Do you may want to dig deeper and find out how your advertising expenditure is correlated with your other promotional expenditure? Do you want to discover whether your daily ice cream sales are correlated with the daily maximum temperature? Or maybe, you wish to know how the sales of a specific product, say product 'A', correlate with promotional expenditure, advertising expenditure and the efficiency of the sales force. Whatever be your problem, you can successfully address it with Regression Analysis and get answers to your puzzling questions.
Regression and Correlation Analysis are usually performed together. While 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 the number of salespeople, a regression model can be built and fine-tuned for such predictions.
For instance, the monthly sales of a Pizza Chain may depend on the number of delivery boys, the cost of advertising, the number of outlets, the variety of pizzas offered, the number of existing customers and the competitors' activities index. Other examples would be, forecasting the sales of automobiles based on the income levels and competitive intensity, or predicting a retail store's 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.
We will first identify a set of variables that are affecting your sales. These variables will then be used to make predictions. For example, it could be macroeconomic variables such as housing starts, GDP growth rate and new automobile purchases, or marketing mix variables like the advertising expense and the number of sales or service people. Once these variables are identified, data on them is needed for at least twenty-five to thirty observations. We will then build a linear equation by using statistical methods like the least squares algorithm. We can also use categorical predictor variables like the level of education of the consumers.
Regression Analysis works best with numerical predictors. Once the equation is constructed, we will 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 the sales of burgers, to the sales of cement and heavy machinery. The only necessary condition is the availability of the appropriate numerical data on predictor and predicted variables.
If a lot of variables exist, we will filter out some variables to reduce the co-linearity, or perform factor analysis to combine the correlated variables. Then a "best prediction" equation is determined from the set of independent variables. The 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 will then test the prediction model we build, to find out how accurate it is in actual field conditions or how it performs with the data left out during the model-building process. We will then modify the prediction model by adding or dropping variables if needed.
Do you want to know more about the variables that are impacting your sales? Do you want to change the elements in your marketing mix for targeted sales? If yes, then outsource regression analysis, as it is best sales forecasting tool that you can use, to forecast your sales with greater accuracy. Apart from regression analysis, we also offer Logistic Regression Analysis and several other research services that can provide your company with greater business insight.
With our latest technology, data analytical capabilities, domain expertise and business intelligence, we can provide your business with quality regression analysis services that can give your company a competitive edge.
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