Surveys were pretty much the only game in town for understanding customers, you know, until recently. You'd send out some questions, wait for responses, analyze the results, and maybe learn something useful. But here's what tends to surprise a lot of brands - they're sitting on mountains of customer feedback that's way richer than any survey could ever capture. Omer Kehat, VP of Product at Revuze, has spent the past year basically rethinking how companies actually use this data, and what he's found is rather illuminating.
"We've been focusing a lot on consumer reviews and how they help brands really understand what customers love and don't love about their products," Kehat explains. But Revuze has been taking this to a whole new level by combining AI with multiple data sources to create what he calls "actionable recommendations and insights" rather than just another dashboard to stare at.
The company won BIG Innovation Award for this work, and for good reason.
The Why Behind the What
Brand managers typically open their week looking at last week's sales report. Something went up. Something went down. The report tells them what happened, but here's the thing that usually drives people crazy - it doesn't tell them why.
"Everybody can tell you what's happening, but it's really hard to say why things are happening," Kehat notes. Sure, you might see that sales increased after you ran a discount. That's obvious. But what's really driving customer love for one product versus another at a very granular SKU level? That's the insight that actually matters.
Revuze was founded on the premise of helping brands uncover the why behind the what. Over the past two years, the company found two critical things that changed their approach completely. First, you get much richer answers when you look beyond just consumer reviews to include social data, customer support tickets, Reddit discussions, and other sources. Second, AI can actually cut through all that noise in ways that traditional analysis never could.
"Using AI can really cut a lot of the hard work that we had to do traditionally," Kehat observes. Those large language models can create summaries, recommendations, and call-to-actions based on the data rather than forcing humans to manually analyze everything.
From Flood to Focus
Having managed reputation from both marketing and HR perspectives over the decades, I've seen firsthand how overwhelming this data flood can become. We've got all these systems and platforms generating information constantly. The good ones have nice interfaces with filters and reports and dashboards. But you're still doing all the analytical heavy lifting yourself.
"We don't want to position our platform as a platform that will be creating unemployment in different areas across the organizations. We're really there to empower and enable those people to get to those insights and get to those understandings much, much quicker."
This is actually the right way to think about AI integration. It's not replacing your team's expertise - it's giving them something like an entire consultancy backing them up when they need to understand what happened on Tuesday at three o'clock when call volume spiked and sales tanked.
What boggles Kehat's mind is how many brands still don't have a proper voice of the customer platform. Some had social listening tools but removed them because they couldn't cut through the noise. Others are still relying primarily on surveys. Many simply don't recognize how critical voice of the customer is to brand reputation and growth.
"There are so many brands that are still not in that understanding that how critical voice of the customer is to your reputation," Kehat says. Revuze is trying to solve this with affordable pricing, which is a significant departure from traditional market research firms or big agencies like Deloitte or McKinsey that throw manpower at the problem and charge accordingly.
The Category Context Problem
Here's something most people don't consider about general-purpose AI. If you tell it "my battery life was 10 hours," that statement means completely different things depending on context. For a laptop, 10 hours is pretty average. For a cell phone, it's not impressive. For a drone, it's absolutely off the charts.
Revuze has spent seven or eight years building what they call category context LLMs - specialized language models that understand these nuances within specific product categories. "Understanding the context of the category is really critical, especially when analyzing reviews and social content," Kehat explains.
For a consumer shopping for a vacuum cleaner, ChatGPT might give them a decent answer. The risk of buying the wrong vacuum isn't that high. But for a brand manager deciding whether to invest millions in a new product line or marketing campaign? The error rate or risk of making the wrong decision becomes dramatically higher.
"They need to understand the reasoning. This is not just coming because the chat is biased or polluted with data from Reddit or whatever other forums," Kehat notes. The data needs to be grounded in actual reviews and real customer purchase experiences. That's where specialized LLMs have genuine advantages over general-purpose ones.
This mirrors what we're seeing across industries. The race toward boutique or highly specialized AI agents just makes sense. You've got Walmart selling tennis rackets, sure. But you're not winning Wimbledon with equipment from Walmart. Specialization matters.
What Most Leaders Miss About Voice of Customer
Kehat sees two main misconceptions from CEOs and founders. First, they downplay the significance of having "just a couple hundred reviews." They're missing how prominently reviews appear in modern shopping experiences - on product detail pages, in search results, across every customer touchpoint.
Second, many brands remain surprisingly conservative in their thinking. Tell them "voice of the customer" and their first thought is still "let's do a survey." Surveys are fine, but they're literally a century old technology. There's a reason they were the primary tool back then - it was the only practical way to reach customers. But we've evolved since.
"Those brands are just, you know, they're not getting it. They're not getting it and they're not adapting to where we are today. And we're seeing it. Those brands, ultimately, they lose out."
The survey problem is particularly acute. Anyone who's completed a post-transaction survey knows how those typically go. Unless you're genuinely upset, you probably just click through with high scores to get it over with. The data ends up heavily skewed, and there's not much actionable meat in the results. You need to go where the real conversations are happening.
The Shift That Matters
When I asked Kehat what mindset shift he'd recommend to teams integrating AI into their products, his answer was refreshingly direct: embrace it. Don't be tentative. Don't overthink it.
"Our ability to move so much faster is now open, then it allows us to fail much quicker and recuperate," he explains. Revuze has made plenty of bad decisions over the past year. The difference is they can turn things around quickly and remedy mistakes faster than ever before.
The traditional approach meant careful, slow deliberation on every decision. You couldn't afford to fail. Now? Fail fast, learn quickly, and deliver better products. That's the actual advantage of AI integration - not replacing humans, but accelerating the learning cycle.
This reminds me of what happened with radiology. Everyone predicted AI would eliminate radiologists immediately. What actually happened? There are more radiologists than ever. They can do more work, serve more patients, and scans became cheaper. Win-win-win for everybody. The work shifted, but it didn't disappear.
The Real Value of Reputation
Here's something I've learned from decades in B2B marketing - if most companies just focused on improving their brand reputation, they could probably lift revenue by 10% without changing anything else. Just reputation alone. That's how powerful it is.
But reputation management is a daily, weekly task. It requires actually listening to what customers are saying across all channels, understanding the patterns, and responding appropriately. The brands winning at this aren't necessarily the ones with the biggest budgets - they're the ones with the best systems for turning customer feedback into business decisions.
Revuze's approach of combining multiple data sources with specialized AI represents what voice of the customer platforms should actually be doing. Not just collecting data. Not just displaying dashboards. But providing specific, actionable recommendations that help brands understand exactly what needs to change to drive better outcomes.
Month two through 12 matters in subscription businesses. But really, it's about months 33 through 36 - that's where your profit lives. You can't get there without truly understanding and responding to customer needs. In B2B especially, loyalty is everything. You need to lean into these conversations constantly.
Moving Past the Dashboard Era
The shift Revuze is making - from SaaS dashboard to AI-powered recommendation engine - represents something bigger happening across business software. We spent the last couple decades building ever-better ways to visualize data. Charts. Graphs. Filters. Drill-downs. All useful, but still requiring humans to do the analytical thinking.
Now we're entering an era where the software can actually tell you what to do based on that data. Not in a deterministic "follow this algorithm" way, but in an "here's what we're seeing across all your data sources, here are the patterns that matter, here are the specific actions that would likely move the needle" way.
That's substantially different from traditional business intelligence. It's why Revuze is positioning their platform not as a replacement for human expertise, but as a force multiplier that lets smaller teams punch above their weight class.
For marketing directors and account executives managing multiple clients - the exact audiences we typically write for at Business Intelligence Group - this matters immensely. Your ability to quickly surface insights and deliver recommendations is becoming the differentiator. The brands that figure this out will eat the lunch of those still manually analyzing spreadsheets.
Twenty years ago, I was putting all these pain points into PowerPoint presentations, trying to identify patterns across customer feedback manually. It was slow. It was incomplete. It missed things. Having lived through that world, I can tell you - what Revuze and similar platforms are building would have been transformative back then. It's transformative now.
The brands that win over the next decade won't be the ones with the most data. They'll be the ones who best understand what that data is actually telling them and who can move fastest to act on those insights. Voice of the customer isn't just another data source. When done properly, with the right technology, it becomes your strategic compass for basically everything that matters.