Sports Analytics Continues to Create Opportunities

Haynes Henrickson, president of Turnkey Intelligence, provides three reasons why sports analytics creates opportunities for people who want to work in athletics. The money list:

  • The opportunity for advancement is now a reality, and ISN’T confined to database/technology-specific positions.
  • The growing presence of third-party companies in the sports analytics space
  • Sports business education’s increased emphasis on the importance of analytics

He concludes by noting that sports analytics has become the growth area for the industry.

Intersection of Sports, Analytics Moves From Field to Front Office

Analytics in sports has tended to focus on players and roster management. This thought remains rooted in the Moneyball-supported view of sports. Yet, sports extends beyond players and roster management. Analytics is moving from the field to the front office as these job openings show.

A person looking to break into this field needs to know cluster analysis and multidimensional scaling. To set his or her resume apart, this person should know factor analysis and conjoint analysis.

Additionally, this person would need to know how to communicate results to different audience with communication including both written and oral forms. A demonstrated ability to link analysis to tactical recommendations is essential.

Finally, this candidate should have experience with creating, collecting, and analyzing data from surveys.

Through Otterbein marketing major, we offer the opportunity to acquire these experiences and skills through our degree program.

Retailers Should Kill Their Apps

Aaron Strout argues for retailers to develop optimized websites and against creating apps. His argument centers on two issues. One, a retailing app does not clear the warranted reasons list. Two, app usage appears more hedonic in nature than utilitarian.

In the first issue, Strout outlines three hurdles for an app to clear, including:

  1. Urgency. When you think of apps that address urgency, consider real-time traffic or location data, stock information, banking information or other similar data...
  2. Repetitiveness. ...This might also be the case for an app that helps pay for parking meters, checks weather, or even your e-mail client...
  3. Boredom. This obviously plays more into the “game” or “entertainment” space.

Few if any retailer's app clears those three hurdles. Hence, most retailers should stop supporting or developing an app.

In the second issue, Strout concludes that people use apps for entertainment purpose. Shopping, here, though is not a form of entertainment. Instead, entertainment revolves around social media sites such as Facebook and Twitter, or games.

Strout's post corresponds to an analysis that Dr. David Taylor and I performed, which was recently published as well as shared at an analytics forum (see slide deck below). In this research, we found that how recently a consumer had been to the store would amplify the relationship between intention to use a retailer's app and completing a purchase from that retailer as well as share information. We conclude that app develop and support make little sense for most retailers.


Fries' Guide to Analytics Implementation

In a previous blog post, Dish's research director Patti Fries discussed the satellite provider's cultural and behavioral shifts related to market intelligence. Fries also discussed how she influenced this change. Annie Pettit, who lived blogged the presentation, provides the money list:

  • Individually, Fries completed 72 project in the first seven months at Dish. The results justified her requested 100% budget increase.
  • Dish followed an undifferentiated segmentation approach. Through research, Dish lowered subscriber acquisition  and retention expenses through the development, and implementation of segmenting models.
  • Fries stress to her team that less is more. Instead of providing every last number, her department issues one to two pages of action items instead of 400-pages of tables, and charts.

Based on these comments, it is easy to deduce that Fries was opportunistic, started simple, scored inexpensive victories, and developed a program. It is not clear whether she started with the end in mind (but probably did). Fries' tale underscores the general lesson of how a person could start an analytics program or department at his or her company. This lesson could be the real value of Fries' presentation.

Dish Learns to Listen to the Consumer

Recently, Dish's research director Patti Fries discussed the satellite provider's cultural and behavioral shifts related to market intelligence. Annie Pettit, who lived blogged the presentation, provides the money list:

  • Dish launched its first satellite in 1995 but did not launched its research division in 2012, or 17 years later.
  • Initially, Dish focused on sales, building a subscriber base. Spending on research was limited because the value of research was considered limited.
  • More recently, greater emphasis on research as content distribution has become a commodity service.

The organizational cultural change reflects Dish's emphasis on customer orientation instead of selling orientation. Managers now make decisions with the thought of how will this action (a) make current customers' happy, (b) attract customers from competitors, and/or (c) reinforce our brand identity. In this sense, Dish has adopted market orientation from an organizational culture view.

The organizational behavioral change occurs as Dish improves its competence to generate, disseminate, and respond to market intelligence. Where data collection was random with results kept within departments, Dish now actively and constantly generates market intelligence that is shared across the organization. In this sense, Dish has taken market orientation from an organizational behavioral view.

While Dish remains the number two provider of satellite content providers, its per subscriber financial metrics appear solid. That is, this adopted market orientation posture has improved the company's positional advantage in the market.

How Analytics Saved Netflix

In Netflixed, Gina Keating details Netflix's start, clash with Blockbuster, and triumph in the market. In one particular passage, Netflix's senior executives discussed its business model with reporters on a conference call. During the call, so many reporters attempted to create a Netflix account that its servers crashed. In that moment, Netflix transformed from a hobby to a business. The original senior executive group soon left the company.

Keating focused the remainder of the book on both Netflix and Blockbuster as the two companies battled to the death. Redbox, Movie Gallery, and Hollywood Video played important, but peripheral roles. She provided sufficient details and discussion of external and internal influences that allowed Netflix prevailed.

Of the external influences, Keating dealt with:

  • the failed merger between Hollywood and Movie Gallery;
  • the distraction caused by Carl Icahn for the Blockbuster executive team;
  • the early forays into video-on demand by Amazon, Walmart, and Enron.

Of the internal influences, Keating reviewed:

  • the turnover of Netflix's senior executives as the service moved from introduction, growth, and mature stages of the product life cycle;
  • the relationship between Netflix and USPS that aided Netflix in determining where to build warehouse and fulfillment centers.
  • the use of cluster analysis to provide Netflix's customers with suggestions for additional titles to rent;
  • the models that allowed Netflix's middle managers to formulate a response to Blockbuster's promotional effort, which was causing Netflix's churn rate to increase quickly.

I really enjoyed the aspects of these last three points. The recommendation feature made Netflix feel like an independent video store where employees could provide suggestions to customers because the employees knew the customers that well. This recommendation feature highlighted Netflix's inventory advantage to Blockbuster as well as Blockbuster's employees lack of customer knowledge.

Also, the modeling discussion proved interesting. Briefly, using publicly available information, Netflix could determine when Blockbuster would go out of business. In turn, Netflix avoided harmful price cuts and drastic changes to service plans. Netflix maintained its focus. Ultimately, Blockbuster stopped the promotion just as the model predicted. Netflix reported growth in its subscriber base less than a year later.

Keating packed a lot of discussion, thoughts, and stories into her book. Unfortunately, she does not tie them together. A final chapter that tied the external and internal influences would have provided a better understanding of Netflix's survival, and success.

Keating managed to avoid lionizing Netflix CEO Reed Hastings. Yet, without that final chapter, the reader is left with an incomplete understanding of Netflix. Without those external influences, Netflix could have implemented every action plan and would have achieved inferior financial performance.

Conversely, without those internal influences, Netflex could not have taken advantage of its competitors' misfortunes, and mistakes. The firm would have achieved inferior financial performance.

By combing both influences, Netflix was able to achieve superior financial performance. The proposed final chapter could have hammered the point so that other entrepreneurs, middle managers, and senior executives would better understand how to achieve a similar outcome.

Sample Size Calculation

This post provides directions on calculating sample size.

One, from the IBM survey, start with Question 19 from the sample instead of Question 18 to calculate the population mean, and standard deviation. Be prepared to explain why you use Question 19 instead Question 18.

Two, for D, or level of precision, use +/- 5. That is, you want to the sample to be within 5 points of the population mean.

Three, plug-in your values for D (5), σ (calculated in step one), and z (1.96) into the formula n = (σ²*z²) / D² to calculate your sample size.

Four, if your sample size is greater than 10% of the population, then you need to correct with the formulae nc = (n*N) / (N+n+-1) where n equals the size of your needed sample and N equals the size of the population.

Remember, your population depends on your unit level of analysis.

Friday Fun Question

Most Fridays, I will answer a question or questions from Principles of Marketing students about marketing. This week's question: How do you determine the target market?

Several methods exist to segment a market beyond the simplistic (everyone wants my product) and the gut (young women are very interested in my market offering). More specific methods include using parametric analysis such as regression or ANOVA, and non-parametric analysis such as chi square and Spearman's rank rho. These methods are covered in the Marketing 3850 (Marketing Analytics) course.

Parametric and nonparametric provide critical region where the analysis can decide to reject, or fail to reject the null hypothesis. Typically, the null hypothesis is stated as there is no difference between groups, the means are equal, or the medians are equal depending on the analytical tool.

Beyond those approaches exist cluster analysis and factor analysis. We cover cluster analysis in both Principles of Marketing (MKTG3100) and Marketing Analytics (MKTG3850). The thought is to form groups based on distance, or loss of information, to determine how similar or dissimilar the observations appear. Statsoft provides a longer, more detailed explanation as well as an embedded video.

Similarly, factor analysis relies on the amount of error each group, or factor, shares. Through this approach, the number of observations or variables can be reduced to some type of groups, which allows for classification. We will most likely cover this approach in MKTG3850 for Autumn 2014. This entry from Statsoft provides additional discussion.

Please note that these responses appear brief.

After a segmenting the market, a target market should be selected based on several considerations, including:

  • Size and growth of the market segment;
  • Cost to service the market segment;
  • Firm's ability to meet the demands of that market segment.

That is, the firm should evaluate the attractiveness of each segment before selecting the segment to target.

A Dram for Coolest Use of Cluster Analysis

Luba Gloukhov enjoys single malt scotch. While enjoying a sip, Gloukhov wondered if scotch distilleries could be clustered.

Using a dataset and K-means clustering, Gloukhov was able to create groups, or segments, of single malt scotch.

The analysis is well done and well interpreted. It would have been easier if Marketing Engineering had been used.

Hopefully, Gluukhov will inspire other people to explore cluster analysis to form groups that would not otherwise be apparent.

Cluster analysis would provide a better way to organize this bar.