ESPN.com – B1G numbers: Building the brand

ESPN.com – B1G numbers: Building the brand

“Locally, the support is still there; people are still paying to go watch the team,” Tripathi said, “but on a national level, there has been a bit of a hit on the brand. That’s manifesting in the decline in sales of merchandise.”

Note: Professor Lewis’ wishes that this Kris Kross endorsement from the 90s would have had a bigger impact on the Illini brand:Flying Illini

Elite 8 Recap: Kentucky Dominates Twitter Once Again

As part of the Goizeuta Bracket Buzz contest, we were tasked to determine which of the 4 matchups in the Elite Eight would produce the most pre-game “buzz” on Twitter.  Essentially, we looked at the 24 hour period before tip-off, and collected all tweets that mentioned either team or the match-up in that period.  The Kentucky-Michigan matchup had the most pre-game buzz.  The chart below shows the pre-game buzz for all 4 matchups (it has been indexed with Kentucky-Michigan as 100).

EliteEight

Mike Lewis & Manish Tripathi, Emory University 2014

Twitter Analysis: Who Really Talks About Their Rivals?

It’s rivalry week, and while there is much debate about the best rivalry in college football, it is generally agreed that the Iron Bowl (Auburn versus Alabama) and Ohio State versus Michigan are two of the top rivalry games in college football.  While both sides in these rivalries seem to hate each other, we were curious to determine if the level of vitriol was even or more one-sided in these two storied matchups.  What we found was interesting:  1) discussion around Michigan football seems to encompass A LOT more of the general conversation in Columbus than discussion of Buckeye football in Ann Arbor and 2) after accounting for where the game is being played, the relative level of discussion about the rival school is fairly even in Auburn and Tuscaloosa.

Similar to previous studies, we used geo-coded data from Twitter to serve as a proxy for fan conversation.  We collected all Twitter conversation in Ann Arbor, Columbus, Auburn, and Tuscaloosa for the Monday before the rivalry game in 2010, 2011, 2012, and 2013.  We then calculated the percentage of tweets in that city that were about the opposing school’s football team (“Rival Team Share of Twitter Voice”).   Thus, we had a metric for how much of the conversation in a city was about the rival team.  It is interesting to note that we also determined the average sentiment for tweets in a city that were about the rival football team.  The average sentiment was very negative, but similar across years and cities (translation:  the toxicity of the comments about rivals is the same whether you are in Columbus, Ann Arbor, Auburn, or Tuscaloosa).

We would expect that a rivalry where both local fan bases hated (or were obsessed with) each other at a similar level would have relatively similar “Rival Team Share of Twitter Voice”.  However, we found that in the past four years, regardless of where the game is played, or who won the previous year, the percentage of conversation in Columbus regarding Michigan Football is at least twice the percentage of conversation in Ann Arbor regarding Ohio State football.  Thus, there seems to be a bit of an asymmetric rivalry here with respect to how much one of local fan bases spends its time talking about their rival.  It should be noted that 7% of the population of Columbus are Ohio State students (57,466 out of  809,798) while 37% of the population of Ann Arbor are Michigan students (43,426 out of 116,121).

The Auburn-Alabama rivalry seems to be more even with respect to the level of conversation regarding one’s rivals.  We found that the site of the game seems to change the direction of the ratio of the “Rival Team Share of Twitter Voice”.  If the game is in Tuscaloosa, then local Alabama fans spend more of their time talking about Auburn football than local Auburn fans spend discussing Alabama football.  If the game is in Auburn, then that trend is reversed.  Perhaps the Iron Bowl being played in their hometown adds some more desire to trash talk for the local fans.  It should be noted that 45% of the population of Auburn are Auburn students (25,469 out of 56,908) while 37% of the population of Tuscaloosa are Alabama students (34,852 out of 93,357).

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.

College Football Brand Equity Rankings: The Overall List

Over the last two weeks, we have been reporting our football fan base rankings conference by conference.  Today, we turn to our overall ranking.  We started the list with an analysis of the brand/customer equity of the major conferences.  The Big Ten and the SEC are the leading conferences largely because they have strong TV deals.  That being said, the number one team on our list is not a member of either the Big Ten or the SEC.

Number one on the list is the University of Texas.  The Longhorns have some built in advantages that make it such a powerhouse.  Texas is the flagship school in a highly populated state with an incredible football culture.  Texas is also interesting because it is such a frequent target in realignment discussions.   Texas would bring the most valuable fan base to any conference.   In fact, Texas football is such a valuable property that we doubt that they will move anytime soon.  Texas is a strong enough brand to keep the Big Twelve a viable conference.  This means that Texas has an immense amount of bargaining power within the Big Twelve; which would be lost in a move to the Big Ten or the SEC.

Number 2 on the list is a bit of a surprise.  Based on the numbers, we found Georgia to have the second highest customer equity.  We go into more detail about Georgia football in our SEC writeup.

Number three on the list is the Big Ten’s Ohio State Buckeyes.  Ohio State has many of the same advantages as Texas, as they are the flagship school in a highly populated and football crazy state.

Numbers 4 and 5 on the list also hail from the Big Ten.  We have Penn State in 4th place and Michigan in 5th.  These are two interesting cases, since PSU is obviously in a transitional stage, and may fade a bit over the next couple of years, while Michigan is making moves to become even more profitable.  In positions 6 through 8, we have Alabama, Auburn and Florida.  Our rankings seem to confirm that the SEC and Big Ten are college football’s top conferences.

The 9th place team is one that we haven’t talked about in any of our previous rankings, Notre Dame.  Our guess is that Notre Dame fans will feel slighted by their 9th place ranking.  But, at the end of the day, our approach is driven by a combination of revenue and team quality data.  What we find is that Notre Dame is a great college football brand, but far from the dominant brand their fans believe it to be.

In tenth place we have the lone West Coast team in the rankings.  The Washington Huskies were the surprise leader in the Pac 12, beating out teams like USC and Oregon.

Mike Lewis & Manish Tripathi, Emory University 2013.

PREVIOUS: RANKING THE SEC

Dynamic Pricing Part 2: Price Discrimination and Revenue Management

As we noted in our first dynamic pricing entry, the basic tools for implementing revenue management originated in the airline industry.  In today’s installment, the goal is to provide some insights into how revenue management works in the airline industry and to begin to consider how these tools apply (or don’t apply) to ticket pricing.

As a starting point, it is necessary to specify the revenue management goal.  In the airline industry the most basic goal is to maximize the revenue collected from a given flight (the network structure of airlines can often motivate more complicated goals).  In the case of sports, or performing arts, the corresponding goal is to maximize the revenue produced by a given event.  More generally, we can think of the goal of revenue management is to maximize the value of a firm’s inventory.  Often a distinction is made that revenue management is especially useful when inventory is perishable.  The key point is that if an airliner takes off with an open seat or a stadium has empty seats at game time, that the firm has forever lost that unit of inventory.

The two key ingredients needed to implement revenue management are a system for segmenting customers and forecasts of segment level demand.  In the airline industry, a very basic system of segmentation might be to group customers into a business and leisure segments.  These segments are thought to differ in terms of traits such as price sensitivity and flexibility.  Specifically, business travelers are thought to be willing to pay higher prices and to have more restrictive schedules.  The second necessary input is segment level demand forecasts.  There are many ways of forecasting demand ranging from complex statistical models to simple heuristics but the salient point is that the revenue manager needs to be able to accurately forecast how many customers from each segment will want to travel on a given flight or attend a specific game.

A core concept for implementing revenue management is the idea of “expected marginal revenue.”  For example, let us assume that we are trying to manage the revenue produced by a flight with 100 seats. Also we have a business segment willing to pay a high price that tends to book close to the departure time, and a price sensitive leisure segment that books long in advance.  The revenue manager’s job is to decide how many low priced seats to sell to the leisure segment or, differently stated, to decide how many seats should be reserved for the late arriving business segment.  One way to do this is to reserve just enough seats such that the expected marginal revenue from saving a seat for a business customer is equal to the expected marginal revenue from allowing the seat to be purchased by a leisure customer.

Let us further assume that the business customer pays $1,000 per ticket while the leisure traveler pays $100.  The revenue manager’s decision rule would be to reserve sufficient tickets for the business segment such that the expected marginal revenue produced by the last seat allocated to the business customers is the same as if the seat were sold to the leisure customers.  Returning to our example, if we can sell unlimited $100 seats the revenue manager would reserve enough seats so that there is at least a 10% chance to sell a $1,000 business ticket.

In many ways we could make the preceding discussion sports specific simply by changing “flight” to “game” and by using a segmentation system that is more sports specific (casual versus hardcore fans?).  But as you might expect, when we get to actual implementation it often becomes difficult to directly transfer travel industry yield management techniques to a sports context.  In our next entry we will discuss several of these challenges.

Mike Lewis and Manish Tripathi, Emory University 2013.

Ranking the “Best” Football Fans in the Big 10: Buckeyes are on Top!

We are presenting a series ranking the “best” fan bases in college football.  The study uses data from the past ten years and the rankings are based on Revenue Premium Brand Equity.  For more information on the analysis/methodology, please click here.

As a conference, the Big 10 finished second only to the SEC in overall football brand equity.  The conference added Nebraska in 2011, and will add Maryland and Rutgers in 2014.  The Big Ten has been very successful at creating a network that capitalizes on the appeal of its members.  This fan appeal is also manifested in the top three schools in our rankings; all three schools have football stadiums with capacities over 100,000, and are regularly sold out.

The Ohio State University finished in first place in our ranking of Big 10 fan bases.  In the ten year period of our study, the Buckeyes averaged 2.5 more wins per season than Penn State and Michigan, but also generated 20% more revenue.  Remarkably, Ohio State made this revenue with fewer fans in attendance, on average, than Penn State or Michigan.

Penn State very narrowly edged out Michigan for second place in our study.  Over the course of the study, Penn State and Michigan averaged almost the same number of wins (Michigan had more) and football revenue per year.  However, Penn State’s second place ranking may be short-lived.  The last couple of years have seen a decline in attendance.  This may, of course, in part be due to the recent scandal and sanctions at Penn State.

Indiana and Northwestern are at the bottom of the Big 10 fan base rankings.  Indiana seems to suffer from the same issue faced by Kansas or Duke.  That is, how do you build football brand equity in a “basketball school”?  Northwestern is an interesting case.  A comparison with in-state “rival” Illinois (ranked 8th) is quite revealing.  In the period our study, Northwestern averaged 1-2 more wins per season than Illinois.  However, Illinois average 88% of capacity attendance, while Northwestern averaged 62%.  Illinois also produced 30% more football revenue than the Wildcats.

Michael Lewis & Manish Tripathi, Emory University 2013.

PREVIOUS: RANKING THE BIG 12

NEXT: RANKING THE PAC 12

 

The Best Fan Bases in the Big 10 & Some Details on Our Methodology


Our post on the Best Fan Bases in college basketball generated several interesting comments and questions.  One common request was to see how other schools stacked up.  There were also a number of questions related to the methodology.

Today we start with the complete results for the Big Ten Conference (Our next post will examine the PAC-12).  Indiana comes out on top followed by Minnesota, Ohio State and Wisconsin.  At the bottom of the list we have Penn State and Michigan.  Nebraska is not included in these ratings due to lack of data.

The difference between Indiana and national runner up Michigan highlights the way our method works. For most of the last decade, Michigan and Indiana both struggled on the court.  Consequently, Michigan fans stayed away, while Indiana continued its streak of ranking in the top 15 in the nation in terms of attendance.  We should also add for those that want to claim some sort of bias, Professor Mike Lewis is a diehard Illini fan, and it pains him to have Indiana rank number 1.

It may also be useful to provide a bit more of the methodology used to generate the rankings.  We start with information on men’s basketball revenues reported by the Department of Education.  As an aside, we should also point out that the analyses reported on the website all rely on publically available data. While this data may not be perfect (like just about any other data set), we do not have any reason to believe that the data is systematically biased.

We then build a regression model that predicts these revenues as a function of data that corresponds to team quality and market potential.  The following equation is a portion of the model used (we are trying to keep the stats to a minimum as we expect that 95% of readers just want to see the results):

The actual statistical model included a number of other factors such as dummy variables for each conference and several nonlinear measures of team quality such as a quadratic term for winning percentage.

We use this model to make a prediction of revenue for each school (i) in each year (t).  We call this prediction Revhat(i,t).  We next compute the residual for each observation in the data (Rev(i,t)-Revhat(i,t)).  This residual represents the difference in actual revenues versus the revenues expected based on market potential and on-court performance.  The fan equity rankings are based on the sum of the residuals for the last 5 years (the model is estimated using ten years of data).

A couple of points are worth noting.  First, we do not use a school fixed effect because we are interested in how this residual changes.  Using the last five years is a compromise between eliminating noise that occurs in a single year and also capturing the enduring but evolving fan equity.

A second issue that merits discussion is the role of conferences.  In our model we estimate a conference effect.  The reason we do this is to eliminate the benefits that a weak school can collect simply by being in the right conference.  For example, if we do not control for conference revenues schools like Northwestern actually do very well in the rankings because their revenues are extreme given their (lack of) on-court success.

The issue of conferences is a tough one and one that is beyond the type of analyses we do for the website.  The issue is that it is difficult to disentangle the conference effects from the school effects.  The outcome of this problem is that a school like Indiana ends up suffering in the overall ratings because some of the Big Ten “equity” should really be allocated to the Hoosiers.

The table below shows the rankings of conferences.  As expected the Big Ten leads the way followed by the ACC.  The key caveat for this chart is that the Big Ten network is what pushes the Big Ten ahead of the ACC.

Look out for our next post that will examine the PAC-12

Winners, Losers, and Question Marks from the Men’s NCAA Basketball Tournament

Later tonight, the NCAA championship game between Michigan and Louisville will tipoff in the Georgia Dome.  Even though a champion has yet to be crowned, we can begin to make some judgments regarding the marketing winners, losers, and questions marks of this year’s tournament.  Before we provide our thoughts, please note that tournament success will usually result in greater publicity, fan loyalty, and all the spoils that come with brand equity (think Apple or BMW).

The two teams in the championship game are both interesting stories.  Louisville is a marketing monster, and enjoys the greatest “revenue premium” relative to on-court performance, but Michigan is another story.  Michigan performs poorly on our brand equity metric because history shows that Michigan needs to win consistently to keep the arena packed.  Anecdotally, we had to explain this poor brand equity finding to a distinguished University of Michigan business school professor, who pointed at this year’s excitement as an indicator of fan loyalty.  Given that this was a UM professor, we had to explain using small words, that true fan loyalty means that the fans even show up in down years.

For the two teams in the championship, Louisville is a clear brand equity winner, as they will continue to lengthen their lead on the competition; but the jury is still out on Michigan.  The only potential losers in the Louisville family are the Louisville fans that could be asked to pay higher prices.  As a frustrated University of Illinois fan, Professor Lewis would view this as a very small sacrifice for basketball success.

Michigan faces the challenge of all football schools: the year consists of the football season, spring football, and the remainder is perhaps a tossup between basketball season and football recruiting.  For Michigan to create true basketball brand equity, the school needs to sustain success.  While Coach Beilein’s history suggests this is likely, Michigan could lose multiple underclassmen to the NBA draft.

Two schools that didn’t make the NCAA Tournament also offer an education comparison.  Tubby Smith failed to make the tournament, and was replaced by Rick Patino’s son, Richard.  Given Minnesota’s high level of brand equity (2nd in the Big Ten), this was likely a decision to protect the brand by trying to bring in a dynamic young recruiter.  The most notable team to fail to make the tournament was last year’s winner, the Kentucky Wildcats.  The Emory Sports Marketing Science Initiative makes no pronouncements about Kentucky.  I think we can all agree that Kentucky and Coach Calipari have developed a new and unique business model.

March Madness is known for its Cinderellas.  This year’s top two Cinderella stories were the Wichita State Shockers and the Florida Gulf Coast Eagles.  Our assessment is that Wichita State was the BIG winner.  Not only did the Shockers reach the magical level of the Final Four, but also, at least as of now, Coach Marshall is sticking around.  For a mid-major to build equity, the school needs to sustain success beyond that achieved by an individual coach.  This last point brings us to FGCU.  By his hitting the exit for USC with amazing haste, it is likely that any fan excitement created by reaching the Sweet 16 has left the state of Florida with Andy Enfield.