A too small sales force is a loss of income and a too large or incorrectly allocated one is a waste of resources. Many companies use their sales managers’ gut feelings to determine how many sales people they should have and how they should be allocated. This means that one of the most important decisions for growth and profitability is based on the hidden assumptions of a few people.
A company we recently worked with discovered they were spending too much resources on segments with little potential and too little on segments with great potential – they were wasting resources in some markets while missing opportunities in others.
A sizing and deployment analysis answers the questions:
• How large should the sales organization be?
• How are resources best allocated across geographies, segments and products?
With the current dominance of financial management in most companies, sales executives need to learn to speak the language of finance – they need to be able to present fact-based evidence to effectively argue for and secure the resources necessary to capture the potential of their markets. That evidence has come within easier reach as companies have much more data on markets, costs and sales force activities than they had just a few years ago. Today, most companies have a CRM system of some sort. The data in them are of varying quality but the quality gaps can usually be bridged fairly easily.
We propose to use data on markets and profitability to find the best number and allocation of people, and if any assumptions have to be made, to make them explicit and clear so that they can be valued and so that any risks associated with them can be managed. Experienced sales managers’ instincts are valuable and should be used, but assumptions should be clear and used in a structured approach, as no single person can know all markets and customers and then balance that versus revenue and profit potential.
Traditional approaches to sales force sizing and allocation
The budget approach is based on the faulty argument that “we have as many sales people as we can afford”. This is counterproductive since sales spending should not be seen as a cost but an investment that provides a return. A sales manager in a company that uses the budget approach will likely need convincing arguments to gain support to change the sale force size, as budget levels tend to become anchored and stick over time.
The most commonly used approach is based on historical revenue and margins. A sales person is assigned an estimated target revenue or profitability and sales resources are allocated to segments and products so that each sales person’s territory meets that target. The strength of the approach is its simplicity. Simplicity is valuable as it makes it easier for people to understand and to motivate changes. But revenue and profitability are historical while sales resources should be invested where the future potential is. Therefore this approach is best in markets and for product and service offerings that change very little.
A similar approach is to replace historical revenue and profitability with potential revenue and margins. This approach can be useful as it is forward-looking and relatively simple. It does however not account for the threshold effect and decreasing return on sales (see below), and it requires almost as much data as the more comprehensive return on sales optimization.
With a work build-up approach, companies estimate the amount of work needed to cover targeted segments and add that work up to get the number of people required. It is a good as a complement to other approaches and for new markets where potential cannot be estimated.
Scientific approaches to sales force sizing and allocation
• Optimizing return on sales
Return on sales optimization (ROS) utilizes basic optimization techniques to find the size and allocation of the sales force that provides the highest profit for the company. The return on sales optimization approach has been used for decades in industries with large sales forces like healthcare and pharma. The sales force however is a cost centre but also a means for revenue generation, and since ROS optimizes profits, as opposed to sales cost, it is a powerful tool to gain insights into profit and revenue management.
The profit optimization is based on estimates of potential sales through an estimation of the sales response curve, data on segment and product profitability, and costs for sales resources.
A strength of ROS is that it takes the threshold effect and the decreasing return on sales into account. The threshold effect is a consequence markets and customers requiring a certain amount of sales resources to start generating significant sales. There is no point for a sales rep to spend time to identify a customer’s needs if he does do not have time to finish the deal, and as the word spreads, within the customers’ organizations or between customers in a segment, companies typically spend less sales resources for additional euro of sales. Similarly, as the market potential becomes exhausted it takes an increasing amount of sales resources to secure each additional euro of sales, making it nearly impossible to capture 100 percent share of any market. Not accounting for these effects may lead to incorrect conclusions, especially for large changes in sales force size or allocation.
The weakness of ROS is that it requires estimates of return on sales that sales managers are not used to making explicit. They will feel uncomfortable doing so, at least the first time around, and there will be errors in their estimates. Therefore a ROS exercise should be iterative, to allow for testing and scenarios, and it should be used regularly to enable a feedback and learning process based on actual outcomes. An additional benefit of ROS is that it can generate not only sizing and allocation insights but also sales and call plans.
• Efficient frontier benchmarking
Efficient frontier benchmarking (EFB), as a tool for operational improvement used to benchmark and improve operational efficiency in various industries, has been proven effective for sales force sizing and allocation. It has been used in industries like for example business equipment manufacturing and production equipment manufacturing. It utilizes the statistical techniques regressions and linear programming to determine the optimal size and allocation of the sales force. It can effectively account for differences in markets, competitive situation and market potential, and identify not only the optimal size and allocation but also the efficiency frontier across a company’s sales units. Therefore it has the valuable additional benefit of providing data on the relative efficiency of the internal sales units. With the most efficient sales units identified, a company can identify best practices within the best-performing units and leverage those best practices to make the other sales units as efficient. The profitability impact of making all sales units as efficient as the most efficient one is likely as large as that of achieving the optimal size and allocation of the sales force. To be effective, EFB requires a number (typically more than 10) of fairly homogenous sales districts, some knowledge of competitive activity, and data on sales, sales costs and sales activities that is readily available in most companies.
• Considerations for implementation
The scientific approaches ensure that the decision of sales force size and allocation is based on facts, that assumptions are made explicit and can be tested, and that the decision will have the highest quality possible. However as no approach is perfect it is advisable to use a combination of approaches to balance their respective strengths and weaknesses. For example a scientific approach combined with a simpler approach as a reality check.
Balance number of sales representatives with number of people in sales supporting role
A sales person needs support to be effective. This is especially true in complex industrial sales where many companies have specialized roles for example for technical sales and pre-sales engineering. When managers change the number of sales representatives they also need to scale the number of people in supporting roles accordingly. Take caution as the optimal ratio of sales to sales support people can vary across segments depending on how customers buy.
Where in the business cycle are you?
Market potential is a key piece of information in several of the sizing approaches. The problem is that potential can vary greatly over the business cycle. To determine the optimal size of a sales force size based on potential at the top of the business cycle can greatly overestimate the optimal size and profit potential. Sales resources are expensive to attract and train and having to let go of them due to an error in estimated potential can be a costly mistake. This can vary depending on the economics of the business but as a conservative rule of thumb we recommend companies to size their sales forces using a potential at approximately 25 percent from the bottom level towards the top level of the business cycle.
What is your time perspective?
The optimal size of the sales force depends on which time perspective you adopt. This is simply because spending on sales resources is an investment that takes time to bear fruit – new or reallocated sales people will need time to develop business with their new customers and they will need time to learn. During that time they will not be as effective as the current sales people are, but the company will have the full cost (except bonuses) from day one.
Manage risk
A company we recently worked with discovered they were targeting too many new segments, running the risk of not gaining critical mass in any of them, and facing a great risk if their sales expectations were not met.
There are great costs and risks involved in a large change in sales force size and allocation. The scientific approaches to sales force sizing and allocation can provide powerful insights with potential to greatly improve the performance of a company. The conclusions are however based on data that may contain errors. No market data is perfect, markets can change, competitors can react and expected returns may not materialize. Companies with long sales cycles need to take extra care, as they will need to spend more before they see the results of the changes.
The best way to mitigate these risks is to gradually transition towards the optimal size and allocation while collecting new data and reiterating the sizing and allocation exercise to confirm (or contradict) earlier results.
Plan the transition carefully
Besides risk, the most important consideration is probably the competencies of the current people, cost and time to train them, and availability of people to recruit. Managers need to have a structured approach to define the competencies required to sell and identify any gaps versus the competencies of their current or newly recruited sales people. Competence gaps can be for example lack of product or industry expertise, or lack of consultative selling skills. Managers also need to make sure sales people do not end up spending too much time on travelling. These considerations limit how quickly the targeted size and allocation can be achieved.
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