NPS Is More Than a Score—It’s a Source of Feature Signals

In most teams, NPS serves primarily as a satisfaction metric. We celebrate increases, worry about drops. We also receive lists of new customer comments.

However, in my experience, these comments are often treated separately from product research. The team responsible for NPS (often customer success) does try to regularly summarize and classify comments into categories.

‍The problem is that we treat them like afterthoughts, summarizing them into generic buckets. "15 people mentioned missing features" tells us nothing actionable.

As product researchers, we should treat NPS comments as a valuable source of Feature Signals—indicators that some new functionality should potentially be implemented in the product. This involves qualitative analysis of comments from a product perspective. Properly analyzed NPS comments can help us shape our roadmap and guide future research directions.

What is the NPS score?

NPS asks users how likely they are to recommend your product to others. The follow-up question—"What's the primary reason for your score?"—is where users explain their thinking. This is where you get feedback about what matters most to them.

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Unlike structured surveys, where we guide the conversation, NPS comments are beautifully unfiltered. Users mention whatever matters most to them in that moment—the feature they're desperately missing, the workflow that frustrates them daily, or the competitor tool they wish you could match.

What kind of product Feature Signals can we find in NPS comments?

NPS comments are an excellent source of Feature Signals. These are clients’ comments that are indicators that there’s a problem, gap, or friction that we could potentially solve with a new functionality. For a researcher, it’s a sign that certain functionality should be investigated further or potentially addressed by the product team.

The problem is how we typically handle the qualitative data from NPS comments. Most teams get periodic summaries that group feedback into broad categories: "missing features (47 mentions)," "performance issues (23 mentions)," "great support (31 mentions)." These summaries feel useful, but they're too generic to act on from the Product Perspective.

When I see "missing features" with a count, my first question is always: which features? Are people asking for the same thing, or completely different capabilities? Is this a pattern worth investigating, or just scattered wishlist items?

After analyzing responses, I've noticed that within the "missing features" category, you can distinguish several subcategories of Feature Signals.

Features users like in competitor tools

Users often point out functionalities they rely on in other tools but miss in ours. I’ve seen feedback like “thread actions from Slack” or “similar to what Notion offers for databases”.

For the product team, this is a great source of insight into which competitor features are genuinely valued, versus those that are loudly marketed but don’t impact day-to-day work. Users will often even suggest how a competitor’s feature could be adapted in our product to deliver real value.

Features whose absence blocks critical tasks

These comments sting the most because they highlight moments where the product truly fails users. Think feedback like “I can’t generate the reports my boss needs” or “there’s no way to bulk edit, so I have to do everything manually.” Insights like these reveal which functionalities must be fixed.

These aren’t nice-to-haves; they’re dealbreakers that may drive churn. As product researchers, these are the signals we need to prioritize first.

Features that exist but don't work well enough

‍Sometimes, users point out features that aren’t performing as well as they should. In my case, many users complained about the underperforming search function, which turned out to be critical in the CRM context, since it’s all about quick access to the database.

Solving this issue quickly became a top priority. Simply tossing those comments under a general “Needs improvement” bucket could have made us miss an important insight.

Features users don't know you already have

‍This one always catches product teams off guard. Users will ask for something that’s already been live in your product for months.

For the team, that’s a strong signal—not necessarily that a brand-new feature is needed, but that the existing flow should be redesigned to improve discoverability.

I once had a situation where customers kept requesting more filtering options for a board displaying communications. The funny part? The product team had forgotten that this feature already existed—it was just buried so deep no one could find it. We only rediscovered it while planning to build a “new” feature!

Future directions users expect from your tool

‍Users often share their vision for where your product should head next. They may highlight trends that matter to them, like AI-powered email writing features that are quickly becoming must-haves. Or they might describe their “ideal world” scenario—even if the absence of those features isn’t a dealbreaker, especially when the user still leaves you a high score.

This kind of forward-looking feedback can play a big role in shaping your long-term strategy.

How to extract Feature Signals systematically?

As you can see, NPS comments alone can yield many signals for product development. But how do you analyze this data to make it more actionable and grounded in analysis rather than just general intuition about what direction to take?

Let me share how I approached this. My first method was to collect all feature signals in a single Excel file. This meant that in addition to the typical NPS summary, I created a second file focused solely on product insights. This file contained all user comments relating to one of the feature signal groups mentioned in the previous section.

Feature Signals became a place for all signals from various sources. They don't have to be only NPS comments, but it's a good starting point for this type of continuous analysis.

Feature Signals should be categorized by specific product areas. Beyond allowing the entire team to browse through organized data, it's worth summarizing it regularly.

I created charts showing the frequency of the same problem, but not generic ones like "email problems!" Instead, I showed the scale: 14 users since the beginning of the year wanted "to use AI assistance with emails," or 45 users mentioned "the problem of missing email threads," etc. These are very specific data points that help prioritize topics without needing to conduct large research projects for each product area separately.

Why is it worth analyzing Feature Signals?

Systematic tracking of feature signals from NPS comments has transformed how our team approaches product decisions. Here are the key changes I've observed:

We identified patterns that weren't obvious from individual feedback

‍Since we started analyzing NPS comments regularly and in detail, we've discovered very concrete patterns in user responses.

A real-life example: we'd long felt that the way our product presented analytics wasn't sufficiently useful. However, Feature Signals gave us clear guidelines on what and why wasn't working there. We discovered that the main problem with our analytics was the lack of a quick connection between charts and data.

Thanks to the large number of comments we gathered on this topic over time, we uncovered patterns that became a very concrete argument for addressing this issue.

We found quick wins hiding in plain sight

‍Some feature signals, despite not being major changes, gave us space for quick wins. Regular analysis of these comments allowed us to fix small but irritating things that clients mentioned in NPS! This way, for example, we added several new formatting options for notes added in the tool—it was a small change, but it significantly improved daily work and enhanced the experience.

We knew which topics needed deeper research

‍NPS comments aren't always detailed enough to fully understand and properly interpret a need, so in many cases, feature signals are just the first step pointing us toward areas that we as researchers should explore with dedicated studies.

This way we don't shoot blind. From the very beginning, we choose areas that matter to users and save time on researching or developing features that aren't as important to them.

Before developing feature signals, we wasted a whole month researching an aspect that worked poorly in our product and seemed obviously something we needed to work on. However, it turned out that this area wasn't problematic at all! If we had been collecting feature signals back then, we surely would have noticed it was worth going in other directions.

We tracked how priorities evolved over time

After some time, feature signals also started showing us changes in user priorities. Changes we introduced, the impact of AI-related trends, development of alternative products, or integrations—all of this dynamically influenced our users' priorities.

Feature Signals became a very important part of our continuous research practice and helped us stay grounded in realistic present-day needs, instead of sticking to data from several months ago, which unfortunately can become outdated very quickly.

How Survicate can speed up the feature signal analysis

Maintaining such a file and regularly summarizing it is quite time-consuming, though. Over time, I discovered that Survicate could help me with this. Since I was already running NPS surveys in Survicate, all the data was already in the tool, so I started experimenting with Insight Hub.

Insight Hub is a Survicate feature that analyzes large amounts of qualitative data and creates categories based on it.

Survicate's Insights Hub analyzes and categorizes all your feedback

To focus on product features, I needed to create appropriate "Topics." In my case, these were feature signal categories on one hand (from section 3), and on the other hand, product areas that were most important to the team at that moment, like Search, Emails, Contacts (these are specific for each product).

Survicate did much of the work for me. It classified comments under the topics I created, then independently divided them into individual insights with references to specific comments and their counts. The results of such analysis could be viewed in charts, so I didn't need to create them manually in Excel with added data.

Excel works fine, but not every team can afford to have one person manage and update such a file manually. Insight Hub makes this process many times faster.

Making NPS comments part of your product strategy

The most effective product teams don’t wait for quarterly research cycles to understand what users need. They treat NPS comments as a continuous source of Feature Signals that inform both immediate fixes and long-term strategy.

When you systematically track these signals, you’re not just collecting feedback; you’re building a knowledge base that compounds in value over time. Patterns emerge, priorities become clearer, and the difference between nice-to-haves and dealbreakers is easier to spot. Quick wins stop slipping through the cracks, and your research efforts stay focused on what matters most to customers today.

This is where Survicate can help. With I nsights Hub, NPS feedback doesn’t sit in static spreadsheets, it’s automatically categorized, visualized, and connected back to your product areas. That means less manual work, faster discovery of patterns, and clearer guidance for your roadmap.

Turn your NPS surveys into a real product strategy engine. Try Survicate and see how quickly you can move from raw comments to actionable feature insights.