[MUSIC] So the idea that we're going to talk about, preference isolation, is the notion that you have preferences and tastes that are different from the other people in your neighborhood. And as a result, local stores, local restaurants, local merchants aren't motivated to give you what you'd like. So just to motivate this a little bit more, let's think about an example where everyone in your town wears ties. Most people happen to want to wear blue ties and you wear a red tie. Now, think about the problem faced by the manufacturer and seller of ties. If there's a cost associated with producing more variety, and typically there is. The manager of that factory and that store might say, you know what, most people want blue ties, only one fellow wants a red tie, we're not going to make any red ties. And as a result, the person with the taste for the red tie, the preference minority, is not going to get what they want. So, I'm going to explain in the next few slides how this problem can be overcome through online intervention, particularly through sellers who operate on the Internet. To make a more personal example here, I offer one to myself in Philadelphia, why it is that I can't get Vegemite when I go to the local supermarket. Let me show you a picture of Vegemite, I'm sure my friends in the UK, Australia, New Zealand, and other places have seen it. Here's a little kid with a Vegemite all over his face. It's a delicious black paste that you have on toast with cheese and avocado and stuff like that. I look for it all the time when I go to the supermarket in Philadelphia, but I can never find it, why? Because I'm probably one of the only few people that would actually buy it. So the store manager who wants to stock items that are profitable and sell frequently is not going to pay attention to my preferences, I'm a preference minority, and therefore I'm not going to be able to get what I want. The Internet however, could solve this problem. So let's go again, a little bit of background. Here's our slide, our friends at Quidsi. And the research for this particular article that I wrote and published with a friend of mine, Jeonghye Choi at Yonsei University, is based on data that we got form diapers.com. Here's the article itself, the title of the article. If you want to, you can always go read it for more details, but I'm going to give you the flavor of the main findings. The article's called Preference Minorities and the Internet. So let's see how this works. The notion is that if you're selling things online, that gives you the ability to aggregate people. Maybe there's only two people in Philadelphia that like Vegemite,. But in all the towns in America, maybe there's 100,000 if we added all those people together. And even though it wouldn't be efficient for us to serve them in individual shops, we could sell that product over the Internet. That's the basic idea. So preference isolation is going to bring shoppers to the online marketplace instead of the offline marketplace where they're locked out. And it's going to do that in a systematic way. So now let me get into the details and conclusions of this particular study, again using the data from diapers.com that we're all by now pretty much familiar with. So in order to test out our idea that people who were different from their neighbors weren't getting served adequately by offline stores. Jeonghye and I went out and we did a little bit of a field study. Kind of a fun field study and here's what we did. There's a chart on the slide that you can see where we went out and we visited different supermarkets in the Philadelphia area. Three supermarkets were from the Fresh Grocer chain, and two of the supermarkets or stores were Walmarts. Now what's interesting is all of those stores, all of Fresh Grocers, they were all the same sized stores. But their local markets were different with respect to the number of households in the local market that had kids. So again, if you look at the chart, you can see that Store 1, about 10% of the population in the immediate area around the store had young children. They were households with babies. Store 3 on the other hand, about 16% of the household's in that neighborhood where that store was located had children. So what does that mean for our idea or our theory? Well, the people who live in the market where there's only 10% of households with kids are going to be relatively more neglected by the supermarket than in the market where there are 16% of households with kids. So if my friend Chris is the manager of the store, he's going to say, you know what? Not that many people in the 10% market have kids, I'll just have Pampers and few leading brands on the shelf and I won't worry about having a lot of variety. If, on the other hand, he's managing the store with 16% of the target market has children, he's going to say, you know what? A lot of people in this local neighborhood have kids. I better cater to their tastes and preferences. And so what you see in the chart is in neighborhoods where there's a higher fraction of households with kids, the actual stores have more shelf space, more linear square feet and more variety of product on the shelf. Now of course, this doesn't prove our theory but it does indicate that local stores pay attention to the composition of the people who live in the neighborhoods and then they stock merchandise accordingly. As a little side note, this was kind of a fun thing to do in Philadelphia. Jeonghye and I had a pink measuring tape. We were running around trying not to get caught by the store managers measuring how much space was allocated to these things. If you've been to Philadelphia, it's kind of a tough town. It's a place where they boo Santa Claus. At least the football fans do so you have to be a little bit careful if you're running around with a pink tape measuring stuff in stores. Okay, so let me elaborate a little bit more on this next slide with the actual theory that we built up to try and explain this concept. So, imagine we have two different markets, this is just purely a conceptual argument. So, the first market, Market A is a market where there are 100 households with babies or 100 households in the target population. The total population of this particular market is 200 people. So, half of the people in this local neighborhood have the characteristic that we want. In this case, it's whether or not they have children. But you can think about this idea much more generally. It could be half the people want Vegemite or any other product that you can come up with. So, notice in that market, there is one store and the store is 200 square feet in area. And so the manager of that store says gee, half the people in the market have this particular characteristic, let's say households with kids, so I'm going to allocated half of my store to products that cater to those people. And again, this is just a stylized example to make the main point but hopefully you can see where this is going. Now in Market B, again, there are 100 people who have the characteristic we're looking for. In this case, households with children. But the total population of the market is 1,000 people. So these people with kids are a little bit more rare in this case. They're only 10% of the population. Now notice however, because Market B has more people in total, it has 1,000 instead of 200. There's more stores and we're just assuming that the number of stores grows with the population. So, in a market of 200 people, if there's one store and a market of 1,000 people, there will be five stores. Now again, my friend Chris who does a lot of store managing, I guess, he's in Market B and he says gee, I'm running these five stores and each store is 200 square feet in size and I notice that 10% of the households in the local market have children. Therefore I'm going to allocate 10% or 20 feet of the space of my store to serving that group. So what you notice here is even though in both Market A and Market B, the target market is the same size, 100 households with kids in A, 100 households with kids in B. The extent to which the offline market is paying attention to them is very, very different. The customers who live in Market B, everything else held constant, should be more likely to want to buy their products online. In this case, from our friends at diapers.com. So let's see if that is in fact true. So now Jeonghye and I went and we looked at the real data. This is just a map, it's a black and white map, but hopefully you can get the idea. This is an area of Los Angeles County in the United States. And what you notice is in the top map, there's an indicator of how isolated the customers are. We call this the preference minority index, or the PM index. And the darker the color, or the darker the shading, that means customers are more isolated. That's in the top part of the map. In the bottom part of the map, these are the sales to diapers.com. And again, you notice the fifth quintile, or the area that's having the most sales, the darker areas are sort of the same dark areas in the bottom part of the map as they are in the top part of the map. So this is indicating some support for our theory that when customers are isolated, they're more likely to use online merchants instead of offline merchants. So, the next thing that we that we did after looking at the raw data is do what a lot of us do here at the Wharton school do. Whether it's Pete or Barbara or myself, is we ran some statistical analysis or some econometric analysis on the data. And what we did was we tried to see if it was in fact the case that sales at diapers.com were higher in markets where the customers that they were focused on were more isolated. Everything else held constant so we controlled for the education level, we controlled for the number of stores in the area, we controlled for the population density, we controlled for the income. All the things that you might think would affect online versus offline buying, so all of those things were held constant in our study. And what we found was yes, there was a highly statistically significant effect of isolation on sales. In markets where customers were more isolated, they were more prone to go online and sales at diapers.com were higher. So now I'm going to explain the magnitude of the effect which I think is really interesting. Was also very useful for the company for the guys at diapers.com. So most of you I think are familiar with the idea of a percentile, but let me explain that because that's going to be important for understanding these results. So if you've ever taken a standardized test like an SAT test or a GMAT test. Any test at the end of high school or to get into college, those kinds of things. You'll remember that when you get your score back, in addition to the raw score, you typically get a measure of percentile. So, where is it that you ranked relatively everybody else that took the test. So, if you're in the 90% percentile, that pretty good. That means only 10% of the test takers beat you and you beat 89, 90% of everybody else. If you were in the 10th percentile, which I'm sure none of you were, that means that you only beat 10% of the people or in fact, 90% of the people beat you. So what Jeonghye and I did is we used the same concept but we applied it to our preference minority index. So we looked at all of the locations in the United States, all of the areas, all of the zip codes that were really, really isolated in terms of people with kids being relatively rare. And what we found was for zip codes of the 90th percentile on that index, the online sales at diapers.com were 50% higher. So think about that result for a moment. If we had two zip codes that were absolutely identical in all respects and in particular, these two zip codes have the same total member of households with children, 100 here, 100 here, just like in the example. If this was a zip code that was more isolated, the online demand at diapers.com was 50% higher. So, we think that's a very interesting finding and also one that Internet retailers can actually use when they think about online, offline interaction. Now, if you think back to the long tail, you might remember that different brands also have different levels of sales so, the most popular brand has the highest level of sales. In the baby diaper category, that's Pampers. And the second one is the second most popular and so on down all the way out into the tail or into the niche products. So, the same thing happened here, what we found was if we looked at particular products that were niche products and we compared an isolated market versus a non-isolated market. The sales in the isolated market online for niche products were about 125% higher. And I'm going to show you this in a diagram to make it easier to remember. So now here's the diagram that explains everything that we've discussed and brings it all together in one place. And also, importantly, relates it back to that other key idea, the long-tail, that we've already discussed. So let me show what's going on. There are three pieces to this diagram that are important in terms of understanding the overall concept. So first, on the left-hand side, we see Market A and Market B. Those are just those markets that we had previously where we looked at the fraction of households with children. So notice that Market A is the market where off-line retailers are paying a lot of attention to our target customers and households with babies, because they're 50% of the market. And Market B, the offline retailers, aren't paying much attention to our target customers, who are the households with children. And that's reflected at the bottom of the slide with a little thumbs up and thumbs down in the two markets. So what does that mean for the way products are sold and bought online versus offline? So for that, we have to go to the top of the diagram which is our old friend, the long tail. And so just remember by way of review that the long tail is an idea or concept that has a plot of all of the products that are available from a particular seller or a particular merchant. The Y-axis is always the sales of the product and the X-axis is all the products lined up from the most popular to the least popular. So it just so turns out in the diapers category the most popular brand is Pampers, followed by Huggies, then Luvs, and then there's a whole bunch of other products, literally thousands and thousands of different varieties of styles and brands and so on. And one that I have highlighted there is one called Seventh Generation. That's a niche product out in the tail that has lower sales than the other three. Seventh Generation is not available at every offline retailer. So how do we relate this long tail idea back to preference isolation and see how the two things come together? Well if we think about the online retailer, that's our friends at diapers.com, they carry the entire distribution of products. They offer everything, probably have the largest assortment of baby related products and diapers, probably of anyone in the world, actually. Certainly bigger than any physical store. Now if we turn to Market A, in Market A was also pretty good for variety. Maybe not everything is there because physical stores have space constraints and so forth, but Market A has a decent amount of variety, meaning that the offline sellers are quite attractive. Market B, however, most of the sellers are just stocking the popular brand and not really catering to a full range. So in Market B, the amount of product available is much more limited and is just really focused on the most popular brand. That's why in Market B, the customers are more prone to shop online versus offline. 50% overall and up to 125% more online shopping when they're looking for those niche brands that are really impossible to find in those preference isolated markets. [MUSIC]
