Let’s admit it: the iOS privacy changes complicated growth marketing way more than any of us would have expected. The SKAdNetwork API has restricted, aggregated, or delayed all app event data, and lift measurement is unavailable. The changes have also hindered optimization, targeting, and measurement—each of which collectively played a role in ultimately increasing CAC for all advertisers.
While many would argue that B2C brands ended up having too much on their plate as a result of these changes, I’d say that growth is actually a trickier feat for B2B subscription companies. Lately, it has become even more problematic, as VC’s, PE’s and shareholders in general measure CAC in their assessment of a company. But the cost of media is too darn high, and it is negatively affecting profitability and scalability, so now what?!?
As we mentioned in a previous post, the growth teams behind B2B companies are often left to do more with their campaigns, using less. After all, B2B’s typically have longer funnels, and there are very little (if any) conversions to paying users in the first few weeks after acquiring new subscribers. This makes decision-making particularly difficult—and even costly when you consider the rise in CAC. The rise in CAC has gone up by as much as 7x for B2B subscription companies, and has naturally become a major focus for marketers, CMOs, CROs, and more as they explore ways to reduce that cost.
This is why the most sophisticated data-driven subscription B2B companies are exploring ways to reduce CAC, while increasing profitability. Interestingly enough, this would have sounded a little overly ambitious even five years ago, but it has become perfectly attainable thanks to a few different approaches.
Placing focus on customer retention and loyalty
It turns out that about 44 percent of companies have a greater focus on customer acquisition vs. 18 percent that focus on retention.
Some of the most effective ways to take on customer retention include the upkeep of a communication calendar, customer relationship management, and (when relevant) a customer loyalty program. Brand advocacy also goes a long way for B2Bs, which explains why powerhouses such as HubSpot and Slack have fared so well as their loyal user base indirectly handled marketing.
It is worth noting however that if your data-driven B2B is able to identify the most loyal customers—you’ll immediately have an upper hand. That’s because you can nurture them to turn them into their brand advocate so that they can bring profits in the long run.
Introducing a freemium option
You might be wondering what freemium options have to do with lowering CAC. Well, it boils down to simply getting a foot in the door, in an effort to make your targets become accustomed to (and hopefully even dependent on) your service, to further increase the likelihood of conversion. This is surprisingly one of the more overlooked methods of indirect monetization, because you are getting users into using your product before they become more expensive to acquire.
Not only would freemium options help you acquire users while they are still inexpensive, but it would also carve out the path for them to naturally follow, by signing up to more monetized versions of your service. You just need to reel them in first! This also ties in with growth loops.
As we mentioned in one of our previous posts, growth loops are becoming more appealing for B2B’s over funnels, because unlike funnels, they do not end at acquisition. Instead, growth loops are about keeping activities and events going in full circle, like clockwork. By extension, it opens the doors to scalability, thereby playing a role in reducing acquisition costs.
In many ways, growth loops can be considered the engine of growth strategies, and can work nicely with freemium models. That’s because when combined, it can enable product-led growth loops and further propel existing loops.
Using zero- and first-party data to fuel predictive marketing
I decided to save the best one for last. 😇
If you’re not tapping into the power and potential of your zero- and first-party data to power up your growth campaigns and reduce CAC, you are simultaneously leaving a lot of money on the table AND losing a lot of it as well!
In one of our previous posts, we went over how both zero- and first-party data can be used to help your brand get a better grasp of the LTV of your different customers, thereby helping you make strides in acquiring additional customers that demonstrate similar LTV, especially when using a LTV predictive model, which obviously falls under the predictive marketing umbrella. But there is so much more to it than that, and it’s the kind of thing your data team will absolutely geek out over!
So here’s how to take LTV data to the next level. Looking at the future LTV at a cohort level, enables you to adjust your CAC strategy and maintain target profitability. As we explained in a previous post, looking at futurespected data will change the course of your major campaign-related decisions. I mean, you can decide which campaigns are worth keeping, or shutting down completely, or or worth putting more budget into because predictive performance data indicates there will be higher ROAS in the long run.
That unlocks the ability to evaluate the impact of ad spend, before the budget is actually spent! These insights are particularly crucial for B2Bs, considering the minimal conversions that are seen earlier on. Your team can also have a close look at future LTV data at the user level for audience segmentation.
Of course, the use cases for audience segmentation data are plentiful. This includes generating custom lookalike lists for prospecting purposes, and creating custom audiences for remarketing purposes. The data can also be used for intent-based email marketing, and so much more.
Bear in mind, while the possibilities are endless, the common denominator here is that you are leveraging the data and insights you already have, to maximize profitability. Here’s a breakdown of how it all comes together:
- When you create lookalike audiences, you're trying to target users that are similar to your best users. (Read more on that here!)
- When you create lists for remarketing/email campaigns, you're essentially doubling down on users you believe will yield more value.
- When you add predictions to this acquisition, you're upgrading from making smart/intuition-based decisions that are based on historical data, to making decisions based on future outcomes.
Ultimately, going this route will also enable CAC reduction, and AI would be doing all the heavy lifting.
The trials and tribulations that came about the growth marketing realm in the post-pandemic world put marketing teams in the position of having to roll their sleeves up and figure out ways to take on UA and scale while reducing costs, and be profitable while keeping compliance at the forefront. Initially, it sounds like quite the difficult paradox to approach, much less achieve. However, reducing CAC while gaining profitability is absolutely possible, and your marketing team doesn’t have to lose sleep over it! In a nutshell, it all starts and finishes with data: collecting it; extracting insights from it; and activating enhanced acquisition strategies with future value and in mind.