You may want to dig deeper and find out how the advertising expenditure is correlated with other promotional expenditure. Or discover whether daily icecream sales are correlated with the daily maximum temperature. Regression Analysis is your ally here.
How do the sales of a specific product, say Product ’A’, correlate with promotional expenditure, advertising expenditure and the efficiency of the sales force? This vital information is what Regression Analysis delivers.
Regression and Correlation Analysis are generally performed together. Correlation analysis measures the degree of association between two sets of quantitative data. Regression Analysis tries to explore the quantitative relationship between a dependent variable and a set of independent variables.
The correlation coefficient measures the association between two variables. It has a value ranging from 0 (no correlation) to 1 (perfect positive correlation), or -1 (perfect negative correlation).
Correlation is usually followed by Regression Analysis in many applications. Among correlated variables, if one variable can be predicted from others, like sales from advertising, distribution and number of salespeople, we can build a regression model and fine-tune it for such predictions. For instance, monthly sales of a Pizza Corner may depend on the number of delivery boys, cost of advertising, the number of outlets, the variety of pizzas, the number of existing customers and the competitors’ activities index.
Other examples would be: forecasting the sales of automobiles based on income levels and competitive intensity, or predicting retail store sales based on a variety of consumer demographic and psychographic data.
If appropriate predictor variables are used, consumer durables and business products can also be subjects for prediction.
Sales Forecasting is the most common application of Regression Analysis.
Which are the variables that immediately impact your sales?
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We first identify a set of variables that affect sales, and that can be used to predict it. For example, it could be macroeconomic variables such as housing starts, GDP growth rate and new automobile purchases, or marketing mix variables like advertising expense and number of sales or service people. Once these are identified, data on them is needed for at least twenty-five to thirty observations. We then build a linear equation using statistical methods like the least squares algorithm. We can also use some categorical predictor variables like the levels of education of consumers.
Regression Analysis works best with numerical predictors.
Once the equation is constructed, we use the resulting coefficients to predict the value of sales for a new set of predictor variable values. This is a quantitative forecasting method, and the closer the relationship between the measured variables, the higher the quality of predictions made from this. Common applications include predicting sales of burgers, to the sales of cement and heavy machinery. The only necessary condition is the availability of appropriate numerical data on predictor and predicted variables.
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If a lot of variables exist, we filter out some variables to reduce collinearity, or perform factor analysis to combine correlated variables. Then a "best prediction" equation is determined from the set of independent variables. Two criteria are: the statistical significance of the equation, and the amount of variance explained by all the variables included in the equation. SPSS (Statistical Package for Social Sciences) is generally used to build the model.
We then test the prediction model we build, to find out how accurate it is in actual field conditions or how it performs with data left out during the model-building process. We modify the prediction model by adding or dropping variables if needed.
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Regression Analysis is one of the many Research and Analysis outsourcing services provided by Outsource2india. Research and Analysis gives your organization critical business insights into core business strategies. Additionally, we offer Logistic Regression Analysis. Outsource2india has the necessary technology, advanced data analytical capability, domain expertise and business intelligence that creates high levels of measurable business value to customers.