Lenders hate uncertainty. Uncertainty is the black hole into which dollars disappear and returns evaporate. The primary cause of lender uncertainty? The plain and simple answer is a company's inability to accurately project its operating results. There are many variables involved in developing accurate projections.

One of the variables, the sales forecast, is amenable to analysis that can increase its accuracy and, if done properly, give management new insights into the company, its customers and the marketplace. There are four factors to take into account when developing a sales forecast that will lead to more consistent, predictable and credible results: understanding conflicts, reviewing inputs, analyzing results and creating ownership.

Understand Conflicts

A "top-down" approach to setting sales targets is justifiable in stable companies. However, once conditions begin to deteriorate, the need for a detailed "bottoms-up" approach is required. The initial inputs for the sales forecast should come from the operations and sales managers. Operations managers often have the best grasp of the realities of the existing customer base, while sales managers understand the market and potential pool of customers.

In a troubled-company situation, operations and sales managers easily become conflicted between projecting high or low sales. Managers are motivated to paint a rosy sales picture, since presenting a poor forecast could rapidly lead to layoffs or even closure of their business unit/ department. On the other hand, commissions and bonuses are often linked to meeting and/or beating sales forecasts.

The magnitude and influence that these issues exert when operations and sales managers create a forecast must be fully understood to properly review the forecast inputs. Armed with a feel for the political and organizational environment, the financial executive can then begin to critically review inputs.

Review Inputs

Once a "bottoms-up" approach is employed and motivating factors of interested parties understood, emphasis is placed on the various inputs. Start by checking the historical accuracy of past forecasts. Believe it or not, historical results and trends can sometimes be a good indicator of the future. Look at last year's sales by customer—sort in descending order and do an 80/20 analysis. Ask those specifically assigned to each account how they predict that account to contribute during the next year and why. Individual trends should ultimately be done on a customer-by-customer basis.

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Executives that produce bankable forecasts and execute according to their plans will build financial integrity and enjoy the support of external and internal constituents.

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By definition, historical data only addresses past or current customers. Then the question becomes, what of new business? Make sure someone is tracking win/loss/lead conversion rates—many CRM software packages can actually generate some very useful forecasting data.

Try scenario-planning. If you take the gaming and emotion out of the equation, you are still left with the inherent uncertainties in the world in which we live. At its simplest, three scenarios should be used: good, bad and stable outcomes with an overall probability assigned to each. A more complex example would involve a decision tree that lists out specific scenarios by product line, division, region, customer base, etc., and instead of generic classifications of outcomes, specific events such as a strike, drought or government intervention can be used. By rolling these various factors up the decision tree, you will achieve an expected outcome for the revenue forecast.

Apply a Monte Carlo analysis if sales can be assigned probabilities. The tool is very analytical in nature and is easily added to existing spreadsheets. Let's look at an example of using a Monte Carlo analysis:'

A company has several prospects, each prospect will contribute varying amounts to sales should the business be booked (see Table 1). Management has calculated their expected sales from these prospects by summing all the sales these prospects could contribute and multiplying it times their historical win rate, which creates a revenue forecast of $96 million.

Unfortunately, several problems occur when using this approach. First, each prospect represents a discrete, different contribution to sales. The simple average of these amounts very well could equate to a net sales number that is impossible to achieve given the various permutations. Second, you can't win 30 or 50 percent of a project. Winning or losing a customer is a binary event. You either win or lose the business. Third, a weighted average is only a 50/50 likely number—every executive and lender would like to have a higher degree of certainty than flipping a coin to determine their revenue forecast.

Simply put, Monte Carlo addresses these issues by running various scenarios multiple times while applying individual probabilities by project (see Table 2). A financial executive can dictate the reliability of the forecast in a statistical manner and produce an 80 percent likely forecast of $60 million (see Table 3).

Once you've determined the reliability of the inputs, the resulting output must be analyzed appropriately before communicating with different constituencies.

Analyze Results

Some software programs (e.g., SAP, Oracle, Excel, Adaytum, J.D. Edwards and Hyperion) can do a great job of compiling forecasts. No software can ever replace sound financial judgment and operational analysis to craft credible and tangible forecasts. Identifying and linking the key drivers of the business, market size and market share targets to the financial plans is crucial not only for developing the forecast, but more importantly for determining why the sales target was hit or missed.

Other factors should be incorporated into a reasonable sales forecast. The seasonality of goods along with the number of business days available to generate sales can produce dramatic swings from one period to the next. Credit and inventory policies should be linked to all promotional efforts and should track along with sales.

If management is particularly aggressive in its tactics to increase sales, credit policy may need to be changed, approval times reduced, underwriting made more liberal and extended financing allowed. These factors will become important when converting the final sales forecasts into a cash/working capital forecast. Additionally, remember to decouple the sales quotas from the sales forecast: quotas = management tool; forecast = bankable numbers.

After the analysis is complete and a course of action is decided upon, ownership must be established within the organization to get results.

Create Ownership

The sales forecast ultimately becomes a product of consensus and negotiation among the senior executives, which is then communicated to external and internal constituencies. Lenders and investors hold senior management accountable for delivering results. Management must in turn lead their employees and make them responsible. A simple way to convey this very complex task is this: Set targets, measure results and hold people accountable. Following are some tactics that can be used.

Align words and deeds. Doing what you said you would do builds credibility. If you say you will track performance and expect people to be accountable, track results and publish performance across all interested parties in the organization, especially among the sales professionals. Develop an atmosphere of healthy competition. Devote resources to recognizing effort and rewarding results.

Tie compensation to the meeting and exceeding of forecasts. One should be cognizant that management will study the compensation system in an attempt to game it. This shows the importance and gravity of hitting the numbers and generating cash.

Update forecasts regularly, which in a crisis situation means at least monthly. Look into why forecasts are being beaten or not met and identify root causes. Management is often pleased when forecasts are exceeded; however, accuracy is an important factor in distressed situations. Drastically exceeding forecasts can cause interested parties to question management integrity due to valuation implications.

Conclusion

While venture capitalists invest money on ideas and people, traditional distressed lenders and investors place money based on consistency, predictability and credibility. Executives that produce bankable forecasts and execute according to their plans will build financial integrity and enjoy the support of external and internal constituents.