Forecasting Market Demand for an Existing Product

    If the firm already has an existing product in the market, then estimating what the future demand for the product would be will be a matter of assessing the following:

    • Listening to what people say. This includes salesforce opinions, expert opinion, and buyers’ opinions.
    • Assessing what people have done. This generally involves the statistical analysis of past-sales data or related data.

    Salesforce Opinion

    Salesforce opinion generally involves getting a composite of what each salesperson, sales team, or sales unit estimates to be its possible sales volume for the upcoming period based on past history. For this to work, the company sales force should be relatively attuned to their respective clients’ needs. If not, then the opinions will be driven more by blind guesswork than by educated indicators.

    Expert Opinions

    Expert opinions regarding the potential market size and the acceptability of the proposed product can be taken from industry watchers or people with experience in the industry. This can include technical resource people from the Department of Trade and Industry, industry veterans, observers, and insiders.

    A word of caution about expert opinion: just because a person is an expert does not mean that this person has the ability to make reliable predictions about the future. At worst, this person’s guess will only be as good as yours. At best, using an expert’s opinion is mostly a convenient way of passing on the blame to someone else in case a forecast goes wrong!

    But what makes an “expert” an expert in the first place? The underlying assumption here is that an expert truly understands the potential market of a new product, most likely due to years of experience and exposure to the market (i.e., the expert understands how the market thinks and behaves). Perhaps the expert is someone who has done extensive research on the potential market or has spent years selling related products to them.

    In the end, however, the expert who is asked for an opinion regarding the potential sales of a new product is still giving a guess. The only difference is that, because of the expert’s experience, the guess may be a little more grounded than that of a non-expert’s.

    Asking people about their opinions is a form of qualitative forecasting. More quantitative forecasting methods, on the other hand, would involve tools such as time series analysis and regression analysis.

    In 2007, it was estimated that 47 million 9-liter cases of dark spirits (whisky, rum, and brandy) were consumed nationwide. One million of these cases were of imported brands. These figures were extrapolated from retail surveys as there is no industry organization that monitors total sales.

    Furthermore, by calculating the average price of imported dark spirits versus the average price of local ones, it was deduced that imported dark spirits accounted for 11.85 percent of the market in terms of peso revenues.

    Time Series Analysis

    Time series analysis uses data from previous periods to forecast the following period’s sales. The simplest example of this is the use of a status quo assumption: if sales last year were 1,000 units, then expected sales this year could also be 1,000. More likely, however, some form of trending analysis would be factored in: if data shows an average growth rate of 5 percent per annum over the past three years, then the forecast would be last year’s sales multiplied by a factor of 1.05. More statistically-based analyses can be performed using statistical software packages.

    Regression Analysis

    Regression analysis is a more sophisticated statistical method for predicting an outcome based on multiple possible factors. A regression model works by using statistical models to determine the correlation between a hypothetical cause and the effect (in this case, level of sales), again based on historical data. If a factor is deemed to have a strong correlation with the effect, then a regression model—a formula—can be constructed that hopes to determine sales based on highly correlated factors. Factors could include economic indicators such as gross domestic product, the performance of related industries, and leading indicators (economic indicators that tend to precede or affect performance in the target industry).

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