Developing the Revenue Model


The most important component of a projection gets the least attention.

A financial forecast is a critical tool in managing a dynamic business.  When used effectively, the forecast acts as a living, breathing document used by all internal and external stakeholders.  It is the tool that guides the purchase of inventory, allocates time and resources, and represents the framework by which the external world views the company.  In theory, it represents the sum of all of the variables impacting the business and should be the output of a strategic planning process that considers market demand for the goods and services, drivers impacting that market, competitive responses to changes in the market, and the manner in which the firm will penetrate the market in a dynamic environment.

What we have very commonly found is that financial forecasts tend to be estranged from market data and the strategic planning process and are simply linear extrapolations of the past.  Even when masked behind complex Excel models, these projections are not the result of a comprehensive understanding of the business environment and leave the company unprepared to address both the external pressures facing the business and the internal requirements to make sure the forecast is met.

The greatest deviation of actual results from forecasted results occurs in the revenue line.  Our experience is that, given a projected revenue model (units and prices), companies can build reasonably accurate cost models.  The cost side is easier to quantify because, for the most part, the company has or can gather internal data that considers labor, machine time, occupancy, and administrative costs.  Revenue, on the other hand, changes with external market conditions and most companies have limited knowledge of, or access to, external market data and trends. In this article, we discuss how to connect a strategic analysis of the market with an actionable plan using operating metrics to develop greater confidence in revenue forecasts.

Understanding Market Data to Drive Strategy

Too often we hear management teams assume that a historical rate of growth will continue without a basis in market analysis.   They may have an intuitive feel for the competition, for product differentiation, and for overall market growth based on discussions in the field but this thinking is generally not integrated into the forecast, and rarely is based on data.  This carries considerable risk and, unfortunately, the battlefield of corporate history is filled with the corpses of businesses that took their historical growth rate for granted.

Businesses exist and are able to generate revenue because they solve a need for customers at a price supported by a given cost structure.  Their growth, at the simplest level, is constrained by the market they participate in, the competitors they compete with, and their own capabilities. In order to grow, businesses must take market share from a competitor or maintain share in a growing market.  Neither happens by chance, and making informed decisions about actions to achieve these goals requires a clear understanding of the specific customer need (demand), the amount of goods and services needed (market size), how volume is affected by price (elasticity), the number and characteristics of direct competitors (competition), and the availability of potential substitutes.

Obtaining this data is difficult for most middle market businesses, and often requires looking externally.  Even sophisticated private equity firms with specific industry expertise don’t usually conduct these analyses themselves, often relying on strategy consulting firms to assist them.  These firms not only have access to the universe of relevant (and expensive) industry research, but also have extensive proprietary databases, and expertise in developing customized data through surveying. Customized data collection could target buyers’ views on products and services, the factors influencing their buying decision, the expected volume of their need, their opinions about different competitors, and other avenues for satisfying their need.  Firms such as McKinsey, Boston Consulting Group, Bain and others do this type of work for large corporations and investment firms.  Middle market firms need to search a bit harder to find someone who can size the project to be affordable, or they need to try it on their own with limited resources.

The output of this work is commonly a forecast of the size of the overall market, the pricing pressures implied by alternatives/substitutes, the share available to the business’s selection of products and services, and the strategic levers or risk factors in each element.  This is the opportunity for the business.

Putting the Plan in action

Armed with data validating the opportunity and the competitive positioning, a commercialization strategy should be developed that links key operating metrics to specific growth strategies.   Each operating metric should describe how the plan is going to be completed.   For example, if the commercialization strategy is driven by productivity of an expanding industrial sales force, then the revenue model should include such variables as the number of sales representatives, training time, productivity of each representative, specific accounts to be targeted, the length of the sales cycle, and the success rate. In a consumer business with retail distribution, the plan needs to identify the specific targeted retail accounts, the rollout by retailer, and how four-wall productivity will be affected over time.

This “bottom’s up” process is important because it not only explains how the plan will be achieved but it gives quantitative metrics with which to gauge progress on a short-term basis. As better knowledge is gained about each factor, further calibrations can be made, improving the reliability of the forecast.  Many businesses skip this step and believe they are building appropriate revenue models because they break out each segment or product SKU. They may go through long internal processes where they debate each number, but unless the operating metrics are clearly defined (and measured), the effort just disguises a gut instinct for how much the business will grow.

Conclusion

Building an accurate forecast is incredibly difficult and will almost always be wrong. The value in devoting time and resources to developing forecasts is not to predict the future, but to provide management with a tool to continually evaluate potential outcomes under constantly changing conditions. This process enables a culture where informed decisions can be made to aggressively analyze opportunities, consider risks and craft strategies—decisions that lead to greater value creation for the business and its owners.

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