Since iOS 14 hit in April of 2021, the DTC world has been desperately searching for the answer to one crucial question:
How do I get clear attribution?
It’s a valid question — understanding which ads are actually working is the basis not only for in-platform decision-making, but for assessing the value of paid social advertising as a whole.
The favored response of the DTC community?
Third-party attribution tools.
These new APIs promise a clear window into what’s really happening in your account. But all of these tools have one fatal flaw:
None of them pass their measurement data back to Facebook.
And Facebook can’t make optimization decisions with data it doesn’t have.
That means third-party attribution platforms leave you with lots of information … that you can’t take action on.
Or, more specifically, that Facebook isn’t capable of putting its incredible machine-learning capabilities towards.
For months, we’ve been struggling with the same fundamental questions about attribution … and we’re happy to report that we’ve found a tool that may just be the future of media buying and ecommerce in general. And we even have a case study to prove it.
Retina.ai is an AI platform that algorithmically predicts Customer Lifetime Value (CLV) from day one. Meaning — it produces something magical that allows you to see a customer’s future behavior right now.
Wait … what is Customer Lifetime Value, exactly?
CLV is defined as:
In other words, it’s the bottom-line value that a customer brings over the course of a year.
Optimizing for CLV:CAC is more valuable than optimizing for ROAS or simple LTV, because it takes both cost and timeframe into consideration.
In practice, of course, Retina’s not actual magic. But it’s pretty close.
Retina’s sophisticated algorithm intakes historical data and correlates first-purchase behaviors with long-term outcomes.
Then, it applies those learnings to your current customer acquisition, identifying first-time purchasers that are most likely to increase in value over a 12-month window.
The kicker? Retina integrates directly with Facebook Ads manager.
Seriously — we can literally see CLV as a metric when we're buying ads:
This allows Facebook to optimize for LTV:CAC based on predictive data fed back to the platform at the individual user level in real time.
Sounds amazing right? But what about the case study?
Born Primitive, a fitness apparel brand, faced a seemingly unbreakable revenue plateau. This was only compounded by the data integrity issues brought about by iOS 14.
There was only one way to get through this barrier:
Optimize Born Primitive’s LTV:CAC.
Born Primitive’s customer base includes lots of die-hard brand loyalists, and we needed to understand how to find more of them.
Using Retina’s Quality of Customer Report, we confirmed there was a huge difference between the brand’s highest and lowest value customers:
50% of Born Primitive’s customers have a 10-year CLV of $136 or less … but the average 10-year CLV is $366.
That means the top 25% of customers are worth at least 150% more to Born Primitive than the bottom 50%.
But before we could run the test, there were a few hurdles …
The first challenge lay in setting up a baseline LTV model. We had to create an accurate model by ingesting data from Shopify’s order and customer table.
Challenge two relied on feeding CLV data to the Meta platform. The team set up integrations on Facebook CAPI and configured campaign modeling.
The last challenge required us to show proof of increased conversion and LTV metrics. We constructed a scientific test to measure the incrementality of the LTV-based optimization.
We set up a split test to understand the effectiveness of CLV-based optimization.
Hypothesis: Optimizing for CLV in near real-time results in higher CLV customers (and better campaign metrics) than optimizing for ROAS.
The first two weeks focused on connecting Born Primitive’s Shopify to Retina and creating real-time custom conversion in Meta.
Then came monitoring. We watched CLV values in Ads Manager and lined up creative, budget, and plans for testing.
A month into it, we launched the split test. Testing budgets at $30k with a 50/50 split, we compared “Business as Usual” ads to “Real-time CLV-optimized” ads.
Once the dust settled, we reviewed the results and shared with the broader team.
Retina's campaign beat the “Business as Usual” campaign in all categories. We saw three times higher CLV to CAC when optimizing for CLV.
When optimizing for real-time CLV:
Of course, this isn’t a surprising result; if you optimize for CLV, you’ll get better CLV. But here’s the truly shocking outcome:
The CLV-optimized test also won on first-order metrics.
It beat out the business as usual campaign on:
This test proved that optimizing for CLV in real-time finds more relevant audiences at a more cost-effective rate.
In other words, the customers who will be most valuable in the future are also more valuable right now.
We talk about this entire case study and its implications on the latest episode of our podcast, The Ecommerce Playbook. Listen to learn more.
With such a definitive outcome — there's an 85% likelihood of the results repeating — at least one thing’s for certain:
We're gonna run this again for more clients.
And, for those in LTV-dependent businesses like apparel, cosmetics, supplements, and other consumables, running a Retina test through CTC could be the single most important thing you do for your business right now.
Not only will you get the best price on the Retina platform through us, you’ll also get an agency that …
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