marketing resource allocation: calculating the right number of sales reps

Customer Information Strategy – Professor Dominique Hanssens Case Submission #2 – November 5, 2010| Study Group #5: Justin Cohen, Doug Daly, Kevin Morra, Brent Morrison, Nancy Sagar

The Age-‐Old Question: How Many Sales Reps Do We Need? Founded in 1889, C-‐Tek has grown revenue for its grinding wheels, sandpaper and abrasives

products to more than $20 billion in 2004. The company’s organizational structure encourages

innovation and entrepreneurial spirit by giving 33 different business operations their own P&Ls,

their own distribution channels, and their own sales teams. As a result, the company reaps the

rewards of occasional product breakthroughs. Yet on the flip side, this decentralized structure

creates resource allocation challenges; with all of these separate P&Ls, it’s extremely difficult to

judge where the most promising growth investment opportunities lie.

John Sawyers, the sales manager for grinding products, has experienced this issue firsthand –

he’s been losing his battle to gain additional resources for the past five years. His division is

growing steadily, but his 14 sales offices are losing ground to the competition, meaning he is

giving up market share. He has repeatedly proposed to add to his salesforce of 52 reps, but the

company continues to deny his requests. C-‐Tek management’s response to his proposals isn’t

uncommon, since troubled units in any business typically request additional resources in order

to reverse a performance slide. It’s easy to invest in divisions that are doing well, but when

they’re struggling, it’s easy to point the finger at managers or to cut further investment until the

situation turns around. That’s why so many companies slash their marketing budgets during

downturns in the economy – they base their investments on last year’s performance rather than

focusing on the return (e.g. profit) they can generate through various investment levels. John

has found himself in this very situation; fortunately, he’s smart enough to employ a forward-‐

thinking resource allocation analysis.

Group 5 – C-‐Tek page 2

Analytical process

We used the projections developed by John’s “base team” to run a resource allocation analysis

including sales response functions for each branch -‐-‐ that is, expected revenue as a function of

staff level. The first step was to use the team’s revenue projections to “calibrate” our model and

create the sales response functions. After our first pass at the data, we discovered that we

needed to use the “expert user” logit analysis in the ME-‐XL software, since the standard analysis

produced poor response curves like the Twin Cities curve in Exhibit A. By upgrading to the logit

analysis, we generated S-‐shaped response curves that more accurately modeled the impact of

additional resources (sales reps) on total revenue.

Once we calibrated the model by creating those city-‐by-‐city response curves, we ran the

resource allocation analysis to answer these questions:

1. If C-‐Tek continues to constrain John’s staffing level, can he increase his expected net

margins by shifting sales reps among his offices?

2. If John can optimize his staffing resources, what is the optimal (unconstrained) staffing

level and resource allocation among his branches to achieve the optimal level of profits

for the business?

Question 1: Reallocation of his current 52 reps

With a gross margin of 35%, C-‐Tek predicts about $28.7 M in net margin (revenue minus the cost

of the sales force) over the next year. The good news is that John can increase net margins to

$34.2 M by reallocating staff among his 14 branches as shown in Table 1. The reallocation

results in an additional $5.4M in gross profit, a 19% increase over the current sales force

allocation.


Table 1: Expenses and Profits for Constrained Sales Force by Gross Margin %

To achieve these results for the case of the 35% gross margin, John will need to shift sales reps

among the different offices as shown below:

This graph shows that John must eliminate three of his sales offices altogether – San Francisco,

Philadelphia, and High Point – and move those sales reps into more lucrative cities (Cleveland,

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Optimal vs current headcount at C-‐Tek branches while maintaining overall staff levels

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Optimal

Gross Margin % Cost ($000)Gross Margins

($000)Net Margins

($000)20% 7,645.31$ 23,337.63$ 15,692.32$ 25% 7,644.83$ 29,568.64$ 21,923.81$ 30% 7,644.89$ 34,855.04$ 27,210.15$ 35% 7,645.03$ 41,829.65$ 34,184.62$ 40% 7,644.98$ 46,556.82$ 38,911.83$ 45% 7,644.92$ 52,248.65$ 44,603.73$


Atlanta, Seattle, Los Angeles, Boston, Nashville, and Dallas). Such a shift could be quite

challenging for a company like C-‐Tek; their sales reps are likely mid-‐to-‐senior level, have roots in

their various cities, and strong backgrounds in the industry and product lines. A San Francisco-‐

based rep may not be interested in moving to Cleveland, so the company will need to determine

how to best serve customers in each market (can reps work virtually and travel for meetings, or

do they need to be physically located in each city?). If reps must relocate but are unwilling to do

so, the company may face a recruiting challenge to replace their expertise, or they may need to

increase base salaries/commission rates and incur non-‐trivial moving expenses to hang on to

sales stars.

In addition, to fully maximize profit, the analysis produced fractional headcounts at many

branches; C-‐Tek needs to decide whether it is practical for one salesperson to cover multiple

areas or if the staffing levels should be rounded up or down. The company faces additional

other decisions regarding how much of a change in staffing the company can endure without

breaking all current relationships with customers. As these practical issues are addressed, the

projected income would reduce somewhat, but these early stage results are promising, with

total net margin by branch shown below:

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$2,000

$4,000

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Optimal vs current net margins at C-‐Tek branches while maintaining overall staff levels

Optimal

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Unconstrained Case: Optimal Staff Levels and Allocation

For the unconstrained case, every branch sees an increase in staff, which allows the company to

maintain relationships with existing customers while avoiding the relocation challenges and

expenses. The new optimal staff level by gross margin is 97 FTEs at a 35% gross margin as

shown below:

Table 2: Gross & Net Margins w/ Unconstrained Sales Force by Gross Margin %

Even with a minimal gross margin of 20%, the optimal staff level is 83 heads -‐-‐ an increase of

over 50% from today’s level. The new staff is allocated across the 14 branches as shown below:

Gross Margin %Salesforce

size Gross Margins Net Margins20% 83.0 29,304.76$ 17,108.60$ 25% 89.2 37,664.72$ 24,552.21$ 30% 93.6 45,915.72$ 32,151.52$ 35% 97.2 54,130.79$ 39,848.20$ 40% 100.1 62,324.69$ 47,612.63$ 45% 102.6 70,509.00$ 55,427.19$

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Optimal vs current headcount at C-‐Tek branches with unconstrained staff levels

Current

Optimal


Based on this analysis, adding more staff will increase net margins by over $5M versus just

reallocating the sales force. Further, it is often easier for sake of continuity with customers and

employee morale to add staff than to shift or remove staff. However, adding staff poses

additional risk as the company is committing itself to additional SG&A cost. There is also a

question as to how many new people a branch can absorb and still function smoothly (for

example, do they need more support personnel as well?) and whether such a recommendation is

based on an overly optimistic scenario. Therefore, we did a sensitivity analysis on the expected

net margin as a function of total sales people over a range of gross margin levels.

As shown in the graph, there is a point of diminishing returns where additional staff leads to a

lower net margin. However, even for very low margins the optimal headcount is over 80.

Further, considering the flatness of the curves, the staff level can be within 5-‐10 FTEs versus the

optimal level and still reap very nearly all of the potential benefits. With this in mind, we

estimate the relative benefit of different staff levels on overall net margin:

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Salesforce Headcount

Higher gross margins demand a larger salesforce

45% Gross margin

40% Gross margin

35% Gross margin

30% Gross margin

25% Gross margin

20% Gross margin

Optimal Headcount


This chart shows the change in net margin for different staff levels versus the optimal

reallocation of the existing 52 member sales force. As shown in the graph, most of the benefit

to the company happens with a staff level between 80 and 100 people.

Breakthrough Technology

C-‐Tek is blessed with an entrepreneurial culture and history of innovation, and the product

development team believes their next new technology could increase the company’s profit

margin to 40% and increase the total market size by 20-‐25%. With such a dramatic potential

growth opportunity available, the company needs to have adequate sales staff ready to

capitalize.

With a 40% profit margin, increasing staff to 100 FTEs is the most profitable option assuming no

growth in market size (see graph at top of page). As this technology increases market size, a

greater increase in staff would ensure the company capitalizes on this new market opportunity

-‐$2,000.00

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Change in Net M

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Gross margin (%)

Raising headcount improves net margins

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before imitators fill in the gap. However, there are three reasons we can’t make a

recommendation today about the specific additional headcount needed:

1. Growth drivers: If their market size calculation is based on total revenue for the product

line, is a large proportion of that growth being driven by price increases or purchase

volume increases from existing customers? If so, the current sales staff may be able to

capture most of the existing revenue with little incremental effort, meaning that the

need for additional headcount growth may be negligible. But if growth will be driven by

a new customer segment or purchases from new decision-‐making units, C-‐Tek will need

to increase headcount to capture that revenue. Without knowing the level of effort

required to generate various revenue scenarios in the calibration model, we can’t project

total or city headcount with any degree of accuracy.

2. Adoption rate: Over what timeframe will the innovation be adopted? Without knowing

this, we cannot know when or how quickly the market will reach the expanded state. We

would need to know more about the diffusion rate before staffing up to capture it.

3. Timing & sales process: Finally, we need to know exactly when this innovation will be

ready to launch and what the length of its sales process may look like so that we can hire

reps at the appropriate time. For example, C-‐Tek may decide that reps need to start

scheduling initial meetings with prospects using prototype products before the new

product even launches.

We do, however, recommend that C-‐Tek hire us to run this analysis after the innovation has

been released and the team has enough adoption and driver knowledge to provide estimates for

new response functions. At that time, we can run the analysis and tell them how many

additional sales reps they may need (and over what time horizon) to fully capitalize on the

opportunity.


Final Recommendations

C-‐Tek is clearly understaffed in even the most pessimistic of future projections. A simple

reallocation of existing staff (the “constrained” model) may improve net margins by 19%

provided the re-‐shuffling does not lead to burned out staff, difficult relocations, rehiring, and

abandoned customers. Further, at this existing staff level, C-‐Tek wouldn’t have the sales

resources to support the breakthrough technology that is apparently just around the corner.

More importantly, this reallocation solution does not properly address C-‐Tek’s key issue: sales

resources produce future profit, and staffing should not be calculated purely on cost measures or

past performance. Instead, C-‐Tek must invest in resources to maximize future profit. In

economic terms, the goal should be to staff at the point where the marginal revenue for a

salesperson equals the marginal cost of that salesperson, aka the point at which marginal profit

equals zero. This exercise produces the headcount for that point and tells C-‐Tek how many reps

to hire to based on future profit margins.

This dramatic increase in staffing has its own challenges. For example, recruiting and training

the new staff could take significant time given the specialized expertise John’s team may

require. More importantly, this projection tool does not take into account how the competition

will respond. It is quite likely that a surge in selling effort by C-‐Tek will be met by a similar surge

across the industry, thus leading to more sales staff pursuing ever smaller opportunities. To

illustrate this point, note the projected sales as a function of headcount for optimally allocated

staff and a 35% margin shown below:


The baseline sales estimate is $103.9M, so to think a major player like C-‐Tek can double its sales

force and increase sales by $50M without significant competitive response is overly optimistic.

So, since the current staff of 52 is woefully inadequate, and since ramping to 100 appears wildly

optimistic right now, we recommend an intermediate headcount of 80. This level is at or near

the peaks of the sensitivity analysis curves on page 6; it requires $3M less to staff than the 100

FTE level, and it gives C-‐Tek additional staff to exploit the breakthrough technology when it

arises. We also do not recommend asking reps to allocate their time to multiple territories in

order to meet the fractional FTE counts generated by the software.

$110,000

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Headcount

Higher headcount leads to increased sales


Final Staffing Recommendation: 80 Reps

With a total staff of 80 in these offices, projected revenue is just 0.7% lower than the optimal

allocation with fractional headcount.

Efforts and outcomes / Segments

LA SF Seattle Boston Philly Cleveland Atlanta Nashville High Point Dallas Chicago Cincinatti St LouisTwin Cities

Headcount 9 5 6 6 6 7 6 5 5 6 6 5 4 4sales ($000) 12,833$ 6,358$ 13,335$ 12,802$ 7,551$ 14,552$ 10,834$ 9,956$ 5,624$ 9,466$ 13,421$ 9,941$ 6,954$ 10,225$

Efforts and outcomes / Segments

LA SF Seattle Boston Philly Cleveland Atlanta Nashville High Point Dallas Chicago Cincinatti St LouisTwin Cities

Headcount 9 5 6 6 6 7 6 5 5 6 6 5 4 4sales ($000) 12,833$ 6,358$ 13,335$ 12,802$ 7,551$ 14,552$ 10,834$ 9,956$ 5,624$ 9,466$ 13,421$ 9,941$ 6,954$ 10,225$


Exhibit A

Sample response curve using the standard analysis versus the expert logit analysis we used.

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Effort for Twin Cities

Twin Cities response curve -‐ basic model

Calibration Data

Response Curve

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Effort for Twin Cities

Twin Cities Response Curve Using Logit Model (expert users)

Calibration Data

Response Curve

marketing resource allocation: calculating the right number of sales reps

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