Have you ever thought of how automation can affect a TV company?
In case you didn’t, we’ve done it for you.
We’re a TV company and our clients call the shots about how, when, and where they consume content, so if we are not satisfying their needs, they'll cut the chord or go to a competitor's new.
We realized we had to rethink our goals, promotion priorities, and measures of success if we wanted any possibility of developing our subscriber base and driving growth.
Here are three strategic changes that assisted us to strive for -- and deliver growth. Here is how you can too:
1. Align on a development metric, and then hold all advertising accountable for this
Dish Network is a business. Folks are able to choose how they want to participate with us, while it's offline, online, cellular, or each of the above. That means we have needed to bring digital and offline that our advertising is more efficient and powerful our channels.
We have needed to bring digital and offline together so that all our marketing is effective and efficient.
We faced two obstacles in doing this: Ensuring that we tie our online and offline touchpoints and conversion actions effortlessly, and creating a system to utilize information to identify opportunities for getting the maximum out of our investment.
Is use our call center data to inform our electronic advertising and marketing investment. We find value even when their buy cycle may be started by them. And we know that over half our new subscribers will socialize with us on the telephone.
We integrated call conversion data into research so that our electronic advertising could work harder to find us better mobile prospects because phone advertising drives higher conversion rates. In addition, we make it easy for people to click-to-call us Google with call extensions on search advertising. Because of this, one-third of our search advertising conversions are now driven by call extensions.
2. Lean into CLV to reach the customers
We recognize that not all clients will be the same. Some are 5X more valuable than the average, and some have greater attrition scores. The support that is larger is needed by Other folks. Knowing the values and the attributes of the segments that are distinctive and treating them have been crucial to our success.
But we didn't always get it right. It was only recently that we made a change in the way we approach customer lifetime value (CLV) for our advertising and advertising tactics, but notably within digital. We learned we can't have a marketing plan based on channels, including electronic, radio, or TV. We needed a digital-first, omnichannel advertising strategy that helped us reach and differentiate our customers.
The very first step was to ensure we understood the features of customers, then we used that data to inform our advertising and marketing strategies. By way of instance, if we know that specific signs are highly connected with CLVwe pass this data back to Google Ads so it can optimize to attain more of these high-value users.
The results speak for themselves: profitability of our operation attempts have increased 43% since we started using a target return-on-ad-spend bid strategy this year.
3. Let machine learning (ML) direct your investments
Machine learning has been instrumental in showing and also in assisting us scale. Marketing automation enables us to be smarter and alleviates the guesswork.
The guesswork is alleviated by marketing automation from our preparation and allows us to become smarter.
As an instance, we used to manually bidding against keywords on Google Search. With machine learning in Google's Smart Bidding, we are ready to bid toward conversions. We can connect our digital media investments and offline data to improve performance. This approach has led to a 15X increase in traffic and also a 60 percent increase in conversion rate.
Machine learning is also helping us get insights quicker. In the past, we'd want to wait until there was a campaign finished, sometimes before we can tally the results and turn them to marketing actions. However, together with machine learning, we can get to the insights in a single day. We can predict outcomes before they occur. As an example, we can use operation insights to forecast the return on investment to get a campaign that is in flight. Then we could increase or reduce our investments necessary.
Machine learning is also helping us discover and reach customers than if we still do everything manually. We understand the qualities of the people, so we are able to predict the programming package for them and automatically tailor our marketing to their needs. Thanks to machine learning, personalization, and relevance at scale are now realities.