Optimize Media Effectiveness with CLV
As outlined in a previous blog post, media effectiveness first begins with a clearly outlined plan for investment in paid, owned, and earned media, optimized for distinct groups of customers. By leveraging what you know about these customer groups, marketers and media planners can gain insight into what level of investment should be made for each group with the intent of maximizing return on advertising spend (ROAS) and minimizing wasteful spend or resources.
Media, especially paid media, serves another important function: reaching new potential customers and coaxing them further down the funnel. While developing the customer relationships you already have is the most cost-effective way to increase revenue, acquisition will always be an important part of the paid media budget and strategy. If you aren’t constantly bringing new customers into the funnel, the future of your company is pretty bleak. So what would a customer-focused acquisition strategy look like?
The Traditional Approach
When optimizing media spend, most companies focus on conversions or metrics like clicks, shopping visits, leads, or sales. But the magic happens when you connect those conversions with activity that happens later in the customer lifecycle. With that in place, you no longer have to optimize based on the short-term revenue generated by your newly acquired customers. Now you can optimize using the predicted customer lifetime value (CLV) of those customers instead. Often, this means that marketing can bid more aggressively on programmatic channels (like search, digital video, and display), which in turn means more available volume and a business case for larger media budgets.
Let’s take a hypothetical example from a fictitious health and wellness company named Honest Goods. Consider a particular campaign for herbal foot cream and suppose that Honest Goods is running paid search, digital video, and paid social. A typical way to optimize these channels would be to build an attribution model and ensure that each channel is above a threshold marketing return on investment (MROI). Honest Goods might already have an attribution model, or they might just be using last click. In any case, after running the campaign for a while, they would have data that looked something like this:
We’re assuming here that the herbal foot cream costs $100 to purchase. If the threshold MROI is 100%, then the digital video cost per click (CPC) is too high. The digital media specialist running the campaign would need to optimize for an average CPC of $0.50 to meet the target. That would mean less overall volume from the channel, and fewer sales overall, but an improved efficiency. On the other hand, there may be room to be more aggressive in search and paid social: new campaigns could be built out, and bids could be increased if the marginal $/conversion didn’t exceed $50. These are the kinds of optimizations routinely practiced when managing programmatic media, and they certainly work.
Using Customer Lifetime Value
If Honest Goods were using data from deeper in the customer lifecycle to optimize these campaigns, they would have a competitive advantage when buying media. Let’s say that Honest Goods has a CLV model that they can apply to customers newly acquired during the campaign. Instead of simply product revenue per conversion, they now have a predicted lifetime value per conversion.
If Honest Goods were leveraging customer data from deeper in the lifecycle to optimize their campaign for herbal foot cream, they would be able to see that all channels are well above the threshold MROI in terms of the predicted CLV of the new customers acquired through the campaign: they can bid more aggressively, build out new campaigns, and generally capture more volume while remaining well within their efficiency threshold. In other words, they have a business case for more budget.
The media landscape, especially in digital, is fraught with non-transparent business practices, and sometimes outright fraud. And you don’t have to take our word for it. The problem is most pronounced in acquisition campaigns, where first-party data can’t be leveraged as easily and third-party targeting methods have to be used instead. But when the campaigns are integrated with down-funnel customer activity, those third party methods don’t have to be trusted: you will be able to see exactly which channels and tactics are driving customers with high lifetime value, and whether they are doing it profitably. And by drilling down to more granular campaign metrics, you can figure out how to optimize each channel for long-term MROI.
Of course, this kind of approach enables a lean, effective media strategy. But it also makes it possible to go after more scale, as in the example above.
As more and more media becomes programmatic, this kind of acquisition approach will become increasingly important. The cord-cutter population is growing, and the best way to reach it with rich media (like video) is some flavor of programmatic demand side platforms (DSP), or through programmatic publisher interfaces. To get an edge in this space, to eliminate ineffective spend, and to identify under-valued inventory and scale it up, you need campaigns connected to down-funnel performance metrics so that you can optimize for long-term value, not just short-term conversions.