In most product companies, customer acquisition outpaces churn and gets most of the team’s attention and resources. This makes churn an insidious foe—by the time you sit up and take notice, it may have hit alarming numbers. As Jonah Lopin, CEO of Crayon puts it, ““By the time you see an increase in your churn rate, it is six or eight months after the point in time when you actually failed the customer. If churn is your only measure of customer happiness, then you’re always six months too late to influence your future.”

This is why the voice of the customer (VoC) matters. 

Customers are constantly telling you what they think, both directly and indirectly. By curating and analyzing this data, you can actually stay a few steps ahead of churn and manage customer experience a lot better. In this article, we’ll dive into how you can do that.

What is VoC data?

The concept of VoC was first introduced by Abbie Griffin and John R. Hauser in a 1993 MIT Marketing Science paper. They described VoC as a way to gain a "detailed understanding of the customer’s requirements" and to establish a "common language for the team going forward in the product development process." In other words, VoC gives you crystal-clear insights straight from the people who matter most—your customers—and serves as a solid foundation for making smarter product and business decisions.

VoC data can be gathered in three main ways:

1. Direct VoC Data: includes insights gathered directly from customers through surveys, polls, interviews, customer support interactions, NPS surveys, or focus group discussions.
2. Indirect VoC Data: This is the feedback you pick up from sources where customers aren’t speaking to you directly. Think social listening on platforms like YouTube, Reddit, LinkedIn, or X, as well as reviews and discussions in industry forums, Experts Exchange, etc.. Indirect data can also come from non-customers, such as prospects who considered your product but didn’t make a purchase.
3. Inferred VoC Data: This is curated by analyzing how customers interact with your product—what they click on, how long they stay on your site, their purchase history, and more. It's about reading between the lines to understand what customers might not be saying explicitly.

Think of VoC data as not just collecting feedback but as garnering the insights needed to craft a stellar customer experience that directly influences churn rates. 

The link between VoC and CX

VoC data is closely tied to customer experience (CX)—basically, every interaction a customer has with your product or brand. Imagine a SaaS company aiming to cut down on customer support costs. The product lead is considering whether they can make the onboarding process more self-serve, reducing the need for support. What tool might help them achieve this? They start looking into options. 

Now, let’s say you offer exactly what they need—a product analytics solution. Your name will (hopefully) pop up during their research, and they'll be hunting for online reviews or asking around in their network. In other words, they're already forming opinions about your product—i.e. they have entered the CX timeline—long before you even know they exist! 

CX continues throughout their lifecycle with you and even after they cease being your customer. Remember that this doesn’t always have to be a bad thing. Perhaps your client has outgrown your product or built something inhouse or pivoted their business model. But if their CX with you was good, they might always return to you when the need arises again. They will continue to champion you to their peers in the industry. So, both VoC and CX have a long afterlife.

How to Use VoC Data

Start by analyzing your VoC data and categorizing the feedback to get a clear picture of the common problems, pain points, and suggestions your customers are sharing. In practice, this means spotting patterns in what your customers are saying—like common phrases they use to describe why they love your product or the challenges they're running into.

We’ve written about how it’s always better to proactively manage churn than to scramble to win back a frustrated customer. As our CEO, Vikas Kumar, likes to put it, it’s like making a guest feel welcome from the start, rather than chasing after them with apologies once they’ve left!

If you’re really paying attention to your data, you can often spot the early warning signs that a customer might be on their way out. To catch these signals, correlate customer behavior (like purchase patterns and product usage) with their service history (like chatbot interactions, support emails, or calls with the account manager). Look for emerging patterns and take necessary action. Here are some examples.

The tricky part, of course, is that all this data usually lives in different systems. Maybe your customer feedback and tickets are in HubSpot, while product usage is tracked in Mixpanel. How do you bring it all together for analysis? That’s where DataviCloud comes in. We quickly ingest your multi-source data (in minutes, not days) with zero coding and minimal effort from your data engineers. We give you a clear, unified view of all your business data—ads, CRM, billing, product, you name it—and send you insights about churn and customer feedback in the form of a daily bulletin email or a Slack notification, whatever you prefer.

Want to see this in action? Book a Demo >

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