The Importance of Sales Forecasting in a Recessionary Economy

February 26, 2009 – 4:55 pm by Namrita Singh

If revenues and profits are the lifeblood of a business, then sales forecasts are the leading indicator of a business’s financial health. Faced with the pressures of today’s weakening economy, keeping a check on the financial health of a business is essential for planning ahead and preparing the company to best deal with unknown cliffs and craters.

Given the complexity and size of today’s organizations, sales forecasting is no longer an isolated exercise. Rather, it must be integrated into all facets of an organization to produce a business that runs more efficiently; thus saving money, increasing profitability and serving its customers better. Quality people, accurate data, current systems, relevant policies, and streamlined processes can support reliable sales forecasting, but it is a wasteful activity if it is not incorporated by management into the very core of decision making for all key business functions:

• Financial Planning: Sales forecasting is a vital cornerstone of a company’s budget, which helps predict cash flows, monitor prices and control operating costs to guarantee profits.
• Financial Reporting: Wall Street measures the success of a company by how well it meets its sales targets. Inaccuracies in the sales forecasts may lead to unfavorable earnings discrepancies, which result in a decline in share prices and puts the company’s performance and management’s ability under close scrutiny.
• Sales Operations: Sales forecasts help enable an efficient and effective sales force through sales territory planning, refocusing limited sales resources on areas with maximum revenue potential, and fostering better sales collaboration.
• Manufacturing: Sales forecasts drive demand planning and inventory planning, aligning production capacity to avoid expensive and inflated inventories.
• Product Planning: Sales forecasting forces businesses to look well ahead in order to plan their investments, launch new products and decide when to withdraw products.
• Marketing: Sales forecasts provide an evaluation of past, current and future sales levels, allowing marketers to tighten and focus marketing spend on a promotional mix that will maximize returns.
• Human Resources: Sales forecasts also help in human resource planning by avoiding surprise layoffs or scrambling to find new hires needed to meet new business demands.

An argument can be made that without incorporating accurate sales forecasts into decision making, companies are creating many of the serious problems that they will eventually struggle with in the near future. If accuracy is so important, then why is it that forecasts usually turn out to be wrong?  The reasons behind the inaccuracies are best understood by examining the techniques employed in creating forecasts.

• Judgment Based Forecasting – This involves a large degree of reliance on field sales personnel who are directly in touch with customers and understand changing buying behaviors and marketplace patterns. However, such forecasts are enriched by the application of gut feelings and wishful thinking using subjective, extrapolation and naïve techniques.  Without proper documentation of these judgments, large distortions in the forecast can occur when forecasts are passed up sales levels.
• Statistical Forecasting – This involves relying on historical trends to predict short term and long term future performance. All statistical methods either even out the peaks and troughs in sales history to produce trend-based forecasts, or else they look for repeated patterns to create future forecasts. Mathematically, it is possible to forecast sales with some precision. Realistically, however, this precision can be dulled because of external market and economic factors that are beyond a business’s control.
• External Causal Factor-Based Forecasting – This involves relying on industry predictions for growth or shrinkage in market segments, economic predictions of consumer buying power, predictions of the effects of political change, and competitive intelligence regarding new product launches, new advertising campaigns and changes in pricing policies.

None of the techniques mentioned above can provide a good forecast if used in isolation. The method most likely to succeed is forecasting from the ‘bottom up’, and reviewing forecasts from the ‘top down’. This means building a robust forecasting platform that uses field knowledge of the business as the basis of the forecast and enriches it with informed management judgment that leverages statistical analysis of past sales performance, expected market conditions and external causal factors.

But not all businesses are capable of adopting such a robust forecasting platform due to the time, effort and cost involved. The appropriate forecasting technique for a business or organization should be determined by its particular needs and must consider the following set of forecasting attributes:

• Degree of accuracy required – If the decisions made on the basis of sales forecasts have a high risk attached to them, then the forecast should be prepared as accurately as possible. A combination of all three techniques may need to be used even though this involves more cost and effort. Management needs to think through what constitutes an acceptable tolerance of forecast error and what metrics/ governance are in place to monitor accuracy.
• Forecast horizon – The time horizon that the sales forecast is intended to cover can affect the choice of forecasting technique. Is the company forecasting next week’s sales, or forecasting what will happen to the overall size of the market in the next five years? Depending on the quality of field knowledge, judgment-based forecasting may be sufficient for shorter time horizons.
• Availability of historical data – Position in the product life cycle can affect the availability and hence the ability to use past sales data for statistical forecasting. For products at the “introductory” stage of the product life cycle, less sales data and information may be available than when products are at the “maturity” stage, when time series can be a useful forecasting method.
• Access to competitive data – In some markets there is a wealth of available sales and competitor information, while in others it is hard to find reliable, up-to-date information, potentially limiting the ability to use external causal factor-based forecasting.

The relative importance of the different attributes listed above also varies given the nature of current economic conditions. In a recessionary economy where cost containment and resource optimization are a top priority, tolerance for sales forecast errors are lower. A high degree of forecast accuracy is required to ensure inventory control, optimize sales team allocation and plan channel flow. In such times, there is also a greater focus on longer forecast horizons that can guide long term strategy and decision making, unlike in a growth economy where management is rarely worried about forecasting beyond the upcoming quarter.  Forecasting in recessionary times also presents another problem; historical trends are not as relevant and may not be indicative of the numerous unknown variables sure to be present in times to come.

To conclude, in current economic conditions, sales forecasting forces a business to look at the future objectively and can make the difference between just surviving and being a highly successful business. However, there is no denying that it is a tricky job because so many different factors can affect future sales.

Contributors: Ben Niles, Jack Lee, Sanjay Shitole

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