Why Continuous Customer Feedback Matters to Marketers

1. You've identified an "insight gap" — the disconnect between how much marketers value customer feedback and how they collect it. From your position overseeing both communications and marketing, what's the organizational reality behind that gap?

The insight gap is secretly an execution gap. Our research found that 83% of business owners say customer feedback helps drive decisions, but only 54% collect it more than once or twice a year. That’s pretty telling. People value feedback, they just don’t always have the time or processes to collect it consistently. 
I get why. Marketing teams make decisions all day long, and many can’t invest in lengthy research projects to gather customer feedback for every single one of them. When listening feels separate from the work they’re already doing, it’s easy to just keep moving without it. The problem with that is assumptions can start to fill the space where evidence should be. And we all know that today, people’s thoughts, feelings, and preferences are changing as quickly as the news cycle.
We need to stop treating customer research as an occasional project and instead treat listening as part of the work itself. The best time to hear from customers is before the decision is made, while there’s still time to change the work.

2. The insight gap isn't new, marketers have always known they should be collecting more feedback than they do. What has changed about the consequence of ignoring it now versus three years ago?

Three years ago, there was more time between having an idea and putting it into the world. Today, AI can turn an idea into something that looks finished in less than a minute. That speed can be tricky because the end product looks great, but at best it’s a replication of what’s come before and at worst it’s the result of a hallucination. There’s no good AI replacement for asking customers what they want or think right now.
We’re seeing this all play out at work every day. Our 2026 State of Curiosity Report found that half of workers have had to redo work because the right questions weren’t asked at the start, and 46% have seen time or money wasted because assumptions went unchallenged.
The bottom line is: Listening needs to happen more frequently when execution gets faster.

3. "Not slowing existing operations or needing to hire a research firm" is a specific promise. What does a marketing team that has relied on either a research agency or a quarterly survey cycle looks like six months after embedding continuous feedback into their workflow?

Six months in, I’d expect customer feedback to show up in a lot more decisions. Instead of saving their questions for a quarterly study, the team can ask as questions come up: Does this message resonate? Which idea is stronger? Are customer expectations changing? Which brand name or tagline should we use?
AI makes all of this practical by taking on more of the mechanics of building surveys and analyzing responses, while benchmarks give teams immediate context for what they’re seeing. You don’t need a major research project every time you need an answer, but you do need a consistent groundswell of feedback, so it feels like your team is just swimming in the customer’s point of view all the time.
To me, that’s the exciting part. Curiosity becomes actionable. “I wonder” quickly becomes “let’s find out.” The answer comes back while you can still do something with it. 

4. AI survey creation removes the technical barrier to feedback collection. But survey design quality directly affects the reliability of the data. How does SurveyMonkey's AI handle that tradeoff and what does the system do to ensure speed doesn't produce low-quality signal?

Speed is only helpful if you can trust what comes back. A badly designed survey is still a badly designed survey. It won’t produce reliable results.
With Today’s SurveyMonkey, a user can describe what they’re trying to learn in plain language, and SurveyMonkey AI helps build the survey, adjust questions, apply logic, and refine it. Our proprietary AI makes the survey science and research expertise we’ve built in our platform over the last 27+ years more accessible and easier to implement for the business user.
AI can also analyze patterns across responses for you. That gives marketers more time to focus on what they’re actually trying to understand, whose perspective they may be missing, and what they should do differently because of what they heard. That’s where I want AI doing the work. Let it handle more of the mechanics so marketers can spend more time being creative, crafting the questions, and connecting with the humans behind the answers.

5. “Listening faster" and "acting with confidence" are outcomes that require the AI to surface the right signal at the right moment rather than just generating data. What does that signal-to-noise problem look like in practice and how does your approach address it?

Most marketers already have plenty of data. We don’t need more for the sake of having more. We need to know what deserves our attention and what it means for the decisions in front of us.
When SurveyMonkey revisited our company values recently, AI helped us analyze qualitative employee comments and spot gaps between intended values and employees’ actual experiences. Those insights gave us something concrete to act on and helped shape the values we landed on.
The same principle applies to quantitative feedback. A score becomes much more useful when you know how to interpret it. Our upcoming Benchmarks experience puts results in the context of hundreds of thousands of other company data points, helping teams understand what “good” looks like relative to their industry and peers.
That’s a great job for AI. Help me make sense of what we already know, so I can spend my time with my team deciding what needs attention and what to do about it.

6. Integrations with Claude, ChatGPT, and Workday mean feedback collection can live inside the workflows where decisions happen. In practice, what does that look like and what decision does it improve?

We firmly believe human input still matters for every business on the planet. Our goal is to make it easier to get real human input when you need it. If a marketer is already using Claude or ChatGPT, they can create a survey, analyze responses, and access customer insights without leaving that environment. Workday does something similar for employee feedback. Data such as department, location, and tenure can flow directly into SurveyMonkey, so teams can see where experiences differ across a team without manually connecting the dots in the data themselves.
When teams can ask for and analyze feedback inside the tools they already use, it’s much easier to make listening part of the work. A marketer deciding between two messages shouldn’t have to start a separate research project, come back days or weeks later, or download and combine multiple Excel docs into one. They should be able to ask, hear from real people, and use that input while there’s still time for it to shape their decisions.

7. The pre-launch survey has been the default feedback checkpoint for most marketing teams before a campaign goes live. You're arguing that model is insufficient. What does a marketing team lose by treating listening as a checkpoint rather than a continuous system?

A lot can change after a campaign launches. Customer expectations evolve, competitors respond, and the market keeps moving. The feedback you gathered before launch may not reflect what customers think a month or six months later. When a pre-launch survey becomes your only listening checkpoint, you lose the chance to catch those shifts and respond to them.
Another problem I see all the time in marketing is that we ask too late. Teams do the work, get attached to the idea, then ask customers what they think. By then, everyone has an opinion or a favorite, and changing course gets a whole lot harder and more expensive. That’s exactly when you wish you’d asked earlier. 

8. Customer listening embedded in workflow only creates value if the insights reach the people making the decisions in time to change them. What does the organizational infrastructure need to look like for that to happen?

Technology is part of the infrastructure, but how the team operates matters just as much. Insights need to reach the right people early enough to influence the work. Then comes the hardest part: people have to be willing to change their minds when the evidence tells them they’re wrong. 
Our State of Curiosity research found that while 95% of workers say they’re at least somewhat curious, only 30% say their workplace strongly rewards curiosity. That willingness to ask another question, challenge an assumption, and change course based on what you hear is what turns customer feedback into better decisions.
You can build the perfect listening system, but it won’t do much good if your culture punishes the person who asks the hard questions.

9. The market right now is full of AI tools that promise to tell marketing teams what customers want without asking them. You're taking a different position that AI should amplify human feedback, not substitute for it. What's the specific failure mode you're trying to prevent?

I worry about what happens when an AI-generated assumption starts looking enough like an insight that we stop checking whether or not it’s true.
AI is incredibly good at giving us a plausible answer based on patterns in what already exists. But if I want to know whether our customers understand a new message, what they think about a product idea, or why their expectations are changing, I still have to ask them.
AI can help me ask better questions, listen at scale, and make sense of what I hear. But I don’t ever want to lose the human connection that has driven marketing for decades. I still want to deeply know our customers, because that’s where all the great storytelling begins. There’s just no substitute for listening to humans.