Challenge
The lifetime value of DISH subscribers varies greatly. some stay with the service a long time, while others churn after only a year. With high subscriber acquisition costs, the marketing team wanted to begin optimizing their paid media for LTV — but they had to find a way to do so without losing scale.
Solution
Internally, DISH developed a methodology for predicting new subscriber LTV. Working with Invoca, DISH created closed-loop attribution enhanced with subscriber LTV. They could now pass this conversion data into Google Ads and begin considering LTV as a key signal while optimizing their bidding strategy.
Insights
With enhanced conversion data flowing, the team found that Google Smart Bidding was the best solution to efficiently account for the LTV signal in order to predict conversion rate. Using machine learning-based bidding, DISH could value each consumer, each creative, and each moment individually.
Actions & Results
By combining the power of offline first-party data and machine learning-based bidding, the team achieved:
- 500% lift in ROAS
- 15x lift in conversions
- 60% increase in conversion rate
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