Insights

The problem with peanut butter

Let Candela Partners refine your customer profitability analytics and deliver smart business model improvements to drive operating margins.

One-size-fits-all cost allocations could be costing you millions

Most of our clients have a decent sense of customer profitability at the gross margin level.  Many, however, do not have good visibility to fully allocated customer profitability, accurately accounting for indirect costs associated with serving that customer (e.g., sales and marketing costs, customer service costs, onboarding costs, etc.).  Often, clients will "peanut butter" these costs across all customers by allocating according to the % of revenue a particular customer represents. Typically, this does not represent the extent to which a given customer “consumes” that particular cost item.

Understanding customer profitability is critical for any business because it allows a company to identify which customers are most valuable and allocate resources accordingly.  Beyond optimizing resource allocation and investments, identifying and acting on unprofitable customers can deliver dramatic and rapid improvement of the bottom line.

How to fix it

There are several ways to calculate customer profitability that go beyond revenue-based allocation, but one common method is the cost-to-serve model.  This approach requires understanding both the direct and the indirect costs directly attributable to a particular customer.

In our work with clients struggling to maximize ROI on sales and marketing investments, we always dig deep into customer profitability, how it is calculated and whether it accurately accounts for the true cost-to-serve.  

Our results

Here are some examples of how changing allocation methodologies drastically shifted the way our clients understood the profitability of their customers:

  • For a $3B manufacturer, traveling to sell to far-flung small clients was extremely time-consuming and expensive. In addition, the client's products were not selling through at sufficient velocity to warrant multiple sales visits per season.  As a result, these customers were break-even at best. We shifted marketing resources to increase sell-through at the point of purchase and create turnkey programs for sales teams to cost-effectively drive local demand.  We further shifted the sales model to drive small customers to buying shows where agents could efficiently sell to small customers, liberating travel time to be allocated to more productive sales activities.  Productivity per sales agent improved by 18% 2 years post-implementation of these changes.
  • For the same manufacturer, freight costs were borne by the company and not allocated at a customer level.  When allocating freight back to customers, we identified that freight consumed a disproportionate amount of profit per order for smaller orders, regardless of customer size.  Margin on these smaller orders were 300bps less than the average margin on larger orders.  Changing minimum order quantities and instituting minimum thresholds to qualify for free shipping enhanced the margin on these orders by 10%.
  • For a software company, onboarding costs were not tracked at the customer level but represented a significant line item in the indirect costs on the P&L.  We structured a brief activity survey and tracked time spent in onboarding activities for 3 months, using the data to inform onboarding costs by customer segment and size.  Shifting smaller customers to a more self-directed onboarding model shortened time to go-live, increasing bookings-to-revenue conversion by 11%, and reduced onboarding expense for this segment of customers by 19%.
  • For a global outdoor apparel brand, international customer service costs were allocated based on a % of revenue.  When we dug deep into their customer service model, we reallocated these costs based on the cost of resources with specific language capabilities and further by call center data on the number of calls per customer.  Redesigning the support model for low-volume, unique language markets improved operating income in those regions by 24%.
  • For a B2B business service and software company, technical support staff costs were not accounted for when assessing customer profitability.  We analyzed their service case data and shifted the allocation model to assign a standard cost per service ticket multiplied by the number of tickets for each customer, more accurately reflecting the customer-level consumption of support staff resources.  Combining this insight with a needs assessment by customer segment, we identified an opportunity to sell service plans to smaller customers with high-touch needs which transformed the tech support function into a profit center.

Accurately modeling customer profitability is critical to enterprise profitability, and it doesn’t necessarily require detailed activity-based costing initiatives to get to the right answers.  A smart line-by-line review of indirect costs and subsequent revision of allocation methodologies to reflect actual consumption can illuminate substantial opportunities to improve profitability. In each of the above cases, a more realistic view of customer profitability informed strategic actions that materially improved performance.  We worked with clients to adjust their sales and marketing priorities, as well as their service models for certain segments of customers, to drive better results.

Please reach out to us if you suspect that your customer profitability model is covered in peanut butter.