Why Should ABC Care?
ABC’s customer promise is to provide entertainment that matches its audience’s viewing preferences (which is hard to predict and frequently change). This customer promise is not unique, ABC is in fierce competition with other television studios and internet competitors such as YouTube and Netflix. Supply chain efficiency (measured in high viewership & low production costs) is critical to profitably delivering on this promise. 
Digitization allows the supply chain to have greater visibility on demand signals across the network and offering the ability for mass customization.  This is having a significant impact on the media supply chain.
The traditional media supply chain is slow and prone to forecast error (not unlike the TOM Beer supply chain!). Similar to the traditional fashion industry, it relies on a long chain of producers, directors and distributors that each strive to predict end consumer demand and produce content 6-18 months ahead of delivery.  When this works, ABC is able to deliver a hit show to its customers. When it does not, it is left with a product with little customer demand and an expensive replacement process (requiring a long throughput time if starting a new show from scratch or carrying expensive safety stock of extra purchased shows).
Figure 1: Traditional Supply Chain (6-18 months)
This is being disrupted by a smarter and shorter supply chain that leverages real-time viewer data to predict (and constantly re-predict) what content will be popular.
Figure 2: New Data Driven Supply Chain
This happens in real-time, if YouTube initially predicts a video will be popular based on the first set of viewers and puts it at the top of the page but there’s a sharp demand drop the next minute, it will update immediately. As a result, instead of a producer predicting and choosing what is shown to the audience, the consumer is deciding real-time what gets shown by voting with their views, likes, comments and shares. 
Figure 3: YouTube Ranking Factors
Source: NovelConcept.org & 
What is ABC Doing? (Short-Term & Medium Term)
As a short-term strategy, ABC is working with data analytics firms such as Brandimensions and PropheSEE to leverage real-time user data from digital sources such as Twitter, discussion groups and blogs to predict hit shows. Doing so, PropheSEE was able to predict that ABC’s Desperate Housewives would be a breakaway hit two months before it premiered. 
This allows ABC to invest more in high potential shows and have less wastage, improving overall hit production yield (hit shows divided by total shows produced). This allows the supply chain to better predict end-user demand and reduce variability associated with cancelled shows; lowering overall industry costs associated with cancelled shows. 
As a medium-term strategy, ABC is incorporating popular user generated content into its shows. ABC partnered with Jukin Media to license and show popular user-uploaded YouTube clips on Good Morning America.  And ABC’s parent Disney is exploring a competing service to Netflix that could provide ABC further data about its audience.  While this is a good start, ABC could be doing much more.
Recommended Steps (Short-Term & Medium Term)
In the short-term, I recommend that ABC goes beyond its third-party data relationships and conducts deep data analytics (from both television and ABC.com) on what specific characteristics of its existing hit shows are favored by customers. It could use this data to predict which future shows will be a hit. By using deep data analytics, it may be able to identify certain actors, themes, directors and formats that will work well. This data could be used to augment decision making by ABC’s producers. Netflix has successfully done this with its House of Cards series:
“Executives at the company knew it would be a hit before anyone shouted “action.” It already knew that a healthy share had streamed the work of Mr. Fincher, the director of “The Social Network,” from beginning to end. And films featuring Mr. Spacey had always done well, as had the British version of House of Cards. Big bets are now being informed by Big Data, and no one knows more about audiences than Netflix.” 
I would also recommend that ABC shortens the feedback loop from its customers by showing pilot TV shows to online audiences far ahead of the series premiere. Amazon has successfully used this strategy to help predict demand.
“A group of 14 “pilot” episodes had been posted on Amazon’s website a month earlier, where they were viewed by more than one million people. After monitoring viewing patterns and comments on the site, Amazon produced about 20 pages of data detailing, among other things, how much a pilot was viewed, how many users gave it a 5-star rating and how many shared it with friends.” 
In the medium-term, ABC should explore innovative ways to leverage popular YouTube videos and the shorter associated supply chain. For example, Fox has successful created an TV series made up entirely of popular YouTube clips with Terry Crews as a narrator.
“Hosted by star Terry Crews, the show has been a solid performer for the network on its frequently spotty Friday, averaging a 1.1 rating among adults 18-49 since its debut and performing especially well among young men and teens.” 
ABC could also incorporate popular audience generated videos in its broadcast news coverage of special events such as the Grammy’s and the NBA finals. This could include videos from both celebrities at the event and regular audience members. Snapchat has successfully utilized this strategy to attract millennial viewers, showing audience provided videos (“stories”) of major events. 
ABC has the opportunity to combine both the talent of its TV producers and deep data analytics to more reliably predict consumer demand and gain a competitive advantage  – this is similar to how the combined human + IBM Deep Blue team could beat the best human-only and computer-only chess competitors. 
Open Discussion Questions
- Will we lose out on the “art” of making television as companies increasing turn to data? (parallels to The Gap case on using Big Data instead of fashion designers?)
- Should studios invest in bold avantguard shows that push into new directions that are not supported by existing audience data? If so, what can they do to mitigate risk of failure?
- Where will the media supply chain go next?
- Battaglio, Stephen. 2017. “Broadcast Networks’ Premiere Week Ratings Take A Hit As Viewers Watch On Delay.” LA Times. http://www.latimes.com/business/hollywood/la-fi-ct-ratings-premiere-week-20171004-story.html.
- Mcclellan, Steve. 2015. “Does The Web Know Which TV Shows Will Be Hits?”. AdWeek. http://www.adweek.com/brand-marketing/does-web-know-which-tv-shows-will-be-hits-77455/.
- Van Dijck, José. 2009. “Users Like You? Theorizing Agency In User-Generated Content”. Media, Culture & Society 31 (1): 41-58. doi:10.1177/0163443708098245.
- “The Agile Supply Chain : Competing In Volatile Markets”. 2000. Industrial Marketing Management, Vol 29 (1): 37-44.
- Harvard Business Review. 2017. “Digitizing The Supply Chain”, 2017.
- Owens, Jim. 2016. Television Production. New York, NY: Focal Press.
- Gallagher, John R. 2017. “Writing For Algorithmic Audiences”. Computers And Composition 45: 25-35.
- Guo, Zhiling, Fang Fang, and Andrew B. Whinston. 2006. “Supply Chain Information Sharing In A Macro Prediction Market”. Decision Support Systems 42 (3): 1944-1958.
- “Our Work | Jukin Media”. 2017. Jukin Media Inc. https://www.jukinmedia.com/our-work.
- Flint, Erich. 2017. “Disney Unveils New Streaming Services, To End Netflix Deal”. WSJ. https://www.wsj.com/articles/disney-unveils-new-streaming-services-1502226133.
- Davenport, Thomas H, and Jeanne G Harris. 2010. Competing On Analytics. Boston, Mass: Harvard Business School Press.
- Sharma, Amol. 2017. “Amazon Mines Its Data Trove To Bet On TV’s Next Hit”. WSJ. https://www.wsj.com/articles/amazon-mines-its-data-trove-to-bet-on-tv8217s-next-hit-1383361270.
- O’Connell, Michael. 2015. “Fox Orders More ‘World’s Funniest Fails'”. The Hollywood Reporter. http://www.hollywoodreporter.com/live-feed/ABC-orders-more-worlds-funniest-773139.
- Haimson, Oliver L., and John C. Tang. 2017. “What Makes Live Events Engaging On Facebook Live, Periscope, And Snapchat”. Proceedings Of The 2017 CHI Conference On Human Factors In Computing Systems – CHI ’17.
- Thomas C. Redman. 2008. Data Driven : Profiting From Your Most Important Business Asset. MIT Press.
- Newborn, Monty. 2012. Kasparov Versus Deep Blue. New York, NY: Springer.
Note: Word count excludes embedded exhibits and references.