Regression analysis is a common technique in market research which estimates the relationship of independent variables on a dependent variable. More specifically it focuses on how the dependent variable changes in relation to changes in independent variables. A common application of this in market research is understanding how likelihood to recommend (dependent variable) is impacted by changes in wait time, price, quantity purchased (presumably independent variables).
Regression analysis is a great tool for predictive analytics and forecasting in market research. It helps businesses and organizations prioritize efforts to improve an over-arching measure like overall satisfaction, likelihood to recommend, or net promoter score (NPS). By using regression analysis in quantitative research it provides the opportunity make corrective actions on the items that will most positively improve overall satisfaction. For example, if a banking survey reveals wait time is largest predictor of overall satisfaction due to a negative correlation, a bank and its market research consultant would take initiatives to improve wait times and as a result, predictably improve overall satisfaction.
As a predictive technique, it can be used to estimate sales figures of an organization through the inclusion of outside market data. For example a global company can begin to understand how their revenue may be impacted by market indicators such as gross domestic product (GDP), consumer price index (CPI), or other similar factors. By reviewing and entering forecasted market indicators, you can make educated guesses of revenue in future quarters and even years. Obviously, the further in the future you predict, the less reliable the data will be using a wider margin of error.
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