Feedback vs. behavior: What do customers really want?

If I asked you what kind of coffee you like, you’d probably tell me that you like a dark, rich, hearty roast. 

You’d probably be lying.

According to food researcher Howard Moskowitz, just 25% of people actually prefer a dark roast. Most of us like weak, milky coffee. We just don’t like to admit it. We're adults, aren’t we? We’re complex and sophisticated. We’re a little edgy, well-read, cosmopolitan. We like big, bold coffee, which we sip while perusing Le Monde at a pavement café.  

Or at least, we want other people, market researchers included, to think so. 

This is reporting bias, a problem with topical customer research. Customers are people. People don’t always tell the truth. We lie all the time—often without even realizing it. 

So should we skip the research and just do what our gut tells us?

That sounds pleasingly like Mad Men—but sadly, no. Today’s savvy customers have higher expectations, and brands must exceed them or risk obsolescence. 

The trick is to pay more attention to your customers rather than less. You need to gather qualitative data (such as survey responses and qualitative feedback) and quantitative data (such as user data and heat maps) to understand what your customers want.

In this article, we break down: 

• The difference between qualitative and quantitative customer data—and why you need both

• How to bridge the gap between what your customer wants and what your product does 

• Actionable tips based on how we do customer research here at Typeform

Qualitative and quantitative data: Two peas in a pod

In a business context, research is almost always about understanding the customer better.

To get a complete picture of your customers, market, and product, you need both numbers and conversations. The facts and the perceptions.

Gather a few types of customer information to understand your market and plot your next moves: 

Quantitative vs. qualitative data

Quantitative data is numerical and objective. It answers questions like, “How often do our customers log into our software?” and “When are customers most likely to spend more than five minutes using our app?” 

You can use quantitative data to back up a hypothesis or refine a strategy. It also keeps you honest—your gut might tell you people log into your software daily mid-morning, but the numbers won’t let you kid yourself. Getting to know your customers involves both quantitative and qualitative research. Qualitative data yields rich, nuanced insights but often takes more time to analyze. Quantitative research substantiates these insights by putting hard numbers behind claims and opinions—and sometimes even opens up new avenues for further research. 

Customer behaviors vs. feedback

Customer behavior is what buyers actually do, which may differ from what they say they’ll do. For instance, it’s the steps they take to purchase your product or how they use it once they have it. 

The study of this behavior, behavioral analytics, is crucial for understanding your customers. 

Say you want to encourage more users of the free version to become paying customers. Look at how they use it, keeping close attention to how behavior differs between customer types. Do people use the free version differently? Does a particular feature in the free version compel users to convert? The answers to these questions help you make more informed business decisions.

Customer feedback is what your customers tell you about your products. It gives you an idea of what people like and what’s putting them off. Gathering customer feedback helps you spot and fix issues more quickly, double down on popular features and services, and give you ideas about what to develop next. 

Qualitative data is subjective and descriptive. It might be notes on user behavior, customer comments, in-app feedback, product reviews, or responses to open-ended survey questions. 

With qualitative data, you get context and explanations. For instance, let’s say your quantitative research shows that 70% of your users log into your app once a day. You might wonder why that number’s not higher, so you send out a customer feedback form to let users weigh in. That’s qualitative research.

Getting to know your customers involves both quantitative and qualitative research. Qualitative data yields rich, nuanced insights but often takes more time to analyze. Quantitative research substantiates these insights by putting hard numbers behind claims and opinions—and sometimes even opens up new avenues for further research. 

Customer behaviors vs. feedback

Customer behavior is what buyers actually do, which may differ from what they say they’ll do. For instance, it’s the steps they take to purchase your product or how they use it once they have it. 

The study of this behavior, behavioral analytics, is crucial for understanding your customers. 

Say you want to encourage more users of the free version to become paying customers. Look at how they use it, keeping close attention to how behavior differs between customer types. Do people use the free version differently? Does a particular feature in the free version compel users to convert? The answers to these questions help you make more informed business decisions.

Customer feedback is what your customers tell you about your products. It gives you an idea of what people like and what’s putting them off. Gathering customer feedback helps you spot and fix issues more quickly, double down on popular features and services, and gives you ideas about what to develop next. 

For instance, at Typeform we use a few different typeforms to gather customer feedback: 

• A quick Net Promoter Score® (NPS) typeform that simply asks people how likely they are to recommend us to someone else

• A Signup typeform for new users that tells us about their goals, so we can personalize their experience based on their preferences 

• A Customer Effort Score typeform to find out how easy it is to get the support they need

• A Churn typeform to find out why people cancel their subscriptions

Read our complete guide on how to gather better customer feedback: Voice of the Customer—Typeform’s guide to turning customer feedback into action

Zero- vs. first-party data

People often use these terms interchangeably, but zero-party and first-party data are different.

Zero-party data comes straight from the source. It’s the difference between using marketing attribution software to determine how a lead found you and asking them, “How did you find us?” 

Zero-party data gives you greater insight into your customers. Take our attribution example above. Imagine a lead checks out your company because a friend sent them a link to your blog via LinkedIn. Attribution software would credit that lead to LinkedIn—whereas if you asked the lead themselves, they might say they liked your blog content. 

First-party data is information you infer from user behaviors. For example, it shows how people interact with your website, whether they open your emails and click on the links you send, and how often they purchase your products. 

First-party data gives you a clear picture of your users and customers. This is data you own rather than access or purchase from other companies. According to research by Google, it’s essential for your marketing strategy. Companies using first-party data to inform their marketing initiatives saw nearly 300% more revenue and up to a 150% improvement in cost savings.

If you want to know more about the different types of customer data, check out our guide: The 4 types of customer data and what they can do for you 

Bridge the gap between your customer and product

Customer research aims to understand what customers get out of your product or service, so you can give them even more value in the future. 

Let’s look at four ways to use customer data—including both feedback and behavior—to inform your product and marketing strategy. 

1. Gather customer behavioral data

In addition to qualitative feedback, such as free-response answers to questions in customer surveys, collect data on how people use your products or services. 

If you sell digital products, plenty of product analytics tools let you collect and analyze this data from within your platform or app. 

If not, you might have to get a little creative. Maybe you can’t track product usage data, but you could use a web analytics tool to see how many people download your product-feature tutorials. If the same tutorials keep getting downloaded, chances are those features are in heavy use. (Obviously, this only gets you so far. Those features may be the most complex, not the most popular. But it gives you a starting point, which you can verify with more research.) 

For best results, you could combine your behavioral data with a product-use survey. For instance, you could ask all new users why they signed up for your product. We recommend a multiple-choice question with an open-ended “Other reason” option to leave room for unexpected answers. 

2. Evaluate customer feedback 

You may want to leap into action after seeing one bad product review. However, it’s more helpful to zoom out from your data and see what trends and themes emerge. 

Remember to gather both direct and indirect feedback. Collect solicited feedback from surveys, market research, and focus groups, and comb through customer comments on review sites, social media groups, customer support forms, and online forums to capture unsolicited feedback. 

To frame your research, start by identifying the questions you hope to answer about your customers. A few questions we use at Typeform to give structure to our customer research: 

• What are the most popular requests from users? 

• Do people who fit our Ideal Customer Profile ask for different features than average users? 

• Where do our customers struggle—and what can we do about it? 

• What do people think about our new features?

3. Focus on the right metrics 

Your NPS is a valuable indicator of customer happiness—but it’s an output metric, not an input one. In other words, if you do the right thing, your NPS should go up, but it doesn’t tell you the right thing. 

To figure it out, lean on customer surveys to give you a hypothesis of what users most value about your product. Do they like a particular feature? Or is it the simplicity of your user interface (UI)? What do they get from your product that they don’t get anywhere else? Then use this information to identify a metric that tracks the value your users get from your product. 

For example, Mixpanel is a product analytics tool. They figured out their power users ran analytics queries at least three times a week. So, that’s the right metric for them to monitor if they want to measure and create more power users. 

4. Report, follow-up, and iterate

So, you’ve gathered your behavioral data and customer feedback and figured out the right metrics to track. The next step is to assemble all the information into an actionable presentation. 

At Typeform, we review our customer feedback and behavioral data once every quarter and use it to inform our product and marketing strategies for the following quarter. 

A few other ways to use your research to drive action:  

• Use your behavioral data to create new product hypotheses. Then, assess them using A/B testing. For example, if your power users activate a specific feature regularly, see if you can make that feature easier to find in your free version. 

• Use customer feedback to improve your product messaging. If your customers consistently describe a feature the same way, use that wording on your website to clarify your product value to new leads. 

• Use your customer support feedback to identify new content opportunities. If users struggle with the same feature, maybe a video tutorial would help—or you could revisit your UI to make the user experience more intuitive. 

Customer research do’s and don’ts 

Customer research is always necessary to stay ahead of your competition and keep up with changing consumer behavior. 

For effective customer research, try these tried-and-true tips: 

Consider business growth and customer impact

When conducting research, answer your business questions and find a clear path toward growth. Then, it’s time to consider: How can we translate our findings into tangible benefits for the customer? 

Avoid confirmation bias

It’s tempting—but problematic—to approach customer research with a ready-baked solution in your head from the get-go. Instead of figuring out what the customer wants, you look to validate your gut instincts. 

This approach never leads you to find an innovative solution. Instead, you may end up launching a feature you love, but your customers don’t.

It’s natural to have a few assumptions before you start—but make sure you design your research to test your hypotheses, not confirm them. 

Decide if you’re running strategic or designed research 

Strategic research is top-level and more open-ended. It answers questions like, “Should we expand into this market?” Designed research is more concrete and specific. You’re looking for answers to problems like, “Why is this segment of users hardly engaging with our products?” 

Your focus determines whether you’d be better off starting with quantitative or qualitative data. For instance, you might have numerical product data that shows an issue with user engagement. You know the numbers but not the why behind them, so you might decide to run interviews to uncover the underlying problem. 

On the flip side, if you have a broad, strategic question, start with qualitative interviews to understand the issues better, and then run a survey to get some hard numbers to guide your thinking. 

Involve a researcher 

If you’re too close to the topic at hand (say, if you’re the product manager), involve an objective third party to run your customer research. 

So much data exists in what’s shown rather than what’s said. Researchers know how to uncover that nuance. They invite interviewees to go into more detail to discover what they really think and dive deeper into quantitative data to unlock more meaningful insights. 

Know when to start over

Sometimes, quantitative and qualitative data conflict. Maybe your customer's behavioral data tells you everyone’s using a particular feature—but during qualitative interviews, your customers tell you they hate that feature. 

Nine times out of 10, that’s a problem with the research design itself. You may have started the research process to validate a specific assumption—so you’ve only found data that supports your thesis. Or, your researcher may have baked in some bias somewhere because they didn’t run the numbers correctly. 

If this happens, it’s time to start over. You could still salvage your research—for instance, by going back to the raw materials to see where you let bias creep in. But if you started with biased research questions, chances are the results are also tainted. Time to go back to the drawing board. 

Create a research repository

Building a library of previous research is incredibly helpful. You can review previously collected research data and use it to derive fresh insights. 

For example, we had low engagement rates for one of our features last year. We collected relevant insights through customer interviews—and also got a ton of feedback about our online community. This wasn’t data we could use to understand engagement rates better, but it was beneficial for marketing. We took the opportunity to create a quick slide with a few recommendations for the marketing department, too. 

Customer research: the gift that keeps on giving 

There won’t ever be a time when you can rest on your laurels and say, “OK, I understand my customers now.” People change. Markets evolve. If you want to keep your customers, you have to stay curious. 

Great customer research combines multiple viewpoints and data types. You’ll ask strategic and tactical questions. You’ll keep an open mind. And you’ll keep the conversation going. 

To learn how to conduct effective customer research, check out our article: Market research: the ultimate how-to guide.

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 ScoreSM and Net Promoter SystemSM are service marks of Bain & Company, Inc., NICE Systems, Inc., and Fred Reichheld.