WEBVTT NOTE { "characteristics": "public.accessibility.transcribes-spoken-dialog", "language": "en-US", "language-name": "English (US)" } 00:00:06.880 --> 00:00:12.880 Steve. Hello. Hey, Justin. How [are] you doing? I'm doing very well. Doing very well. And I'm glad that we're 00:00:12.880 --> 00:00:18.959 together again, because we did a nice piece of work called the value-seeking consumer. As you 00:00:18.959 --> 00:00:25.640 know, this was a pretty big research project. We had 3,600 brands evaluated by 00:00:25.640 --> 00:00:32.240 consumers. We had almost a million different pieces of consumer data rating brands 00:00:32.240 --> 00:00:38.919 on their price, on their value, as well as our whole ConsumerSignals data set on how 00:00:38.919 --> 00:00:43.880 consumers' behaviors and spending patterns are changing over time. And we discovered something 00:00:43.919 --> 00:00:48.840 pretty interesting and I think pretty useful about this trend called the value-seeking 00:00:48.840 --> 00:00:55.560 consumer. Now, we can quantify that. So about 4 in 10 consumers in the US are considered 00:00:55.560 --> 00:01:01.880 value seeking. We've recently done a global look at this. And we find that almost half of 00:01:01.880 --> 00:01:08.360 global consumers are value seekers. So, there's a few things that I want to ask you about, including 00:01:08.360 --> 00:01:14.160 what is a value-seeking consumer? Sure. Justin, you know, value is an interesting concept, right? And 00:01:14.160 --> 00:01:18.760 what we were able to determine is that these value seekers are looking for something that's 00:01:18.800 --> 00:01:25.720 worth it. Worth it to them. And in terms of that, it goes beyond the price. It's not 00:01:25.720 --> 00:01:32.000 just something that is less expensive, more affordable, but they're seeking something other 00:01:32.000 --> 00:01:36.560 than just price. And so when you look at it, they're looking at deals. They're looking at 00:01:36.600 --> 00:01:42.440 trading convenience. They're looking at some simplicity in their life. So, value seekers are 00:01:42.440 --> 00:01:48.160 interesting. Now, what's a myth to bust with value seeking is you might think that they come from low-income 00:01:48.160 --> 00:01:54.200 households exclusively, but to your point, we found quite a few value seekers out there and 00:01:54.200 --> 00:02:00.400 1 in 4 households making over 200K are value seekers. That's right, that's right. And I mentioned 00:02:00.440 --> 00:02:07.009 that global refresh. We looked at three different income groups globally across the 15 countries 00:02:07.009 --> 00:02:13.929 that we do ConsumerSignals, and 35% of that high-income group are also value seekers. So 00:02:14.009 --> 00:02:19.529 if you're considering when you hear the word value, that means that we're talking only 00:02:19.529 --> 00:02:25.329 about lower-income households, it's really becoming a broad swath of all consumers, of all 00:02:25.369 --> 00:02:31.850 types. I even consider myself a value seeker. Oh, so do I, totally. But that's not the only myth 00:02:31.850 --> 00:02:38.569 that we have to bust here. Because when people or companies hear the idea of value, 00:02:38.649 --> 00:02:44.289 sometimes that's thought of as like a synonym for cheap. And that was one of the other myths that we 00:02:44.289 --> 00:02:51.210 busted. Correct? Correct. So it doesn't equal cheap. Value can happen at every price point. 00:02:51.210 --> 00:02:56.089 And one of the interesting things with our more-value-for-price framework is that it goes up and 00:02:56.089 --> 00:03:03.089 down the price scale, right? So you can be that cost cutting brand that's trying to 00:03:03.089 --> 00:03:09.369 get the person who's truly looking for the lowest price. Or you can be that brand that is offering 00:03:09.369 --> 00:03:16.289 some non price driver that people perceive as a value. That's one of my favorite 00:03:16.289 --> 00:03:22.929 things about this power of the MVP framework is that by taking those million 00:03:23.049 --> 00:03:30.009 consumer responses as they evaluate brands on things like price and value, you can line 00:03:30.009 --> 00:03:36.529 all that data up, do a regression, and that allows you to kind of level the playing field between 00:03:36.529 --> 00:03:43.409 how value is thought of for, say that, lower-priced grocer that you're talking about, and value can be 00:03:43.409 --> 00:03:50.129 also present in that, more high-end, high-experience grocery. And it's about 00:03:50.129 --> 00:03:55.249 providing more value than would have been expected at that price point. Doing that something 00:03:55.249 --> 00:04:01.820 extra. Steve, what are some of the something extras that these brands are offering that is kind 00:04:01.820 --> 00:04:08.579 of common among MVP, more-value-for-price brands. Well, when we looked across a number of 00:04:08.579 --> 00:04:15.419 different subsectors. So here I'm thinking apparel, grocery, hotel, automotive, etc. 00:04:15.420 --> 00:04:21.499 There were a couple of things that stood out. Quality is not a surprise. But people 00:04:21.500 --> 00:04:28.019 associate quality with value. So getting quality for the price that you've just handed somebody. 00:04:28.060 --> 00:04:34.379 The second is trust, [it] is really interesting. And actually, if you're a higher-priced brand, you 00:04:34.380 --> 00:04:39.459 really work the trust angle pretty hard. We found that trust was even stronger with them because 00:04:39.459 --> 00:04:44.139 they've got to win you over. And then there's attitude. And attitude is an interesting 00:04:44.140 --> 00:04:50.579 one. It's just am I being respected? Do they understand me? Are they knowledgeable about 00:04:50.579 --> 00:04:55.659 what they're trying to sell me, be it a product or service? So it's really interesting. And, 00:04:55.700 --> 00:05:01.819 as you use a fairly simple framework, right? We're literally on the x-axis price, on the y-axis 00:05:02.220 --> 00:05:06.739 value. And looking at that regression that you mentioned. If you're above that line, you're more- 00:05:06.739 --> 00:05:11.739 value-for-price brand. And I know that we were able to double-click on some drivers beyond the 00:05:11.739 --> 00:05:16.739 ones I mentioned. So, you looked at grocery pretty hard. What were some of those when 00:05:16.739 --> 00:05:21.219 you double-clicked on a subsector like grocery that you recall more value for price players 00:05:21.219 --> 00:05:26.659 pulling on? Yeah. So some of those common themes definitely come up, although they come up 00:05:26.659 --> 00:05:33.379 in a grocery context. So, take attitude, winning attitude. Well, in the grocery context that's 00:05:33.379 --> 00:05:39.339 the associate, the store associate, how they show up, how helpful they are. That was one of the 00:05:39.340 --> 00:05:44.779 drivers. But then there are some very grocery-specific things like the speed of the checkout. 00:05:44.820 --> 00:05:50.179 Now, we've all stood in line at a grocery store. We have our thoughts about that 00:05:50.179 --> 00:05:56.579 experience when we looked deeper in the consumer data, speed of checkout was important, especially 00:05:56.579 --> 00:06:01.700 when it comes to once you get to the register. So, consumers were willing to wait in line a little 00:06:01.739 --> 00:06:07.939 bit as long as when they got to the register, that process went really well. Our MVP companies, they 00:06:07.939 --> 00:06:14.659 really stood out in that dimension. Yeah. And you know, if I extend it to say something like 00:06:15.140 --> 00:06:20.739 hotels. What's really interesting is right now luxury brands and hotels are having a moment. 00:06:20.780 --> 00:06:25.459 They're doing better than your down-market brands. So what's happening there? They have 00:06:25.459 --> 00:06:32.339 higher revenue per average room. And they're offering up something of more value: 00:06:32.540 --> 00:06:39.540 higher in service [and] location. To your point, the attitude really comes to play. You can run 00:06:39.540 --> 00:06:46.139 across a number of different subsectors and apply the same framework. And it shows just these new 00:06:46.140 --> 00:06:52.419 insights, specific subsector-driving insights. And what I find really interesting is really getting 00:06:52.420 --> 00:06:58.019 into those non-price drivers. So, Justin, hotels are one of my favorite subsectors. Do you have another 00:06:58.019 --> 00:07:04.669 one? Thinking about the automotive space, which I had a recent experience with. I take my 00:07:04.670 --> 00:07:10.629 car to the dealership in order to get its normal maintenance and stuff, in part because 00:07:10.629 --> 00:07:17.469 of the same kinds of MVP differentiators that the automotive sector had in our study. So 00:07:17.470 --> 00:07:22.350 it's things like higher trust. I know I may be paying a little bit more, but I trust the 00:07:22.350 --> 00:07:27.749 service department, how smooth that goes, how friendly those interactions are 00:07:27.790 --> 00:07:32.150 can make a big difference. So that's something that comes to mind for me. You know, 00:07:32.190 --> 00:07:37.269 Steve, we got into the more-value-for-price framework. But I think it's important that we 00:07:37.269 --> 00:07:43.749 discuss why we know that this is a powerful, useful framework. And that comes out in a few ways. 00:07:43.749 --> 00:07:48.829 In our research, there's net purchase intents. And then there is actually seeing 00:07:48.830 --> 00:07:54.349 household share shift. So, when we looked at it, we are able to segment by more-value-for-price players. 00:07:54.350 --> 00:07:59.109 And we should say that a third of brands out there are more-value-for-price 00:07:59.109 --> 00:08:03.629 players, which means two thirds aren't. And it's 00:08:03.630 --> 00:08:07.950 interesting, right? Because it holds up pretty much as you move from subsector to subsector. But when 00:08:07.950 --> 00:08:13.789 you look at them and you look at more-value-for-price players versus the others, what you find is 00:08:13.829 --> 00:08:18.829 that their net purchase intent over a three-year period is consistently higher. So what that means 00:08:18.829 --> 00:08:24.869 is people are planning to spend. So forward intent. Then when we looked at share shift right, 00:08:24.869 --> 00:08:30.430 using credit card data, we saw household share shift to the more-value-for-price players. So in 00:08:30.430 --> 00:08:36.549 the case of, say, grocery, it was a 2% share shift over that same three-year period. Kind of amazing. 00:08:36.549 --> 00:08:43.348 And thirdly, does Wall Street recognize this? Well, we looked at economic value. We looked at 00:08:43.349 --> 00:08:50.030 the multiplier effect, looking at economic value over EBITDA. And we did see a very significant 00:08:50.469 --> 00:08:56.630 correlation with economic value. So you've got the intention, you got the backward-looking share 00:08:56.630 --> 00:09:03.599 shift and then you've got the economic value. So, it's a solid methodology, right? So MVP, not just a 00:09:03.599 --> 00:09:09.359 clever name. Yeah. And it has practical implications for the C-suite because, as you know, 00:09:09.440 --> 00:09:14.479 Wall Street's always looking for alternative data sources. Sure. And they have seen this as well. So 00:09:14.479 --> 00:09:19.999 there are certain analysts using this as a screen. And so, the C-suite is starting to think 00:09:20.000 --> 00:09:26.399 about okay, am I an MVP brand? And they should. Right. Because it matters to consumers and it 00:09:26.400 --> 00:09:31.918 matters to Wall Street. Well, Steve, I really enjoyed working with you, as I always do, but in 00:09:31.919 --> 00:09:38.159 particular on this more-value-for-price value-seeking consumer work. And I look forward to 00:09:38.200 --> 00:09:43.239 what's next. I appreciate that, Justin. And what's next is we're going to deep dive into a number of 00:09:43.239 --> 00:09:49.119 subsectors so that we can better understand what those non-price drivers are. So stay tuned.