Paid vs. Non-Paid: Why You Shouldn't Predict Growth On Paid CAC Alone
Marketers typically talk about blended CAC as the total of new customers acquired in a given period, divided by the total advertising spending in a given period. This information is crucial to the financial planning and forecasting of a direct-to-consumer brand, as it’s directly related to the percentage of top-line revenue associated with driving new visitors to your ecommerce site.
The Pitfalls of Solely Depending on Blended CAC
The Pitfalls of Solely Depending on Blended CAC
The problem with relying solely on a blended CAC as your core optimization lever, is that it doesn’t actually account for how many customers came in via non-paid sources. Most brands calculate this number by looking at the total customers attributable to an acquisition campaign (which is self-reported via the platform). Consider this example:
If Facebook says 75 purchases were attributable to a top-of-funnel campaign and 100 total purchases were made, then 75% of the purchases during that time frame were from a paid source. Most financial models extrapolate this to forecast that having spent X to drive 75 purchases means spending 2X will drive 150 purchases.
This thinking only holds up under the most unsophisticated (i.e. early stage) forecasting models; more importantly, this example only holds up when Facebook occupies 100% of the brand’s ad budget. Start adding in additional channels, and the simple calculation of newly acquired customers via paid sources starts to get quite muddy.
The Challenges of Organic Traffic Attribution
Remember, the core output of this model is “how many customers came from a paid source, and how many came organically?” Given that paths to purchase are not at all linear, and that Google is just as much an address bar as it is a search bar (consumers will often Google your brand name rather than go directly to your site), utilizing last-click attribution associated with Organic traffic is simply not feasible in modern-day marketing.
This is not to mention the nasty habit brands have of ignoring last-click and web analytics to rely solely on Facebook’s self-reported attribution. And we won’t even get into the changes Google instituted, which no longer let you see search queries or ecommerce transaction volume… the point is, sussing out non-brand Organic performance is just not feasible on a 1:1 basis, unless you actually talk to the customer. This is one of the many reasons why brands run our post-purchase attribution survey -- customers are the de facto ground truth here, because nearly every other data source you’ll encounter is either a) incentivized to credit themselves or their partners, or b) unwilling or unable to provide you with the right data point.
Understanding Paid vs. Non-Paid, Through a Customer Acquisition Lens
Unpaid top-of-funnel acquisition channels are, quite simply, those not driven directly from a measured paid media source. Some examples include the ever-present word-of-mouth, owned and earned social, traffic influenced by press, and orders driven via non-brand SEO initiatives (one of the more difficult channels to master).
As an exercise, we’d recommend listing out all of your channels, both paid and non-paid. Utilizing your Fairing attribution survey, start attributing orders to their top-of-funnel source. The goal is to calculate the percentage of orders which came directly from top-of-funnel advertising, and which came organically. You can then calculate a blended CAC based on your own attribution survey data and what each platform reports.
Of course, you’d hypothetically want to get the percentage of non-paid attributable orders as high as possible -- this leaves you better protected from rising CPMs and conversion tracking issues (i.e. iOS 14 and ATT) that are certainly in every marketer’s future. Again, we recommend solving this through surveying consumers post-purchase -- not only because it’s where you’ll pick up the most data on organic attribution, but also because the best way to future-proof your data collection practices is to simply ask customers for their data in a transparent fashion.
Calculating a New Blended CAC
Operationally, your final step is to calculate your new blended CAC. Removing Facebook’s self-reported ROAS from the equation allows you to hone in on your own attribution model, and become more comfortable diversifying your media spend off of platforms that may not provide as much reporting data as Facebook.
That last point is notable when considering your competitive advantage in CAC. So many DTC brands (and their investors) focus on paid CAC simply because that’s where measurement is plentiful, and thus, predictive models can be created… even if they’re wrong. Said another way: brands willingly spend ad dollars on potentially inferior platforms because those platforms spit out metrics.
We don’t recommend that. Spend your ad dollars on ads, and spend your measurement dollars on measurement. As long as you have a measurement safety net – i.e. attribution surveys – providing data for any and all channels, you can make more intelligent and independent predictions about customer acquisition.
Paid CAC is an important metric, but it's not the only one you should be relying on to predict growth. Understanding the breakdown of paid vs. non-paid sources is crucial to accurately forecasting customer acquisition costs and making informed decisions about media spend.
Utilizing post-purchase attribution surveys is a powerful way to gather data on organic attribution, which is often overlooked in favor of self-reported attribution from platforms like Facebook.
By calculating a new blended CAC based on your own attribution model, you can improve accuracy in your predictions about customer acquisition.