A Fine-Grained Picture of Viewer Behavior: The Key to Growth for Multiple System Operators

Viewership analytics, using the data they already collect more effectively, will allow multiple system operators to enhance revenue and cut costs on the path to achieving high performance.

With digital video recording and video on-demand, the video experience has become a personalized product, tailored to a particular user. However, despite the proliferation of content and viewing options for consumers, video revenues for multiple system operators (MSOs) are flat.

Getting back on the growth track means leveraging video offerings better. MSOs already have the data they need, but it is often scattered throughout the organization. Most companies lack a centralized capability for leveraging data from all sources.

With mastery over data they already collect but do not yet exploit, companies can:

• Market more successfully (retain customers, reduce churn, encourage on-demand video purchases and purchase of upgraded packages).

• Anticipate opportunities and challenges as video-viewing continues to spread across multiple platforms.

• Improve their negotiating position with content providers.

Without a centralized data capability, analysis cannot identify important behavior patterns or viewer segments in the information that cable operators collect. Instead, operators often segment viewers according to longstanding rules of thumb (for example, by age group or income category). Instead of relating usage on one platform to others, companies will report on each platform in ways that do not permit such comparisons. So when it comes time to decide on bundles or other marketing strategies, companies’ moves are often based on what worked the last time, or on hunches. It would be far more effective to use data to reveal potential sources of revenue enhancement or cost saving in viewer-behavior data.

• Revenue enhancement. Analysis can reveal, for example, that customers who watch Program X also tend to like Program Y, so bundling the two together is a promising move. It can also reveal exactly to whom that new bundle ought to be marketed.

• Cost cutting. The better a cable operator can analyze how different market segments see a particular channel, the better the company can bundle that channel for marketing purposes. And, the better it can see the return on investment of that particular channel, when it comes time to negotiate with providers.

How can MSOs exploit their data better, and stop “flying blind” in their quest for enhanced revenue and lower costs? The answer, we believe, is a change in the way companies do both data collection and data analysis. A comprehensive viewership analytics solution will involve:

• Identifying the key data elements and components and creating a new architecture in which they are truly integrated and relatable. This architecture must encompass both familiar data sources and new ones like video head-ends, over-the-top (OTT) servers, Nielsen, customer profile and data from social media.

• Forging the tools required to extract, transform, cleanse and aggregate data from different sources into a single integrated view.

• Creating an analytic workspace that can relate the data to questions that need to be answered (for instance, statistical tools to determine “if a customer watches X, they are most likely to also watch Y,”) allowing MSO to improve programming decisions by clustering channels that are most likely to be watched together.

• Offering an output capability to transform analytics into action (via audience segmentation, viewership forecasting and others). For example, this capability should make it possible to improve return on investment in content promotion by identifying the most promising customers to receive targeted “splash screen” marketing of additional bundles or premium content.

Bruce Brown is a senior manager in Accenture’s Communications, Media and Technology industry management consulting group. He has more than 10 years’ experience helping companies grow, retain, and improve the value of their customer base. Brown works across multiple industry segments, including wireless, wireline, cable and media.
 
Lan Guan is a senior manager in Accenture’s Management Consulting CRM group specializing in predictive analytics. She has 15 years analytics experience in data mining, statistical modeling, forecasting and optimization. Guan has worked across multiple industry segments, including wireless, wireline, cable, retail and financial services.
 
Jim Wheless is a senior executive in Accenture’s Analytics and Marketing Services global group. He has 16 years of experience leading revenue growth engagements in sales, marketing and customer management organizations within the communications, media and technology industries.
 
James Wildenburg is a senior executive in Accenture’s Communication, Media and Technology/Operations group. He specializes in leading projects that require advanced procurement solutions and large-scale transformation. Wildenburg has worked across numerous industries including communications, transportation, automotive, consumer products and aerospace.