Senior executives speak the language of money. Yet few Customer Success leaders feel comfortable with the numbers. And as software executives are facing increasing pressure on the bottom line, failing to connect activities to financial results inevitably leads to layoffs affecting you and your team. Making the case for Customer Success ROI is critically important.
As described in the RevSetter webinar, Proving Customer Success as a Profit Center, proving the ROI for your team can be done. You can build a compelling business case in two different ways: intuitively or empirically. The former is faster and easier, provided it lands effectively with your audience. And while the latter takes more time, effort, and expertise, it can eliminate any doubts about your team’s business impact for even the toughest of skeptics.
The Intuitive Argument For Customer Success ROI
Ultimately, everything in business boils down to revenue and cost: make a product for a buck and sell it for two. The difference, of course, is profit. When it comes to internal functions, the impact of added revenue or reduced costs compared to the operation’s investment is often referred to as margin contribution. And for every dollar spent, investors expect a return, no matter where it occurs.
The simplest and easiest way to show Customer Success ROI is to demonstrate having it versus not having it. Sitting down with your CFO to do an interactive, “back of the envelope” calculation is often all it takes. Below is an example.
Say yours is a $20M Annual Recurring Revenue company and your department costs the company $1.2M per year, considering salaries, benefits, taxes, facilities, computers, software, training, etc. Let’s assume your company’s Net Recurring Revenue is 105%, in other words, installed base revenue grows 5% per year through purchase of incremental licenses, up-sell or cross-sell, net of churn and down-sell activity.
Now subtract from that growth everything you do in Customer Success. This may include value planning and realization for new customers, earning trust, intervening on their behalf as necessary, building the case for renewals, executing account turnarounds and “saves,” and identifying (and potentially closing) new business opportunities. Citing specific examples alone, you can propose a credible estimate for what the annual growth would have been had it not been for your team.
Now multiply the ARR by the NRRs with and without the contributions of Customer Success. In this case, $20M*105% = $21M and $20M*95% = $19M. Subtract the two results: $21M – $19M = $2M. This is the total topline impact you assert that your team makes. Now subtract the cost of your operation: $2M – $1.2M = $800K. This is your margin contribution; the operating profit or free cash flow Customer Success generates over and above its cost of operations. The Return on Investment is simply the margin contribution divided by the total cost ($800K/$1.2M = 0.67 = 67%).
So, for every dollar the company gives you, you pay back $1.67. Not bad! And it doesn’t count the year-over-year impact of revenue growth and retention that comes from serving customers over the long term. Nor does it include your impact on company valuation as a multiple of the incremental revenue or the added pre-tax margin.
But what if your CFO doesn’t buy the hand-waving? What if they fold their arms and say they want more than just anecdotes to prove your case?
The Empirical Argument For Customer Success ROI
As a matter of physics, downstream results always come from upstream processes. That’s because everything in the universe obeys the law of cause and effect. But in a complex system, many internal processes and external factors combine to produce an outcome, making the contributions from each factor more difficult to estimate. Below are four objective, mathematical approaches to consider, each with advantages and disadvantages.
Before vs. After. If your company at one time did not have the Customer Success function and later added it, then comparing the results before and after the change can show your team’s impact. This is perhaps the simplest and easiest comparison to make, but it also assumes that nothing else changed in the interim. For example, your company may have introduced a new product, new pricing, or improved another aspect that could have influenced the results. Global economic or competitive conditions may have also changed.
A/B Test. The randomized, controlled experiment is science’s “gold standard” for causal arguments. You randomly assign a treatment (Customer Success) to one group while leaving the control group alone. You then ensure that all other factors remain the same for both groups—same types of customers with the same needs, they purchase the same products, work with the same salespeople, etc. Therefore, the difference between the test and control groups is the treatment effect. This approach builds the strongest business case, but it assumes the control group will not receive the Customer Success treatment, an experiment your executives may not want to run.
Regression. By applying statistical tools and building predictive mathematical models, you can estimate the relative effects of multiple factors affecting NRR. For example, you may find that degree of need-solution fit, product usage, and customer service satisfaction predict 95% of renewal revenue. Customer Success often directly impacts product usage, so by varying its value in the predictive model, you can estimate the change in average NRR that can attributed CS. But once again, the other factors (fit and customer service) can vary, too, and correlation isn’t necessarily causation.
Synthetic Controls. Finally, using more advanced techniques, it’s possible to create causal inferences from certain sets of data without resorting to controlled experiments. For example, you may have CS in one region and not in others, and by comparing NRR and other factors between the regions, it’s possible to estimate what happened vs. what could have happened. But this approach is subject to experimenter bias (data are used to support a predetermined outcome), the analysis is more involved, and you must have the right data to do it.
Data-driven approaches can better satisfy skeptics, but there’s no guarantee that your results will convince them. Be sure to propose your approach and the assumptions you’ll be making before you do the work. By obtaining their commitment in advance for what will be considered acceptable “proof,” you can avoid the critics moving the goal post when you present your results.
One Final Thought
If you do the work, you will likely show very attractive returns in exchange for your company’s investment in Customer Success. Be sure to use the easiest approach that still makes a strong, credible argument with your C-levels and investors.
And while justifying your existence and increasing your budget are important, the greatest ROI in Customer Success doesn’t come from what happens on your team, but what happens across the enterprise. This is shown below.
If you quantify the reasons why customers cancel their agreements, typically 60-80% has to do with unmet expectations for quality and value in your offering, 20-30% has to do with the relationship with your brand, poor customer service, or objectionable business practices, and 5-10% has to do with customer-specific issues, such as change in need, or financial hardship. This means your team affects only a fraction of the problem—customer retention and growth are therefore enterprise-wide concerns.
It also means that Customer Success is uniquely positioned to trigger and facilitate change and improvement in products, services, and processes with other functions in the business. Working closely with the CEO and C-level staff to identify and lead continuous improvement projects delivers orders of magnitude greater impact on revenue, cost, and company valuation. To the extent you and your team facilitate beneficial, companywide transformation is yet another compelling reason to invest in your work.
About the Author
Ed Powers is principal consultant at Service Excellence Partners, helping organizations make breakthrough improvements in customer loyalty by addressing why they leave—and why they stay and buy more. His specialties include neuroeconomics, analytics, and continuous improvement.
Ed also teaches Data-Driven Decision Making for Customer Success, an instructor-led course covering statistical methods for analyzing operational impact and ROI, customer health, and continuous improvement. A self-paced version is available on Udemy. Also available on Udemy is Advanced Customer Success, helping director, VP, and C-level Customer Success leaders achieve enterprise-wide breakthroughs in customer retention and installed base growth.
