Revisiting McPhee’s Theory of Exposure and the Long Tail

Ever since Chris Anderson published The Long Tail: Why the future of business is selling less of more (2006) marketers have been pondering the implications of a world where the constraints of traditional brick-and-mortar retailing have fallen away as online stores can profitably carry products that appeal to only a select few. Anita Elberse, a Harvard Business School professor, in Should you invest in the long tail (2008) linked these post-Internet era ideas with McPhee’s (1963) old-school Theory of Exposure to bring balance to the thought-space. McPhee’s theory asserts two principles: (1) the most popular products/services (hereafter just “products”) in the fat head enjoy a natural monopoly among casual users, the majority of users in any product category, because these are the products that they can most easily gain awareness of; and, (2) even when casual users become aware of niche long tail products they tend to prefer the mass-market fat head products because these products have been optimized to appeal to a more diverse set of users, while niche products are generally optimized for aficionados. These two factors (lesser known and less appealing) put long tail products in double jeopardy.

We tested McPhee’s theory against Yelp’s review data and found that his ideas were generally supported, but we made some fresh observations in the process. As expected, we found that the venues (most Yelp reviews are for businesses like restaurants and hotels that are more accurately described as venues than products) reviewed by Yelp participants formed a long tail (Figure 1), where the most reviewed businesses are in the fat head. While some have found the head to be separated from the tail in the classic Pareto 80-20 split (80% of venues in the tail), we used a two-step clustering algorithm and found that 90-10 was a more natural split.


Figure 1. A two-step clustering algorithm found that 90% of venues reviewed on Yelp are in the long-tail.

The same clustering algorithm was also used on reviewers, where they were segmented based on the proportion of niche venues in their reviews. It was found that reviewers fit into four natural clusters as described in Table 1. How was McPhee right? The pure fat head customers (cluster 1) are the most satisfied (average star rating of 3.82) and loyal (5840 checkin-ins per business) consumers among the main-stream businesses that are 97.3% of their experience.


Table 1. There are four natural clusters among Yelp users based on the proportion of long tail businesses they review.

What’s new? Our interesting discovery was the cluster 2 consumers who are almost as happy (3.77 stars) as the mainstream cluster 1 consumers, but are responsible for a much larger number of reviews that have a monopoly on the readers’ assessment of review helpfulness. It seems that experiencing roughly 25% niche venues may be some kind of novelty sweet-spot that most inspires consumer-generated media, and most enriches consumer knowledge in general. Therefore, McPhee’s theory seems correct but may not tell the whole story of consumer experience in the long tail. Does experience with this mix of venues empower cluster 2 consumers to make better comparisons between the niche and mainstream? Is there something about the personality of cluster 2 consumers that make them more skillful review writers? Are they more allocentric? Many unanswered questions, but a fascinating phenomenon nonetheless.


When do reviews adequately portray a product or service?

It’s not always about quantity

Generally the more reviews you have, the more they converge on a consensus assessment of the experience provided (Figure 1). That suggests to the prospective customer that the experience is very predictable and low risk.


Figure 1. Only 35 reviews, but the rating distribution is starting to converge on 4 stars.

But, that is not always true. Some experiences are polarizing: some people love them while others hate them. Sometimes random chance will bring these two sides together in near equal numbers, with results confusing to the prospective customer (Figure 2).


Figure 2. Many reviews, but consensus is polarized.


There is another dimension to many review sites (Figure 3), where readers assess the helpfulness of reviews. We found that this information tends to bring clarity to even polarized reviews, however the review sites do not present this information in a helpful way.


Figure 3. Review sites have different ways of indicating the helpfulness of reviews, but while they display this information for each review and sometimes the object of the review it is uncertain what the implications of this information is.

We found that readers’ assessments of helpfulness gradually peak and then decline over time (Figure 4). It seems that the peak is the point where the reviews finally begin to capture all the information necessary for consumers to make an informed prediction of whether the reviewed object or entity is right for them. We call this point information sufficiency. Since matching expectations with outcomes is the key to customer satisfaction, we recommend that consumers be told when the available reviews might not allow them to make an informed prediction of their outcome. This signal may be best sent with a simple set of icons that indicate whether information sufficiency has been reached (Figure 5).


Figure 4. The helpfulness of a set of reviews was observed to peak over time. The time at which the peak occurs is denoted the point of information sufficiency. The vertical dotted line matches one in Figure 6.


Figure 5. Since matching expectations with outcomes is the key to customer satisfaction, we recommend that consumers be told when the available reviews might not yet allow them to make an informed prediction of their outcome.

Helpfulness and satisfaction interact

Our final observation on this issue is that information sufficiency seems to mark the minima of overall customer satisfaction (Figure 6). We believe this occurs because after information sufficiency is reached prospective customers are better able to predict if they will be satisfied with the experience by reading the reviews. Therefore, customers that expect to be unhappy stay away and those that participate are a self-selected better match for the product or service being offered.


Figure 6. The vertical dotted line matches one in Figure 4 that marks the point of information sufficiency. Customer satisfaction steadily decreases until this point, and increases thereafter as customers who are a good fit for the value offering are better able to self-select into the experience while those who are a poor fit stay away.

The consumer ecosystem

As one of the many people who downloaded the data Yelp posted in its Kaggle data mining competition, I couldn’t help but start thinking about what other value Yelp could provide beyond the consumer services that are the core of its business. I have an idea for a business intelligence application:

Businesses generally define their market in terms of the consumers within, and the other businesses with which they compete in offering similar value to the consumer. This is a very inward-looking way of defining a market. A better way is to define it through the eyes of your customer. Figure 1 was constructed using a methodology described in a later section from some of the Yelp data released for its Kaggle competition. It shows a cluster of businesses in Phoenix, AZ that have been classified into the same business categories. On the surface it seems like a strange mix of radio stations and newspapers, however they are clustered together because they all are categorized as mass media. This depicts a set of competing businesses as typically conceived.


Figure 1: A traditional map of market competitors united by their mass media categorization.

Figure 2 focuses in on one of the businesses in Figure 1, KUPD 98 FM, and depicts its place in a diverse business ecosystem constructed from the other businesses reviewed by listeners who wrote a review of KUPD. The central position of Delux Burger among all these businesses is surprising, counter-intuitive and a potentially valuable insight. However, the true value of the data pattern is that it gives KUPD a deep, multi-faceted insight into the behavior of its listeners. The potential applications of this insight are broad and certainly include arming the KUPD ad sales manager with evidence to show the 71 businesses in Figure 2 the potential value of advertising on KUPD. What might KUPD be willing to pay Yelp for this insight, updated on an ongoing basis? I think that being able to provide this information to any business listed on Yelp would constitute a minimum-viable product.


Figure 2: The business ecosystem of which KUPD is a member. Unlike the traditional market structure depicted in Figure 1, this ecosystem contains no direct competitors (i.e., radio stations). This graphic depicts the businesses that KUPD listeners frequent, they are principally united by their patronage of Delux Burger.

Figures 1 and 2 depict parts of two large networks of businesses listed on Yelp. In Figure 1 that network is created by linking businesses together that were classified into the same business categories (e.g., Discount Store, Nightlife and Music Venues), the more categories a pair of businesses share the more similar those businesses were considered to be. The overlapping web of categories creates a densely connected business network. The network was then pruned to a minimum-spanning tree, the smallest set of connecting links needed to link all the businesses together into a network structure of minimum total length. The simple set of links depicted in Figure 1 is from that minimum-spanning tree. The widely-known Girvan-Newman algorithm was then used to find communities among the businesses in the network. These communities are considered to be the true sub-markets within the overall network. Figure 1 depicts one such community of businesses, connected by the minimum-spanning tree.

A similar procedure was used to generate the network depicted in Figure 2, except in that case businesses were linked because the same people wrote a review of each business. The length of each link was based on the number of reviewers two businesses shared, as well as the number of stars the reviewers awarded each business. Again, the minimum-spanning tree algorithm was used to simplify the network, and Girvan-Newman communities were identified. Figure 2 depicts one such community, except in this case we have a diverse business ecosystem patronized by the same consumers.

As I stated in my introduction, these business ecosystem insights are highly actionable from a managerial perspective. This is particularly true in the way they suggest potential partnerships among businesses that do not compete, yet share the same customers. However, even when viewed from a traditional competitive analysis perspective, the consumers’ view of the market makes it obvious what the real competitive dynamics are. For example, in Figure 2 Delux Burger is the central influence that organizes the ecosystem. There are several other restaurants that, while they only occupy a peripheral position still attract enough attention to be on the consumer’s radar. What of the other restaurants whose food is utterly unremarkable, nether good, nor bad, and thus unworthy of review? Their absence focuses the attention of the competitors in the ecosystem on those who are their real competition. This information should also be a wake-up call for those restaurants that are overlooked. If that insight causes those restaurants to raise their game, then the depiction of Figure 2’s ecosystem might change, prompting all the businesses in the system to want to update their knowledge and business practice on a recurring basis.

I’m working on a web application to display this analysis for all the businesses in this dataset. When it is ready it will be at: