The Use of Season Ticket Incentives in Major League Baseball

Journal of Applied Sport Management Journal of Applied Sport Management

Volume 12 Issue 1 Article 5

3-1-2020

The Use of Season Ticket Incentives in Major League Baseball The Use of Season Ticket Incentives in Major League Baseball

Kaitlin Poe

Missouri State University

John Drea

Illinois College

, [email protected]

Follow this and additional works at: https://trace.tennessee.edu/jasm

Part of the Education Commons, Social and Behavioral Sciences Commons, and the Sports

Management Commons

Recommended Citation Recommended Citation

Poe, Kaitlin and Drea, John (2020) "The Use of Season Ticket Incentives in Major League Baseball,"

Journal of Applied Sport Management

: Vol. 12 : Iss. 1.

https://doi.org/10.7290/jasm120105

Available at: https://trace.tennessee.edu/jasm/vol12/iss1/5

This article is brought to you freely and openly by Volunteer, Open-access, Library-hosted Journals (VOL Journals),

published in partnership with The University of Tennessee (UT) University Libraries. This article has been accepted

for inclusion in Journal of Applied Sport Management by an authorized editor. For more information, please visit

https://trace.tennessee.edu/jasm.

e Use of Season Ticket Incentives in

Major League Baseball

Kaitlin Poe

John Drea

Abstract

A study of Major League Baseball season ticket promotional incentives found that

the most popular types of incentives provided to season seat holders (SSHs) were

exclusive oers, complementary items, discounts, ticket options and services, and

payment plans. Oering a payment plan to SSHs was positively associated with

higher average game attendance. Payment plans were more commonly associated

with teams with higher winning percentages over the past three seasons and with

teams that lled a higher percentage of their stadium capacity. Teams that ll more

of their stadium capacity were also found to oer fewer categories of season ticket

incentives to SSHs.

Keywords: Season tickets, promotional incentives, Major League Baseball, MLB,

sports promotion

Kaitlin Poe is a graduate student in the Department of Kinesiology at Missouri State University.

John Drea is a professor of Business at Illinois College.

Please send correspondence to John Drea, [email protected]

Journal of Applied Sport Management Vol. 12, No. 1

https://doi.org/10.7290/jasm120105 https://trace.tennessee.edu/jasm/vol12/iss1/5/

Poe and Drea

Introduction

One of the major sources of revenue for Major League Baseball (MLB) teams

is ticket revenue. Ticket revenue can be subdivided into four categories: season

ticket revenue (full to partial packages,) group ticket revenue (blocks of tickets for

an individual game that are sold to an organization), suite and club seating rev-

enue (exclusive seating where revenues are retained by the team), and individual

game ticket revenue. Previous research has identied that winning is a signicant

inuence on total attendance (Davis, 2009; Langhorst, 2014), though the eect

of winning is dierent for dierent types of tickets. Individual game tickets are

largely inuenced by team performance during the current season; however, since

season tickets are sold prior to the start of the season, they are inuenced by the

anticipation of team performance for the upcoming season (Drea et al., 2016).

For many teams, season ticket sales are the starting point for total ticket sales

for a given season. ere is an emphasis among most MLB teams to not only

maximize full-season ticket sales, but to also get season seat holders (SSHs) to

commit to renew or upgrade their season tickets as early in the o-season as pos-

sible. is enables a team to determine what its inventory of remaining tickets will

be available for group and individual game sales. ere is also an emphasis to fo-

cus on new SSHs acquisition and on retaining/upgrading SSHs from lower priced

packages to higher priced packages in future years. A study of season ticket sales

for the Pittsburgh Pirates (Mullin et al., 1993) reported that 80% of the increase in

season ticket sales came from 20% of existing Pirate SSH renewing and upgrad-

ing/increasing their purchases. It is not an overstatement to suggest that season

tickets are the backbone of MLB ticket sales operations.

e purpose of the current study is to examine the use of incentives for the

sale and renewal of MLB season tickets—how incentives are used, the categories

of incentives is use, and any evidence of their eectiveness.

Background

Season ticket sales sta typically have three primary goals

: to increase the

number of renewals, to increase the revenue stream from existing SSHs (upgraded

packages or increase the number of tickets), and to expand the SSH base. It is also

important to recognize that direct ticket revenue is only one part of the revenue

stream tied to ticket sales. Other sources of revenue that are derived from tick-

ets sold include concessions, parking, and auxiliary services (o-premises team

properties that generate revenue from fans). Since these derived revenues are a

signicant addition to ticket revenue and SSHs are high volume ticket consumers,

the lifetime value of SSHs is of importance to MLB ticket sales managers (Drea et

al., 2017).

Overall, four factors have been identied that inuence the decision to renew

season tickets: administration and tangible services, team performance, social

Based on private conversations with MLB and college season ticket sales directors.

e Use of Season Ticket Incentives in Major League Baseball

and related concerns, and love for sports (Chen et al., 2009). SSH satisfaction was

found to be most closely correlated with the variable management of the event

and facility (Chen et. al., 2009), and the two best predictors of renewal were the

variables administration and tangible services and team performance.

ere appears to be a dierence of opinion between fans and sports market-

ing directors regarding which promotions are most eective at inuencing atten-

dance. Dick and Turner (2007) examined how National Basketball Association

(NBA) marketing directors and NBA SSHs perceive the eectiveness of various

activities (including promotional incentives for encouraging NBA season ticket

renewals. Starting with a listing of twenty dierent promotional incentives that are

used by NBA teams, promotional giveaways at the door was ranked by SSHs as the

most eective promotional technique; however, this technique was ranked as #13

of 20 by the marketing directors. Partial season ticket packages were ranked #1 by

NBA marketing directors, but only ranked as #5 by SSHs. e idea of a disconnect

between fans and sports marketing directors over which promotions are most ef-

fective was also found by Lanzillo (2010) in a study of the eects of promotion on

minor league baseball (MiLB) attendance. While the research was not limited to

season tickets, minor league baseball fans indicated the most eective promotions

were hat/cap giveaways (4.15 out of 5), t-shirt giveaways (4.05), and ticket dis-

counts (3.93). MiLB team ocials indicated the most eective promotions were

reworks (4.64), in-game entertainment (3.62), and ticket discounts (3.57). Fans

seem to be more focused on tangible promotions, while marketing sta seem to be

focused on activities that add to the entertainment value.

One of the eective uses of promotional incentives is in response to a service

failure. Burton and Howard (2000) reported on eective strategies used by the

Portland Trailblazers of the NBA to recover SSHs aer a protracted work stop-

page. ese strategies included the use of gis as a “tangible atonement for the

service breakdown,” oering SSHs free attendance to a team scrimmage, and the

use of all sta, the head coach, to personally calling SSHs to urge renewals. In the

year following the work stoppage in which some had predicted a 20% decline in

attendance, the Trailblazers sold 97.4% of their ticket inventory, in comparison to

a league-wide average of 88%. Ticket exchange options have also been suggested

as a way of increasing satisfaction for SSHs (Sche, 1999), though this research

was conducted for performing arts SSHs.

Female fans are an understudied area in season ticket research, yet the re-

search that does exist suggests they may be accessible through a dierent pro-

motional mix. Women are more likely than men to view attendance at sporting

events as a means to spending more time with family (Davis et al., 2010). Other

researchers have noted signicant dierences in how men and women respond to

promotional incentives in sports. Hansen and Gauthier (1993) found that women

purchased a greater quantity of team merchandise in comparison to men. Other

research has found that in comparison to men, women are more likely to remain

team loyal under adverse conditions (Fink et al., 2002). Given that women cur-

Poe and Drea

rently constitute only 30% of all “fans” of MLB (Sorilbran, 2019), developing a

specic promotional mix towards this target audience is worth further study.

ere has been little research into the eectiveness of the use of incentives to

encourage MLB season ticket sales. One of the challenges to doing research on

MLB season ticket sales and attendance is that MLB teams do not publicly share

specic season ticket sales data, and the percentage of total ticket sales that are

season tickets is believed to vary widely depending on the franchise. is makes it

impossible to conduct a direct examination of the eects of the MLB-wide eects

of specic season ticket incentives on dierent season ticket packages. Individual

teams can conduct this research by using their own attendance and incentive data,

combined with other independent variables that potentially inuence attendance,

such as winning percentage, day of the week, and weather (Drea, 1991); however,

the lack of publicly available season ticket data from all teams makes it impossible

for sports market researchers to draw MLB league-wide conclusions on the eects

of season ticket incentives.

A second consideration is that many season tickets are purchased by ticket

brokers and then resold to either institutional buyers such as hotels or resold in the

secondary ticket market (i.e., StubHub). is means that these individuals who are

sitting in season tickets seats are not the same individuals each game. e primary

motive of a ticket broker in purchasing season tickets is prot, while the primary

motive of an individual buying season tickets is usually related to the direct use of

the seats and the entertainment value of the seats. As a result, the incentives pro-

vided by the team to the season ticket buyer do not extend to the individual who

purchased the ticket on the secondary market. Since ticket brokers are motivated

by prot, incentives that cannot be converted into revenue (throwing out a rst

pitch, MLB-TV app, etc.) are less likely to be eective.

Methodology

A review of the websites of all 30 MLB teams was conducted between January-

March 2019. Data collected from each web site included the incentives provided

by each team to season seat holders for three categories of season ticket packages:

full season (81 games), half season (40-41 games), and 20-game packages. For

the 20-game packages, no distinctions were made among the variety of 20-game

packages available (e.g., 20 games chosen by the SSH, 20 games vs. division rivals,

20 games chosen by the team). Once SSH incentives were collected, six catego-

ries were created in advance: discounts, complimentary items, exclusive items for

SSHs, ticket services and options, a SSH club/lounge, and miscellaneous. Incen-

tives were placed into one of these six categories which are in Table 1.

e Use of Season Ticket Incentives in Major League Baseball

Table 1

Categories and Examples of MLB Season Ticket Incentives

8



Table 1

Categories and Examples of MLB Season Ticket Incentives

Incentive

Category

Examples

# of MLB teams with 1+ of

these incentives

Discounts

% off team merchandise

% off game concessions

Single-game ticket discounts

Group tickets at a special rate

Party suite discounts

27 teams

Complementary

Items

Payment plans

Scoreboard messages

Spring training tickets

On-field batting practice views

SSH gifts

28 teams

Exclusives Items

for SSHs

Ticket priority for postseason games

Run the bases

Pregame, on-field recognition

Private SSH events

Player autograph sessions

29 teams

Ticket Options

Unused ticket exchange

Same seats for all games

Online ticket management tools

Ability to resell tickets

SSH-only theme night presales

25 teams

SSH

Lounges/Clubs

Club membership for SSH

10% off food and beverages in

lounge/club

9 teams

Miscellaneous

Special Event/Concert Presale

“Hit for your seat”

SSH ID card

Collectible pin

7 teams

Additional 2018 data was collected for each team on average game attendance, fan cost

index (average cost for a family of four to see a game), win/loss percentage, market size, stadium

age, and average stadium capacity filled. Market size was assessed in three ways: Metropolitan

Statistical Area (MSA) population, Nielsen TV market size, and a subjective measure of market

size (Bleacher Report) based on team operating behavior. In addition, we examined some

specific incentives provided to SSH by some MLB teams: a SSH gift/promotion package; a SSH

event/appreciation; a media guide, yearbook, and/or newsletter; a parking incentive (a free pass,

Results

A series of independent sample t-tests were conducted to examine the variable

average game attendance when each SSH incentive was provided, in comparison

to when each incentive was not provided. Independent t-test results are provided

in Table 3. Only one incentive, payment plans for SSHs, was found to be statisti-

cally signicant (t = 3.127, p = .01) and in the expected direction (providing the

incentive is associated with increased attendance. Average game attendance for

the twenty-four MLB teams that oer SSHs a payment plan was 30,773, compared

to 21,042 for the six MLB teams that do not provide this incentive.

e Use of Season Ticket Incentives in Major League Baseball

Table 3

Independent Sample T-Tests, Comparing Attendance When Dierent Types

of SSH Incentives Are Made Available

10



expected direction (providing the incentive is associated with increased attendance._ Average

game attendance for the twenty-four MLB teams that offer SSHs a payment plan was 30,773,

compared to 21,042 for the six MLB teams that do not provide this incentive.

Table 3

Independent Sample T-Tests, Comparing Attendance When Different Types of SSH Incentives

Are Made Available

Variable

Attendance Mean

if Offered

Attendance Mean

if NOT Offered

T-value

(sig.)

Payment Plans

30,773.67 (n=24)

21,042.17 (n=6)

3.127 (.010)

Discounts

28.467.67 (n=27)

32.064.67 (n=3)

0.577 (.615)

Complimentary

27,820.93 (n=28)

42,917.50 (n=2)

3.417 (.132)

Exclusives

28,731.07 (n=29)

31,620 (n=1)

0.310 (.759)

Ticket Options

29,298.12 (n=25)

26,743.60 (n=5)

0.552 (.604)

SSH Lounge/Club

26,512.78 (n=9)

29,819.33 (n=21)

0.949 (.357)

Miscellaneous

25,597.37 (n=8)

30,001.91 (n=22)

1.341 (.199)

SSH Appreciations

26,724.00 (n=7)

29, 467.52 (n=23)

0.755 (.466)

Trips/Extra Incentives

30,118.00 (n=2)

28,735.18 (n=28)

0.235 (.848)

Misc. Gift/Promo Pack

30,490.25 (n=12)

27,718.78 (n=18)

0.806 (.429)

Events/Appreciations

25,734.50 (n=6)

29,600.58 (n=24)

1.336 (.202)

Media Guide, Yearbook, or Newsletter

32,539.50 (n=6)

27,899.33 (n=24)

1.078 (.315)

Parking Benefits (Pass, Discount, or

Reserved)

25,652.72 (n=18)

33,589.33 (n=12)

2.548 (.018)

MLB-TV App

25,109.63 (n=8)

30,179.27 (n=22)

1.597 (.129)

MLB teams that provide payment plans are significantly more likely to be teams that

have filled a higher percentage of stadium capacity. Teams that offer a payment plan for SSHs

filled 70.96% of stadium capacity, compared to 51.27% for teams that do not offer a payment

plan (t = -2.541, p = .017). Additionally, MLB teams that consistently win are also more likely

to offer a payment plan to SSHs, in comparison to teams that do not offer a payment plan,

Teams with a payment plan had a .513 winning percentage, compared to .455 for teams that do

not (t = -2.331, p = .027). On the surface, this would initially suggest that MLB teams that win

more and have higher attendance would likely have higher ticket prices, and a higher ticket

prices would trigger the need for a payment plan for SSHs. While the fan cost index (FCI)

(Statista 2019) for teams with a payment plan was found to be higher than the FCI for teams

MLB teams that provide payment plans are signicantly more likely to be

teams that have lled a higher percentage of stadium capacity. Teams that oer a

payment plan for SSHs lled 70.96% of stadium capacity, compared to 51.27% for

teams that do not oer a payment plan (t = -2.541, p = .017). Additionally, MLB

teams that consistently win are also more likely to oer a payment plan to SSHs,

in comparison to teams that do not oer a payment plan, Teams with a payment

plan had a .513 winning percentage, compared to .455 for teams that do not (t =

-2.331, p = .027). On the surface, this would initially suggest that MLB teams that

win more and have higher attendance would likely have higher ticket prices, and

a higher ticket prices would trigger the need for a payment plan for SSHs. While

the fan cost index (FCI) (Statista, 2019) for teams with a payment plan was found

to be higher than the FCI for teams without a payment plan ($237 FCI vs. $208

FCI), but the dierence was not statistically signicant (t = -1.325, p = .196). It

is noteworthy that the FCI is likely to change from year to year for all teams in a

relatively similar pattern, making the gap ($29) between teams with and without a

payment plan relatively constant from year to year.

One other variable, parking benets, was also found to be signicant; how-

ever, the means for MLB teams that provide parking benets were lower than

those who do not provide this benet. ere were several other variables where

the mean attendance was actually lower when incentives were provided, includ-

ing SSH events/appreciations, discounts, complimentary items, etc. e likely ex-

planation for these ndings is that teams that already have lower attendance are

seeking to increase season ticket sales by oering a greater number of incentives to

potential SSHs. e top four teams for percentage of stadium capacity lled (Bos-

ton, Chicago Cubs, San Francisco, and St. Louis all lled >92% of their stadium

Poe and Drea

capacities) only oer an average of incentives in 3.75 categories. By comparison,

the remaining 26 MLB teams oer incentives in 4.27 categories.

One of the surprising ndings is that most MLB teams do not signicantly in-

crease the number of incentives in a season ticket package as the number of games

in the season ticket package increases. For example, Table 4 compares the incen-

tives oered by the Los Angeles Dodgers, Detroit Tigers, and Cincinnati Reds for

full, half, and twenty game season ticket packages. e value of the incentives

provided remains relatively constant, even though the value of the season package

for a given seat location substantially increases.

Table 4

Quantities of Incentives Oered to Season Seat Holders for 20, Half, and Full

Season Packages for ree MLB Teams: LA Dodgers, Detroit Tigers, and Cin-

cinnati Reds

12



Table 4

Quantities of Incentives Offered to Season Seat Holders for 20, Half, and Full Season

Packages for Three MLB Teams: LA Dodgers, Detroit Tigers, and Cincinnati Reds

Los Angeles

Dodgers

Detroit Tigers

Cincinnati Reds

20 game package

TOTAL

Discounts (2

incentives)

Exclusives (3)

Ticket options (2)

Payment plans (2)

9 SSH incentives

Discounts (7

incentives)

Complimentary (3)

Exclusives (6)

Ticket options (3)

Lounge/Club (1)

Appreciations (3)

Miscellaneous (1)

24 SSH incentives

Complimentary (3

incentives)

Exclusives (6)

Ticket options (1)

10 SSH incentives

Half season package

(40-41 games)

TOTAL

Discounts (1)

Exclusives (3)

Ticket options (2)

Payment plans (2)

9 SSH incentives

Discounts (7)

Complimentary (3)

Exclusive offers (6)

Ticketing options (3)

Lounge/Club (1)

Appreciations (3)

Miscellaneous (1)

24 SSH incentives

Complimentary (2)

Exclusives (8)

Ticket options (1)

11 SSH incentives

Full season package (81

games)

TOTAL

Discounts (2)

Exclusives (4)

Ticket options (1)

Payment plans (2)

9 SSH incentives

Discounts (7)

Complimentary (3)

Exclusive offers (6)

Ticketing options (3)

Lounge/Club (1)

Appreciations (4)

Miscellaneous (1)

Payment plan (1)

24 SSH incentives

Complimentary (2)

Exclusives (9)

Ticket options (1)

12 SSH incentives

This does not mean that the actual incentives provided are identical (full season SSHs

may receive different “exclusives” than 20-game SSHs), but it does indicate that the volume of

incentives changes little as the number of games on a season ticket package increases.

In order to identify which season ticket-related independent variables were related to

average game attendance, multiple regression (stepwise) was used, using a criterion of

probability of F to enter < .05. (Average game attendance was used instead of total game

attendance, since not all teams had 81 home games due to rain outs/postponements that were not

is does not mean that the actual incentives provided are identical (full sea-

son SSHs may receive dierent “exclusives” than 20-game SSHs), but it does in-

dicate that the volume of incentives changes little as the number of games on a

season ticket package increases.

e Use of Season Ticket Incentives in Major League Baseball

In order to identify which season ticket-related independent variables were

related to average game attendance, multiple regression (stepwise) was used, us-

ing a criterion of probability of F to enter < .05. (Average game attendance was

used instead of total game attendance, since not all teams had 81 home games due

to rain outs/postponements that were not made up if the game had no impact on

the standings.) e goal was not to create a predictive model of average game at-

tendance; rather, the purpose was to see if it was possible to isolate season ticket

sales variables other than team performance (winning percentage) that contribute

unique variation toward average game attendance.

It is important to recognize there is a signicant limitation to this analysis,

which uses regression to predict average game attendance, not season ticket sales.

Average game attendance has been used as a surrogate variable because the num-

ber of season tickets sold by each MLB team is not publicly disclosed.

Predicting

average game attendance would assume that season tickets as a percentage of total

tickets sold is relatively constant, and anecdotal information suggests this is not

likely to be a valid assumption. e present analysis serves as a starting point if

such data does become available in the future.

Table 5

Stepwise Regression Results: Model Summary

e authors have spoken to two MLB teams as well as a representative of MLB on multiple

occasions in an attempt to obtain this information, including oering to sign NDAs, but both teams

deferred to MLB and MLB declined to provide the data.

13



made up if the game had no impact on the standings.) The goal was not to create a predictive

model of average game attendance; rather, the purpose was to see if it was possible to isolate

season ticket sales variables other than team performance (winning percentage) that contribute

unique variation toward average game attendance.

It is important to recognize there is a significant limitation to this analysis, which uses

regression to predict average game attendance, not season ticket sales. Average game attendance

has been used as a surrogate variable because the number of season tickets sold by each MLB

team is not publicly disclosed

. Predicting average game attendance would assume that season

tickets as a percentage of total tickets sold is relatively constant, and anecdotal information

suggests this is not likely to be a valid assumption. The present analysis serves as a starting

point if such data does become available in the future.

Table 5

Stepwise Regression Results: Model Summary

Model Summary

Adjusted R

Standard Error of the Estimate

.742

.550

.496

6515.03

Dependent Variable: 2018 Average Game Attendance

Predictors: (Constant), 2016-18 Average Winning %, Bleacher Report Market Size Ranking, Club/Lounge

Table 6

Stepwise Regression Results: Coefficients

Unstandardized Coefficients

Standardized

Coefficients

Std. Error

Beta

Sig.

(Constant)

-9266.867

12341.526

-.751

.460

2016-18 Avg. Winning %

89474.075

22955.855

.567

3.898

.001

Bleacher Report, Mkt Size

-318.461

146.195

-.310

-2.178

.039

Club/Lounge

-5533.373

2682.830

-.284

-2.063

.050



- The authors have spoken to two MLB teams as well as a representative of MLB on multiple occasions in an attempt to obtain

this information, including offering to sign NDAs, but both teams deferred to MLB and MLB declined to provide the data.

Table 6

Stepwise Regression Results: Coecients

13



made up if the game had no impact on the standings.) The goal was not to create a predictive

model of average game attendance; rather, the purpose was to see if it was possible to isolate

season ticket sales variables other than team performance (winning percentage) that contribute

unique variation toward average game attendance.

It is important to recognize there is a significant limitation to this analysis, which uses

regression to predict average game attendance, not season ticket sales. Average game attendance

has been used as a surrogate variable because the number of season tickets sold by each MLB

team is not publicly disclosed

. Predicting average game attendance would assume that season

tickets as a percentage of total tickets sold is relatively constant, and anecdotal information

suggests this is not likely to be a valid assumption. The present analysis serves as a starting

point if such data does become available in the future.

Table 5

Stepwise Regression Results: Model Summary

Model Summary

Adjusted R

Standard Error of the Estimate

.742

.550

.496

6515.03

Dependent Variable: 2018 Average Game Attendance

Predictors: (Constant), 2016-18 Average Winning %, Bleacher Report Market Size Ranking, Club/Lounge

Table 6

Stepwise Regression Results: Coefficients

Unstandardized Coefficients

Standardized

Coefficients

Std. Error

Beta

Sig.

(Constant)

-9266.867

12341.526

-.751

.460

2016-18 Avg. Winning %

89474.075

22955.855

.567

3.898

.001

Bleacher Report, Mkt Size

-318.461

146.195

-.310

-2.178

.039

Club/Lounge

-5533.373

2682.830

-.284

-2.063

.050



- The authors have spoken to two MLB teams as well as a representative of MLB on multiple occasions in an attempt to obtain

this information, including offering to sign NDAs, but both teams deferred to MLB and MLB declined to provide the data.

As expected, 2016-18 average winning percentage was the most important

predictor of the dependent variable 2018 average game attendance (Beta = .567

in a three-predictor model), followed by the market size as assessed by Bleacher

Report (Beta = -.310) and the presence of a club/lounge for SSH (Beta = -.284).

Poe and Drea

As expected, larger market teams were signicantly associated with higher av-

erage game attendance (Kendall’s Tau-B = -.361, sig = .005). e presence of a

club/lounge only for SSHs was associated with lower average attendance (26,513

attendance when a SSH club/lounge is available, compared to 29,819 average at-

tendance when there was no SSH club/lounge available.)

Discussion/Recommendations

ere are ve broad categories of promotional incentives to season seat hold-

ers (SSHs) that are common for most MLB teams.

• Twenty-nine MLB teams oer exclusives available only to SSHs, such as post-

season ticket priority, access to private events, autograph sessions.

• Twenty-eight MLB teams oer complimentary items, ranging from tickets to

o-season activities, watching batting practice, etc.

• Twenty-seven MLB teams oer discounts on some team-controlled factor,

such as discounts on concessions, individual game tickets, merchandise, etc.

• Twenty-ve MLB teams oer ticket options and services for SSHs, such as

mobile access, personal ticket representative, ticket exchanges.

• Twenty-three MLB teams oer payment plans for SSHs.

Within each category, however, there is variation in what promotions teams

oer. As an example, the Detroit Tigers oer SSHs seven dierent incentives in

the “discounts” category:

• A discount on the price of each ticket (compared to individual game prices)

• A season parking discount

• A discount on individual game tickets

• A discount on team merchandise

• A discount on an appearance by “Paws” (Tiger’s mascot)

• A discount to buy group tickets at the season ticket price

• An enhanced party suite discount

By comparison, the Tigers’ division rival Chicago White Sox oer only four

discounts: a discount on suites, a parking discount, a discount on a party area,

and a discount on individual game tickets. Another division rival, the Kansas City

Royals, oers only one discount (individual game tickets). e amount of dier-

ences between teams in the types of incentives oered within categories suggests

a need for research at the league level to identify what works and what does not in

comparable MLB markets.

As previously noted, research into the eectiveness of promotional incentives

in MiLB and NBA has suggested dierences of opinions between what fans/SSHs

perceive as motivators for attendance, and what team marketing personnel per-

ceive as motivators for attendance (Dick & Turner, 2007; Lanzillo, 2010). It is also

13



made up if the game had no impact on the standings.) The goal was not to create a predictive

model of average game attendance; rather, the purpose was to see if it was possible to isolate

season ticket sales variables other than team performance (winning percentage) that contribute

unique variation toward average game attendance.

It is important to recognize there is a significant limitation to this analysis, which uses

regression to predict average game attendance, not season ticket sales. Average game attendance

has been used as a surrogate variable because the number of season tickets sold by each MLB

team is not publicly disclosed

. Predicting average game attendance would assume that season

tickets as a percentage of total tickets sold is relatively constant, and anecdotal information

suggests this is not likely to be a valid assumption. The present analysis serves as a starting

point if such data does become available in the future.

Table 5

Stepwise Regression Results: Model Summary

Model Summary

Adjusted R

Standard Error of the Estimate

.742

.550

.496

6515.03

Dependent Variable: 2018 Average Game Attendance

Predictors: (Constant), 2016-18 Average Winning %, Bleacher Report Market Size Ranking, Club/Lounge

Table 6

Stepwise Regression Results: Coefficients

Unstandardized Coefficients

Standardized

Coefficients

Std. Error

Beta

Sig.

(Constant)

-9266.867

12341.526

-.751

.460

2016-18 Avg. Winning %

89474.075

22955.855

.567

3.898

.001

Bleacher Report, Mkt Size

-318.461

146.195

-.310

-2.178

.039

Club/Lounge

-5533.373

2682.830

-.284

-2.063

.050



- The authors have spoken to two MLB teams as well as a representative of MLB on multiple occasions in an attempt to obtain

this information, including offering to sign NDAs, but both teams deferred to MLB and MLB declined to provide the data.

e Use of Season Ticket Incentives in Major League Baseball

worth noting that MLB has emphasized a need to increase diversity in its fan base

and employment (Castrovince, 2018). Given the dierence in beliefs by fans over

“what works” in promotion and the dierences in wants from diverse fans, MLB

teams should be encouraged to allow fans to tailor the promotional incentives to

their own needs. e Milwaukee Brewers already do this, allowing potential SSHs

to choose promotional incentives from dierent categories based on their desires.

A new full season Brewers SSH can choose one incentive from a “silver” category

and one from a “blue” category, while a new 20-game SSH can choose one incen-

tive from the “blue” category (silver and blue are team colors). ese incentives

are shown in Table 7.

Table 7

A Sample of the Menu of Promotional Incentives for Milwaukee Brewer SSHs

(New full season SSHs choose one silver and one blue incentive. 20 game SSHs

choose one blue incentive)

16



incentives from different categories based on their desires. A new full season Brewers SSH can

choose one incentive from a “silver” category and one from a “blue” category, while a new 20-

game SSH can choose one incentive from the “blue” category (silver and blue are team

colors). These incentives are shown in Table 7.

Table 7

A Sample of the Menu of Promotional Incentives for Milwaukee Brewer SSHs

(New full season SSHs choose one silver and one blue incentive. 20 game SSHs choose one blue incentive)



Silver Incentives (partial list)

Blue Incentives (partial list)

Two Diamond Box tickets for a 2019

game

A “fast pass” for kids running the bases

Taking batting practice at Miller Park

A SSH polo shirt

An autographed print of Bob Uecker or a

star Brewers player (Yelich, Braun, Cain)

An autographed print of former owner/MLB

commissioner Bud Selig or a star Brewers player

(Hader, Aguillar)

Watching pregame batting practice on the

field

A Sandlot/Cinco de Mayo theme night bobblehead

pack

A family play day at Miller Park

Johnson Controls Stadium Club passes

Sliding down Bernie Brewer’s slide

Breakfast with team mascots

Movie night on the field

Luncheon with GM David Stearns

SSH refillable cup

From “Season Tickets,” 2019 (https://www/mlb.com/brewers/tickets/season-tickets).

An additional element in the consideration of season ticket incentives is the ability of

social media to increase the effectiveness of season ticket incentives. The Pittsburgh Pirates

include the type of social media interactions between SSHs and the team as an input into their

predictive models of which SSHs are likely to renew or buy season tickets (Vijayan, 2011). Any

efforts to diversify the MLB SSH base is likely to include fans who are younger and have a

greater emphasis on female fans. Both of these groups tend to be significant consumers of social

media. Teams are advised to consider how social media can be used in conjunction with

incentives that target under-represented groups (women, younger fans, fans of color).

Previous research into dynamic pricing (Drea & Nahlik, 2016; Sweeting, 2012) has

indicated that the use of dynamic pricing typically results in an increase in profitability for teams

An additional element in the consideration of season ticket incentives is the

ability of social media to increase the eectiveness of season ticket incentives. e

Pittsburgh Pirates include the type of social media interactions between SSHs and

the team as an input into their predictive models of which SSHs are likely to renew

or buy season tickets (Vijayan, 2011). Any eorts to diversify the MLB SSH base is

likely to include fans who are younger and have a greater emphasis on female fans.

Both of these groups tend to be signicant consumers of social media. Teams are

advised to consider how social media can be used in conjunction with incentives

that target under-represented groups (women, younger fans, fans of color).

Previous research into dynamic pricing (Drea & Nahlik, 2016; Sweeting,

2012) has indicated that the use of dynamic pricing typically results in an in-

crease in protability for teams that use it. Dynamic pricing allows ticket sellers

to move closer to a market equilibrium price by increasing the price on a good

that is scarce as the event horizon (game date and time) approaches. Alternatively,

when there is an oversupply of tickets relative to demand, the expectation is that

Poe and Drea

dynamic pricing in the secondary market would fall. e issue is whether the same

concepts would apply to SSH when the event horizon is the date when all tickets

are no longer reserved for season ticket packages and therefore become available

for individual purchase. While season tickets prices would not rise or fall as this

event horizon approaches, some teams do provide incentives that encourage SSH

to renew early. Renewal aer the date in which individual game tickets go on sale

may result in a lower perceived seat value, since some premium seats may no lon-

ger be available for a full or half season.

References

Burton, R., & Howard, D. (2000). Recovery strategies for sports marketers. Mar-

keting Management, 9(1) 42–49.

Castrovince, A. (2018). MLB sees continued increase in diversity. MLB News.

https://www.mlb.com/news/major-league-baseball-increases-diversity-

c271509694

Chen, S., Salazar, W., & Fitzgerald, L. (2009). Factors aecting collegiate sports sea-

son ticket holders satisfaction and renewal intention. American Alliance for

Health, Physical Education, Recreation, and Dance National Convention.

https://aahperd.confex.com/aahperd/2009/nalprogram/paper_12043.htm

Davis, M. (2009). Analyzing the relationship between team success and MLB at-

tendance with GARCH eects. Journal of Sports Economics, 10(1), 44–58.

Davis, P., McDonald, H., & Karg, A. (2010). e role of gender in determining sea-

son-ticket holder satisfaction. ANZMAC 2010: Doing more with less: Proceed-

ings of the 2010 Australian and New Zealand Marketing Academy Confer-

ence, ANZMAC, Christchurch, New Zealand.

Dick, R., & Turner, B. (2007). Are fans and NBA marketing directors on the same

page? A comparison of the value of marketing techniques. Sport Marketing

Quarterly, 16 (October), 140–146.

Drea, J., & Nahlik, A. (2016). Dynamic pricing in Major League Baseball: Issues

and challenges. Atlantic Marketing Journal, 5(3) 59–70.

Drea, J., Marlow, L., & Mense, L. (2017). Applying customer lifetime value to ma-

jor league baseball season tickets. Journal of Applied Sport Management, 9(2),

37–49.

Drea, J., Mense, L., & Stewart, A. (2017). e role of WAR in predicting MLB team

attendance: An exploratory study. Proceedings of the Society for Marketing Ad-

vances, 408–417.

Fink, J., Trail, G., & Anderson, D. (2002). Environmental factors associated with

spectator attendance and sport consumption behavior: Gender and team dif-

ferences. Sport Marketing Quarterly, 11(1), 8–19.

Hansen, H., & Gauthier, R. (1993). Spectators’ views of LPGA golf events. Sport

Marketing Quarterly, 2(1), 17–25.

Langhorst, B. (2014). What do your fans want? Attendance correlations with per-

formance, ticket prices, and payroll factors. Baseball Reference Journal, 43

(Spring). http://sabr.org/research/spring-2014-baseball-research-journal

e Use of Season Ticket Incentives in Major League Baseball

Lanzillo, J. (2010). e attitudes of minor league baseball fans and team administra-

tors toward the use of promotions. PhD dissertation, Wilmington University.

Mullin, B. J., Hardy, S., & Sutton, W. A. (1993). Sport marketing. Human Kinetics

Publishing.

Sche, J. (1999). Factors inuencing subscription and single-ticket purchases at

performing arts organizations. International Journal of Arts Management,

1(2), 16–27.

Sorilbran. (2019). Marketing to sports fans: Viewership and demographics [Info-

graphic] e Shelf. https://www.theshelf.com/the-blog/sports-viewership

Statista. (2019). Fan cost index of Major League Baseball teams in 2018 (in US

dollars). Statista. https://www.statista.com/statistics/202611/fan-cost-index-

of-the-major-league-baseball/

Sweeting, A. (2012). Dynamic pricing behavior in perishable goods markets: Evi-

dence from secondary markets for Major League Baseball tickets. Journal of

Political Economy, 120(6), 1133–1172.

Vijayan, J. (2011). Pirates tap BI to boost attendance. Computerworld, October 10,

(18)2.