Dish Network’s CMO, Jay Roth, explains how the company’s transformative approach to digital marketing is driving growth.
We’re in the TV business. Our customers call the shots on how, when, and where they consume content, so if we’re not satisfying their needs, they’ll cut the cord or go to a competitor’s brand.
We realized we needed to rethink our goals, marketing priorities, and measures of success if we wanted any chance of growing our subscriber base and driving profitable growth.
To get there, we made three strategic shifts that helped us strive for — and deliver — growth. Here’s how you can too.
1. Align on a growth metric, then hold all marketing accountable to it
Dish Network is an omnichannel business. People can choose how they want to engage with us, whether it’s offline, online, mobile, or all of the above. That means we’ve had to bring digital and offline together so that all our marketing is more efficient and effective — not just our individual channels.
We’ve had to bring digital and offline together so that all our marketing is more efficient and effective.
We faced two challenges in doing this: Ensuring that we tie our online and offline touchpoints and conversion actions together seamlessly, and creating a system to use data to identify opportunities for getting the most out of our spend.
One unique thing we do is use our call center data to inform our digital marketing investment. We see value in leveraging our call centers to help acquire new customers, even when they may start their purchase cycle digitally. And we know that more than half our new subscribers will interact with us over the phone before signing up.
Because phone marketing drives higher conversion rates, we integrated call conversion data into paid search so our digital marketing could work harder to find us better phone leads. We also make it easy for people to click-to-call us on Google with call extensions on search ads. As a result, call extensions now drive one-third of all our search ad conversions.
2. Lean into CLV to reach the high-value customers
We recognize that not all customers are the same. Some are 5X more valuable than the average, and some have higher attrition scores. Others need greater technical support. Understanding the attributes and values of these unique segments and treating them differently has been critical to our success.
But we didn’t always get it right. It was only recently that we made a shift in how we approach customer lifetime value (CLV) for all our marketing tactics, but especially within digital. We learned we can no longer have a traditional marketing plan based on linear channels, such as TV, radio, or digital. We needed a digital-first, omnichannel marketing strategy that helped us differentiate and reach our most high-value customers.
We didn’t always get it right. It was only recently that we made a shift in how we approach customer lifetime value.
The first step was to ensure we deeply understood the attributes of high-value customers, then we used that data to inform our marketing strategies. For example, if we know that specific signals are highly correlated with CLV, we pass that data back to Google Ads so it can optimize to reach more of these high-value users.
The results speak for themselves: profitability of our performance campaigns have increased 43% since we began using a target return-on-ad-spend bid strategy earlier this year.
3. Let machine learning direct your investments
Machine learning has been instrumental in showing us what works, what doesn’t, and in helping us scale. Marketing automation alleviates the guesswork from our planning and allows us to be smarter with every dollar we spend.
Marketing automation alleviates the guesswork from our planning and allows us to be smarter with every dollar we spend.
For example, we used to manually bid against specific keywords on Google Search. Now, with machine learning in Google’s Smart Bidding, we are able to bid toward actual conversions. We can also connect offline data to our digital media investments to improve performance. This new approach has resulted in 15X lift in conversions and a 60% increase in conversion rate.1
Machine learning is also helping us get to insights faster. In the past, we’d need to wait until a campaign was over, sometimes months, before we could tally the results and turn them into marketing actions. But with machine learning, we can get to these same insights in an afternoon. We can predict outcomes before they happen. For example, we can use real-time performance insights to predict the maximum return on investment for a campaign that’s in flight. Then we can increase or decrease our investments as needed.
Machine learning is also helping us discover and reach far more high-value customers than if we were still doing everything manually. We better understand the qualities of these people, so we can predict the best programming package for them and automatically tailor our marketing to their needs. Thanks to machine learning, personalization and relevance at scale are now realities.
