Allocating Headcount When Data Is Lacking but Opinions Aren't

 

Here's a tale of two sales districts: District A with three reps and District B with five. The manager in District A is happy with his staff, generating, on average, $750,000 in sales per rep. District B's sales manager is clamoring for more resources, claiming that she could increase sales by $850,000 if she had one more rep. So you could pick up an easy $100,000 in incremental revenue, with no incremental cost, if you transferred one rep from A to B. Easy money, right?

But wait a minute. Does it maybe make better sense to leave the sales force in District A intact and add new sales reps to District B? If an additional sales rep costs $150,000 and gross margins are around 30%, adding an additional rep would produce an incremental gross profit of 30% x $850,000 or $255,000 for that $150,000 investment, an ROI of 70% ($255K-$150K/$150K). Compare that to the $30,000 gross profit gained by reallocating one person from A to B.

 

Setting, allocating, and reallocating budgets for marketing and sales headcount can be a daunting task filled with emotionally and politically charged landmines — especially when the process also involves allocating costs back to products, market segments, channels, and geographies. Most companies still set headcount budgets the old-fashioned way, based on some combination of percentage of sales, sales potential, competitive pressure, intuition, or simple inertia. Yet with just a little information, most companies can make much smarter, more defensible decisions.

How It Works
Absent historical data, the relationship between headcount and business outcomes is mostly related to managerial beliefs about how the market operates. These beliefs can be summarized in a sales response function, an Excel model used to determine what marketing or sales force sizing and allocation is most cost-effective or optimal. The model predicts the sales volume and profitability from a particular marketing or sales force allocation plan or determines which plan is most profitable for a given situation.

To start this process in your own company, break the marketplace into segments according to the way marketing or sales resources are currently allocated. Then the interested parties — marketing and sales managers, sales reps, marketing planners, marketing researchers, new product developers, and senior managers with overall budget approval — gather to outline their assumptions regarding the:

This assumption-gathering session often includes market growth projections, new product introduction assumptions, competitive and environmental forecasts, and other general or segment-specific market predictions. When hard data are not available (and they rarely are), subjective inputs and best guesses are quite workable. The approach typically takes one to two days of management time (see figure 1).

Once you have that information, you can build a simple Excel model to see the implications of your combined judgments. Best of all, there is no need to reach consensus. Different scenarios — optimistic, pessimistic, best guess — will lead to different, conditional recommendations: "If Jim is right about the responsiveness of the market, we ought to add a rep to District A. However Sally's projections are not that optimistic. Sam, what do you think is most likely to happen in District A?"

An Example from the Pharmaceutical Industry
When Syntex Labs introduced Naprosyn, it allocated a modest selling effort to the drug, and sales began to take off. Figure 2 shows a simple version of the Syntex Labs marketplace — Naprosyn was using only 97 of Syntex's 430 sales reps. Its existing plan was projected to generate $226 million in profit.

 

If Syntex were to redeploy sales resources from some of its other products to Naprosyn, the firm could increase its profit to $270 million, a leap of more than 20%, with no increase in the sales budget. Syntex decided to affect a partial reallocation of effort and saw an increase in profit of more than $25 million over its previous sales structure.

A more systematic, disciplined approach using a combination of available data and subjective best guesses as inputs into a response model generally produces a 5% to 15% increase in sales revenue with the simple reallocation or redeployment of existing resources. With little-to-no increase in spending, you, too, can identify the optimal resource allocation plan or help explain the impact of various alternatives in a way that escapes the intuition-based planning quagmire.

For a working sample of a sales response model, please click here.