# Market Potential and Sales Forecasting

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2 Potential and Forecasting 2 estimate but not part of Brand A s sales potential estimate. Brand A may also lack the product capacity to serve the entire product market. Thus, in practice, there is almost no probability that in competitive product markets the estimates for sales potential will equal the market potential estimates. Forecasts, which will be discussed later, are the predictions of how much will actually be sold during a given time period. The market forecast is the prediction of how much of all brands in a product category will be sold in a given time, while sales forecasts predict sales of a single brand. For the remainder of this section, we will look at several means of calculating market potential. Our focus will be on market potential and not sales potential for two reasons. Most sales potential estimates are derived from market potential estimates. That is, to calculate sales potential, you generally must calculate the market potential first. Sales potential is typically expressed as a percentage of market potential based on market share predictions. Second, in practice there is very little difference between sales potential and actual sales forecasts. Generally companies try to capture as many customers as they realistically believe they can. If so, then they sales forecast will essentially equal the sales potential estimate. Market potential is frequently used to estimate whether or not expansion into new markets will be feasible. Markets in this case are most frequently defined geographically, but need not be so. For example, a company may be considering expansion into parts of the country where their product is not currently sold. Therefore, they may want to estimate the potential to sell to customers in these new locations. However, market potential can also be estimated when a firm wants to sell to new demographic groups. For example, the NFL recently began offering team jerseys cut and styled for women because research indicated that many women fans would rather not wear team apparel styled for men. This represents expansion into a new demographic market. Demographic and geographic market definitions are commonly applied together when estimating market potential. Chain Ratio Method of Calculating Market Potential The chain ratio method kind of assumes that what happens for one market will happen proportionally in another market. So if we have information on sales or market share in one market, we can extrapolate those results to another market. One common application of the chain ratio method is where a local or regional company wants to calculate potential sales nationally. In this case, the chain ratio method would follow several steps. First, identify the demographic characteristics of the customer groups to whom the analysis applies. Because the chain ratio method relies heavily on secondary data, often population data from the Census Bureau, it s best to describe markets in terms of sex, age, and income. Although we may use more complex demographic descriptions for other purposes, data for the chain ratio method will be more available, timely, and accurate if the demographic description is kept simple. Sometimes data giving estimates of population size by occupation and some psychographic variables are available. If the data are available and reliable, then they can be used. If not, keep the market description simple. For example suppose a brand of expensive upscale cookware produced in California sells primarily to women between the ages of thirty-five and sixty-four with household incomes

4 Potential and Forecasting 4 estimate as a starting point for market expansion planning is better than simply making those decisions from intuition alone. Buying Power Index We begin with the simplest method for estimating market potential, excluding just guessing, of course. The Buying Power Index or BPI was initially developed by Sales and Marketing Management magazine. The index is very easily calculated with readily available data and is intended to help marketers compare the retail purchasing power of specific locations in the United States. The BPI is not specific to any product or product category. It simply gives a comparative measure of consumer purchasing power relative to purchasing power nationwide. To understand the BPI, let s begin with its formula. You can see by looking at the formula that it is a weighted sum that describes an area s purchasing power relative to the United States. To use as a measure of market potential, BPIs can be calculated for several areas of similar size or population and then they can be compared to each other. Note again that BPI is not specific to any particular product; it describes a given geographic region. Data to calculate the BPI are readily available from the Census Bureau and from the Bureau of Economic Analysis. BPI figures are most frequently calculated for metropolitan areas generally at the county level. Regression Based Index Methods The BPI described above is a very simple method for figuring market potential. As such, it suffers from limitations, not the least of which is its breadth. It is not specific to any product category, but refers to retail spending in general. With a little research, often through internal and external secondary data, marketers can uncover data that provide market potential insights that are specific to their product categories. Of course, the issue here is whether the extra effort to obtain and analyze the data produce results that are sufficiently explanatory to be worth the effort. Like the BPI, regression based index methods convert variables to percentages of the relevant population with the given characteristic, hence the term index. Unlike the BPI, regression based index methods are not one size fits all, but are researched and customized to fit the given situation. In general, regression based index methods follow several steps. First, researchers should consider what factors may predict or explain demand for the product. This requires understanding markets well, which of course is not always easy. Researchers should in particular focus on population factors related to product sales. For example, suppose a company is considering launching a low cost wireless internet service for families. Pricing may be based on the number of people in the household rather than on data usage. Because the company must install equipment to provide the service, they wish to focus on states with higher population densities so that more people can be reached with less equipment investment. However, it could be that more densely populated areas have more

5 Potential and Forecasting 5 public spaces with internet, so this variable is in question. Ultimately, they decide to see whether population density, family size, head of household education, and household income predict current internet service sales. Second, the researchers must collect the data and then create indexes by converting the data to reflect the percent of the base population that possess the characteristics under study. For example, the researcher studying the internet service would need to obtain data by state on the following variables: sales of internet provider services by state (which would probably available from the industry association or perhaps even from state regulators), population density, family size, education of the reported head of household, and household income (all of which are available from the Census Bureau). Convert the data to population base percentages would be relatively straight forward. In this case, the base population would be the U.S. population, so for each variable, the researcher would need to divide the state figure by the U.S. population figure. For internet service sales, sales in each state would be divided by nationwide sales to yield the percentage of sales in each state. Similar index calculations would be made for the other variables to indicate the degree to which the state was above or below the average for the U.S. Third, the researcher would enter the data into a regression analysis and calculate which variables predict internet service sales and the nature of the relationships. For example, using internet service sales as dependent variable and population density, household education, and income, and family size as independent variables, the researcher would estimate the regression equation to see which independent variables were statistically significant. Independent variables that were not significant could be removed from the model and the equation re-estimated. The SPSS output below shows the regression coefficients from such an estimation. The first estimation showed that household size was nonsignificant. That variable was excluded and the model was re-estimated. Output from the second estimation is below. The estimation yielded an R 2 of 73.9%, suggesting good overall model performance. Coefficients a Model Unstandardized Standardized t Sig. Coefficients Coefficients B Std. Error Beta (Constant) Density index Education index Income index The equation estimated by the model would be as follows:

6 Potential and Forecasting 6 Note that the independent variables are the indexes of population density, education, and income. These are the variables expressed as a percent of the total population s mean of that characteristic. Fourth, to compare the market potential for particular states, the data for the states would be put into the equation to calculate the sales index for that state. For example, suppose that the internet service company wants to compare the market potential indexes for Massachusetts and Rhode Island. The calculations would yield the following: RI: Sales Index = ( ) + ( ) + ( ) = 1.14 MA: Sales Index = ( ) + ( ) + ( ) = 1.54 The calculations suggest that Massachusetts would be a somewhat better market for the new internet service than Rhode Island. Importantly, this method of market potential estimation does not give an actual estimate of market potential. It is an index that helps compare locations for their potential relative to the average nationally. In this respect it is similar to the BPI. However, in this case, the estimates are specific to the industry and variables that matter in terms of purchase. Therefore, while it is somewhat more difficult to use than the BPI, it does give results that are likely more relevant to marketing managers. FORECASTING Forecasting is more fraught with uncertainty because of its fundamental difference from market potential estimation. Forecasting focuses on how much people will buy and not on who could buy. Therefore, as noted earlier, forecasting is about making predictions of future behavior. Forecasting methods range from very simple guesses to highly complex econometric models. In this section, we will focus on some of the simpler methods of forecasting. Judgmental Forecasting Techniques As the name implies, judgmental forecasting techniques rely on the judgment of knowledgeable people to make informed guesses about sales in the coming period or periods. Judgmental methods have the advantage of simplicity, which means they can be completed with relatively low costs and in relatively short time frames. However, judgmental forecasts are not typically based on data and do not typically involve any empirical analysis. As such, they may be prone to error and bias, depending on who is involved. That said, judgmental forecasts should rely on multiple participants, which may mean that the forecasts are somewhat more accurate. Of course, the accuracy of these forecasts depends greatly on how knowledgeable and insightful the participants are. Salesforce Composite. In organizational markets, salespeople should know the needs and plans of their customers. They should have a good sense from their customers about how much their customers

8 Potential and Forecasting 8 Simple Quantitative Forecasting Methods Some even relatively simple forecasting methods rely on empirical data analyses to arrive at sales forecasts. We will discuss two of these simpler quantitative methods in this section. Time Series Analysis. Let s begin by defining the term time series. A time series is simply some variable reported at regular time intervals. In forecasting, the variable reported over time is usually either unit or dollar sales. Time series may be analyzed using highly complex statistical models or they may be analyzed using simple visual inspection. If the patterns that exist in past data hold true for the future data, then the forecaster should be able to project forward to predict future sales. Look at Exhibit 1 to the right. It shows a hypothetical time series graph. Note that time is plotted on the horizontal axis and sales is on the vertical axis. In time series plots, this arrangement is always the case. As you look at the graph, do you see any patterns in the plot? If you look carefully, you should actually see more than one pattern occurring at the same time. That s because many time series, particularly of sales, tend to follow somewhat predictable recurring patterns. For example, many retailers have increasing sales in the months just preceding Christmas, followed by a decline in sales after the holiday season ends. Exhibit 1. Time Series Plot If retailer sales were graphed, this relatively predictable pattern would likely be easy to spot visually, assuming that monthly or even quarterly sales data were available. To better understand how to discern the patterns that may exist in time series data, it is useful to know the possible sources of variation in much time series data. Look at Exhibit 2 to the right. It shows the same hypothetical time series graph as above. However, in this case it also shows the overall upward trend of sales over the time period covered by the time series. Thus, the first source of variation in time series data is called the trend. The trend shows the long term movement of the data. Trend is generally the longest term source of variation in time series data. For short term sales forecasts, trend is not especially useful. However, many sales forecasts attempt project sales out several periods. For such longer term forecasts, trend must be considered. Exhibit 2. Time Series Plot showing Trend

9 Potential and Forecasting 9 Marketing managers, especially those in the United States, tend to be very focused on short term performance. Therefore, shorter term sources of time series variation are more useful for forecasting activities. The Exhibit 3 to the right shows again the same time series data as on the previous page. The red line shows the second source of time series variation, called cyclicality. When studying sales data, variation from cyclicality generally occurs with broad macroeconomic conditions. For example, the business cycle refers to the repeating ups and downs of gross domestic product over time. During periods of expansion, sales cyclicality may reflect this general pattern of increasing growth while during periods of recession, the general pattern may be one of slower growth or even contraction. Exhibit 3. Time Series Plot showing Cyclicality A third source of variation in time series was described briefly earlier. This variation generally occurs over the course of a calendar year and generally captures the patterns that occur by month or by quarter. Look at Exhibit 4 to the right, which shows the same time series plot as the previous graphs. The red line shows the third source of variation, referred to as seasonality. The term reflects the fact that much intra-year variation in sales occurs by season. For example, as noted earlier, retailers often experience spikes in sales during the Christmas holidays and declines after the first start of the year. Seasonality is the shortest term source of variation in time series. Close visual, and as we will see in a moment, mathematical, inspection of seasonality is useful for making the types short term forecasts of interest to many Exhibit 4. Time Series Plot showing Seasonality marketing managers. The fourth and final source of variation in time series is simply random variance that occurs in every period. This is often referred to simply as noise. Because it is random, it cannot be predicted, meaning that even the best forecasts by any method cannot be perfect and will always have error.. Many mathematical approaches exist to remove some of all of the first three sources of variation from the time series in order to see the underlying structures of the patterns in sales. Some of these approaches are quite complex, while others are very simple but still effective in forecasting future period sales. In the following section, we look at one of the more common mathematical forecasting methods, the moving average.

10 Potential and Forecasting 10 Moving Averages. The concept of a moving average is really very simple, though they are best explained by example rather than by definition. Exhibit 5 shows a sample calculation of a moving average for hypothetical sales data. The calculations are very straightforward. Several points should be made about the example. First, notice that the example shows a three period moving average. Thus, the first entry in the moving average column begins with the third quarter of The first two spaces are blank because the moving average starts with the average of the first three quarters. Second, moving averages need not be limited to three periods. Mathematically, one can calculate moving averages to include as many time periods for data are available. Generally, however, moving averages rarely exceed five or seven periods. Third, time periods need not be quarters; they can be any time interval useful to the analysis. Quarterly and monthly data are common in moving averages of sales. Finally, moving averages (or time series generally for that matter) can be of any variable useful for analysis. Our emphasis here is on sales because it is the most commonly forecasted variable. However, variables such as event attendance or store traffic are also amenable to these analytical methods. The reason moving averages are useful for forecasting is illustrated in Exhibit 6 on the following page. This exhibit shows the time series plot (blue line) of the data in Exhibit 5 as well as a plot of the moving average (red line). The main feature of the plots in Exhibit 6 is that the line of the moving average has less variance in it than the plot of the original sales data. In other words, the moving average plot is smoother than the plot of the original data. This illustrates what moving averages do; they smooth time series data, removing some of the variance and making the general direction of the time series easier to project forward. Time Period Sales Three Period Moving Average 2010 Q1 32, Q2 28, Q Q4 31,050 30,990 30, ,103 Average of 2010 Q1, Q2, and Q3 Average of 2010 Q2, Q3, and Q Q1 34,540 32,193 Average of 2010 Q3, Q4 and 2011 Q Q2 26,780 30, Q3 32,460 31,260 and so on Q Q1 31,770 34,760 30, , Q2 30,920 32,483 Exhibit 5. Moving Average Calculation

11 Potential and Forecasting Exhibit 6. Plot of Time Series and Moving Average The smoothing effect of moving averages can be seen with greater clarity in Exhibit 7 below. This plot shows real sales data from 1997 to The blue line shows the original time series and the orange line shows a five period moving average of the data. Notice how the five period moving average removes much of the variation from the original time series, especially seasonality. What remains is the trend, which is upward, and the cyclicality, which follows the broad wave shape of the curve Exhibit 7. Time Series of Sales Data and Five Period Moving Average

12 Potential and Forecasting 12 To actually forecast using the moving average plot, the forecaster could simply extend the smoothed line out one quarter or more. The forecaster could visually adjust the angle based on where the moving average is relative to the trend and cyclicality of the original time series. What is especially helpful regarding the data in Exhibit 7 is that the moving average appears to have moved into a period of upward average sales rather than being near the top or bottom of the curve. Predicting points of upturn or downturn is more difficult when using these simple forecasting techniques. Importantly, forecasting using time series and moving averages can produce models that can predict with reasonable accuracy when upturns or downturns may occur, though their exact timing and magnitude can be difficult to pinpoint exactly. These models, called ARMA (autoregressive moving averages) and ARIMA (autoregressive integrated moving averages) models create functions that can closely reproduce even complex time series patterns, allowing forecasters to mathematically project future sales rather than relying on visual analysis only.

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