The polite answer to this question is “no”. In some circumstances the answer should be “yes”. In this post I will discuss two situations in my career where we ended up “firing” thousands of customers but managed to increase revenues and profits at the same time.
Let’s begin by setting some context. First, you need to assess where in the technology adoption life cycle your solution realistically sits. Is it in the visionary or early adopter stage? Are you caught up in the tornado? Or are you realistically in the late majority or laggard phase? As your solution moves through the adoption life cycle, the value each customer brings to your firm evolves. In the early stages customers provide a never ending source of ideas and validation for how your solution can solve the problem set you are targeting. Feedback and solution iteration are constant. Once your solution has reached the back half of the technology adoption lifecycle, the approaches to solving the market’s problems are well established and validated. The focus shifts to operational excellence, efficiency, and solution cost reduction.
In my career I have had the opportunity to lead several product management organizations. Twice in my career I have led programs to rationalize a segment of a solution’s customer base. In both cases the solutions were definitely in the late majority/laggard phase of the technology adoption life cycle. The first solution involved Electronic Data Interchange (EDI) and the second involved online fax outsourcing. The foundation of EDI can be traced back to the Berlin Airliift in 1948. The history of fax machines can be traced all the way back to 1865 with the introduction of the Pantelegraph. Needless to say both EDI and online faxing fit all the characteristics of late majority/laggard technologies.
When I joined both of these firms I did an assessment of each of their product/service lines. What both of these solutions had in common was that they had large existing customer bases that operated on a month by month basis, had no term contracts, and billing was based on actual usage. I led my teams in an exercise to analyze each customer base. We ended up producing an analysis like this:
First we tiered out the customer base based on the size of their annual revenues and we correlated that with customer support activity. It was not too surprising to find that 18% of the customers only accounted for 0.3% of the solution line’s total revenues, but they were responsible for over 34% of all customer support tickets. This is not uncommon, especially for solutions that have been in the market for years, if not decades. The customers with the smallest revenues tend to be infrequent users of the solution. When we drilled deeper into the customer support ticket data we found something that looks like this:
The smallest customers were consuming support resources at a significantly higher rate than other customers. Most of their support requirements involved how to use the solution – only 14% of the tickets involved a defect in the solution. One common theme is that the smallest customers almost never invested in formal education and training. The cost of a day of training represented a significant portion of what they would spend in a year on the solution; these customers also consumed finance and administration resources at a rate that was disproportionate to their revenue contribution. An invoice for each customer had to be generated each month, a payment processed, and when required collection activities for overdue accounts were required. Basically continuing to serve these customers was unprofitable.
Once we presented our analyses to senior management, the CEOs made the decision to go forward with a customer rationalization program. It is important to note that in each case the pool of impacted customers was over 2,000. In each case we eliminated the lowest tiers of month to month pricing for each solution. Instead we offered prepaid annual subscriptions. Customers could either adopt one of the prepaid subscriptions or leave the service. The costs of the prepaid subscriptions were fixed between 200% to 250% of what the customers on average had been paying before. We had an extensive process of notifying customers and up to a three month period to move to the new pricing plan. 30% to 45% of the customers decided not to move forward with the new pricing. But more than 50% did agree and the result was that revenues from these customers exceeded what the entire impacted customer base had been delivering before. Profitability increased also due to lower customer support requirements and lower finance and administration costs since 12 monthly bills and payments were replaced with just one annual invoice and cash application. A side benefit was that cash flow was accelerated as well by having one payment cover the entire year of service.
Firing customers is not a recipe for success. I believe it is important for product managers to develop a deep understanding of the dynamics and contribution each customer makes to their business. Analyzing and segmenting customers using revenue tiering analysis and pareto analysis is a good start. I would also encourage product managers to do a moneywheel analysis of their product/solution’s sales transactions. You should focus first on organically growing your business. Later on you can make an investment in optimizing your customer base using strategies and tactics like those discussed in this post.