Predicting Future Sales
A sales forecast is simply a predicted turnover in a given period based on an agreed marketing plan set against a targeted external marketing environment. Sales forecasting forms a critical part of any business plan, because it underpins cash flow forecasting, stock control, staff recruitment and marketing activities. A sales forecast dictates stock allocation and production decisions. Over-optimistic sales forecasting may result in stock sitting on shelves. Likewise, a conservative forecast, means not enough products are produced to satisfy customer demand in the future.
So, failure to accurately predict turnover is likely to result in poor management sales decisions, and lost business opportunities. To help manage risk and uncertainty, businesses should generate a systematic and structured sales forecasting method.
Sales forecasting provides a basis for setting targets, budgets and business goals. Most businesses produce an annual and monthly sales management forecast. These financial targets represent the measure the success or failure of a firm's business strategy. For instance, a high level sales forecast, can be used to set the targets of individual salespeople. It literally changes the behaviour and motivation of employees. Future turnover is also instrumental in dictating budgets for production capacities, financing requirements, staffing levels and marketing spend.
A sales forecast also gives investors, the confidence in the future profitability of a company. Healthy turnover predictions are one of the key metrics, when valuing an existing business for sale. Investors will rapidly lose confidence, if management fail to meet their own forecast. Investors will undoubtedly challenge sales management forecasts, to determine whether the assumptions are realistic. Similarly, firms applying for a small business loan from their bank, will be asked to justify the loan application, by outlining detailed forecasting assumptions.
A sales forecast also helps allocate staff and resources to business opportunities. As some complex larger sales prospects can involve lengthy sales cycles, the need to allocate specialist people and other pre-sales resources becomes essential. By working closely with front line staff, managers can resource larger campaigns effectively, and maximise the chances of closing the deal.
A sales forecast also gives management a systematic way to cope with unexpected change. Businesses are forced to constantly reshape their forecast, due to changing external factors. These factors include booms and busts, unexpected staff turnover, new market entrants, product recalls, fashion style changes, political events, new legislation and regulatory guidelines. When businesses are merged, bought or sold they must also adapt their forecast accordingly, (to reflect the change in ownership and expectations). Without the ability to predict an alternative outcome, emotive and irrational blame may ensue.
There are many different techniques in which to forecast future sales. The method used depends upon the type of industry, the nature of the product or service sold, and the time frame of the forecast. In principle, there are two main categories. Firstly, the 'time series' methods, relies heavily on the use of historical data to extrapolate an intended outcome. Time series forecasts are commonly used in technical and wider macro economic analysis. They are also used in financial planning by extremely large companies (who have a long history of trading data on which to base future assumptions). For example, a rolling moving average forecast, produces a prediction based on historical performance data over a given time period. Rolling moving forecasts provide mathematical data point, weighted averages to extrapolate a future trend. The forecast can also be exponentially smoothed for presentational purposes.
Secondly 'judgemental methods' tend to rely less on mathematical statistical formula, and more on the most relevant factors in a given business scenario. They are based on the users opinions, knowledge, experience, and the use of relevant quantitative assumptions. Judgement does not mean 'sticking your finger in the air' and hoping for the best. Judgement forecasts are about applying common sense, to rationalise, interpret and quantify, the varying factors which influence future sales. Businesses are free to use a combination of both methods to forecast. Firms want to work out, what it must to sell to break even, what it could sell (based on the maximum market size), and what it is only able to sell (based on its internal resources capabilities). When producing a sales forecast, managers will need to quantify and list down all the sales assumptions in numeric terms.
Most small firms create a spreadsheet of sales figures using historical data, and then apply a variety of the assumptions.
Firstly, by using internal or 'controllable factors', managers can improve the forecast up or down (so that some reason and intelligence is applied). Controllable factors could include recent new business sales conversion ratios, existing renewal retention rates for repeat purchases, and resources capability. For instance, there are only so many man-hours a project manager can work on site. Firms may also be restricted by geographical or transportation delivery limitations, physical stock management, access to front-line trained workers, advertising budget and support capability. Lastly, don't forget price volatility. In competitive markets, undercutting tactics by competitors can make a mockery of your forecast. You can should make reasonable assumptions about break even point, sales volume, price point and a desired gross margin. As price setting is under your control, it is a controllable factor.
Secondly, think through the 'uncontrollable factors'. These are factors which are out of the direct control of the business owner, but which still have a direct impact on the possibility of future sales outcome. Uncontrollable factors include Inflation. If inflation is running at 5%, will you raise prices by 5% to keep up? Next build-in fluctuation due to seasonality. Most businesses face peaks and troughs in the purchasing behaviour of their customers. This includes annual holidays, significant sporting events, traditional shopping sales and products orientated around markets reliant on weather (such as barbecues and ice cream). Next, think about market expansion or contraction. The market demand for most products or services follows a life cycle of rapid growth, expansion, stability and decline. Lastly, determine whether new entrants will reduce your market share.
There is no such thing as an accurate forecast. Aim to get it a close as possible, with an acceptable margin of error. Accuracy is crucial for a number of reasons. Firstly, an over-optimistic sales forecast can result in over production, causing unnecessary cash to be tied up in stock. Secondly, investors have a greater degree of confidence in the management team if promises are kept. Whereas trust and integrity is lost because of overoptimistic forecasting. Thirdly, staffing levels need to match fluctuations in demand.
So what are the best ways to improve the forecast accuracy?...
First, verify your working assumptions. Identify as many of the controllable sales assumptions as possible. Then ask tough questions, to check the assumptions are correct. For instance, businesses that are limited by employee man hours, can easily identify the maximum physical capacity of that person to generate sales over any period. Managers should write down the assumptions or include them as detailed notes in the spreadsheet. This helps when checking past calculations, to ensure that there is no room for misinterpretation or confusion or forgetfulness, (regarding how the assumptions were originally derived). Spreadsheets can become notoriously complicated to unravel, when validating working assumptions.
Second, know your history. Understand why previous forecast targets were missed, and use this knowledge as lessons for the future. Existing businesses can use historical forecast data, to tighten up the relevancy and accuracy of the most recent underlying assumptions. Historical data includes financial statements, sales call reports, accounting records and post sales customer feedback forms. Also ask direct line salespeople for their opinions, and ask key customers for their future buying intentions.
Thirdly, do your market research. New business start-ups do not have the luxury of relying on historical sales data, upon which to build realistic sales assumptions. So grab as much quantifiable environmental data from the existing target market, customer demographics, customer surveys, local business groups, management research companies and so on. The 80/20 rule could also be applied to the forecast, in that 20% of the firm's prospective clients will provide 80% of the revenue. Who are these clients? Can you list them by name or by prospective campaigns identified during a start-up phase?
Lastly, sales forecasting software can also be used to help improve forecast accuracy. Packages such as QuickBooks Premier or Quicken Small Business, can help you make a prediction based on historical data. These software forecasting packages can help novice users, by prompting them to enter key management information to produce an initial forecast.
We hope this short guide has helped you think through the issues, when implementing sales forecasting methods within your business!