Tiffany Serbus-Gustaveson 0:18
Happy Thursday everybody. I am Tiffany Serbus-Gustaveson, a digital strategist with BWG Connect and we are a network of knowledge sharing group, we stay on top of latest trends challenges whatever is shaping the digital landscape, we want to know and talk about it. We are on track to do at least 500 of these virtual events this year due to the increase in demand to better understand the digital space. And we'll also be doing at least 100 in-person small format dinners this year. So if you happen to live in a tier one city in the US feel free to send us an email, we'd love to send you an invite, the dinners are typically 15 to 20 people having a discussion around a certain digital topic, and it's always a lovely time, we spend the majority of our time talking to brands that's how we stay on top of the latest trends. We'd love to have a conversation with you. So feel free to send me an email at Tiffany@bwgconnect.com. And we can get some time on the calendar. It's from these conversations we generate the topic ideas we know people want to learn about. And it's also where we gain our resident experts such as Yogi who's with us today. Anybody that we have TISA collective community has come highly recommended for multiple brands within the network. So if you're ever in need of any recommendations within the digital space, please don't hesitate to reach out to myself or the team, we have a shortlist of the best of the best. And we'd love to provide that information to you. Also note that we do partner with a talent agency, a formerly BWG talent now Hawkeye Search that we can put you in contact with as well? Should you have any hiring needs? few housekeeping items. We want this to be fun, educational, conversational. It's all about you your comments and questions. So definitely put any questions you have comments into the chat that q&a. Or if you feel more comfortable, you can always email me at Tiffany@bwgconnect.com. And we will be sure to get to them. And we will probably wrap up around the 45 minute mark to give you ample time to get to your next destination spot. So with that, let's rock and roll and start to talk about competitive data from Amazon reviews strategies for data driven marketing. The team at Yogi have been awesome friends in the BWG community and network. So I'm going to kick it over to our panelists. If you can give an intro, then we can dive into the information. And that'd be lovely. Thank you guys

Gautam Kanumuru 2:25
are here reserve thanks for the introduction, Tiffany. And thanks everyone for spending the Thursday afternoon, morning, depending on where where you are with us today. So excited to be here. My name is Gautam. I'm co founder and CEO of Yogi. Prior to founding Yogi was a product manager at Microsoft and VP of Engineering at a startup called clarke.ai. So I used to have been in the Natural Language Processing space for a very, very long time. And kind of have worked on products at Microsoft like Xbox Cortana, windows, things like that. So I always like to kind of put a PSA out there that if you are having any issues with any Microsoft products, I probably know are one degree removed from the person that owns the product. So I can always pass that feedback along. But yeah, and then I was lucky enough to be kind of a member of Forbes 30 under 30 for work in AI and enterprise software.

Tiffany Serbus-Gustaveson 3:25
Awesome. Thank you, and congrats on that. Thank you.

Spencer Kelty 3:30
Well, thank you everyone for being here. I'm Spencer, Head of Marketing here at Yogi. I've worked as Head of Marketing at brands like constructor of work as an agency strategist, really just focused on, you know, creating cutting edge technology experiences that improve how customers interact with brands. So I'm really excited to be here today. And we're gonna dive right in. So, in this topic, the first thing to really drive home is that reviews are impacting consumer decisions. We've seen a lot of data on this, especially since the pandemic, large percentage growth rates in the volume of reviews, large growth in proportion of consumers reading reviews before making their purchases. And you know, on the other side of that, too, consumers are more and more likely to switch brands if they're convinced by reviews by word of mouth by any sort of feedback in their network. So this is all to say that from the consumer side reviews are an amazing source of data. And we're going to get more into that. But I also wanted to kind of impress that reviews are really important on the retailer side and they're just becoming more and more important every day. This talk is focused around Amazon. So we're going to specifically focus on how Amazon is reusing reviews as data to really drive that point home. And the biggest thing here are Amazon review summaries. I'm sure everyone's somewhat familiar with them at this point. It started out as a small test earlier this year that was on a couple of products for a small subset of users. You may have seen them on electronics. Like this example, on the right is a Dyson air purifier is one of the first products that got an Amazon review summary. But they've started to roll out to a ton of different categories and a ton of different products. And it's very clear with the speed at which Amazon is rolling them out to new accounts and new categories that they work to convert customers, they're leading to better outcomes, better sales rates, things like that. So the interesting thing here is these are entirely driven by reviews. And this is Amazon's first generative AI push this is the first thing that they're using this new generative AI technology to drive is based on review data. So these are aggregated from product reviews, they're going to highlight certain key attributes that consumers like and dislike, they have very specific formats, generally focused on a couple of things that are positive, and one or two things that are negative to kind of give this full quick introduction to how people really feel about that product at an attribute level. Why these are really interesting, and we're going to get more into this as we go into what some of these actionable, competitive advantages marketers can get out of reviews is that these are a great 500 foot view of a product that Amazon is putting together for consumers. But brands can use these as well. They're very easy to harvest information from it's very quick to get a lot of information from a lot of reviews very quickly. And anybody who's used Bizarre Voice and similar products can say that not all platforms access Amazon data very easily. So sometimes it can be a little bit of a struggle to pull your information from your Amazon reviews. And these can really be be helpful not only just for your products, but for those competitive products that you want to do a quick analysis of, but we're gonna get a little bit more into that here.

Tiffany Serbus-Gustaveson 7:23
And is the presentation available? The attendees?

Spencer Kelty 7:27
Absolutely. So there'll be a couple of things that will we'll share out and follow ups to attendees, including the presentation,

Tiffany Serbus-Gustaveson 7:33
theater. Well, thank you.

Gautam Kanumuru 7:37
Yeah, and just to kind of continue along that line that Spencer was talking about, I think one of the interesting things to think about, because we're talking about kind of competitive review analysis and how to take advantage of it is where is kind of where will Amazon go from this AI Review Summary sort of sort of piece. And I think, at the end of the day, one of the problems that this is really solving for the end customer, the one that's going to make the purchasing decision is it gives a really great just summary at the end of the day of what is this product good at what is this product bad at right and gives me more context as a shopper on? Will this product work for me? Yes or No, given my specific scenario, I think the thing that we used to be able to kind of rely on as kind of conceptually is that, hey, if there's two different toothpaste products, one is rated 4.5. And the other one is rated 4.2, people would probably be more likely to purchase the 4.5 random one. But I think the interesting dynamic that happens now with these AI review summaries is that it might turn out that the AI, the Amazon review summary for the 4.2 product might actually mention that this is very good tasting. Whereas one of the complaints for the 4.51 might be Hey, there's a little bit of an aftertaste in me as a shopper if taste is the most important thing I care about when it comes to toothpaste, I actually might start to go with the 4.2 rated one. And the reason I mentioned this is because the next step that starts to the going to likely be started to iterate it on within Amazon is starting to look at these from a characteristic by characteristic basis and starting to give shoppers that insight into like, hey, if if what you really care about his tastes when it comes to toothpaste, here's how people are talking about it in reviews. And here's the better products that might be there for you. And the reason that that now becomes important as we think kind of as brands and as sort of Amazon experts is how do we start to integrate not just review analysis, but competitive review analysis into our decision making into our processes downstream so that we can start to deal with more of this nuance. And at the end of the day, us the yogi system, we're focused on it analyzing reviews and ratings is what we do. It's what we kind of live in brief. But whenever we're talking to folks that are kind of looking to start on this journey, we always just break it down into two options. So the first option is manual review analysis. And so this is just starting with a simple process of just somebody on your team, or maybe two or three team members, just going through the iterations of gathering data and what people are saying about reviews. So what this usually looks like at the end of the day is you take maybe your top five or 10 products on, let's say, two retailers, and maybe take your competitors, five or 10, top products on their retailers on to retailers. And then you kind of just go through the information in a pretty 10,000 foot overview kind of kind of level. So usually what that ends up looking like is, Hey, how is our volume been changing? How's the average rating been changing? And then you kind of scroll through some of the recent reviews just to see if there's any trends that are popping up? For any of these? Is it that when you look at one star reviews, is it that you're seeing five different complaints? Or is it the same complaint coming up five times. And there's, there's different ways to do this, what we've seen with some customers is they just go full manual on this other the others might have a digital shelf tool, like a prophet Taro, for example, or stack line, that'll give them the star rating and volume information. But again, when it comes into what people are actually talking about, which plays a role in the generative AI summaries downstream and everything like that, that's still where there's a little bit of kind of that manual nuance. And there's just a few sample questions that that you could answer based on this. Like how are our competitors reviews changing after a brand refresh, for example, or common keywords that are coming up in competitors negative reviews. Then the second option is when you start to do more AI powered review analysis. And this is where tools like Yogi come come into play. where this starts to become very, very important is when you want to take the next step, especially from a depth, perspective, and depth honestly, across two dimensions, depth in terms of the level of product catalog that you want to analyze, and then depth from the level of questions that you want to analyze as well. So what I mean by product catalog is, if you're a brand that has 50 products, for example, sold across five, or six or 10 different retailers, in a fairly competitive market, where there might be five other competitive brands that each also have 50 products, you're now looking at 300 products that you need to keep an eye on across, let's just say five different retailers. So that's 303,000, like, you're talking about 1000s of different just pages that you have to keep track of from that perspective. And that just isn't feasible for a person or a team of people to do especially when you want to look at those on a weekly basis, even for some folks a daily basis. And so just that piece of it from the depth of product catalog is one important dimension. The other important dimension is just the level of analysis that you want to do from these right, this isn't just what keywords are coming up, or frequency of keywords, when you really want to start to get into Hey, what are the different themes? What is the sentiment of the theme? When people talk about taste? In my reviews? What do they think when people talk about taste in the market? What do they think, am I above and below? Am I trending upward? Am I trending downward? All of those kinds of information, that next level of depth that you can use to do anything from influencing your PDP pages to figuring out what product innovations to try to push your team on. That is where the AI powered review analysis starts to become super, super useful.

Tiffany Serbus-Gustaveson 13:49
Somebody who's done manual review analysis, it's yeah, very painful. And I remember distinctly with Amazon is us having to extract those more negative reviews that had to do with logistics, and had a delivery time or packaging issue, whatever. So is AI able to extract that or what does ai do with those comments and reviews? Yeah, yeah, it's

Gautam Kanumuru 14:14
a great question. So at the end of the day, when you look at a system like Yogi, what we do is, you know, he's essentially going to go through all of the reviews for a product or across across the market, and break down the mentions based on topics of conversation. So if there's a review that said, I've been giving, I've been giving this dog food to my golden retriever for five years. I used to give them a different dog food, but they used to scratch an itch a lot ever since we switched no more scratching and itching. But every time I ordered this, it does take two weeks to deliver versus two days. And they give it a two star review. Because of that. With something like Yogi you're gonna be able to go in and the system is gonna say, Hey, this is a two star review on Amazon On a talks positively about the product and skin and coat health, but negatively about logistics. And that is when you can start to filter those out. Or even we've had some clients have some success of reporting those to Amazon being like, hey, look, this review has nothing. This is a negative review that has nothing to do with the product should not be listed and not bring down our average rating.

Tiffany Serbus-Gustaveson 15:20
And how far is the AI going back? Is it all reviews that are on sites? Or is it a certain time limit?

Gautam Kanumuru 15:26
Yeah, so for us, it's anything that we can get exposure to at the end of the day. So if we're able to go on a retailer and pull reviews all the way back from 2015, we'll do so. And that kind of changes retailer to to retailer,

Tiffany Serbus-Gustaveson 15:39
be able to segment it. So if you did have a product change or a packaging change in a certain time span, you could compare the difference between the years.

Gautam Kanumuru 15:48
Yep, yep, yeah. So you're able to within the platform kind of break down different timeframes. So that you can look at like pre reformulation, post reformulation things like that? Well,

Spencer Kelty 16:01
one thing I wanted to call out just because we're talking about, you know, shipping issues or deliverability, issues, things like that. One thing that I see very commonly among retailers is, they know that they're seeing those in reviews, and they're frustrated that they're pulling their ratings down. But they don't know how much of an issue it is. Because, you know, let's say it's 5% of your overall review volume mentions shipping and handling issues. Being able to benchmark that against the competitive space is critical. And not only just for that, obviously, for any topic, but I think it's interesting because that topic does tend to come into mixed reviews, where maybe it's not just the focus theory, maybe it's not a one star review that says it arrives broken maybe to go through this point, it's a two star review that says or a three star review, or going a four star review that says I love the product, but I'm having shipping or handling issues. That's the kind of thing that most manual analysis just isn't going to discover. And when you're trying to figure out how much impact is shipping, shipping issues having or is any issue having on our product review on our sales, being able to go in and use an AI tool that's going to extract that individual idea from the review, and not just distill the review down to that mention of shipping issues and the review score, but rather figure out exactly in the context of the review what it's really saying that lets you get a lot better context. And then benchmarking that against the whole market suddenly gives you the ability to say, is this really an issue? And if so, where does the stack rank and all the other issues we're aware of and the things that we want to work on to improve? Great point. Kind of getting a little bit more into that kind of the the next thing I want to go over is the kind of questions that review analysis can answer in the competitive front, especially? And what sort of outputs do these questions have? Like, what are the actual actions that a team is going to take based off these. So I mean, first, you know, you're kind of in the realm of of marketing claims and positioning. That's kind of one of the big categories we plan. Most Yogi users are in marketing. They're people that are trying to figure out how to message in marketing campaigns, how to message on PDPs, how to change the positioning and the competitive landscape, things like that. So being able to look at your reviews and see what themes have the most positivity around them. And on the other side, look at your competitors, and understand where they're failing, where there's whitespace, based on what's coming up in their reviews, lets you improve these marketing claims. optimize your campaigns around the keywords that there's opportunities with that your competitors are leaving, leaving things to be desired on, or the ones that you're already nailing it at. So you can lean into what's working and lean away from the things that lead to you getting poor reviews. Earlier, both them gave an example of toothpaste like you have to to toothpaste brands. One has a higher rating, but one is doing a lot better on taste. Well, if you discover that your competitors doing poorly on taste, and they're the leading brand in the category, that's something that you need to push in your marketing campaigns in your PDPs. Push that thing that you're seeing your brand, outperform your competitors on and you're going to see your marketing campaigns perform better. You're going to see your PDP convert better, you're going to see those results basically anywhere you're able to apply that optimization. And then I mentioned this quite a bit, but benchmarking on the competitive landscape is just a massive thing that a lot of brands are just unable to do with the data they have been able to go in and Understand how what you're seeing from your consumers, benchmarks against what other brands are seeing from their consumers is just kind of a bit of a superpower. Most most of the time, you know, you look at your competitors, marketing campaigns, you're doing competitive analysis, you might even be talking in focus groups or surveys to their customers. But what you're not able to see are, you know what their overall brand sentiment is, without a tool like an AI review analysis, you're not able to understand exactly to a granular detail how they feel about attributes compared to your brand, or compared to other competitor brands, being able to pull in that full category view, where you see basically every product in the category, and understand exactly based on review data, how they're looking at how consumers are looking at each attribute, each feature, each theme and those products, really does unlock a whole lot of different opportunities and options on how to go about iterating your marketing claims and your product iterations. And, you know, lastly, is just understanding what the white spaces and the opportunities in the market, this is something that brands pay a lot of money. Market research for. This is something that takes a lot of time, traditionally, this is the kind of thing you might go out and hire a consulting firm, or to spend a lot of time doing focus groups and testing to really understand what that whitespace is. But the fact is, those answers are likely in reviews. If you're in a somewhat saturated category with a lot of review volume, the answers are there, people are going to tell you what matters most to them. And you're going to be able to very clearly see if you lay out all of those reviews in a structured way, what themes have the lowest consumer sentiment, what themes are not contributing to, to overall positive product scores. And from there, you can quickly understand what some opportunities might be in your product innovation and iteration space. And we're going to talk a little bit more about that later, we've actually got some examples of some things that brands have done. So but for now, I'm gonna turn it over to go with them for kind of our first big example here in competitive benchmarking,

Tiffany Serbus-Gustaveson 22:19
and just a reminder, questions, comments, put them into the chat, and we will get to them as we go. I had one question about the themes. So are you seeing what the summary is that they can actually extract the price point theme? If somebody is complaining that it's too high or rating that it was such a great price?

Spencer Kelty 22:39
To go? Oh, yeah, yeah, go for it. You go ahead Gautam?

Gautam Kanumuru 22:45
Yeah, we do see pricing value, or I would actually put it this way we see value come out with the Amazon review summaries more than price. So it's not explicitly like a lot of the summary that we've seen aren't so much like people think this is priced too high. As much as they don't feel like they're getting as much bang for the buck for it. So that's kind of the way that we we tend to see it, or we've seen it play out more more often. And generally speaking as well, I think, just when we talk about reviews overall, because I think we're at the point of having analyzed hundreds of millions at this point. But most people talk from the context of like, what value they're getting. And what we often see with price and value is as soon as another theme performs poorly, price and value inherently goes down. So it could be that you're a $5 product. But when the jar gets shipped, and they open it, it looks half empty. Even though you're getting the quantity that was advertised instantly, you're gonna see complaints about packaging, you're gonna see complaints about price and value. Regardless of if there's another product that $10 That is, again, more expensive, you get a little bit less, but it seems more full. You'll see that discrepancy at the end of the day. So we what we generally say is like price and value is the most extreme fragile, I guess. That's obviously

Tiffany Serbus-Gustaveson 24:16
from that perspective. Very interesting. Thank you.

Gautam Kanumuru 24:20
Yeah, yeah, for sure. Yeah, and then like Spencer said, I think the, the thing that we thought would be most useful for this discussion is just to honestly to show examples of a lot of the questions that that Spencer laid out. So the first one is, again, staying on the theme of competitive benchmarking is actually something that we pulled from the hair market at the end of the day or scrunchies, specifically, so skoon see is a brand owned by Cohn air, that's kind of the at least they claim to be kind of the creators of the spreadsheet. Although this is again, very kind of commonly used, but one of the The most talked about topic of conversations when it comes to scrunchies. Is durability at the end of the day, like how long does this product last. And one of the interesting things is, students is always kind of thought positively about their their durability. But when you actually dive into the reviews and compare all the mentions of durability for their product versus the rest of the scrunchie market, you actually see a difference in sentiment. So if you kind of look at that third column on the on the table, on the left side, you'll see the average sentiment is at negative 0.1. Just worth noting that our sentiment goes from a negative one to a one. But you can also see it from an average rating perspective. So on the left hand side, you'll see that whatever people talk about durability with Scunci, the average rating tends to be a 3.26 verses for the market. When people talk about durability, the average tends to be a 3.81. So you can obviously see there's a big, big difference from that perspective. So, again, as the scuzzy team, how do you kind of react to this? I think there's a few ways that you could you could look into it, just purely, hey, we're not doing well from a durability perspective. But the other interesting thing worth noting is it's not like anybody's doing amazing at durability as well, right? Like a market average of 3.81 isn't necessarily great. And so everybody has room to improve from a durability perspective, but we especially have room to improve from from a durability perspective, then that leads to kind of the second thing, which is, hey, from a marketing claims perspective, what what should we be doing? Or maybe from our PDP perspective, what should we be doing, probably shouldn't be advertising, that we're the most durable product out there that this is going to last two years or things like that, because what that's going to do is create a mismatch of expectations for consumers. And that's eventually going to start to lead to people who purchase it thinking they're going to be able to use it for two years, it breaks after a month, they're going to write you a one star review, you're going to start to see the negative impact of that overtime. So what what should happen is you should lean into something else that you're doing? Well, I think one of the things that students he was really outperforming was just from color and design perspective at the end of the day. So that should be the the point that they they lead into more from from kind of a short term perspective. But then when you start to look medium to long term, this is a kind of data. It's definitive, it's quantitative. It comes from real user feedback that you can share with your product teams with your r&d teams, to really drive change at the end of the day, because we know when we talk to marketers, when we talk to eCommerce folks, our customers, they see this feedback come through, and they see the kind of downstream effects and we've talked to plenty that do have a tough time convincing the rest of the company that this is something that they have to rally behind and make changes on. But when you have data like this, that you can take back, we've been able to see a lot of folks kind of influence downstream changes, because of that a

Tiffany Serbus-Gustaveson 28:10
powerful data to be able to Yeah, take any emotion or opinion out of it within the organization and use the numbers. Exactly.

Gautam Kanumuru 28:21
So yeah, so that's just an example from kind of the the airspace so we can jump to a completely different category.

Spencer Kelty 28:26
I have a couple a couple of quick things to add here, just because I think they're worth highlighting one, because I think that goes some go some said this great, but I do want to emphasize it a little bit. The PDP relationship between claims and reviews that you see coming in is is one that we see is very strong. When you talk about something in your PDPs, it shows up at a much, much higher rates in your reviews, then if you don't talk about it. And that's entirely due to we'll go through set expectation set. So there's this this feedback loop that you can have, where if you're not focusing on things in the PDP, that lead to the best consumer experience that are the best of what your product has to offer and match the real world consumer experience, you start to see poor reviews come in. And you'll see more of them if you're focused on things that are worse experiences. But by switching that we commonly see brands that align their PDP to what they have the most positive sentiment in and what they're beating their competitors that we see that lead to a positive feedback loop, where the right people are buying the product, the people that care about the things that your product excels at, leading to higher reviews, which as we all know, higher reviews correlates very strongly to higher conversion rates. So it's this thing where by just understanding, you know for school and see here, for example, understanding that your durability is trailing In the market, obviously, a marketing team sees this and they want to get product on board to improve it. But well, what happens in the in the meantime, like that's not going to get a hit for a while, that's, that's going to be a six month or a 12, a 12 month and 18 month process to get done. But in that meantime, just focusing away your pee pee in your marketing language can have massive impacts to your review scores, your conversions and your sales. Sorry to jump in there, both mice really wanted to lean in on that.

Tiffany Serbus-Gustaveson 30:30
That is like, because they you're doing like, instead of the brand messaging to the consumer, you're using the messaging from the consumer now be your brand.

Spencer Kelty 30:39
Exactly. It's consumer led marketing.

Tiffany Serbus-Gustaveson 30:42
It's consumer led marketing. Wow. super powerful.

Gautam Kanumuru 30:48
No, it's cool stuff. I think to that point. There's another one, we didn't include it in these slides. But I just honestly thought it was funny where it could just be as simple as just meeting your customers where you are. So we have some clients that like the spaghetti space, for example. And one of the things that's been coming up as the amount of people that use the word noodles, instead of spaghetti has just been exponentially growing over the past two years. And so yeah, if you're like, hardcore into Italian food, somebody's calling spaghetti noodles is not right. But if that's what's been happening, that's where consumer behavior is trending. There is stare to be one, especially on things like Amazon and online for people to searching for noodles and expecting to see kind of spaghetti. So yeah. Is this things as small as that?

Tiffany Serbus-Gustaveson 31:38
And it's easier to take? Yeah, that's true, actually. Interesting. Very cool.

Gautam Kanumuru 31:47
Awesome. Yeah. So now the next the next example is from a completely different industry. So and this one is maybe looking a little bit more in medium to long term into what's what's possible. And so this is an example from filters, air filters at the end of the day. And one of the interesting thing that gets pulled out from from an air filter market is how do people know if this product is actually working or not. And so there's a really, really interesting correlation between, like the amount of kind of five star reviews that you're getting, and the amount of people who say, when I pulled this out, whatever, one month, three months later, it was all brown, look at everything that it caught. Oh, it's, it's awesome, because I know that it's working, essentially, at the end of the day. And so now this becomes the kind of thing that you can again, use from a marketing perspective, as well as as maybe a product innovation perspective, at the end of the day, on the marketing end, as you can tell that the way that people are perceiving quality in this industry is the visual kind of bad stuff that they see when they pull it out. So the more that you can market to that the more that you can even kind of show that in your your Amazon pages or your marketing campaigns downstream, the more it's going to make it make a difference at the end of the day. So again, meeting consumers where they are and their mindset. But then even Hey, from an innovation perspective, is there something cool that we can do that when people pull it out, it's going to even highlight it even more all the different particles that that we were able to capture with with kind of our filters. And so again, completely different industry, but it again, starts to fall on the same theme of just at the end of the day. By taking that next step to being closer to customers and understanding them that little bit more. There's a lot of different downstream decisions that you can make that will definitely bear

Tiffany Serbus-Gustaveson 33:42
bear fruit. So the grosser the better for this product. Exactly. Yeah, yeah.

Spencer Kelty 33:52
Well, it's interesting, too, because I have absolutely no data to support this. This is me as a consumer, not not me as somebody in the data. But I remember for a while I started to see like recycled filters, like recycled paper filters, and they were more of a brown tone to them. And I haven't seen them for a few years, like when I went to replace filters this past year, they weren't there anymore. So I wonder if that was the brands realizing that by having more color in the filter, you're disguising how well it's actually working. So I think that's really interesting. There are brands that are definitely on top of this and are in their reviews looking at this stuff. But if you're doing it manually, it's it's as you said, Tiffany, it's a very tedious process.

Gautam Kanumuru 34:43
For sure, for sure. And yeah, we can just kind of jump into what one more example that's also from the innovation perspective at the end of the day. So this is an interesting one from Purina that honestly goes a few years back but one of the things that they're able to see is that the amount of people that mentioning that they were putting pumpkin on top of their standard dog food was increasing over time, specifically with the idea that it helps improve digestion. And so this was an insight that they're able to see kind of early on. And with one of the sub brands within period, a period and beyond, that tend to be kind of more focused on natural food and ingredients, they were able to introduce kind of pumpkin as one of the core ingredients, you can see it there on the packaging, as a way to kind of promote positive digestion. And so this was a product that went through kind of a new launch, and it ended up performing really, really well. And so the graph on the right hand side just kind of shows the average rating over time, which at the end of the day is kind of what we tend to see with new product innovations at the end of the day is you start to see this big climb in positive reviews, that generally start to come. And it does start to level off at some point. And really, the success of the product is almost dictated by where it ends up leveling off at. Because it's not going to stay at the peak. Those tend to be those super early adopters, even people that get it as part of promotional reviews or things like that. But where do you end up leveling off, if you level off too low, the products probably not going to succeed. And if you can level off high, you've definitely found yourself a winner. So this was a good example of one that was able to kind of level off in a really strong area for the dog food space, which is kind of that 4.5 to 4.6 average rating range. Awesome. I know you have a few more examples, Spencer. Yeah, sounds good.

Spencer Kelty 36:37
So Tylenol, this is one of my favorite examples, because it just highlights a couple of different ways that you can use your views. So Tylenol launched, this new products dissolve packs their little powdered Tylenol products and a little, a little packet. And when it launched, the reviews were quite a bit lower than Tylenol standard, you know, this is a massive brand that has a whole lot of cash. So when a product is performing just a little bit lower for them, they took a lot of notice. And they started actually using Yogi as as part of their initiative to improve that product and bring it up to their standard. And in just a couple of days of analysis, they actually had two major findings. One was that the vast majority of their one star reviews were from people using the product wrong. They were taking these powdered packets, ignoring the directions and pouring them in a in a bottle or a glass of water and trying to dissolve them. Because that's how consumers are trained to interact with packets of of powder. They think that it's like their their emergencies or liquid IVs that they're going to pour in their bottle of water, shake it up and drink it. But these work differently, and the instructions said that they're supposed to be poured directly on the tongue. But people were trained, they were trained in a certain way of interacting with a product. So realizing this Tylenol double down on their messaging around no water needed. So you can see the image on the screen. They put this on their PDP in their carousel, they added a bullet point on their PDP, no water needed pour directly on Tang, and I believe they even actually made some packaging changes to feature it a lot more heavily on the packaging. So people didn't have to look at the instructions to really understand how to use it, it was right there front and center. The other change that they were able to make was they realized the number of reviews that the percentage of reviews that mentioned the product as fast acting was outperforming all of their other products that weren't currently being marketed as fast acting. So from that they were able to reclassify this product into their fast acting line and start messaging around its fast acting efficacy and marketing as something that consumers found was much faster than regular Tylenol. These two changes were able to make a massive, almost immediate impact in reviews by leaning into what people were experiencing both on a positive note and and leaning into that fast acting side and engaging with what people liked most about the product. While going in and making some some smart marketing changes, some smart packaging changes to combat the things that were leading to bad experiences. They were able to create this absolutely massive effects on their product review scores. And you know, like Gotham said When a new product comes out, there's usually a peak, there's usually a point where when those promotional reviews come in those first early adopters use the product, the people that are very brand loyal, start engaging with the product, you usually see it start at the high point, and then it's going to drop off and kind of plateau. So when a product launches, and it's it goes down, off of launch, and doesn't come back up, that's when there's a problem. So they were able to counteract and actually go against that usual flow of things, and bring it up to kind of a second wave spike, and then plateau off at, you know, that 4.8 range, which is, you know, an excellent product score for for any category. And, you know, really counteract what they were seeing early on, as you know, the product that had the potential of failing, and not through any fault of the product. So it's a great example just of how you can find themes and trends, both on that positive and negative side doesn't really touch so much on the competitive angle. But by understanding how people are trained by other products in the space to use your product and interact with your products, the expectations that are set their impact your marketing, the impact how people interact with your product in the in the real world. And that's the kind of thing that doesn't always come out in focus groups in testing. That's real world stuff that you can't always plan for yet to be ready to see what the data comes back and tells you see what your consumers tell you. And, you know, big props for Tylenol, being able to take that data and really quickly make changes that made a night and day impact on their brands.

Tiffany Serbus-Gustaveson 41:39
I just basically think like, if Tylenol didn't have eCommerce, this was 2030 years ago, and they would have put that on the shelf. When lately it probably would have been a discontinued product.

Spencer Kelty 41:51
Yeah, absolutely. And, and there's a decent enough chance that they might not have even known why it wasn't resonating, or they might have had the wrong conclusion about why the product wasn't working.

Tiffany Serbus-Gustaveson 42:00
Yeah.

Spencer Kelty 42:03
So we've got one, one more and then we'll we'll go to questions. So this one is for, for Nestle for coffee mate. And this is you know, all about, you know, looking at voice of customer in a competitive light. So, Nestle coffee mates, obviously a massive consumer brands, I think you're gonna find very few coffee drinkers that haven't heard of or used Nestle coffee May. For them, there wasn't a whole lot of expansion opportunities within that demographic, you know, coffee drinkers had heard and used of Nestle. So they started looking using Yogi to understand what about alternative beverages? What is the space looking like for people that aren't coffee drinkers. And on the left here, we actually have, you know, when they started doing their analysis, the vision of what the entire space was like for alternative beverage messages. So these are the percentages of reviews that mentioned alternative beverages, and showing what percentage of of Nestle was it was around 11%. So coffee made is the leader in this space, they're massive, and they were only getting 11% of review volume that mentioned alternative beverages. They realized that this was a big opportunity that they were missing. They have focused their marketing, obviously, on their core audience of coffee drinkers, but there's not a whole lot of massive gains, you can get in that space anymore because they're saturated. So they started looking at this and saying, Well, what about people who are tea drinkers, hot chocolate drinkers, cider drinkers? What can we do to market to them. And you know, one thing too is interesting is they started realizing that people were also using coffee made in baking. And in recipes, it wasn't just a beverage thing anymore. There were other uses. I believe that there was mentioned that people pouring it over their ice cream, things like that. So they started realizing these alternative uses weren't just a corner case, this wasn't just a few people using their product in a weird way. This was actually something that market was telling them they needed to address. So on the right hand side, this is a graph that shows as they started incorporating alternative beverage and alternative use messaging and marketing message marketing campaigns into their strategy. This is the volume of reviews that mentioned alternate uses over time. So the majority of these we're going to be mentioning of hot chocolate, mentions of tea but also some other other corner cases, but just by adding some imagery, adding some messages in there. PDPs and I believe that even today, the actual marketing campaign around putting it in hot chocolate, they were able Will to quickly change this, this view, change this landscape. And really do this by looking at that whitespace understanding that there are people out there that don't really think of using coffee may in other beverages. So just another example of having that broad view and being able to understand where your brand sits in context and figuring out, well, what else is there? What else? Are people using our product for? What are those corner cases that might become core value and core audiences for our products? Fantastic

Tiffany Serbus-Gustaveson 45:34
case studies, I mean, just absolutely. Unbelievable, really, to be able to, it looks, it's like, oh, that's so obvious, but it's not obvious. But it is obvious if you have the right tools. And you're looking at the data. Absolutely.

Spencer Kelty 45:48
I mean, this is the kind of thing that, you know, you could see those reviews that mention other uses, you can see the reviews, you know, in the case of Tylenol, that that say, you know, it didn't dissolve in water or something like that, you can see those reviews. And I'm sure that, you know, people on these teams, even without Yogi we're seeing them. But without looking at the macro without seeing the context, seeing the frequency at which they're coming in seeing the individual sentiment impact, you can't really have that consumer led action, you can't really take it as a data point. It's it's that qualitative versus quantitative data. You know, qualitative data is very hard for brands to take action on what quantitative data you can stack, right, you can understand what the impact is going to be, you can prioritize. And that's really what what comes out of this. And that's even that's true on the competitive side when you're looking at how to prioritize messaging in relation to your competitors. But also, when you're just looking at your own product, it starts to become a lot easier to stack rank.

Tiffany Serbus-Gustaveson 46:54
Hmm, absolutely. A final questions, comments, get them into the chat or the q&a, and we will get to them in these final minutes. So curious about the brands that you work with that are doing best in class here? How often are their marketing teams going into the PDP and revamping

Gautam Kanumuru 47:13
the copyrighted? Yeah, I think probably the fastest that we see is we have a couple brands that will refresh everything quarterly and opportunistically refresh. Some, like key products almost on a monthly basis. Yeah, yeah, I think that's the fastest that we've seen. And we have like two customers that fall into that. I think what we've seen is the behavior to date for a lot of folks is this is a once a year exercise. And I think we're start we're starting to see people try to shift that to at least twice a year to quarterly,

Tiffany Serbus-Gustaveson 47:49
essentially is like the goal. Yeah, it seems with this data, like monthly totally makes sense. But in my previous world, thinking, well, we used to write that content and just kind of read it, forget about it, move on to new product, you know, keep writing. And it's that's a whole different flow for a team, but very impactful. I think what

Spencer Kelty 48:10
we see right now is that smaller organizations tend to like take this new data and change their workflows faster, which, you know, as you could assume, a little bit more agile in their workflows. But I do think it's worth saying that the the large enterprise category leading brands, they're, they're moving quicker, they're starting to see the value of this data and see that, yeah, PDP shouldn't be a set and forget thing anymore, they need to be something that you're looking at constantly. And I would also just add that, you know, often the data that you have informs the tactics and informs the strategy. If you don't have this data, maybe there isn't a reason to be in your PDP, tinkering with it and changing it every three months, maybe it is a once a year thing. But if you have data like this, that shows very clear trends shows very clear, emerging ideas or emerging competitive opportunities, then, you know, why wouldn't you be in it every every quarter or even every every month? You know, we have, we have different things, you know, that give you opportunities to see stuff as they're happening. So you don't necessarily need to even be just practically digging all the time, sometimes the system will be able to pull out emerging things for you. And it'll give you the opportunity that if if something's happening, you don't necessarily need to be thinking 10 steps ahead, you can see it as it's emerging. Even if you didn't expect to see something. I mean, great example of that is like, you know, new competitors entering the space. You know, somebody comes in, and they're doing a big push into a new category. They're doing a ton of a ton of paid Review. pushes or, or something like that or a ton of promotion something that's bringing in a lot of review volume. Like you can see competitive spaces have a lot of change in the review landscape basically overnight with a with a big product launch where you're seeing people react to new features, new attributes, new ideas in the space.

Tiffany Serbus-Gustaveson 50:19
Absolutely. Very cool. And I love Joe in the audience is like, in a nutshell, it always reviews make adjustments to copy and images and enjoy the benefits them. Absolutely.

Spencer Kelty 50:33
Yeah, yeah, you gotta Joe. I mean, it's, it's really basically it's not that complicated. When you get down to it. It's basically just letting your consumers and not just your consumers but your competitors consumers to guide where your brand and where your product goes. You know, being very consumer led and agile is kind of the name of the game.

Tiffany Serbus-Gustaveson 50:55
Yeah. Awesome. We'll Gautam. Spencer, always a pleasure. Always appreciate the Intel the expertise you guys bring to these events. We definitely encourage follow up conversations with the Yogi team. And we'd love to have a conversation with you. That's how we stay on top of the trends and the things that you all want to learn about. So feel free to email me at Tiffany@bwgconnect.com. With that it's a wrap. Thank you guys. Happy Thursday, and take care and see you in another events. Thanks, everyone.