CAC Payback Period – To Include Gross Margin or Not?

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The CAC Payback Period, also known as Months to Recover Customer Acquisition Cost (CAC), measures the number of months of subscription revenue it takes to recover the costs to acquire one customer. This metric is one of the many ways to look at the capital efficiency and profitability of a SaaS business.

The basic way to calculate this metric is to divide CAC by Average Revenue per Customer  Account or ARPA. Depending on whether you are using monthly or annual data, you may need to multiply it by 12. Some advisors recommend incorporating gross margin into this calculation, some do not.  I would say that both are good metrics; each gives you slightly different information.

Measuring Customer Acquisition Efficiency

In the early days of SaaS, calculations of Months to Recover CAC often did not include gross margin (and many still don’t include it).  The payback period without gross margin measures the velocity and efficiency of customer acquisition. The metric shows how effectively a company is executing in Sales and Marketing to acquire new customers quickly and efficiently.  It is also a good indicator for cash runway and cash burn.

Too long a payback period for your business model is a quick indicator that something needs to be improved, either CAC or ARPU or both.  Patrick Cambell of ProfitWell writes about using Months to Recover CAC as a way to analyze your customer acquisition strategies, both as a whole and for specific customer segments.  CAC payback period can be used to analyze a cohort of customers, or all your customers on average.  

Measuring Profitability

More recently, however, it has become common to include gross margin in the Months to Recover CAC calculation. Gross margin incorporates the cost of delivering and supporting the product during the payback period.  Gross margin isn’t included in the cost of customer acquisition and is generalized for the whole company, or a class of products, not usually by customers or groups of customers.  It is also not usually something that a company can improve in the short term.  Although SaaS gross margin benchmarks have improved by about 10% over the past 10 years due to greater efficiency and competitiveness in hosting costs, SaaS gross margin benchmarks show the least variation of probably any metric that OPEXEngine collects.

Adding gross margin to the equation shows the cost of acquisition AND delivering the subscription, which is also a valid measurement.  It makes the calculation look more at overall profitability rather than just the efficiency of the specific customer acquisition cost and velocity.  Both ways are useful depending on what aspects of the model you are trying to optimize.  

Adding Gross Margin to the Calculation Extends the Payback Period

Tomas Tunguz was one of the first to write about including gross margin (apologies to anyone else who wrote about this earlier than 2015) and mentions benchmarks of around 15 month payback periods.  Adding gross margin to the equation extends the payback period – it is hard for a company to achieve less than a 12 month payback period which often has been touted as a target.  Companies selling smaller contracts to the SMB have shorter payback periods while companies selling enterprise subscriptions tend to have longer payback periods,  extending beyond 18 months.

Both Versions of the CAC Payback Period are Good

We have introduced benchmarks for both ways of calculating the metric. One reason that I like to look at both is that the efficiency of customer acquisition is so critical to the success of a SaaS company, and profitability is also important. Topline efficiency is something that company management can more quickly impact and change.  At the same time, fully understanding the profitability of your SaaS model is important as well which is why many companies will find it more valuable to include gross margin.

Deciding whether to include the gross margin metric in your Months to Recover CAC calculation depends on the reason your company is using the metric.  Determining why you are using this metric in many cases can be just as meaningful as the metric itself.

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