Context, 1P data and LTV
Last week we announced that we’ve just released the ability to start our contextual engine with 1st Party audiences, (properly managed for privacy).
Why, one might ask, would a contextual company announce it can now start with 1P audience data - and why is this important?
Onboarding a brand’s 1st Party data as the basis for building lookalike and predictive audiences is now table stakes for advanced contextual platforms. The ecosystem in 2021 requires this capability in order to bridge the audience and contextual worlds.
The evolution of this development is to directly impact brand KPIs. And the predominant one now is LTV. As the ecosystem evolves toward being managed against privacy, LTV is what savvy brands are now beginning to optimize toward.
During the reign of the cookie and 3rd Party data, brands outsourced both prospecting and retention to third party networks, both large and small. In this context, brands had to depend on the third party networks for a feedback loop. The end result was eventually a conversion - which happened on the brand’s site - but what happened before that conversion didn’t have a great deal to do with the brand.
It’s very different going forward. Both the delivery method (cookie), and targeting data (3P browsing), are either going away or changing. To fill this void, brands have begun to step up and take back control of their future.
It seems the winning KPI will be Customer Lifetime Value (LTV). Brands today are building their own programmatic stacks and related partnerships. They are comfortable using some of their own 1st Party data now and that data is about as good as it gets since the brands have valid pools of real converters.
We’ve worked with brands on large and small campaigns which target LTV as the main, and sometimes only, KPI. We’ve learned that understanding the LTV parameters that are handed to us is one thing. Learning how to put intent behind those LTV parameters on the other hand is the real trick.
Why is understanding intent critical to LTV? That’s an easy question. While the brands have LTV values and targets, what they don’t have is a broad understanding of consumer patterns online, including content consumption. While they no longer need partners to place and optimize the media, they do need partners to unpack what drives LTV.
Adding an intent signal to an LTV effort creates a 1+1=3 effect. Here’s why;
LTV is a numerical expression of value. It does not have, on its own, any knowledge of why that value exists (ie how did the customer wind up with that value)
Adding intent to LTV, in our instance content consumption, provides an actionable window into what is motivating customers.
Adding LTV and intent produces results. In one instance we were able to deliver a 33% lower CPA leveraging LTV as a bid parameter. That’s the 1+1=2.
Here’s the “three”. Adding intent to LTV produces a profile of a consumer that forms the basis for predictive modeling, along with other attributes. Once you do that, the brand is now leveraging their knowledge of value and the partner provides intent. By cycling this each month the brand can consistently look for new customers based on intent signals and LTV will bring the CPA down.
It’s a powerful combination of benefits that come from one motion. We think it’s a side benefit of losing the cookie and the related 3P data.
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