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If you’re part of a B2B-focused business that has been noticing a significant rise in CAC in recent years, trust me you are not alone. In fact, research shows that for B2B businesses, CAC has gone up by as much as 60 percent, compared to stats from five years ago.
This begs one question, which we will let our friend Jerry Seinfeld ask…
Okay, we’ll tell you. 🙂
Growth teams behind B2B brands are beginning to take note of the fact that the payback strategy, which is also referred to as the return-based strategy, is a prime example of “more is more.” That’s because the CAC strategy limits a brand’s ability to scale, because it only locks the price they are willing to pay. Meanwhile, the payback strategy allows growth teams to get a lot more out of that limit. It enables teams to focus on the return, and also does not limit networks to target new users within limited pools.
B2B growth teams have had enough of making constant adjustments to their user acquisition campaigns, due to concerns revolving around CAC. The volatility of results, costs, and budgets—combined with the need to collaborate with multiple departments each time made it all too much to handle.
Unlike B2C’s, B2B’s typically have to do more with less, as they have very little (if any) conversions to paying users in the first few weeks after acquiring new subscribers. As such, the growth teams behind B2B subscription companies usually optimize user acquisition campaigns for upper funnel events, for the sake of ad network algorithms to carry out their functions. Most B2B growth teams also measure the cost per paying customer. This is based on the conversions that took place (if any), while making campaign decisions using naive models or proxies.
While that sounds good in theory, the measurement (and decision-making) based on CAC and/or very naive models is actually detrimental towards the element of scalability. The reason for this is because when acting on minimal conversions and limited data, it’s nearly impossible to make informed decisions. The likelihood of making a costly mistake becomes high. As the saying goes, “The smaller the sample size, the more likely that it is unrepresentative of the wider population.”
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The misinformed campaign decisions can go both ways. For instance, marketers may end up killing campaigns that have a high profitability potential that had little to no conversions during the first few days. On the flip side, they might double down on campaigns just because they had a conversion or two during the first few days, just to find out later these conversions simply do not represent any actual trend—those initial conversions may have just been incidental.
This is why B2B marketing teams are increasingly focusing more on making decisions focused on LTV/Payback. This is done by looking at wider data points (instead of just cost-per-conversion), which can provide an indication of future performance. These data points include behavioral first-party data, which includes elements such as product engagement, team member invites sent out/accepted, creation of new projects/activity, etc. It also includes looking into zero-party data, such as insights from onboarding questionnaires.
This is also done by shifting from naive CAC models for decision making, to more advanced LTV models. Side note: This is an approach you can take on in-house as part of your value-based acquisition process, which we further elaborated on in a previous post. Last but not least, teams are making the switch to a payback strategy by leveraging ML projections to fuel futurespected decision making—either internally (as seen in the case of Monday.com) or using predictive marketing products, such as Voyantis 😉. The latter is a more feasible option. After all, we all understand the hassle that is tied to internal product development, such as the constant back-and-forths with the engineering teams, frequent meetings with R&D/data science/design teams, and maybe even discussions with UX and UI consultants. The efforts may be worth it in the long run, but it comes at the cost of massively delaying actual UA efforts in the process.
WAIT. There’s still more you need to know before making the switch. After all, the payback period refers to how long it takes for a customer to “pay back” the amount of money your company spent to acquire them—but what about the rest of their relationship with your company? Well, this is where LTV data takes center stage.
Remember how I previously mentioned that with payback strategy, more is more? The same pretty much applies with LTV data—more zero- and first-party data points build stronger profiles, which can be used to power up LTV-optimized campaigns. The fruits of LTV-optimized campaigns for B2B companies include reduced churn, increased ROI, and yes—significantly greater returns on CAC as the revenue accelerates and accumulates.
At this point, all online companies, especially B2B SaaS brands, best make the switch to the payback strategy. The CAC strategy is quickly becoming detrimental, and there is so much that can be gained from capitalizing on LTV-optimized payback strategy. I mean, you’ll have all the raw data needed to make well-informed campaign decisions, including keep/kill decisions, achieve exponentially greater profitability for the long-term, AND improve the customer experience all-the-while. That’s one heck of a growth loop if you ask me, and a total win-win for all!
