Two Data-Driven New Year’s Resolutions for Digital Entertainment Providers

As we move bravely into 2015, it’s a great time to think about steps that can be taken to make improvements to our businesses. Many executives in this industry have two areas that are top of mind: customer retention and advertising performance.

Customer churn remains a major concern for cable, satellite, and telco TV leaders as a growing number of subscribers are either opting out for alternative video options such as Netflix, Hulu, and Amazon Prime, downgrading their cable or satellite TV packages as they add these offerings.

For instance, about 2.9 percent of the 100 million-plus American pay-TV customers say they are “very likely” to cancel their services in the year ahead, according to a survey by Frank M. Magid Associates. The likelihood of dropping subscription TV services is highest among younger consumers ages 25 to 34, where 4.9 percent say they are very likely to cut the cord.

Additionally, in the second quarter of 2014, the U.S. pay-TV market lost a net of 305,000 customers, according to institutional research firm MoffettNathanson.

Understanding and responding to the triggers for churn

Companies in this industry serve huge customer bases that often comprise millions of subscribers. As such, it can be extremely challenging to keep track of individual customer behavior and preferences, much less signals that may indicate a customer is about to depart.

But it’s not impossible.

There are numerous ways to use customer data and analytics tools to predict and act on indicators of churn. These include warnings that subscribers share in contact center interactions. Predictive analytics can be used to help contact center associates identify a customer who is at risk of defecting based on verbal cues he has shared (e.g., dissatisfaction with the range of programming available for the TV package in use).

Meanwhile, transactional data can help determine whether the subscriber is a high-value or potential high-value subscriber who the company should strive to retain.

Predictive models can be used to determine whether an offer for free incentives (e.g., three or six months of premium network services) based on the customer’s viewing habits or interests is likely to retain him—not just for the duration of the promotional offer but for an extended period of time.

Understanding why customers leave is just as important. While competitive offers play a role in subscribers jumping ship, customer defection is more often the result of a series of issues such as inconsistent service performance or perceptions of overage charges that result in abandonment.

Analytics can be applied against social sentiment shared by subscribers in online forums and social media channels. Here, the tools can be used to pick up on emerging issues, such as problems with a provider’s digital video recorder (DVR) service. These insights can then be used by decision-makers to get in front of a technical issue and resolve it before it results in widespread subscriber dissatisfaction.

Targeting ads more effectively

Brands habitually use numerous channels in their efforts to target high-value customers. However, this blast approach often fails to recognize the most effective channel or channels for reaching specific customer segments based on their behaviors, what their expressed interests are, and how they are influenced by friends and trusted advisors. Additionally, marketing leaders are under enormous pressure to justify how ad dollars are spent and to generate returns. Consequently, not understanding customers’ channel preferences leave marketers perplexed on determining the impact of advertising spending in specific channels.

For instance, an August 2014 CMO survey conducted by Duke University’s Fuqua School of Business reveals that only 15 percent of marketers can determine the impact of social media marketing using quantitative approaches.

Analytics tools can be used to determine where target customer segments are residing (e.g., Facebook/Instagram; consumer review websites; etc.) as well as the type of content they’re most likely to find engaging (e.g., video).

Analytics can also be used to identify behaviors of target consumers and to determine the likelihood that a consumer will, say, click on a display ad for a local car dealership while reading through customer reviews on a third-party automotive website.

Going a step further, marketers can also use analytics tools to determine the optimal media mix for weighting ad spending.

Subscribers and consumers share a tremendous amount of behavioral information and sentiment in digital channels. Analytics tools offer fantastic opportunities to learn more about these customer preferences and needs that can be used to drive successful business strategies in 2015.


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