What Moneyball Can Teach the TV Industry About Advanced Analytics

Moneyball and Advanced Analytics

Prior to 2002, the Oakland A’s baseball team faced a seemingly insurmountable challenge: field a competitive team with a fraction of the player payroll of teams in larger markets.

The currency that determined a player’s value at the time came from his batting average, runs batted in, and home runs. If a player had high statistics in these areas, he was in high demand. Thanks to the draft, the A’s could select players with high stats. But as soon as the players became free agents they would go to larger markets where they could get more money. The A’s couldn’t match the high salary bids of teams from larger markets. These teams had higher TV revenues and ticket sales because of their larger population, so they could pay more for players.

How could the A’s beat a system designed to favor large market teams?

In order to find a way to compete, general manager Billy Beane used advanced analytics to take a closer look at what determined a player’s impact on winning. Beane discovered that metrics such as on-base percentage and slugging percentage had a bigger impact on winning games than the metrics used to determine the salaries of players.

With his discovery, Beane went out and hired players better suited to win games. He found players who had low batting averages and RBIs, but high on-base and slugging percentages. The players had low traditional measures, which meant the A’s could afford them. They had high game-winning measures, which meant the A’s could have a winning team.

The A’s analytical efforts helped the team reach the playoffs in 2002 and 2003 with a payroll of $44 million. Their competitors had payrolls as high as $125 million. The industry had priced players based on the accepted currency at the time, but Beane used additional analytics to find players with more value than their market price.

Valuing TV advertising with advanced analytics

So how does this apply to TV advertising? Today we have a clear currency for pricing advertising space on television. Viewer data for major TV programs is measured and available to anyone interested in purchasing advertising space. That viewer data serves as a common currency to price the value of running an ad during these programs.

Programs with higher ratings can sell advertising for a higher price. Shows with low viewership don’t even get measured. This creates a challenge for the salespeople responsible for justifying the value of ad spots during those shows. With additional analytics, media sellers can track viewership on these unrated programs. They can then identify opportunities where an audience may be underpriced due to the lack of ratings.

Just as analytics helped the A’s find baseball players who were undervalued in their impact on winning games, advanced analytics can help media sellers identify undervalued programs that can unlock significant additional impact for a given advertiser.

Using analytics to find a target audience

From an advertiser’s perspective, it doesn’t matter whether a program is rated or unrated. What matters most is the ability to reach the right audience, not just any audience. The more information an advertiser has about who watches a given program, the greater the opportunity to select the right programs that best fit the desired audience.

What matters most is the ability to reach the right audience, not just any audience.

Consider an example of two different TV shows that have the same rating. One show has significantly more ski enthusiasts than the other. A local ski resort can use this data to pick the show that best fits its audience. This results in a much higher return on the ski resort’s advertising investment.

Take another example: a car dealership that focuses on luxury vehicles. With the correct viewer data, the dealership could ensure that they only buy ads in programs that have high-income audiences.

In both of these situations and in many others, advanced analytics can help advertisers get more value out of their ad dollars by putting ads in front of target audiences.

More TV data = more advertising efficiency

Efficient, targeted advertising is in high demand in the TV ad industry, and the most successful media salespeople are using advanced analytics to meet that demand. Using advanced TV data, however, carries what I’ll call the risk of transparency. What if the viewer data says the program performs worse than expected for a given advertiser? The answer to this question has two parts.

Efficient, targeted advertising is in high demand in the TV ad industry, and the most successful media salespeople are using advanced analytics to meet that demand.

First, the data can help a media salesperson put the advertiser in the best available ad space with the highest potential for success. While one show may not fit the campaign, the data will likely identify another option that fits better. In the end, the impact advertisers see on revenues will determine their likelihood to advertise again. Thus, matching them to the best possible alternative will increase their likelihood of success.

Second, advanced analytics can point media salespeople to new potential advertisers who value buying space on a given show. Matching a show to the right advertiser can likely command a higher CPM given the greater value to the advertiser of reaching the desired audience.

Bring moneyball to the TV industry

As marketing becomes increasingly data-driven across every method of advertising, content owners with the best data will win. Just like the Oakland A’s baseball team, using advanced analytics in media sales can identify opportunities undervalued in the marketplace. It can differentiate media salespeople by providing more value to advertisers that helps them win in their business.

Tom Roberts
Tom Roberts
Chief Marketing Officer
Tom is a seasoned marketing leader with decades of marketing experience. Before his role as CMO Sorenson Media, Tom served as senior vice president of marketing at Sprint. Tom also served as CMO for Dobson Communications, which was later acquired by AT&T. Prior to that, Tom ran marketing for the western US at Verizon Wireless.