Literary Insights

Here is a summary of the book Sport Business Analytics: Using Data to Increase Revenue and Improve Operational Efficiency:

The book discusses how data analytics can be applied in various areas of the sport business industry to drive revenue growth and improve operational efficiency. It is edited by C. Keith Harrison and Scott Bukstein and contains contributions from sport business professionals and academics.

Some key topics covered:

How the Orlando Magic uses analytics to optimize ticket pricing, sales, and fan engagement
Secondary ticket market dynamics and how analytics is changing primary and secondary market interactions
Best practices in customer relationship management and fan engagement analytics
Data-driven approaches for season ticket holder retention and new customer acquisition
Digital marketing analytics, social media strategy, and communicating sponsorship value
Talent analytics to evaluate employee performance and data visualization techniques
Teaching sport business analytics in academic programs

The book provides a practical overview of analytics applications across sales, marketing, social media, human resources, and other areas. The goal is to demonstrate how a data-driven approach can enhance decision making and strategy in the sport business. Real-world examples and case studies are included from teams, leagues, and companies.

Overall, the book aims to highlight the growing role and impact of analytics in the sport industry for improving fan engagement and driving incremental revenue. It covers current practices and emerging trends in sport business analytics.

Here is a summary of the key points about the business analytics book and its potential contributions:

The book was published in 2016, which marked the 100th anniversary of the American Educational Research Association (AERA). The AERA conference theme that year (“Public Scholarship to Educate Diverse Societies”) reflected a celebration of using data and evidence to inform decisions.
A decade ago, data analytics was not seen as a viable career path in the sports industry. The book aims to bring analytics more into the mainstream of the sports business.
The goal of the book is to provide practical strategies for collecting data and converting it into meaningful insights that can drive competitive advantages for sports organizations.
Key focus areas include ticket sales, corporate partnerships, fan engagement, sponsorship valuation, customer relationship management, digital marketing, market research, and data visualization.
The book aims to help sports organizations utilize data-driven decision making to optimize revenue generation.
The editors hope the book will be timely, relevant, and enjoyable for students and industry leaders interested in learning about business analytics applications in the sports world.

Here are concise summaries of the contributors:

Michal Lorenc is a 14-year veteran of Google who heads up Google’s Ticketing & Live Events Group, overseeing partnerships in sports, music, and ticketing. He is a co-owner of a minor league soccer team and a graduate of Loyola University Chicago.

Suzanne Malia Lawrence is a professor at California State University, Fullerton. She studies topics like female football fans, stereotypes in athletics, and athletes’ concussion experiences. She has won multiple research awards.

Jay Riola oversees business strategy for the Orlando Magic. He started as an intern in 2006 and has an MBA from the University of Florida.

William Sutton is a professor at the University of South Florida and a consultant for the NBA, WNBA, NHL, and other major sports leagues. He focuses on strategic marketing and revenue enhancement.

Manish Tripathi is a professor at Emory University’s business school. His research focuses on analytical models of consumer behavior, social interactions, and technology adoption.

Here are the key points summarizing the interests:

Multichannel strategies - Developing and managing strategies across multiple channels (e.g. online, mobile, social media, in-person) to reach customers and meet business goals.
Market structure and entry - Analyzing the competitive landscape, dynamics, and opportunities to enter new markets or market segments. This can involve assessing factors like bargaining power, barriers to entry, substitute products, etc.
Structural models - Statistical and econometric models that estimate relationships between different variables and outcomes. These models can help assess pricing, competition, consumer choice, and other factors.
Bayesian statistics - A branch of statistics based on Bayes’ theorem that incorporates prior knowledge or beliefs along with observed data to estimate probabilities and model outcomes. Useful for prediction and forecasting.

In summary, the interests focus on applying analytical techniques like multichannel strategies, economic and statistical modeling, and Bayesian methods to understand market dynamics, competition, and customer behavior. The goal is to drive data-driven decision making and business strategy.

Sport teams have used analytics to assist with on-field strategy and player personnel decisions for over 50 years, but have only recently begun applying analytics more broadly to core business functions.
Sport business analytics strategies have evolved to emulate practices from other industries like airlines and hotels, such as variable and dynamic pricing models based on demand.
Key application areas for business analytics in sports include ticketing systems, customer relationship management, social media and digital marketing, corporate partnerships, and market research.
Teams use analytics to set ticket prices and inventory levels, balancing goals of maximizing attendance and revenue. Approaches include demand modeling and surveying fans on preferences.
“Dynamic pricing” adjusts ticket prices in real-time based on factors like demand, team performance, day of week, etc. Aims to increase season ticket sales while maximizing per-game yield.
CRM analytics identifies potential new customers and predicts likelihood of renewals to inform sales strategy. Goal is to understand customer lifetime value.
Social media and digital marketing analytics facilitates targeted campaigns and website optimization. Teams learn from digital innovators.
Partnership analytics quantifies ROI and value to help maximize revenue from corporate deals.
Ongoing market research analytics provides insights into fan preferences and experiences.

Here is a summary of key points about average ticket prices:

In 2015, around 45% of NBA, MLB, NHL, and MLS teams used dynamic pricing for single game tickets, which allows prices to fluctuate based on demand.
NFL teams were not allowed to dynamically price tickets until 2015, when around 25% of teams adopted the practice.
Secondary ticket marketplaces like StubHub and Ticketmaster also use data and testing to optimize their pricing models. For example, StubHub experimented with all-inclusive pricing but found it decreased revenues compared to a base price plus fees model.
Teams are using customer relationship management (CRM) data to get insight into fan behavior and preferences, in order to improve the fan experience and potentially increase ticket sales. For example, the Mets partnered with SAS in 2014 to analyze fan data to personalize experiences.
The average ticket price itself is not directly discussed in the passage, but the summary provides context around data-driven pricing strategies teams are using with the goal of optimizing ticket revenues overall.
Measuring return on investment (ROI) for corporate sponsorships is a challenge in the sports industry. About one-third to one-half of companies don’t have a good system to measure sponsorship ROI.
Sponsors want help from sports properties in measuring ROI and return on objectives (ROO), but most properties are not delivering this.
Key sponsorship objectives include improving brand awareness, driving sales, client entertainment, etc. Evolving metrics to measure ROI/ROO include sponsor recall, brand perception, media impressions, social media engagement, and lead generation.
Market research via focus groups, surveys, etc. provides insights to help sports teams improve revenue generation, branding, fan engagement and satisfaction. Research can inform new initiatives and evaluate current ticket sales, concessions, fan experience approaches.
Social media and digital marketing analytics help gauge the value of campaigns. Sports teams analyze impression-based metrics (followers, clicks) and attention-based metrics (quality of engagement). Platforms also drive ticket sales.
The summary covers the value of analytics for corporate partnerships, social media/digital marketing, and market research in the sports industry. The key is using data to make informed decisions and improve fan engagement.
Analytics can provide valuable insights to guide business strategy in sports, but organizations face challenges in successfully adopting and implementing analytics. Issues include lack of strategic alignment, communication gaps between analytics personnel and decision-makers, and failure to translate insights into action.
Analytics initiatives should be integrated with the organization’s overall strategy. Companies successful with analytics have a strategic analytics plan tied to corporate strategy.
Communication and collaboration between analytics teams and other executives/departments is vital. Bridging communication gaps and distrust of analytics is key. Engaging decision-makers in the analytics process facilitates buy-in.
Analytics insights must be translated into business decisions and actions to provide economic return. Organizations can struggle to use analytics insights effectively despite access to more data.
Examples of effective sports analytics uses include dynamic ticket pricing, targeted fan engagement via social media, enhanced concessions through purchase data analytics, and data-driven sponsorship evaluation and activation.

The chapter stresses strategy alignment, communication, and collaboration as critical for analytics adoption success in sports business. It provides an overview of how analytics can guide key business decisions when implemented effectively.

The ticketing marketplace has changed due to technological improvements and disruptive forces like secondary ticket marketplaces and mobile ticketing options. This has forced teams like the Orlando Magic to innovate and offer fans more flexibility and options.
The Magic conducted research on “casual fans” to understand what prevents them from attending more games. Key findings were that these fans are price-sensitive, look for ticket discounts, make additional in-arena purchases, and are less aware of the NBA schedule.
To address these issues, the Magic launched the Fast Break Pass in their mobile app in 2013. This pass bundles discounted upper-level tickets with a seat upgrade, allowing price-sensitive fans a way to attend games for less while still getting lower-level seats.
Research on loyal season ticket members found they wanted more exclusive experiences and benefits. So the Magic added mobile-only benefits through their app for these fans.
These innovations combining research insights, flexible ticketing options, and mobile technology have helped boost Magic business performance and enhance the fan experience.
The Orlando Magic launched a new mobile ticket product called the “Fall Fast Break Pass” in 2014-2015, sold exclusively through their app. It offered discounted admission to a package of home games for Florida residents.
The passes used mobile-only ticketing and randomized seat assignments to optimize ticket sales while driving attendance. Over 15,000 seats were assigned and it attracted many new customers.
In 2015-2016 they expanded the Fast Break Pass product line to include passes covering different parts of the season. Over 5,000 passes were sold that season.
The Magic also used their app to provide exclusive benefits to season ticket members, such as using credits from unused tickets (“Magic Money”) to upgrade seats or purchase concessions.
In 2015-2016 they upgraded their app using VenueNext’s platform, further integrating services like mobile ticketing, parking, ordering food, and enhanced loyalty features.
The app improvements aimed to increase ticket usage, customer satisfaction and loyalty, especially among season ticket members.

Here are a few key points summarizing the discussion on primary versus secondary ticket markets in sports:

Primary market refers to tickets sold directly by teams/leagues, while secondary market involves reselling of tickets, often via online brokers.
Secondary market has grown substantially in recent years due to search engine optimization and digital advertising, becoming a billion dollar industry. Many teams still rely on outdated sales models focused on phone calls.
Teams typically do not participate in secondary market but brokers resell tickets at higher prices, disrupting the primary market. Teams try to limit digital ticket availability but fans still use secondary market.
Season ticket holders commonly resell tickets via brokers. Secondary market sells at higher volume/faster rate than primary market.
Consumers prefer paying premium for fewer individual tickets versus discounted season tickets. Secondary market caters to this demand better than primary market.
Teams are slowly adapting to secondary market but still have an adversarial relationship in many cases. Secondary market’s pricing data can help teams optimize their primary pricing.
NFL and MLB teams have a large number of home games scheduled throughout their seasons, often with multiple games per week. This includes preseason, regular season, and potentially playoff games.
Teams sell season ticket packages to secure revenue, but this can lead to an oversupply of tickets when season ticket holders can’t attend every game.
Ticket resale platforms like StubHub provide a secondary market where ticket holders can resell unused tickets. Teams try to restrict this to boost season ticket sales.
There are legal debates around whether a ticket purchase is an actual product that can be resold or just a revocable license. Courts have upheld resale rights.
Teams sometimes withhold or restrict sales of the best seats to try to force sales through their own channels. But fans increasingly prefer buying online.
Ticket brokers have become more innovative with digital sales while teams lag behind. Teams rely on brokers to sell excess inventory but don’t strategically utilize the secondary market.
The Dodgers case shows how flooding the secondary market with too many tickets lowers prices and indicates lack of consumer demand. The secondary market reveals the real market value.
Executives are willing to sell tickets on the primary market at lower prices than what they could get on the secondary market. This contrasts with their use of dynamic pricing, which aims to maximize revenues by pricing tickets based on demand.
Secondary market data reveals actual consumer demand and broker interest in a ticket product. Lack of secondary market demand indicates issues with consumer interest, as seen with UFC, NASCAR, and MLS events.
Winnipeg Jets have high average resale prices (ARP) on the secondary market, but low inventory suggests their market may be untested. Brokers must weigh factors like arena capacity and market softness.
Brokers spread investments across teams and leagues to manage risk. They react to news and events that affect ticket demand and pricing.
The Mayweather-Pacquiao fight showed how limited ticket supply and news of the fight ignited demand. Short selling inventory that brokers didn’t yet have added further pressure.

Here are the key points from the TicketNetwork data on Tom Brady and ticket markets:

Tom Brady’s involvement in Deflategate prior to Super Bowl XLIX caused panic and volatility in the secondary ticket market for the game.
The NFL delayed ticket distribution until just before the Super Bowl, unlike previous years when tickets were distributed earlier. This limited supply caused ticket prices to skyrocket on the secondary market.
Some brokers had to cancel pre-sold orders at lower prices to take advantage of the price surge, leading to consumer complaints and lawsuits.
When Brady’s suspension was overturned right before the 2015 season, it again caused volatility and price spikes in the secondary market for the Steelers-Patriots opener.
The data shows significant fluctuations in average resale price (ARP), median listing price (MLP), and get-in price (GIP) around both events involving Brady and ticket supply.
The analysis indicates Brady’s high-profile status contributed to secondary market instability when coupled with uncertainties around ticket availability. Short selling practices exacerbated the volatility.

Here are a few key points about customer relationship management (CRM) and database management in the context of sports business:

CRM refers to the infrastructure, tools, and strategies used to manage relationships with customers and prospects. The goal is to increase customer value and loyalty over time.
Database management is an important component of CRM. It involves collecting customer data, organizing it, and leveraging it to improve marketing, sales, and customer service efforts.
In sports business, CRM and database management help teams track interactions with fans, understand fan behaviors and preferences, segment fans based on value and traits, and deliver personalized communications and experiences.
Common CRM tools used in sports include software systems like Salesforce, Microsoft Dynamics, HubSpot, and proprietary systems built specifically for sports teams. These tools store customer data, interactions, transaction history, and more.
Key information sports teams track includes contact info, demographic data, purchase history, engagement data (e.g. email opens, website visits), preferences, feedback, and more. This data powers fan segmentation, targeted campaigns, and customer analytics.
CRM and database management are critical for sports teams to grow their fan bases, boost ticket sales, improve renewal rates, increase ancillary revenue, and deliver top-notch fan experiences. Effective CRM is essential for success in the modern sports industry.

Does this help summarize some of the key points about CRM and database management in sports business? Let me know if you need any clarification or have additional questions!

Here is a summary of the key points about customer relationship management (CRM) systems in sport business:

CRM helps businesses store, track, analyze, and manage customer information and interactions. It utilizes a database to organize customer data for retrieval and analysis.
Common CRM tools used by sport teams include Microsoft Dynamics, Salesforce.com, ticketing systems like Archtics, and email marketing platforms like Oracle Eloqua.
Important information to track in a CRM system includes:
Individuals - name, contact info, demographics like gender, birthday, marital status, etc.
Companies - sales interactions and deal progress over time
Interactions - phone calls, emails, meetings with individuals/companies
Purchase history - ticket purchases, products bought, season ticket holder info
Attendance data - ticket scans to see game attendance
Growing the database requires capturing new leads through email collection, surveys, website forms, etc. Integrations with ticketing and other systems help expand data.
CRM data enables segmentation, targeted communications, and better understanding of customers. This drives sales, retention, and overall business growth.
CRM is vital for growing the number of customers in a team’s database. Teams must constantly add potential new customers as some percentage of existing customers will not renew each year. Methods for growing the database include:
Data collection tables at stadiums to capture info of attendees
Community events and business tradeshows to identify prospects
Purchased lists of company contacts
Webpages where fans can provide contact info
Sales reps identifying new leads
The goal of CRM is to treat each customer as an individual, not the masses. Info like birthdays and anniversaries allows personal outreach.
Targeted campaigns utilize CRM data to identify subsets of leads more likely to purchase:
Transactional - Prior buyers, high spenders
Behavioral - Email open/click rates, web visits, social media engagement
Demographic - Income, propensity to attend, family status
Geographic - Proximity to venue

The key is using CRM data to target specific individuals rather than mass marketing. This allows for personalized outreach and sales focused on those most likely to purchase.

Here is a summary of the key points regarding The Aspire Group’s ticket marketing, sales, and service philosophy:

Ticket sales are a critical revenue stream and health indicator for sport properties.
The Aspire Group specializes in ticket marketing, sales, and service (TiMSS). They provide outsourced services, strategic consulting, marketing/revenue services, and sports investment optimization.
Aspire creates an annual TiMSS plan for each partner outlining strategies and tactics to grow their fan base. This plan follows an 8-point philosophy:

Research
Retain
Grow
Acquire
Capture
Communicate
Close
Performance Analytics

The plan identifies goals, strategies, tactics, and a timeline for deployment.
Case study: Aspire implemented the first outsourced ticket sales and database marketing roles in collegiate athletics through their Fan Relationship Management Centers.
The TiMSS plan is designed as an ecosystem where each point interconnects and performs optimally together. Data and measurement are critical at the start and end.
The Ticket Marketing, Sales, and Service (TiMSS) plan is created by Aspire Group based on market research to guide ticket sales strategies and tactics.
The strategies focus on retaining avid fans, growing casual fans, and acquiring new fans, with a priority on increasing engagement of current fans over acquiring new ones.
The tactics involve capturing fan data, communicating with targeted campaigns, closing sales, and analyzing performance.
Aspire uses an 8-point philosophy encompassing the strategies and tactics.
Case study of Georgia Tech Athletics Fan Relationship Management Center demonstrates how Aspire conducts research through post-game and post-season surveys to gain insights into fan preferences and behaviors to improve customer satisfaction and ticket renewals. Key metrics analyzed include Net Promoter Score, value-weighted renewal rate, and top box satisfaction rates.

Here are the key points I gathered from the summary:

The TiMSS plan prioritizes customer retention as the number one goal through tracking renewal rates and providing unique experiences for loyal fans.
Growing current customers is the second priority, by increasing attendance frequency, ticket quantity purchased, and spend on upgrades. This is done through stepped ticketing products.
Acquiring new fans is focused on segments most similar to current purchasers. Acquisition strategies include group sales, promotions, ticket bundles, sampling programs, and referrals.
Capturing fan data provides insight to build relationships and align products to customer needs, through surveys, lead generation, and expanding knowledge on current fans.
A “crawl, walk, run, sprint” progression implements market research strategies starting with simple demographics and building up over time.
Tracking renewal rates, utilization of service/retention teams, and personalized technology are key for high customer retention.
Heat maps help concentrate marketing dollars and sales efforts efficiently based on geographic data.
RMC operates a variety of fan data capture techniques at the Georgia Tech FRMC, transitioning from basic methods like surveys to more advanced web tracking and CRM integrations.
The FRMC tracks the effectiveness of each data source for generating ticket sales and revenue.
Captured fan data allows for personalized, targeted communication via the fan’s preferred channel (usually email). Targeted emails see open rates over 260% higher and click-through rates 100% higher than mass emails.
Email campaigns are followed by phone calls to walk fans through offers. Call efficiency improves when following an email touchpoint.
During the sales close, all fan interactions are tracked in CRM and ticketing systems to tie back to original lead sources and measure effectiveness.
Performance analytics examines the ROI and ROO of each element of the TiMSS plan to evolve strategies and tactics season to season.
Individual sales consultant performance is also measured through revenue output, call efficiency, and demonstration of key leadership characteristics. This identifies top performers.

Here is a summary of the key points from the chapter excerpt:

Customer lifetime value (CLV) and brand equity are two important metrics for managing customer relationships and fan engagement in sports.
CLV quantifies the economic value of a customer relationship over time. It is useful for managing season ticket holders since their repeat business has high lifetime value.
Brand equity reflects the loyalty and passion fans feel for a team brand. It drives ongoing fan engagement and buying behavior.
Detailed customer data on retention, usage, and purchases allow sports teams to estimate CLV and use it to guide customer-level marketing.
CLV models need to incorporate factors like team performance that impact fan retention.
Brand equity is an intangible asset based on psychological preferences. It must be measured by observing market behaviors correlated with fan loyalty.
Implementing CLV and brand equity analytics requires investments in data collection and statistical modeling. However, these concepts can provide the foundation for more analytical, customer-focused marketing.
CLV and brand equity represent complementary metrics - CLV is focused on the economics of loyal customers, brand equity reflects the attitudinal loyalty driving those behaviors.

Here is a summary of the key points about the time value of money:

The time value of money is the concept that money available now is worth more than the same amount in the future due to its potential earning capacity.
This core principle of finance holds that provided money can earn interest, any amount of money is worth more the sooner it is received.
Time value of money concepts help inform decisions involving tradeoffs between money amounts received or paid at different times, such as calculating present and future values of single amounts and annuities.
Key time value of money formulas include present value, future value, and net present value. These formulas allow you to calculate the worth today or in the future of a single amount or a series of payments.
An understanding of time value of money concepts is crucial for financial decisions involving comparisons of cash flows occurring at different times, such as capital budgeting, loan repayments, and investment evaluations.
Proper application of time value of money principles provides a more accurate way to compare financial alternatives and make informed financial decisions.
The paper discusses customer lifetime value (CLV) and links it to team performance metrics like winning percentage. CLV increases as reselling rates and winning percentages increase.
Fan equity and social media equity are estimated for NFL teams using statistical models. Fan equity is based on home revenue premiums while social media equity uses Facebook/Twitter followers.
The Cowboys have the highest fan equity while the Steelers rank highest in social media equity. Social media equity provides a national measure unconstrained by pricing and capacity.
There is a connection between CLV and brand equity. Brand strength, impacted by team performance, influences long-term fan behaviors like retention rates. CRM and mobile technologies can strengthen the brand-customer relationship over time.

Here are the key points I gathered from the overview of the role of analytics in a sport business organization:

Analytical positions are responsible for a wide range of tasks including database management, data integration, email marketing, market research, digital/social analytics, advertising analytics, sales reporting, and season ticket renewal tracking.
The role requires multitasking across these different responsibilities related to collecting, analyzing, and acting on data.
The breadth of responsibilities highlights how critical analytics is across many functions within a sports organization.
Effective analytics involves both efficiency (optimizing data systems) and effectiveness (informing business decisions).
Keeping the analytics simple, aggregating data appropriately, and leveraging public data sources are important considerations.

Here are the key points from the passage:

Efficiency and effectiveness are primary principles for an analytics department. Efficiency is doing things right, while effectiveness is doing the right things.
Keeping analytics simple often leads to greater efficiency. A quick heuristic that explains 75-85% of a relationship can be better than a complex model that takes weeks to develop but only improves accuracy marginally.
Aggregating data can lead to better decisions about areas like media budget allocation, customer focus, and staff distribution.
Public data like census information and Google Trends can provide useful external context to make decisions about pricing and advertising spend.
The examples demonstrate how relatively simple analysis of internal and external data can yield meaningful business improvements through more informed decision making. The key is focusing analytics on the most high-impact issues.
The Tampa Bay Buccaneers analyzed their digital marketing spend and decided to shift more budget to the beginning of the NFL season. By tripling their digital spend in August and September, they generated a 22x return on investment.
Mapping ticket buyer locations allowed the Buccaneers to see where fans were concentrated and target billboards and other marketing efforts accordingly. They could also overlay sponsor locations to see proximity to fans.
Direct marketing is more targeted than mass marketing, aimed at specific segments with tailored offers and calls to action. It is trackable and results are measurable.
Direct marketing ads have three main components - the list, the offer, and the creative. The list and offer account for 80% of effectiveness, creative just 20%.
A/B split testing allows direct marketers to test different versions of ads and offers to optimize performance. By only changing one variable at a time and measuring conversions, marketers can determine through statistical testing which version performs better. This “test and learn” approach helps improve direct marketing efforts.

Here are the key points on fan engagement, social media, and digital marketing analytics at Duke University:

Case Study: #DukeMBBStats Data Visualization Platform
- Duke Men’s Basketball created a data visualization platform to engage fans and enhance game broadcasts.
- The platform analyzes and visualizes play-by-play data in real time.
- It increases fan engagement by making games more interactive and data-driven.
Fan Profiles
- Duke uses analytics to segment fans and personalize engagement.
- Fan types include diehard fans, social followers, corporate partners, etc.
- Targeting specific fan profiles allows for more relevant content and experiences.
Key Takeaways
- Analytics help identify and segment fan bases for better engagement.
- Data visualization and real-time stats enhance fan experience.
- A data-driven approach enables more personalized outreach and experiences.
- Analytics inform strategic decision making around resource allocation and ROI.

In summary, Duke University leverages data and analytics across fan engagement, social media, and digital marketing. A data-driven approach allows them to better understand fans, create more relevant experiences, quantify results, and optimize resource allocation for ROI. Advanced analytics transforms fan engagement and fuels business growth.

Duke University is using sports analytics for player development, marketing segmentation, and other purposes, following the trend of adopting analytics in college athletics.
One innovative way Duke is using analytics is through its fan-facing data visualization platform called #DukeMBBStats.
#DukeMBBStats digitizes historical Duke basketball statistics back to 1905 and allows fans to explore and share data visualizations.
The platform bridges academics and athletics on campus, provides unique content for fans, and represents a new type of sports analytics engagement between teams and fans.
#DukeMBBStats caters to modern fan behavior - being mobile-optimized, social media integrated, and focused on giving fans value beyond just selling to them.
This platform matches the passion of Duke’s famously devoted “Cameron Crazies” fanbase by providing a new way for them to engage with the program’s statistics and history.
The Chicago Cubs have a long World Series drought but still draw big crowds due to the history and aura of the team and Wrigley Field. Fans support the Cubs as an identity, not just to see a winner.
Duke basketball faces uncertainty when legendary coach Mike Krzyzewski retires. The program needs fans to appreciate Duke athletics as an identity beyond just wins and losses.
Fan engagement platforms like #DukeMBBStats help fans feel understood and involved. This can increase time spent on the website and provide opportunities for cross-marketing.
Analytics allow athletic departments to better understand fan behavior and preferences. This enables more targeted and effective marketing rather than mass blasts.
Creating detailed fan profiles based on purchase history and engagement allows programs to segment fans and cater communication and offers. This results in more relevant outreach.
Digital marketing is becoming increasingly important for sports teams and leagues to engage fans and drive revenue. Strategies like social media, email, SEO, and digital advertising can help reach and connect with fans.
For ticket sales, teams can use digital tactics like targeted emails, social media promotions, and retargeting ads to boost ticket purchases. Analytics allow teams to segment and target different fan groups.
Sponsorship revenue can be increased by creating digital assets and custom content to deliver value for partners. Digital also provides new inventory like branded social content, emails, live streams etc.
Merchandising sales can be driven by using social media and influencers to showcase products. Digital also facilitates impulse purchases through quick online checkout.
Digital allows teams to distribute exclusive content to engage fans. This includes behind-the-scenes video, player interviews, contests, etc.
Data and analytics should inform digital marketing strategy. Insights on fans’ interests and behaviors can optimize how teams reach, attract and retain loyal audiences.

In summary, digital marketing gives teams and leagues a way to build deeper connections with fans, gather data to inform business decisions, and open up new revenue opportunities. A thoughtful digital approach is becoming essential for success in the sports industry.

Here are the key points summarizing the passage:

Digital marketing has allowed sport teams to connect with fans and promote events in unprecedented ways. Tools like search, online video, and digital advertising give teams flexible and responsive strategies to build their fanbase and drive revenue.
Ticket sales now happen predominantly online, so teams need digital strategies to support this key revenue stream. Research shows most ticket buyers use search engines, team/league/venue websites, and social media when researching and purchasing tickets.
The Washington Wizards used Google AdWords campaigns focused on ROI metrics to optimize ticket sales during their successful 2013-2014 season. This strategy brought in many new customers and drove their highest online ticket revenue.
Video content is increasingly how fans consume sports online. Short clips and highlights are extremely popular across devices. The Orlando Magic capitalized on this by using targeted video ads to portray their games as an immersive, authentic experience for tourists.
Digital analytics allow sport teams to continuously improve marketing campaigns by evaluating performance metrics and adjusting spending accordingly. Data-driven strategies have proven successful for revenue goals like ticket sales.
The Orlando Magic used highly targeted video ads on YouTube to promote ticket sales to fans in Brazil. By showing ads in Portuguese that featured realistic game environments, they increased attendance from Brazil by 35% year-over-year.
Digital marketing provides new opportunities for sports teams to engage potential fans during the discovery and purchase process. Over a third of sports event discovery happens on social networks and venue sites, which are prime targets for digital marketing campaigns.
Display and video advertising allow teams to reach a wide audience, even with a limited budget. The Orlando Magic was able to successfully target international fans without a huge marketing spend.
Analytics are critical to understand user behavior and optimize digital marketing campaigns. The minor league soccer team AFC Ann Arbor used YouTube analytics to refine their video ads and boost engagement, leading to higher than expected game attendance.
Key digital marketing metrics include audience data, acquisition sources, user behavior, and conversions. Tools like Google Analytics allow teams to collect this data and turn it into actionable insights.

Here are a few key points on communicating the value of sports sponsorship:

Sponsorship is a partnership between a brand and a sports property to achieve mutual objectives through an exchange of rights and benefits.
Historically, properties valued sponsorship based on exposure and impressions. This is changing as sponsors demand ROI data.
Properties can value assets through inherent valuation (based on the asset’s attributes), relative valuation (benchmarking against similar assets), and comparable valuation (using sales data of comparable assets).
To communicate value, properties need to understand sponsors’ objectives, quantify how assets help achieve those objectives, and use metrics and data sponsors value. Metrics may include exposure, engagement, branding lift, sales lift, etc.
Properties should present valuation in a clear, concise format sponsors can understand. This includes highlighting the value in business terms like ROI, not just impressions.
Valuation should tell a story that resonates with sponsors using data, comparables, benchmarks, and case studies. The goal is building an insightful, compelling business case for sponsorship.
Effective communication requires understanding sponsors’ goals and mindset. Presenting valuable data that links to business outcomes is key to retaining and growing sponsorship revenue.
Sports sponsorship is a major revenue stream for many sports organizations, involving deals between sports properties (teams, leagues, etc.) and corporate partners.
Sponsorship inventory includes venue signage, media advertising, intellectual property rights, experiential marketing, and jersey sponsorships.
Historically, sports properties have valued sponsorships by providing “recaps” - PowerPoints showing activation images - rather than quantitative data.
The economic downturn and rise of digital analytics led sponsors to demand more transparency and data on their spend.
Sports properties can better value assets using inherent (impressions), relative (ratios), and comparable (market prices) valuation approaches.
Inherent valuation analyzes the number of impressions generated by inventory items. Digital analytics now allow more accurate impression counts.
The industry needs professionals who can connect sponsorship values and metrics to provide the transparency sponsors now expect. Strong analytics skills are critical for the future of sports sponsorship.
Measuring impressions is important, but not all impressions are equal in value. Sport organizations need to focus on reaching the right demographics that align with their corporate partners’ business objectives.
Digital and mobile sponsorships often have lower CPM rates, but can still deliver significant value by providing detailed engagement metrics and reaching younger demographics.
Comparable valuation based solely on price can be problematic. Sport organizations should focus on communicating the unique value they can provide to different partners based on demographics, marketing goals, etc.
Sport organizations should utilize a combination of inherent, relative, and comparable valuation models when pricing sponsorships. This provides a more holistic view of value.
Effective communication with sponsors is critical. This involves understanding the audience, establishing credibility, using persuasive institutional rhetoric, and effective presentation of information.
Market research and data analytics are crucial for driving innovation and growth in the sports industry.
Understanding target audiences through market research provides insight into customer preferences and motivations. This allows sports organizations to create tailored offerings.
Live analytics during sporting events allow adjustments in real-time based on fan engagement and behavior. For example, concessions and merchandising can be adapted.
The authors provide a case study on market research about women as sport spectators. Surveys uncovered motivations and constraints that can inform marketing efforts.
Live analytics at a college football game guided real-time changes to concessions based on sales data. Popular items were restocked, while slow-sellers were discounted.
Overall, market research and live analytics enable data-driven decision making to better serve fans and maximize revenues. Sports entities must leverage these tools to innovate and keep pace with fan expectations.

Here is a summary of the key points from the excerpt:

Women are an important demographic for sports marketers to understand and connect with, as recognized by pioneers like Bill Veeck decades ago through promotions like Ladies’ Day.
There is a growing body of academic literature examining women as sports fans and spectators, including studies on gender differences in attendance, motivation, fandom, and experience. However, more research is still needed on women’s everyday experiences as fans.
Qualitative research reveals women can feel stereotyped, empowered, or viewed as “outsiders” at sporting events dominated by men. Scholars recommend viewing women’s sports spectatorship as a process.
Sports marketers have often failed to connect with women by just creating “pink” merchandise or marketing the same products without adapting them sufficiently to women’s needs.
Analytics and market research should aim to deeply understand motivations and experiences of various fan demographics like women. Quantitative and qualitative data can be gathered.
The book excerpt provides a rubric for “Live Analytics” - collecting intercept survey data from fans at a live sporting event when emotions are high. Steps include getting approval, assembling a survey team, logistical preparation, identifying a sample, surveying at optimal times, recording data, and analyzing results.

Here are a few key points from the summary:

Market research is important for sport organizations to understand their fans, particularly demographic groups like women. The data can reveal insights into preferences that can drive business decisions.
The NFL commissioned a study on women fans that found hygiene, healthier food options, accommodations for kids, and apparel choices were priorities.
Montana State University held its first football clinic for women fans called “Gridiron Girls.” It went beyond just teaching football basics and focused on engaging women through interactions and experiences with players.
Though no formal data was collected at the inaugural event, there are plans to conduct intercept surveys and use Live Analytics in the future to gather insights from participating women.
The clinic gave women an active voice beyond just “shrink it and pink it” marketing approaches. Innovative aspects included the focus on engagement and experience, as well as a sponsored gift of a keychain that empowered women football fans.
Overall, the case illustrates how understanding and engaging a demographic like women fans through research and experiences can lead to innovation and new business opportunities for sport organizations.

Here is a summary of the key points about talent analytics:

Talent analytics refers to the use of data and analytics to make better decisions about managing talent and human resources in an organization. It helps identify and develop top talent.
Talent analytics is being applied in the sports industry by organizations like the NBA and Aspire Group. The NBA uses data to identify high-performing salespeople and develop training programs. Aspire Group uses their “WHOPPPP” model to evaluate employee performance.
Other industries like retail, technology, and manufacturing also use talent analytics successfully. Key applications include identifying characteristics of top performers to guide hiring, optimizing incentive programs, predicting retention risk, and providing personalized coaching.
Potential benefits of talent analytics include improved hiring, more effective training, higher employee engagement, and better retention. Challenges include data availability, proper statistical analysis, and organizational adoption.
Overall, talent analytics allows organizations to leverage data to make more informed talent management decisions, develop people more effectively, and improve workforce quality and productivity. Sports teams/leagues and other businesses can benefit from this trend.

Here is a summary of key points about using talent analytics in the employee hiring and performance evaluation process:

Talent analytics refers to using data and analytics to assess and optimize the workforce, including recruiting, performance evaluation, and workforce planning.
It allows companies to make data-driven decisions about their human capital rather than relying on gut instinct.
Basic talent analytics involves metrics like headcount, turnover, and recruiting stats. More advanced analytics assess things like employee engagement and satisfaction, supervisor effectiveness, and predicting workforce needs.
Sport organizations are starting to use talent analytics more. For example, the NBA implemented a “Sales DNA” initiative to evaluate sales executives based on empathy, ego drive, and ego strength.
The Aspire Group uses an acronym “WHOPPPP” to evaluate candidates on characteristics like work ethic, humility, ownership, etc.
Some teams use personality tests like the Caliper profile to identify the best job candidates based on traits that correlate with performance.
The benefits of talent analytics include hiring and retaining the best employees, identifying top performers, predicting workforce needs, and making human capital decisions based on data rather than guesswork.
Overall, talent analytics helps sport organizations optimize their workforce productivity, satisfaction, and engagement. As analytics becomes more pervasive, expect to see its applications in managing people expand.
Talent analytics involves using data and analytics to improve talent management decisions related to hiring, retention, promotion, team composition, and more.
Successful sport organizations like the Cleveland Indians, San Francisco Giants, and Aspire Group use talent analytics models to better understand what drives sales performance. Metrics like work ethic, integrity, passion, and results are factored in.
Other industries also use talent analytics successfully. Google has a “people analytics team” that uses data to guide people management decisions. Harrah’s uses analytics to put the right employees in the right jobs.
Talent analytics can be used in hiring to identify traits that correlate with success and assess candidates objectively versus relying on intuition.
Ongoing tracking of employee performance and fit allows organizations to make better decisions about development and advancement opportunities.
Overall, talent analytics brings more data and objectivity to talent management, rather than relying solely on subjective opinions. Sport organizations can follow the lead of innovative companies in utilizing these approaches.

Here are a few key points on the importance of data visualization and data-driven storytelling:

Visualization is key to understanding large volumes of data. It leverages preattentive processing - the ability to rapidly process certain visual properties like color, size, motion subconsciously - to help people digest data more efficiently.
A strategic framework for effective data visualization includes knowing your audience, introducing the value of visualization, keeping it simple, retelling old stories in new ways, using comparisons, and balancing data and design.
Data-driven storytelling involves discovering and communicating insights from data that lead to business action. Effective stories focus on the audience, are structured around a narrative, use clear and simple visuals, and convey value.
Tips for finding and sharing actionable stories include focusing on business priorities, involving stakeholders early, iterating through feedback, considering alternative perspectives, and translating insights into recommendations.
The goal is to turn complex data into clear visual narratives that engage audiences and drive data-informed decisions and strategies. Careful design and storytelling are crucial to bringing numbers to life.

Here is a summary of the key points regarding the number of nines in the passage:

The passage states that nines occur 6 times in the first 10 digits (1-10).
It also states that in the first 100 digits (1-100), the number of nines is 20.
For the first 1,000 digits (1-1,000), there are 100 nines.
The pattern continues that in a set of N digits, the number of nines is roughly N/10.
So in general, as the number of digits increases, the number of nines increases proportionally at approximately 10% of the total number of digits.
The passage provides the examples of the number of nines in the first 10, 100, and 1,000 digits to demonstrate this pattern.

Here are a few tips for using comparisons effectively in data visualization:

Index scores: Calculate index scores that compare metrics to a baseline. For example, you could index revenue over time to a base year. This allows you to easily see percent increases/decreases compared to that baseline.
Sparklines: Add tiny charts next to your data points to show trends over time. Sparklines provide context and comparisons without taking up much space.
Dual-axis charts: Use dual axes in a chart to compare two metrics with different scales, like revenue and profit. Make sure to clearly label each axis.
Small multiples: Show the same chart for different segments, like revenue by product line or region. The repetitive design facilitates comparison across the visuals.
Color coding: Use color to divide your data into logical groups for comparison. For example, color code performance metrics by department.
Reference lines: Add dashed reference lines to indicate goals, averages, past performance, etc. This gives context for comparing your current data.
Filtering: Allow users to filter the data by segments or time periods to generate comparisons. Interactivity empowers users to do their own analysis.

The key is to intentionally build in comparative elements so the data doesn’t stand alone. This adds context and meaning that brings the story in the data to life. Choose comparison techniques that make sense for your specific data and audience.

Here are some key points for teaching a sport business analytics course:

Provide an overview of how analytics is used in the sport industry, including key applications like player performance, fan engagement, ticket pricing, etc.
Cover both foundational analytics skills (data analysis, statistical modeling, visualization) as well as sport-specific concepts and cases.
Use a mix of lectures, hands-on exercises with real data sets, guest speakers, and group projects to make the course engaging.
Assignments can include short homework problems to apply skills, individual data analysis projects, and a group project focused on a sport business case study.
The exam can cover both theoretical concepts as well as practical applications of analytics to demonstrate learning.
Emphasize how analytics is becoming increasingly essential for sport organizations and equip students with skills to add value.
Make the course interactive and highlight real-world examples of analytics in sports for motivation. Collaborate with local teams/leagues as possible.

The key is balancing technical analytics proficiency with practical application to the sport industry. This gives students a foundation to utilize analytics in their future sport business careers.

Teaching a sport business analytics course presents unique challenges due to changes in data availability and complexity. Course design and delivery methods need to adapt.
Sport organizations are demanding analytically-trained talent as they rely more on data to drive decisions about pricing, sponsorship, customer relationship management etc.
The author teaches a course focused on analytical techniques for the business side of sport organizations, rather than player/team performance analytics.
Industry leaders look for 5 key skills in analytics hires: asking the right questions, gathering data, modeling data, interpreting results, and communicating findings. Finding candidates with all 5 is difficult.
CRM and individualized marketing are becoming more important as teams try to deepen relationships with fans. Analytics helps provide a 360 degree view of customers.
Adoption of analytics has helped sports teams grow revenues beyond expectations.
The course introduces analytics concepts broadly at first before focusing on sport-specific applications. Readings, examples from other fields, and the instructor’s experience guide course content.

Here is a summary of the key points regarding business analytics in sport management education:

Davenport and Harris (2007) classified analytics into three types: descriptive, predictive, and prescriptive. Descriptive analytics focuses on gathering and organizing data, while predictive analytics uses past data to forecast future trends. Prescriptive analytics goes further by offering solutions through methods like optimization and experimental design.
Good supplemental materials include articles from MIT Sloan Management Review, Harvard Business Review, and Forbes. Textbooks are still emerging in this field.
Key statistical tools covered are Excel and regression analysis. Students need basic Excel skills and should understand regression outputs. In-class exercises apply these tools to real sport business cases like ticket sales forecasting.
Topics covered include CRM, ticketing strategies, sponsorship ROI, social media analytics, scenario analysis, and database marketing. Real examples from sport organizations are used to engage students.
Assignments and exams align with course materials and business applications. Students work individually and in pairs to solve problems similar to those faced by sport organizations.
Faculty can enhance sport analytics courses by attending conferences, hosting analytics events, and teaching students how research can help solve real sport business challenges. Sport management programs that embrace analytics will be seen as innovative leaders.

importance, 220–221

Here is a summary of key points about sports sponsorship and analytics:

Sponsorship is a major revenue stream for sports teams and leagues, providing brands access to fans and partner assets.
Effective sponsorship requires data and analytics to quantify value, set pricing, prove ROI, and optimize partnerships.
Teams use data to segment inventory, demonstrate assets’ value, create tailored packages, and prove impact.
Sponsorship analytics track impressions, exposure time, sentiment, reach, engagement, sales lift and more.
Partners want insights into fans, assets’ performance and ROI to justify spending and optimize partnerships.
Data helps teams sell the value to sponsors, while allowing sponsors to measure impact and returns.
Key metrics include exposure time, impressions, sentiment, reach, engagement, website traffic, sales lift and more.
Advancements in data collection and analytics enable more rigorous measurement than traditional exposure-based valuation.
Data-driven sponsorship optimization benefits both teams/leagues (revenue) and sponsors (returns on spend).

In summary, analytics is crucial for maximizing the value of sponsorship deals for both sports properties and brand partners through quantifying assets’ value, optimizing partnerships, and proving ROI.

Here is a summary of the key points from the chapters:

Chapter 1 discusses the evolution and impact of business analytics in sport. It covers how analytics has grown in importance and application in the sport industry.
Chapter 2 examines analytics and ticketing innovations at the Orlando Magic NBA team. It looks at how the team uses analytics to understand ticket demand and optimize pricing.
Chapter 3 explores the growth of the secondary ticket market and how it is becoming a primary market. It covers issues like ticket pricing strategy in relation to secondary markets.
Chapter 4 is about customer relationship management and fan engagement analytics. It discusses collecting customer data to drive CRM and fan engagement.
Chapter 5 outlines the Aspire Group’s ticket marketing, sales, and service philosophy. It covers strategies for acquiring, retaining, and growing fan bases.
Chapter 6 covers empirical research methods for season ticket holder management and fan engagement. It discusses survey techniques for gaining insights.
Chapter 7 is on developing and measuring data-driven direct marketing initiatives. It examines using data analytics to target marketing campaigns.
Chapter 8 discusses fan engagement, social media, and digital marketing analytics at Duke University. It covers Duke’s use of analytics for fan engagement.
Chapter 9 is about leveraging digital marketing to engage consumers and drive revenue. It examines digital marketing analytics.
Chapter 10 looks at communicating the value of sports sponsorship through analytics. It covers sponsorship valuation methods.
Chapters 11-14 cover additional topics like market research, talent analytics, data visualization, and teaching sport analytics.

In summary, the book explores the many applications of data analytics across different aspects of the sport business, emphasizing how analytics is transforming the industry.

#book-summary

Sport Business Analytics - C.Keith Harrison