologies we had, partially due to constraints related to the regulatory nature of our specific company and partially due to the specific culture and internal processes we needed to contend with. In our next chapters, we will be looking to leverage AI technologies to help us enhance the data analysis (align comments to reasons, analyze more facets of the data, identify trends, etc.), as well as create follow-up actions (project plans to help build upon actions and magnify additional areas of opportunity for our teams).  

A number of new startups are developing AI-native survey and VoC solutions (along with many of the existing survey players who add such capabilities to their offerings). TheySaid and Perspective are two names that come to mind. What these companies promote is the notion that with AI one can potentially replace the asynchronous and rigidly defined surveys with a more customer-tailored and dynamic approach to collecting customer sentiment. Those approaches are very worth keeping an eye on as they mature quickly to points of high value.   

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Sara Bochino and Boaz Maor are veterans in the Customer Success world and have worked at a number of companies, leading customer-facing teams in different formats.