Dynamic Pricing and the Dual Entitlement Principle: When is Dynamic Pricing Price Gouging?

This article in the Kansas City Star discusses the Kansas City Royals’ dynamic pricing plans for the post-season.  The key excerpt from the article is…

“Diamond Box seats located behind the dugout on the lower level normally sell for $39 in the regular season. That price jumps to $155 for a wild-card game or the divisional series, $220 for the championship series and $275 for the World Series.

That represents increases of 297.4 percent, 464.1 percent and 605.1 percent. Seem high? Several professionals in the field say they are among the sharpest increases they’ve ever seen for any event.”

The obvious question is “are these prices really too high?”  The knee jerk response from dynamic pricing advocates is usually that the prices are fair since the prices are set by the market.  The concern I have with the idea of market prices being “fair,” is that fairness is subjective.  In other words, it is the consumer that gets to make the judgment as to whether a given practice or price is fair.

There is an academic theory that speaks to this issue of fairness. The theory of “dual entitlement” basically says that consumers evaluate prices with the belief that while the firm is entitled to a profit, the consumer is also entitled to a fair price.  In the case of increasing prices of post-season games, the dual entitlement principle suggests that while the team is entitled to some price increase, the consumers should not be exploited with exorbitant prices.

What is the downside to violating this principle?  The Royals should be concerned with whether these prices damage their stock of fan loyalty.  As a small market team, the Royals are likely to have more losing seasons in their future.  If they want fans to stand by the team during the tough times, it seems like extracting every last dollar during a rare playoff series might be a bad idea.

So when is dynamic pricing price gouging?   Whenever the fans think it is.

Is Purple Pricing really Customer Friendly?

The latest version of dynamic pricing is Northwestern’s purple pricing.  We pointed out a while back that this program seemed primarily designed as a means for extracting revenue from visiting fans.  This video explains in more detail how the system works and how it contains advantages for fans.  From a consumer behavior perspective, the purple pricing system contains a significant benefit.  The system starts with a high price and prices decrease until the section is sold out.  Customers are protected as the price eventually paid is the LOWEST price at which tickets are sold.

So how does purple pricing compare to other dynamic pricing efforts?  Perhaps the biggest difference is that the price structure is largely dictated by the school rather than the market.  In NU’s program the school sets the initial price and from there the prices can only come down.  This means that NU can potentially be leaving revenues on the table.  The second and more subtle factor is related to how the system impacts consumer’s decision making process.  The system pushes consumers to buy quickly at higher prices in order to avoid being left out.  The system compensates by providing the safety net of all consumers paying the lowest price.   The system works for Northwestern if the fear of being left out leads consumers to pay a bit more than they would like.  If enough consumers feel this pressure then the low price guarantee is irrelevant.  In this way, purple pricing transfers risk to consumers. Of course, while this transfer of “pricing” risk might have negative implications for customer relationship management, the Northwestern program seems much more targeted to extracting revenues from Michigan and Ohio State fans.

 

Red Wings & Bruins Top NHL Social Media Equity Rankings

Last week, we published our ranking of NHL Team Fan Equity.  We have coined the term “Fan Equity” as a sports specific version of customer equity.  This metric is driven by “economic” measures of loyalty.  But, we do realize that a fan base also includes factors such as passion and engagement that may be (to some extent) overlooked in an economically driven ranking.

As a second look at fan base quality in the NHL, we use an approach that removes factors such as non-revenue maximizing pricing policies and capacity constraints that limit our ability to measure the customer equity of a hockey team.  The ranking we present today is based on what we call “Social Media Equity”.  The ranking is developed as follows:  First we collect information on each team’s social media presence such as the number of Twitter followers and Facebook likes.  We then develop a statistical model that quantifies the relationship between these social media metrics and measures of performance such as the team’s winning percentage for the last three years, and market factors including median income and metro area population.  We also include each team’s number of tweets in the model.  We then look at the difference between predicted social media followers and actual social media followers.  This delta between predicted and actual followers is reflective of Social Media Equity.

This social media based rankings has both pluses and minuses.  On the plus side, fan interest is not constraint by either high prices or stadium capacity.  On the negative side, while liking a team on Facebook or following them on Twitter shows fan interest, we can’t economically quantify this interest (these teams are businesses after all).

The number one team in our social media ranking is the Detroit Red Wings.  This is not a surprise, as the common wisdom is that the Red Wings are the number one team in Detroit (at least according to Professor Lewis’ sister in law).  The Red Wings have great social media presence on both Facebook and Twitter.  In positions 2 through 5 we have the Bruins, Devils, Flyers and Avalanche.  These are all big time fan bases with the exception of the Avalanche (actually we are not sure about the Devils but there was an episode of Seinfeld involving face painters so we assume Devils’ fans are indeed very passionate).

The Avalanche is where the story gets interesting.  While the Avalanche rank only 21st in the league in Twitter followers and 12th in terms of Facebook likes, they have achieved these results in a relatively small market, while often struggling on the ice (16-25 last year).  Our model suggests that Denver is potentially a strong hockey market.

The Social Media Equity results are a bit different than the economically driven results of last week.  Notably, the Canadian teams drop quite a bit.  In the social media rankings, Montreal finishes 6th, Vancouver 8th and Toronto 10th.    We can only speculate as to why the results differ.  Perhaps the previous results overrate the fan bases of the Canadian teams because Canadians are too nice to balk at high prices.

At the bottom of our list we have Tampa Bay in 5th from the bottom, Ottawa in 4th, the Kings in 3rd, Columbus in 2nd and Anaheim in last place.  Later in the year we will combine our various rankings to come up with a list of the best and worst sports cities.  We expect that Tampa Bay will be a front runner for one of those lists.  Ottawa performed similarly poorly in the economics based list so the verdict is in, Ottawa is the worst hockey city in Canada.  LA has a decent social media presence, but when we adjust for team performance and market size the results are not pretty.  Finally, perhaps Columbus should call themselves the “Buckeyes” rather than Blue Jackets.

The Winnipeg Jets are excluded from the rankings because the team moved from Atlanta during the period of the study.

Mike Lewis & Manish Tripathi, Emory University 2013.

Coaching Hot Seat Week 3 – Mack Brown and Lane Kiffen

Periodically, we like to do what we call “Instant Twitter Analyses.”  We do these in situations where consumer opinion is the key to understanding a sports business story.  In the case of “coaches on the hot seat” customer reactions are a critical factor.  While sports are a bit different than most marketing contexts, the basic principle that unhappy customers signal a problematic future remains true.

During this college football season we have been tracking fan base reactions to their coaches.  As we all know, there are two prominent programs (USC and Texas) with coaches in trouble.  The point of today’s post is to show how the Twitterverse has been reacting to these two coaches this season.

In the picture below we see the daily negative and positive posts for these two coaches.  The patterns and levels are remarkably similar.  But it does seem that Brown has a few more defenders at Texas (despite having two losses).   In fact over the first three weeks of the season Brown’s percentage of positive posts is 47.8% while Kiffin’s is 45.7%

This data indicates that in the court of public opinion these coaches are both in about the same shape.  We also suspect that an extended hot streak would save both coaches.  Perhaps the most interesting thing about this data is what it says about each job and fan base.  In the past we have ranked Texas as having the most loyal customer base and Forbes has ranked Texas as the most valuable athletic program.  To add to the Texas advantages, it seems that the fans are also a bit less critical.

 

 

 

NHL Fan Base Rankings: Americans may like Hockey, but Canadians Love it

PLEASE NOTE THAT THESE ARE OUR 2013 RANKINGS, FOR OUR 2014 RANKINGS AND IN-DEPTH STUDY, PLEASE CLICK HERE.

For our NHL Social Media Equity Rankings, please click here.

A quick search of the Internet about who has the best fans in any sport will lead to multiple articles and rankings.  These rankings tend to rely a lot on personal opinion, and very little on any type of analysis.  The best of these studies tend to use a little bit of data concerning metrics like attendance, or maybe how many “likes” the team has on Facebook.   Occasionally, the ranking will be some type of weighted average of several pieces of data.  The vast majority of these approaches are badly flawed.  In the case of looking at raw numbers such as attendance, a frequent mistake is to ignore that attendance is driven by winning rates.  If this is the case, then such a study inflates winning teams’ fan bases by including bandwagon fans.  In the case of using a weighted average of multiple criteria, we still have the problem of not accounting for winning rates, but we also have the problem that the “weights” for each factor tend to be arbitrary.

What we do in our rankings is to use a wide variety of data and some statistical modeling to get around these issues.  We use something called a revenue premium approach to assess a team’s fan equity (value of the fan base).  The basic procedure begins with a statistical model that predicts a team’s box office revenues based on market potential (population and median income), team quality (winning rates) and other factors (such as team payroll).  We then compare the predictions from this model with each team’s approximate box office revenues to determine which teams over and under perform.  More details on the approach are available here.  In today’s post, we rank NHL fan bases using the above approach.  Later in the week, we will present results that rank teams based on social media equity (rather than the economic value of the fan base).

Using the past three years of results, we find that the best NHL fan bases live north of the border.  In first place, we have the Toronto Maple Leafs.  The Leafs pack the fans in despite charging the highest prices in the league.  The key point is that while the Leafs have been up and down the last few seasons, the fans continue to show up and pay premium prices.

In second and third place, we have Edmonton and Montreal.  The Oilers ranking second may be a bit of a surprise given some of their recent struggles on the ice.  But Edmonton continues to sell out their building on a regular basis, while charging fairly high prices and losing more than half their games in recent seasons.  Remember, Edmonton does this with a metro area population that barely exceeds one million.  The Canadiens are number three on the list.  A comparison between the Canadiens and the Chicago Blackhawks might be instructive.  These two clubs are fairly similar in box office performance. The Hawks sell a few more tickets but Montreal charges higher prices.  But, Montreal achieves their results in a metro area a third the size of Chicago’s, and without being one of the best teams in the league.

In positions 4 through 6 we finally see the Americans represented.  The Penguins come in 4th, the Rangers 5th and the Flyers 6th.  Our initial reaction to these results was that Pittsburgh is a heck of a professional sports city.  The Steelers were the leaders in our study of social media equity in the NFL.  The Rangers and the Flyers are both solid franchises across all dimensions.

One of our favorite parts of doing these rankings is determining the bottom five.  It’s fun because we typically get to be insulted by folks from all over (thankfully, the Trashers left Atlanta so we are spared the local abuse*).  San Jose and Anaheim are 5th and 4th from the bottom, respectively.  Californians seem to be the opposite of Canadians (take it as a compliment or insult).  Third from the bottom is the Phoenix franchise (We’re not even sure of their name). Second from the bottom we have the Ottawa Senators.  This is just embarrassing for a Canadian team.  Let us respond to the Ottawa fans right now.  We don’t care that you sell out – read the description of the method.  In last place, we have Dallas.  Why would anyone move a hockey team from Minnesota to Dallas?

*On a related note, the Winnipeg Jets are excluded from the rankings because the team moved from Atlanta during the period of the study.

Mike Lewis & Manish Tripathi, Emory University 2013.

Twitter Analysis: College Station Buzzing About Alabama

It’s amazing what a difference a year (or ten months) can make.  Last November, Johnny Manziel was a redshirt freshman leading a two-touchdown underdog team into the hostile environment of Bryant-Denny Stadium.  This weekend, the Heisman-Trophy winning, John Hancock-machine leads the sixth-ranked Aggies into a matchup with Alabama that is undoubtedly one of the most anticipated games of the college football season.  ESPN College GameDay will be in College Station, even though the game is on CBS.

We decided to use Twitter to study (1) how much more chatter is there about the game this year versus last year and (2) how is the chatter different between the two campuses?  Our key findings: (1) The pre-game chatter has increased over 600% in College Station and 350% in Tuscaloosa as compared to last year and (2) The level of pre-game chatter was over 3oo% greater in College Station versus Tuscaloosa in 2012 and over 400% greater in 2013.

The methodology for our study is quite straightforward.  As in our Michigan-Notre Dame “rivalry” study, we used Twitter data from Topsy Pro Analytics.  We essentially collected all of the tweets originating from College Station, TX and Tuscaloosa, AL in the Sunday-Wednesday period before the game in 2012 and before the upcoming game.  In the pool of tweets from College Station, we counted how many of them mentioned a term that was related to Alabama (e.g. “Alabama”, “Bama”, “Tide”, and “Saban”).  We divided this number of tweets by the total number of tweets collected from College Station.  We performed this analysis separately for 2012 and 2013.  This gave us the Twitter Share of Voice for the match-up in 2012 and 2013 in College Station.  We did the exact same thing for the pool of tweets from Tuscaloosa in 2012 and 2013, but we looked for Texas A&M related terms (e.g. “Aggies”, “TAMU”, “Manziel”, and “Johnny Football”).  We believe that the Twitter Share of Voice metric is a good proxy for the level of game related chatter in the two markets.

The results indicate that while both communities seem to care a lot more about the game this year than they did last year, the Texas A&M community cares a lot more about the match-up than the people in Tuscaloosa.  We look forward to performing a similar analysis when Alabama plays Auburn later this year.

Mike Lewis & Manish Tripathi, Emory University, 2013.

Twitter Analysis: Michigan Cares More about Notre Dame “Rivalry”

Last week, Notre Dame Coach Brian Kelly described the Michigan-Notre Dame game as not “one of those historic, traditional Notre Dame rivalries.”  These comments helped invigorate discussions, newspaper columns, and College GameDay signs debating the magnitude of the Michigan & Notre Dame rivalry.

Rather than listen to “experts” tell us about the significance of the game (or fabricate memories of the game), we decided to use Twitter to study how much people cared about the game in South Bend, IN and Ann Arbor, MI.  The setup for our study was fairly simple.  Using data from Topsy Pro Analytics, we were able to examine tweets originating from South Bend and Ann Arbor.  We compiled a list of words that could be used to describe Notre Dame (e.g. “Notre Dame”, “ND”, “Fighting Irish”) and a list of words that could be used to describe Michigan (e.g. “Michigan”, “UM”, “MICH”, “UMICH”).  We then collected all tweets that mentioned any of the Notre Dame related words and originated from Ann Arbor.  We also collected all tweets that mentioned any of the Michigan related words and originated from South Bend.  We believe that these tweets are capturing the level of “rivalry” that each campus has toward the other campus*.

For the game played on September 7, 2013 in Ann Arbor, we looked at tweets on September 5th and 6th (pre-game).  We also examined tweets on September 8th (post-game).   We computed the Twitter Share of Voice for tweets about Notre Dame in Ann Arbor and for tweets about Michigan in South Bend for both Pre and Post-game.  As an illustration, to compute Twitter Share of Voice for Notre Dame related tweets in Ann Arbor, you simply divide the number of tweets that mention Notre Dame in Ann Arbor by the total number of tweets in Ann Arbor.  We believe that this Share of Voice metric helps control for the relative sizes of Twitter bases in the two cities.

The results from the 2013 game are very interesting.  Pre-game, the Twitter Share of Voice in Ann Arbor for Notre Dame related tweets was 60% higher than Twitter Share of Voice in South Bend for Michigan related tweets.  This implies that people in Ann Arbor cared more about the game (at least on Twitter) than people in South Bend.  Post-game, the Twitter Share of Voice went up by 57% in Ann Arbor.  The sentiment (ratio of positive to negative tweets) of the post-game tweets also rose by 40%, whereas there was no change in sentiment in South Bend (Michigan won the game).  We could interpret this as Notre Dame fans were relatively unaffected by the loss.

Perceptive Michigan and Notre Dame fans could argue that these results are skewed because the game was played in Ann Arbor.  We have excluded tweets from the day of the game to try to correct for any game site effects.  However, to get a better understanding of the “rivalry”, we performed a similar study of the 2012 game which was played in South Bend.  Even though the game was played in South Bend, the pre-game Twitter share of voice was 18% higher in Ann Arbor. The Notre Dame victory only created a 14% increase in the post-game share of voice in South Bend, and a 23% increase in tweet sentiment.  Thus, looking at data from the past two years, there seems to be an asymmetry in this “rivalry”.  That is, it seems Michigan cares a lot more than Notre Dame.

Mike Lewis & Manish Tripathi, Emory University 2013.

*Obviously, both of these universities have alumni all over the world.  We are limiting our study to South Bend & Ann Arbor because we believe this (1) captures current students and (2) is the cleanest way to separate out the two fan bases.

Instant Twitter Analysis: USC angrier but Texas Cares More

When teams lose fans get angry and coaches get fired.  Twitter now allows us to get an instant picture of fan anger.  Over the first week and day of the college football season, two coaches have emerged as mostly likely to be run out of town.  According to Topsy, Mack Brown has been the subject of the most negative tweets (2,550) but Lane Kiffen has the highest rate of negative to positive tweets (2 times as many negative as positive tweets).

USC fans are angrier but Texas fans are more involved.

Tebow Fatigue?

In the past, we’ve discussed Tim Tebow in the context of the brand equity he created for the University of Florida.  With his recent departure from the New England Patriots, we thought it would be interesting to see how fans reacted to his being cut this time around (as compared to in April from the NY Jets).  The chart below simply illustrates the ratio of positive to negative tweets that mentioned “Tebow” on April 29, 2013 [when Tebow was cut from the Jets] and on August 31, 2013 [when Tebow was cut from the Pats].  The ratio dropped from 1.55 to 1.05.  Thus, while overall there were still more positive than negative tweets when Tebow was cut from the Pats, the ratio has declined dramatically from the first cut by the Jets.  There were also fewer mentions of Tebow overall.  Does this signal Tebow fatigue?

Mike Lewis & Manish Tripathi, Emory University 2013.

Social Media Equity: The NBA

A challenge in evaluating fan bases in professional and college sports is how to adjust for capacity constraints.  Unlike most consumer categories, teams have a limited number of seats to sell.  One way to get around this issue is to look at team revenues.  But this approach also has some strong implicit assumptions in that we must assume that teams are trying to price in a manner that maximizes revenue.

The world of social media provides an opportunity to look at fan base support without worrying about capacity or pricing issues.  To look at NBA teams “social media equity” we collected follows and likes from Twitter and Facebook.  We then created a statistical model that predicts these measures of social media engagement as a function of market size, tweeting activity and team performance for this past season and for the season before that.  We then compared each team’s actual follows and likes against the model predictions.  This method attempts to control for short term fluctuations in winning percentage and market differences.

The top team in terms of social media equity is the LA Lakers.  The Lakers crush the competition both in terms of raw numbers and in our model.  In second place, we have the Miami Heat.  This one is interesting, and we suspect that the Heat results may be a bit misleading.  While the Heat does very well currently it is not possible to separate out how much of the social media equity is driven by the team versus by LeBron.  This is something to watch as we collect more social media data over the next few years.  In third place, we have another non-surprising result in the Celtics.

It is the next three teams that are surprising as Golden State ranks 5th, New Orleans ranks 6th, and Charlotte ranks 4th.  The case of Charlotte illustrates the value of our model based approach.  In absolute terms, Charlotte performs relatively poorly in terms of social media metrics.  However, when we adjust for team performance and market size, the team does fairly well.  This indicates that the Charlotte market has fairly resilient fans, and likely speaks to the potential of the market if a consistent winning team is developed.

At the bottom of the list, the most surprising result is the New York Knicks’ 27th place finish.  This is doubly interesting because when we ranked fan bases in terms of “economic” support, the Knicks were number one.  What these two results imply is that the Knicks’ fan base is economically valuable but not engaged (at least in terms of social media).  The Knicks play in the largest market but have only about 20% of the social media activity of the Lakers.

There were a couple of other interesting findings from this study.  First, the number of Twitter followers was uncorrelated with the number of times a team tweeted.  This suggests that fans follow based purely on their feelings for the teams, rather than the entertainment of following an interesting Tweeter.  We also found a very high correlation between the two social media platforms as the social media equity estimates across the two platforms exceeded 0.91. However, when we looked at the correlation between the social media equity and the economics based fan equity the correlation was just 0.3.  We will leave this disconnect between social media and revenues for a future post.

Mike Lewis & Manish Tripathi, Emory University, 2013.