David Baekholm is chief growth officer at Epidemic Sound, a Swedish-born music technology company with offices in the Netherlands, the U.S.A, South Korea, and Germany. He's responsible for ensuring that the company has a comprehensive understanding of its customers.
One such way of identifying this is through a recent project to create a productive lifetime value model that could help Epidemic Sound spend the right amount of marketing budget on the right customers, and increase its paid subscribers.
The tough economic climate called for us to have a more cost-effective approach to spending. We needed to create a lifetime value model that could help us be more strategic with our budget.
That’s why it’s essential to create an accurate and informed LTV model that identifies higher value customers.
At Epidemic Sound, we found that the money we were spending on our customers was disproportionate. Because we hadn’t identified our higher value customers – for example, paid subscribers – we were spending as much of our budget reaching our lower value customers.
Within four months of introducing our new LTV model, we were able to identify our higher value customers and grow our paid subscriber base by 80%. Here’s how we did it.
We took several steps to grow lifetime value. For example, we have translated our site into a host of additional languages.
As we learned more about our customers and their behaviours, we needed to ensure that these learnings were reflected in the LTV model. We were able to then add more layers to the knowledge we had on our customers, finding out what plan users had, what country they live in, and what licence they bought.
We also expanded the model to include other customers, such as:
- One-off licensees — Those who need just one, or a few, tracks for a single production.
- Partners — These are the integrated partnerships we have with Adobe, Canva, Pinterest, and others.
- Enterprise clients — A subscriber (usually a business) who needs a bespoke subscription.
We took several steps to grow lifetime value. For example, we have translated our site into a host of additional languages. We can now see everyone engaging in their native language, which adds an additional layer to the model, and we have discovered that people are more likely to engage in their language of preference.
We have also introduced our own app as we know that when users install it, it will lead to a higher LTV.
You need an aggregator – like AI – that not only knows your data, customers, and preferences, but also has its finger on the pulse of everything that’s going on in the market.
It’s important that you have a thorough grasp on your data and remember to treat it like milk – it has an expiration date.
Beyond that, my advice is to start as simple as possible. Many LTV models fail because they aim for too much complexity and sophistication from day one. Start with a simple model and be prepared for ongoing iterations.