Distributors are increasingly aware of the need to understand customer profitability, and more tools are available than ever before to help with this process. This article examines how to deploy profitability tools and how to avoid making critical business mistakes in the process.
This is the first article in a three-part series on customer profitability analytics.
Over the past few years distributors have increasingly focused on understanding the profitability of specific customers or even individual transactions. There are many flavors of the concept, which goes by names such as top grading, cost to serve (CTS) and customer stratification. The idea is that by calculating the gross margin, expenses and working capital involved in each transaction at a granular level, a distributor can make better decisions about pricing and resource allocation. Why continue letting “loser” customers suck all your profit? Instead, identify the high-profit customers and take really good care of them so they never leave.
This analytical approach to profitability is long overdue, and it has generally been a healthy exercise for the industry. It’s important to understand where money is being made and where it is not. Customer profitability analytics have helped many companies improve gross margins, reduce service costs, uncover operational inefficiencies and deploy limited sales resources more effectively.
The analysis usually produces compelling charts that rank customers by net profitability. These invariably show “gold” accounts producing the majority of profits on the left side, “lead” accounts that lose money on the right side and the break-even “silver” and “bronze” accounts in the middle. The thinking that results from these charts is along the lines of “If we could just get rid of those lead guys we’d double our profit.”
But as my colleague Mike Emerson says: “If I could just shoot every hole like my best ones, I’d be on the pro golf tour.”
Most distribution executives know intuitively that their businesses are a mix of profit contributors and profit detractors. The challenge is not recognizing how wonderful it would be if everyone were above average; the challenge is actually getting there.
It Starts with ABC
Almost all customer profitability analysis tools are based on the concepts of activity-based costing (ABC), a method of breaking down shared expenses to specific activities so they can then be assigned based on how customers consume the activities. For example, if Marla, a warehouse employee, costs $30,000 annually and picks 3,000 order lines per year, then the average customer is assumed to incur a picking cost of $10 for each line it orders.
Dissecting all the activities, cost drivers and transactions like this for everyone in the organization and mapping them to all customers is no small effort. It gets tedious and resource-intensive. To keep the project manageable, the majority of distributors who go down the ABC path make a lot of simplifications. They often limit themselves to a handful of characteristics that are easy to extract from their business system. A common approach is to simply use a count of outbound order lines along with cost factors for different delivery types (e.g., vendor drop ships, warehouse deliveries or counter pick-ups).
Software is available that can help deal with the volume of data generated by ABC analysis. The best packages provide tools for cleansing data, clear graphics to visualize the profit picture, and analysis capabilities to enable managers to slice and dice different combinations of customers, products and sales territories on the fly. These tools provide data to replace anecdotes, helping management move beyond endless debates with sales reps about what is “good” business. They arm sales teams with quantitative information for setting prices or negotiating with customers. It is not uncommon for a company to gain a full point of gross margin simply by making sales reps more clearly aware of profit-draining customers.
It is important to apply common sense and recognize that precise numbers are not necessarily accurate ones. An old professor of mine used to make fun of our tendency to treat numbers as somehow more real when they appear on a spreadsheet or colorful graphic. Beware of
false precision, or what he called “seat of the pants to the fourth decimal.”
Consider Your Assumptions
In our firm’s experience, there are two common sources of material inaccuracy. The first is oversimplification. Getting an 80 percent accurate picture is usually sufficient for decision-making. Unfortunately, some ABC simplifications can take that picture below this 80 percent threshold. In the warehouse it is common for order changes and expedites to consume 40 percent of the outbound labor force, especially in highly automated facilities or those that use zone or batch picking. In back-office functions it is common for errors and discrepancies to consume more than half of the labor cost. For example, payables clerks can often process hundreds of “three-way matched” invoices (i.e., where the purchase order, receiving document and invoice all line up) in the time it takes to reconcile a single mismatched one. If a model assumes that a customer’s payables cost is based purely on its invoice quantity, this effect will never be seen. Rather than trying to reduce invoices from all customers, a far more effective approach might be to address the few customers with high error rates.
Putting too much faith in “close-enough” numbers almost put one East Coast distributor out of business. The company’s cost-to-serve calculation estimated delivery expenses based on a customer’s distance from the warehouse, which seems reasonable. In reality, cost was driven almost entirely by truck routing. A remote customer at the far end of a standard route was actually cheaper to serve than a closer one that wasn’t adjacent to any other deliveries. After implementing pricing and service adjustments based on the flawed assumptions, the company found its trucks bouncing back and forth through traffic-clogged cities, half empty and usually late. Its overall revenue dropped by far more than expected, and its expense levels actually rose. The company was forced to abandon the strategy and had to work for more than three years to regain the business lost from its estranged customers.
This is an extreme example. The point is not that assumptions are always bad, but rather that distributors must be careful about how they make and interpret the results. If the cost formula relies solely on a count of order lines, then the answer will always be some form of “get the customer to order less often.” But it may be that a better answer is to implement a “perfect order” process in which no human intervention is required. This might let the customer order more frequently so it maintains lower average inventory levels. If the ABC analysis is too cursory, the better approach may never be identified.
Selling Costs Are Different
The second source of inaccuracy is in demand creation, what distributors loosely call selling costs. These include all the things a distributor does that lead to a customer’s intention to buy, such as prospecting, relationship building and consultative sales calls. The activities that come after the customer’s intention to buy can be considered “demand fulfillment” services, including order entry, warehouse picking, delivery and the invoicing cycle. ABC is very good at assigning demand-fulfillment costs to customers because they are largely transaction-driven. All other things being equal, twice the order lines equals twice the cost.
But demand creation costs are not amenable to ABC because they aren’t connected to individual transactions. While it’s relatively easy to tie Marla’s pick to a specific customer, how can the cost for prospecting, lunch-and-learns, travel time and sales meetings be accurately allocated? ABC comes from the world of manufacturing, where factory labor costs are often higher than material costs – the opposite of what is typically seen in distribution. Our benchmarks indicate that only 25 percent of most distributors’ labor expenses
are in production-line jobs that can be readily tied to specific transactions, such as warehousing, order processing and purchasing. Other costs can be made to look like they are customer-driven on a spreadsheet, but the reality is that they will not change as a result of anything any individual customer does or stops doing.
To accurately measure selling costs, management needs to understand how sales reps actually spend their time and how sales resources vary throughout the customer lifecycle.
Careful with the Sales Whip
Customer profitability analysis is often promoted as a way to help sales reps find more desirable business. Of course reps should have at least a rough idea of the company’s net profit and how it is influenced by customer and product mix, but there needs to be care to not overestimate their influence over the profit levers.
Our firm has seen many attempts to incorporate cost-to-serve factors into sales rep management and compensation. The majority of these have either foundered or been counterproductive. In a typical example, a construction supply distributor found its sales reps ordering factory drop ships from thousands of miles away for items that were currently sitting on the shelf of its local warehouse. Why? Because their commissions were tied to the CTS formula, which showed that warehouse orders were more expensive than direct shipments from the vendor.
The cardinal rule of incentivization is: Don’t reward or punish for factors that are outside the individual’s control. It isn’t effective and generates anxiety and resignation. Realistically, reps lack control over the key drivers of CTS. They can’t influence warehouse productivity, truck routing or, in many cases, the customer’s ordering cycle. Worse, they rarely understand the complex formulas and almost never believe that calculations are accurate. Most times they see them as a ploy to reduce their pay or deprive them of the fruits of their own labor. They inevitably become frustrated trying to do something they are not well-equipped to do instead of what they were hired to do.
Encouraging sales reps to focus on the “gold” customers may lead to the exact opposite of what is needed to grow. You want your best hunters going after market share, not making buddy calls to service big, loyal customers. Today most distributors can’t afford to have their most expensive sales resources focused on anything but pioneering new growth opportunities and attacking the competitors’ crown jewel accounts.
The bottom line is that customer analytics are a proven platform for improving profitability. But they are not a solution in a box. Understand the underlying assumptions and limitations to ensure that the right conclusions can be drawn and acted on. In some cases, the results may be sufficient to take immediate action for margin or cost improvement. In others, the most important output will be identifying the areas of business that warrant deeper investigation.
Part 2 of this series will explore how to use customer analytics to drive profit improvement.
Steve Deist is a partner with Indian River Consulting Group, a firm focused on market access for distributors and manufacturers. Contact him at firstname.lastname@example.org.