Ask Us Anything:
Hidden Trends in Your Product Feedback

Donna Perlstein (Moderator):
Thanks everybody for joining. Hi and welcome—uh, welcome to the webinar Hidden Trends in Your Product Feedback. So glad you can join us today. My name is Donna Perlstein and I’ll be your host.

So before we dive in, just a few quick housekeeping notes:

• The session will run for about 35 minutes, followed by Q&A.
• Feel free to drop your questions in the chat at any time.
• We will be sharing a recording after the session, so no need to worry if you miss anything.
• And finally, be sure to stay on until the end because we have some really great insights you won’t want to miss.

Let’s get started. I’m going to introduce our speakers today. We have Dan Cropsey and Omer Kehat joining us.

Dan is currently the President of Harmony Marketing Technologies and Advisor. He has 30+ years of global product strategy, development, and management experience with industry-leading marketing analytics firms who serve thousands of brands, retailers, agencies, and media companies. Welcome, Dan.

And then we have Omer, who is the VP of Product at Revuze. Omer brings over a decade of experience in the online shopping industry, with a background in analytics, natural language processing, machine learning, and transforming unstructured to structured data. Welcome, Omer, as well.

Let’s take a quick look now at the agenda. You can go to the agenda screen slide, sorry.

So, we’ll start by discussing some of the key challenges that product teams face and the common pain points they encounter. Then we’re going to explore why diverse data sources combined with AI-driven insights are essential for making informed decisions.

Next, we’ll dive into the power of the voice of the customer—how real consumer feedback can guide product strategy and innovation. From there, we’re going to walk through how this transformation is happening in real time, and we’ll give you an exclusive sneak peek at what’s next.

Finally, we’ll wrap up with the Q&A session, so be sure to jot down any questions you have along the way, and we’ll be sure to answer them at the end of the segment.

Over to you, Dan.

Dan Cropsey (Nielsen):
Well, thanks for a very kind introduction. I love what I do. Like Donna mentioned, I’ve been proud to build solutions used by thousands of consumer brands and their leaders.

I think the job of the product manager is super hard—and it’s getting harder. Consumer preferences are changing faster than ever, which means the competitive landscape is also changing incredibly fast. Product life cycles are way shorter. Product innovation demands are much more intense. And while all that’s going on, the data they rely on to save the day is exploding—yet still very fragmented and siloed.

Thankfully though, AI is also exploding. That comes with some pretty high expectations, unfortunately. And God forbid in today’s day and age that you make a mistake as a product leader, because negative product reviews are plastered right on your e-shelf, right next to the purchase button. And that same negative feedback is also broadcast to anyone that requests it via search and GenAI tools.

The industry stats echo that. It’s amazing to see that new product success rates are actually getting worse. According to the Harvard Business Review and Nielsen, only 15% of new products actually see their second birthday. And 68% of CPGs are struggling to get actionable insights from their data.

So, the challenge is tough, right? It amazes me how much is going on for product leaders. Their job is uber important—their success metrics are literally the company’s success metrics: sales and profitability. The scope of what they do is incredible—from market awareness, to portfolio planning and innovation, to design, manufacturing, distribution, pricing, advertising, and promotion.

Thankfully, the core of all that activity is fundamentally grounded in understanding and meeting the needs of your end customer and your retail sellers. This role actually has two customers. And to get that right today, you have to lean heavily into data and advanced analytics—AI, machine learning, and the like.

The good news is that this function is not shy about using data. They look at retail sales, segmented sales, pricing, shipping, consumer reviews, social media, and so on.

Now, if I had to pick one type of data that is most underutilized today, I’d say it’s voice of the customer. By that I mean product reviews, social media, surveys—this feedback is underleveraged across the board.

Historically, that data has been sparse and biased. Only the biggest brands had enough volume to do it properly. It was hard to justify enterprise-level investments when only a subset of your portfolio had enough feedback. It was also slow—manual surveys, keyword analyses—it all lagged other data sources. And the language was often complex or unstructured, making it costly to summarize and integrate.

That’s the past. I’m happy to say that recently, this has changed dramatically. The barriers are down. There’s no excuse anymore for not injecting this type of data across your entire product lifecycle.

So what changed?

1. More data – Society has opened the floodgates. Consumers share everything—especially in eCommerce.

2. Easier to use – Thanks to AI, interpreting and summarizing data is faster and more actionable.

3. More vital – Customers are reading and reacting to feedback before making purchases. Product managers now have to stay ahead of both consumers and competitors.

Omer Kehat (Revuze):
Dan, I just want to add that all of these are excellent observations. I really want to double down on what you said about “no excuses.”

The fact that brands face headwinds—competition, multi-channel challenges, even macroeconomic pressures—there’s no excuse for product leaders not to use voice of customer data. And luckily, the age of AI has made it much more accessible.

We’ll talk about how we make this data actionable and recommendable from the voice of the customer. Good points, Dan. Thank you.

Dan Cropsey:
Alright, so as you can probably tell, I’m pretty bullish on using this data—and others are too. We’re starting to see the evolution of this accelerate. It won’t be long before this data is injected into every major product decision.

The most savvy, data-driven brands are already using these insights for product innovation and market positioning. But right now, it’s still often ad hoc.

New tools like Revuze are making it easier to bring these insights into product assortment, design, marketing, eComm descriptions—injecting it into action, in context.

Ultimately, though, to maximize this potential, the industry will need to re-engineer how things are done. You’ve got to bring together what people do and what people say—into a single AI-based solution. Now is the time to break out of your departmental silos. Tools like Revuze can make that happen.

Dan Cropsey:
If you recall earlier, I mentioned that stat: 68% of CPGs aren’t getting the most out of their data. A lot of that has to do with siloed solutions that stall at “what happened” and leave product managers wondering “why” and “what to do about it.”

Imagine a world where your tools anticipate those next questions and proactively provide answers.

For example, if your sales dashboard shows a drop in performance, wouldn’t it be great if it immediately pulled in related product feature feedback to help identify the cause?

If pricing systems show increased sensitivity, the natural next question is: “Is this a value perception issue, or temporary inflation?”

Assortment tools could identify a portfolio gap—and immediately ask: “Is this feature even relevant to your brand? Or a fad?”

If your loyalty metrics drop, are consumers seeing feature parity? Or is there an emerging unmet need?

Even your customer care system might flag a rise in returns. Are they due to product quality, mismatched descriptions, or exaggerated claims?

Ultimately, I believe voice of customer data will evolve from describing the past to predicting the future—assortment gaps, loyalty loss, and more.

I’m excited to see the “why behind the buy” get its proper due in decision-making. Omer, I know you share that excitement—and I’m curious to see what Revuze has coming next.

Omer Kehat:
Thank you. Great. Thank you, Dan, for that opening. I think now we can take this to the second part of the webinar where we’ll talk about the Revuze solution and the transformation we bring to brands—and how we help solve many of these issues and pain points Dan mentioned.

Before we talk about Revuze, I just want to illustrate our vision. We’ve been working on solving a very simple—but very powerful—problem statement:

“What do our customers really think, and how can we transform that insight to drive our business?”

It sounds simple, but it’s very hard to do. Most solutions today are static or fragmented—they don’t give you actionable steps. Some only work at the brand level, others can’t go down to the SKU level. Some only focus on one niche: surveys, panels, communities.

But the data is there. The issue is not access—it’s transformation and activation. That’s exactly what we do at Revuze.

We’ve been helping brands for many years gain that clarity, helping them drive innovation and outpace the competition. We took a lot of commonalities that we saw in market research and automated them. We streamlined them and allowed organizations to look at their entire voice of the customer data and use that data to drive innovation and enhance their marketing communication and messaging.

We’re working in over 2,000 CPG categories. We’re covering over 600 different channels—not only ratings and reviews, but also social, POS data. You’ll see the slide in a few minutes outlining all the different data sources we connect to and can bring in. You’ll also see the list of brands we’re already serving today in various categories and different industries, all helping them transform this voice of the customer data into actionable insights and recommendations.

A little bit about our platform—just to give you some understanding of our tech stack and how we get things done.

We have a very robust data collection layer, where we hook into and integrate with multiple data sources and bring in all the information about your products—what’s happening on social, in the category, your competition—anything that helps us understand your voice of the customer.

Then we have a layer that’s completely proprietary, where all the magic happens. This is where we organize the data, cleanse it, do all the analysis and processing using advanced GenAI and LLMs to understand what people are talking about, what their sentiment is, how we can translate that into recommendations, and how we can summarize and aggregate that into meaningful ways that later drive specific actions.

From there, we have an entire suite of applications available to view the data, take action, and see all the information.

In this presentation, we’re going to give you a sneak peek of our product hub, which is aimed at solving exactly the problems Dan previously spoke about. As product leaders—how do I make sense of all this information? What should be my next step? How do I build my portfolio? What should I launch next? Which channels should I focus on? What’s my positioning and communication strategy?

We tried to look at the entire product life cycle—from research to ideation to concept testing to launch—and see how we can cater to all these different phases.

Using our deep research engines, we help understand things like purchase motivations, unmet needs, usage purpose analysis, white space opportunities, and emerging trends. From that, we can create different use cases that help product leaders across every phase.

That includes market trends, health scoring, competition analysis, SWOT analysis, purchase motivations, innovation planning (bottom-up or top-down), identifying product defects, understanding return rates, and even initiating surveys through our platform to validate concepts before a product is launched.

All of that is baked into the platform.

Welcome to the recommendation era. It’s no longer just about insights or analytics—it’s about action. We help brands understand what they need to do, when they need to do it, and for which product.

Let me give you a sneak peek into our Product Hub.

Right now, the demo is based on data from the running shoes department. Like I said earlier, we cover over 2,000 categories and subcategories within the CPG world. So we can easily feed in data from other categories and allow you to look at the products important to you.

Imagine I’m a product leader in a top running shoe brand. It’s Monday morning—I’ve caught up on emails and now I open up the Revuze Product Hub. Immediately, I see interesting stats about incoming reviews and rising or declining discussion topics. I see what’s happening in social—what videos people are watching, what they’re mentioning about my brand or competitors.

And then, I scroll down and get into the “Take Action” section. This is where the platform really helps. I see alerts—reviews mentioning returns and refunds. Returns are a huge challenge for many online brands. I read that around 17–27% of all products bought online are returned.

I can also see reviews mentioning product defects, one-star reviews, and products that are currently in decline. It’s not just about seeing numbers—it’s about understanding why people are returning products.

One click, and I can explore return reasons. I see complaints about size issues, comfort, quality. I even see which products these reviews came from—three specific SKUs. Now I can click and drill into one SKU to see a full 360-view.

I get a summary of all reviews. I can see what people mention positively and negatively. I can click to read examples of what they’re saying about comfort, for instance. I see the star breakdown. I can view SWOT analysis—strengths, weaknesses, opportunities, threats—and what’s driving 5-star vs. 1-star ratings.

It’s not just about my product, though. I can also look at competitors.

From the Competitor 360 View, I can select another brand, understand their positioning, what consumers are saying about their products, why people return them, what their strengths and weaknesses are, and what new launches they’ve introduced. I can track innovation, see which brands are gaining momentum, and even identify lesser-known brands that are worth keeping an eye on.

As I’m browsing, I might find a product from a competitor that inspires me. Let’s say it has interesting tech or design. I can add it to one of my Innovation Boards.

I can later go to my Innovation Board and see all ideas I’ve saved. Maybe I decide one is mature enough to transform into an innovation kit. Or maybe I want the system to create an idea automatically.

I click “Add Idea,” select one of my products, and let the system recommend innovation based on consumer wish lists—things like, “I wish the shoe came in red,” or “I wish the laces were longer.”

The system suggests an “Adaptive Chameleon Shoe”—inspired by other products outside the category, like color-changing car paint from BMW.

I can generate a full kit: overview, benefits, features, target audience, inspiration sources, even design suggestions. I can talk to engineering and marketing with this ready-to-go idea. I can launch a survey to validate it. I can export everything as a PowerPoint and pitch it internally.

That’s just a sneak peek. There are many more use cases on the platform, which you can see on the left-hand menu.

As promised, for everyone attending this webinar, we’ve created a special giveaway. We went into the platform and extracted innovation ideas from multiple categories and products. You’ll receive a report that includes ideas for innovation and improvement across various product types. That’s our gift to you from the Revuze team.

Donna, back to you.

Donna Perlstein:
Thank you, thank you. I hear an echo—sorry about that.

Okay, so that wraps up our main presentation. Now we’d love to hear from you. Feel free to ask any questions—go ahead and type them in the chat box and we’ll try to get to as many as possible.

Let’s give everyone a minute or two and then we’ll dive in.

So I do see a few questions coming in.

We have a question on: How do you weed out the bias and fraud that we know is prevalent in VoC product review data? Omer, Dan—want to take that?

Omer Kehat:
Yeah, I’ll take that one. It’s a great question. Comes up a lot in discussions with customers.

Obviously, in the world of AI, many retailers are struggling with bots and fake reviews. We address this in a few ways.

First, we rely heavily on verified purchase reviews. Many retailers already have guardrails in place to verify reviews, and we definitely use that flag as part of our process.

We’ve also added guardrails to identify patterns in fake or bot-generated text—so we can weed them out and flag them on our platform.

Of course, this is an ongoing race, as the fraud tactics get more sophisticated. But we’re also advancing our tech stack to detect and handle these at a very high success rate.

Donna Perlstein:
Thank you. Let’s see—another question here: Can Revuze integrate with PLM or PIM systems?

Omer Kehat:
Oh yes—we definitely integrate with multiple systems. We’ve already done integrations with PIM and PLM platforms, including Salsify, Oracle, SAP, and InRiver, to support these use cases and export our data into those systems.

Donna Perlstein:
Thank you, Omer. I think I see another question: Does Revuze cover all categories as well as global markets?

Omer Kehat:
Great question. As we showed on the slide, we currently cover over 2,000 categories in CPG and other industries—ranging from fashion, electronics, food and beverage, home appliances, beauty and personal care.

Our technology is agnostic—we can also analyze reviews for restaurants, hospitality, or other verticals.

Regarding languages, yes—we have full global language support and cover sources worldwide, including EMEA, APAC, and even the Chinese market.

Donna Perlstein:
Thank you, Omer. Another question here: Where does Revuze fit within a company’s innovation stack, and how can data-driven insights take product development to the next level?

Omer Kehat:
That’s a very interesting question. It varies by organization.

We aim to be a one-stop shop that brings in all external data. Internal first-party data can also be imported, but our focus is on capturing everything outside the company’s four walls.

We realize we won’t take over every desktop, but we hope to be a daily tool for product leaders—something dynamic, used weekly or daily.

There’s so much incoming data that things shift quickly, especially in volatile categories. Our platform helps you stay on top of it.

Dan Cropsey:
And let me add one more thing to that. Product managers rely heavily on product attributes—flavor, size, packaging, health claims. Traditionally, they’ve used those to analyze sales.

With Revuze, you can now connect those same attributes to consumer feedback. You can tie purchasing behavior to actual sentiment, using structured voice-of-customer data. That level of alignment didn’t exist before.

This will completely change how people think about innovation and marketplace needs.

Donna Perlstein:
Oh, thank you guys—thank you Omer, thank you Dan.

So that brings us to the end of the webinar. Thank you to our speakers, and to all of you who joined today.

We hope you found the discussion valuable. If you have any additional questions or would like to explore these insights further, please don’t hesitate to reach out. We’re happy to answer.

Also, keep an eye out for upcoming webinars and events—we’d love to have you join us again.

Thanks once again for taking part in this webinar. We hope to see you in the future. Thank you, guys—enjoy the rest of your day.

Dan Cropsey:
Thanks.

Omer Kehat:
Thank you.