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.