On the ball: How ESPN uses BI and analytics to give sports fans the ultimate viewing experience

With the help of BI and analytics tools, ESPN can now make data-enabled decisions, and truly understand how its broadcasts are being received by fans

Sport consumption has seen a huge increase in recent years. With record viewing numbers for the likes of Premier League last season, and a novel willingness from consumers to pay for exclusive online content. As a result, sports is a lucrative source of revenue for network and programming brands.

Alongside this increase in popularity, modern television offers fans a more up close and personal experience at a fraction of the cost compared to attending live sporting events. Viewers can watch from the batter’s eyes, and encounter the huge right hand made by MMA fighters. In many ways, the viewing experience has evolved to become what Doug Kramon,  ESPN’s senior director of fan support and customer care views as a ‘virtually there’ experience.

However, in an era when TV watchers have more options than ever, and with beautiful camera angles not enough to retain viewers, broadcasters are looking for innovative ways to keep their audience engaged.

The battle for viewers

For networks like ESPN, part of the challenge of keeping viewers engaged comes down to understanding their expectations and experiences. Sports conjure up a range of emotions you simply don’t feel when you buy a pair of trainers. Fans feel elated when their team  scores the winning try and devastated when they miss the final penalty.

At the same time, if a fan misses any part of the action this can create an emotional reaction, that often leaves ESPN’s Fan Support inundated with inbound customer service issues. This information can come from a plethora of sources, including email, chat, calls, SMS, or even indirectly complaining via social media channels. The result, a vast amount of data to sift through to uncover the underlying problem.

For ESPN, the key barrier was being able to cut through the noise, to understand the live conversation and pinpoint key issues their fans were experiencing. On top of that, ESPN needed to be able to understand the severity of these issues. Was it limited to one fan, or thousands of fans?

Whether you’re a sports broadcaster or a cosmetics brand, providing exceptional service is crucial to retain customer loyalty.  However this can be tricky when feedback is hitting you from every angle. So what are the key steps ESPN implemented to help provide the ultimate experience that keeps fans coming back?

Tackling the challenge

1. Tuning into sentiment

Understanding the sentiment of data is pivotal to identifying key issues experienced by fans. With the help of Domo and RXA, a leading applied artificial intelligence and data science company, ESPN deployed a solution capable of analysing sentiment. This worked to extract the real-time feelings coming from conversations across different customer channels. Using artificial intelligence (AI) to classify the positive, neutral and negative opinions fans were expressing.

2. Unifying the voice of the fan 

It’s not only conversation data that hits the contact centre, there is also a mass of discussion happening on other online platforms, from Twitter feeds, and personal blogs to online forums such as, Reddit. Using Domo’s platform, ESPN is able to connect siloed data from conversations happening online, with data coming in directly to Fan Support, and house it all in one place. By unifying these disparate data sources, ESPN is able to map out all of the data to have one unified voice of the fan.

3. Real-time view 

Customers today have high expectations. They anticipate that businesses will meet them where they are and when they want. As such, ESPN fans respond to experiences that are timely and tailored to their specific needs, and reject those that aren’t. To meet these customer demands, ESPN deployed real-time data analytics to understand issues fans were experiencing as they happened, such as lagging, freezing or lost service.

4. Automating processes to identify key broadcast issues

Automation is key when it comes to customer care generally. For ESPN, they needed to not only see customer service issues in real-time, they also wanted to be able to deflect these issues from their contact centre and promote fan self-service. By harnessing BI and analytics, ESPN is able to identify key words regarding the product, the related issues, the frequency of that issue, and the associated sentiment. All of which are then presented in a concise summary that allows leaders to act immediately. For example, sharing responses through chat bots, FAQs alerts and live site announcements to let fans know the issue is being addressed,  which ultimately relieves strain on Fan Support teams.

5. Utilise AI-powered alerts to notify problems

Through AI, ESPN is able to track how a fan is consuming sport from various TV providers to understand what the fan in the digital seat is viewing. Using thresholds, ESPN receives AI-powered notifications and alerts via mobile when a number of issues related to a specific TV provider tips over the set limit. This provides information in the moment, so that customer service leads can collaborate with the appropriate tech/product department to get the issue resolved as quickly as possible.

The ultimate result

With the help of BI and analytics tools, ESPN can now make data-enabled decisions, and truly understand how its broadcasts are being received by fans. By mobilising data and AI, ESPN customer service leads can spend less time digging around for the problem and more time focusing on how to improve the overall virtual experience for its viewers. By tapping into customer sentiment, ESPN has created a unique business advantage. The impact of all of these efforts together, has meant that ESPN’s customer satisfaction is up by 9%, and customer self-service by 200% year over year.

Advanced customer analytics, at incredible speed and precision, isn’t just a possibility now, thanks to an array of converging technologies – it’s an imperative for companies hoping to connect tightly with their audience. Indeed, we are seeing this reflected in business behaviour. According to research conducted by Harvard Business Review Analytic Services, 60% of enterprise business leaders say customer analytics is extremely important. Companies who utilise real-time customer data are those that achieve customer retention and loyalty.

For other companies looking to rationalise and consolidate their customer data the best piece of advice I can give is to start by looking at a solution that can bring together customer data from multiple touch points, while effectively promoting quality data governance.  Without a joined-up data solution, businesses are at an increased risk of a garbled customer view that offers no real value. With a solid data governance protocol, data is clean and free of errors which will grant the integrity needed to attain the modern consumers expectations.