Analyzing Market Research Data - Forecasting Techniques

Forecasting is the use of historic data to determine the direction of future trends. Forecasting is most commonly used to determine future sales/demand size as well as budget allocation. 

There are two basic approaches to forecasting:

1. Quantitative Forecasting: Quantitative forecasting mostly pertains to numbers or figures, such as sales forecasts, budget forecasts or numerical projections. It is usually based on the history of a company’s figures and takes into account the industry’s history as well. Quantitative forecasting is typically used when the situation is ‘stable’ & historical data exist i.e. existing products; current technology. Eg. Forecasting sales of mature products.
2. Qualitative Forecasting: Based more on expert opinion and judgment, qualitative forecasting usually doesn’t rely on history. It can be used to determine a company’s strengths, weaknesses, opportunities and threats, as well as to predict performance. Hence, qualitative forecasting is used when the situation is vague & little data exist i.e. new products; new technology. Eg. Forecasting sales for an altogether new innovation.

Methods of Qualitative Forecasting:

1. Jury of executive opinion: Under this method, the opinions of a group of high-level managers often in combination with statistical models, are pooled to arrive at a group estimate of demand.
2. Sales force composite: In this approach each sales person estimates what sales will be in his/her region. This forecast are then reviewed to ensure they are realistic and then combined at the district and national levels to reach an overall forecast.
3. Consumer market survey: This method solicits input from customers or potential customers, regarding their future purchasing plans. It can help not only in preparing a forecast, but also in improving product design and planning for new products.
4. Delphi method: This is a group process intended to achieve a consensus forecast. There are three different types of participants in the Delphi method:
   • Decision-makers/experts
   • Staff personal/coordinator
   • Respondents
   • The decision makers usually consist of a group of five to ten experts who will be making actual forecasts, the staff personal or coordinator assists the decision makers by preparing, distributing, collecting and summarizing a series of questionnaires and survey results. The respondents are a group of people often located in different places, whose judgments are valued and are being sought.
5. Nominal group discussion: Like Delphi technique, the nominal group discussion involves a panel of experts and unlike the Delphi technique, the nominal group technique provides an  opportunity for discussion among the experts. Here seven to ten experts are asked to write down a list of ideas about the area of forecast. All ideas are then discussed and ranked to reach to a consensus.

Methods of Quantitative Forecasting:

1. Time Series: The time series technique predicts the simple assumption that the future is a function of the past. In other words, they look at what has happened over a period of time and use a series of past data for forecasting. Some of the tools of time series method are Simple average, Moving average and Exponential smoothing.
   • Simple average is the simplest way to forecast assuming that the demand in the next period will equal to the average of the past demands. Here the demands of the previous periods are equally weighted. By averaging, we try to detect the pattern or central tendency of demand. But we have to be careful about the fact that, if the underlying pattern changes over time simple averaging will not detect this change.
   • Moving Average: The moving average forecast uses a number of recent actual data values from several of the most recent periods to generate a forecast. Once the number of past periods to be used in the calculations has been selected, it is held constant. Moving average is useful if we can assume that market demands will stay fairly steady over a period of time. A 4-month moving average is found by simply summing the past 4-month data and divided by 4 and so on. The average “moves” over time, in that, after each period elapses, the demand for the oldest period is discarded and the demand for the newest period is added for the next calculation. This overcomes the major shortcoming of the simple averaging model. Moving average can be simple as well as weighted. A weighted moving average is where a weight can also be assigned to some time periods (like Diwali/festivity) which is different from rest of the periods. 
   • Exponential smoothing: Exponential smoothing is a sophisticated weighted moving average forecasting method that is still fairly easy to use. It involves very little record keeping of past data. Exponential smoothing is distinguishable by the special way it weighs each past demand. The pattern of weights is exponential in form. Demand for the most recent period is weighted most heavily; the weights placed on successively older periods decrease exponentially.
2. Regression model: Regression model is a causal forecasting technique output that establishes a relationship between variables. There is one dependent variable and one or more independent variables. Historical data establishes a functional relationship between the two variables. If there is one independent variable it is called simple regression, otherwise, it becomes multiple regression.