Want to know how to increase sales? Of course you do. Every small business owner wants more sales and profits.

But the question is how to do that. Where should your activities be focused? Do you know which factors affect your business the most? Is it the amount of rainfall? Or is it the direction of the economy?

Regression analysis can help answer those questions.

What is Regression Analysis?

Regression analysis is a mathematical method that determines which independent variables have the most effect on a dependent variable. It helps to determine which factors can be ignored and those that should be emphasized.

To put this explanation in everyday terms, let's consider an example. Suppose you're operating a food truck selling fruit juices made with watermelons, kiwis, mangos, lemons, oranges and a few other fruits. Since all of these fruits will spoil over time, controlling waste is important, and the amount of each fruit to buy every day for inventory is a critical decision.

In this case, the dependent variable is sales and the independent variable is the high temperature for the day.

After plotting historical sales and temperature data on a chart and using a regression analysis formula, you find that sales are higher on days when the temperature is higher. This makes sense. Consumers are more likely to buy a glass of watermelon/mint/lemon/lychee juice with cool, crushed ice on hot, dry days than chilly, rainy days.

So, the next step is to look at the data and place inventory orders based on the forecasted temperatures.

This example illustrates several advantages of regression analysis.

Making Predictions and Forecasts

Forecasting future results is the most common application of regression analysis in business.

As with the example of the juice truck, regression methods are useful for making predictions about a dependent variable, sales in this case, as a result of changes in an independent variable – temperature.

Another example is when insurance companies use regression programs to predict the number of claims based on the credit scores of the insureds.

Improving Business Efficiency

Managers exploit the advantages of regression models in finding ways to improve the efficiency of business processes.

The owner of the juice truck used regression techniques to determine more economical order quantities based on weather forecasts. This same analysis might even help him in scheduling work hours for employees and also lay the groundwork for ordering another truck to exploit a different location.

Supporting Business Decisions

Business owners are always looking for ways to improve and use resources effectively. Making decisions is never a sure thing, but regression analysis can improve the odds for getting better results.

Suppose the marketing department wants to increase the frequency of radio and television ads. What is the likelihood that the increased ad frequency will lead to a rise in sales? Will the profits from any sales growth be enough to offset the cost of more ads?

A regression analysis could provide some insight into the connection between increased advertising and profitable sales growth.

Analyzing Results and Correcting Errors

Regression models are useful to analyze the actual results from decisions that might seem, at first, intuitively correct.

For example, extending store hours might be expected to increase sales. But, will it?

Liquor store owners in one state lobbied for the right to stay open on Sundays, thinking this would increase sales. However, regression analysis revealed that total sales for seven days turned out to be the same as when the stores were open six days. The only difference was the increased cost to stay open the extra day.

Finding New Opportunities

With the increased capacity of today's computers, point-of-sale data from actual sales and reams of information from governments and industry associations, it is possible to mine this data to find previously unknown relationships between independent variables and dependent variables.

Regression Analysis in Action

Walmart is a good example of a company that has used this technique.

Walmart management wanted to know which products customers purchased before a storm. A regression analysis of the company's vast sales database revealed a surprising answer. Strawberry Pop-Tarts. Sales for this ready-to-eat pastry increased seven times the normal rate before a hurricane. Beer, of course, was the top-selling item.

Recognizing their customers' fondness for pop-tarts and beer before a storm, Walmart store managers in the path of a storm would order increased quantities of both items.

Astute small business owners will recognize the advantages of regression methods in helping them better manage their businesses.