Every good product manager today wants to understand how customers are perceiving and interacting with the product.

They want to know which features are working, which ones are proving to be the bottleneck and which new features are being requested.

This kind of information straight from the customers is critical in building a strong product roadmap

But it is not always easy to get this information. As a product manager, therefore, you’d always have this question in your mind - “how to get better, accurate, granular customer insights to inform product roadmaps?”

In this article, we will answer this question.

How Product teams are using customer service data currently?

Almost every product team today uses a product analytics application like Mixpanel to get quantitative product insights.

This is an excellent first step in understanding how your customers are using your product.

It is also a great way to track metrics like Adoption rate, Daily/Monthly Active User, NPS, Retention Rate, etc.

By the way, if you’re struggling with a low NPS rating, there’s an article we’ve recently on this topic where we talk about how you can improve your Net Promoter Score using Customer Sentiment Analysis.

Coming back to product analytics.

It can also give you insights into some beneath-the-surface metrics like who your power users are, what makes a power user, and popular features among others.

But these insights are not complete.

And if you’re using these insights, you fall into one of these two categories: either only focused on the quantitative data or not using qualitative data to its full potential.

1/ Are your insights ‘all quantitative, no qualitative’?

Suppose you are a ride-sharing app and your product funnel looks something like this:

Users enter location > They book a ride > They complete the ride.

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Your product analytics app tells you that most users are dropping off while booking a ride. That is a good data point to have.

But the problem here is, with just this data you will never be enough to make necessary product changes. You need to answer why. Why are so many users dropping off there?

Customer service data will help you understand just that.

More on this example later in the article.

2/ Or, are the insights ‘all quantitative, less qualitative’?

Next comes product teams which also incorporate some forms of qualitative insights.

These insights typically come from user research. User research can vary depending on the company, but typically it includes analysing customer reviews, surveys, NPS, and user interviews.

While these are good sources of customer information, the biggest drawback with them is that these alone can not give you the whole picture.

This is because all of these need to be procured from the customer. As a result, tools like NPS can be biassed, generic, and reactive.

Then there are also product bug reports that come from the customer service team. Most of the time, these insights are generated based on manual tagging and shared on an ad-hoc basis. 

These reports are also not holistic and are sometimes inaccurate, which results in a lack of confidence in them. 

Customer Service Analytics + Product Analytics = The ultimate fodder for a killer product roadmap

We already know how crucial customer service insights are, and how automated tagging can help create impactful user reports that feed straight into the product feedback loop.

But you may still think. That's all fine, but how exactly?

Well, to illustrate, let's go a bit back in the article.

Remember the ride-sharing app's example?

This is their typical user flow. Users enter location > They book a ride > They make the payment > They complete the ride.

We established that most users were dropping off at the 'book a ride' section.

With this data in hand, you can examine your customer service data for additional insights.

This is what you discover: The users were actually facing a problem while processing payment. Clueless, they go back to the previous section and drop off from there.

While the product analytics tool attributes the drop-off to the 'book a ride section', it's actually the payment section that needs to be scanned.

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As you can see here, customer service data can validate or negate your assumptions, enrich your existing insights, and add new dimensions - all of which will eventually help you tweak your product roadmap to incorporate your customer needs.

How to use customer service data for a better product roadmap?

A product manager shouldn’t have to work with a stale product roadmap.

You can leverage customer conversations to get an in-depth understanding of what’s driving your users, their pain points, their favourite features, and so on. 

The more granular you go, the more you can understand the small problems and wow points. Then feed this information into your product roadmap and prioritise features that would move the needle.‍

Looking for some inspiration?‍

Do check out our podcast episode with Nick Moreton from Hotjar where he talks about representing customer support insights in their product feedback loop. I’m sure you’ll find some aha moments there!

There can be several ways, but let’s talk about a couple use cases that we see our customers like Hotjar, Hopin, and Gousto love.

1/ User churn analysis

There are often underlying reasons behind why a customer cancels.

- Was it a repetitive product issue that bothered them? 

- Or the product wasn’t what they had expected in the first place.

- Maybe there are some features which are deal breakers that your competitors have.

It's a good idea to set up a weekly report that gives the exact answers to these questions so that you can confidently get to the core of product issues.

Then use this information to inform your product roadmap and ultimately improve customer retention.

2/ Feature adoption analysis

This one is especially useful when you launch a new product or feature.

Keep an eye on how the new feature is being received by the users by tracking your customer service data in real time.

A spike in related topics where customers show negative sentiments would be a good place to do an in-depth analysis.

Maybe the entire new feature as a whole was not well-received, but there are often cases where users do not appreciate a part of it.

For example, in the ride-sharing app, you launch a feature where users can record audio for the driver giving them directions to use when the GPS is unreliable.

Now, the users absolutely love this feature, but the audio duration is limited to 1 minute. So people share their feedback with customer support.

If you go by the overall volume of a topic, you may be tricked into believing that the customers didn't appreciate the feature or that it was not functioning properly.

But the reality is different. Customer insights will help you understand these.

Closing thoughts

If you’re using customer service data to spot product bugs, you’re already ahead of the competition.

But to fully utilise the potential of customer service conversations, you need a mechanism that’s automatic and uses AI to tag tickets.

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One that understands your customer data and accurately tags your support conversations in real time, giving you granular information about your product bugs.

This will help you to identify product issues quickly and in much more detail. The result is quick product bug fixes and a better customer experience.

Want more product related content? here's an article we've written recently on using customer service data to uncover product bugs in realtime.