Make Informed Business Decisions Using 5 Analytics Tricks

    [This article was written by Dawn Castell.]

    Regression Analysis

    Regression analysis is the estimation of the ratio between two or more variable inputs. The output, or dependent, is the result of the variable, or independent, inputs. This analysis helps business owners predict the outcome of a given set of circumstances or actions. Common uses for this data are predicting product demand, traffic patterns, and the cut-off point for variables such as price and wait times. Fundamentally, businesses use regression analysis to forecast outcomes and optimize operations

    Linear Regression Analysis

    Regression analysis falls into two categories; linear and logistic. Choosing linear regression vs logistic regression depends on the number of relevant variables required to obtain the desired result. Linear regression examines two inputs directly correlated to a dependent output. For example, a business could forecast the number of cold beverages sold in relation to climate temperature. Armed with this information, a purchasing agent could gauge the ideal amount of product to keep in inventory. Linear regression analysis is a powerful tool. Business managers should comprehend and employ this method to improve bottom lines.

    Logistic Regression Analysis

    Logistic regression analysis considers several factors and businesses typically use it to make a yes/no or true/false determination. Again referencing the beverage seller, logistic regression analysis would consider data such as the demographics and density of the customer base. The dependent variable in the logistic analysis would differ with changes in independent variables such as income level, foot traffic patterns, and time of day. This analysis may answer a question such as whether or not it would be profitable to stock cold beverages on a winter weekend evening.

    Uses of Regression Analysis

    Creative uses of regression analysis can help businesses grow. One use is a long-term overview of a comprehensive data set without any preconceived purpose. This comprehensive overview may reveal new insights not readily apparent or even sought after. To perform this type of analysis, the analyst should compile data from as many relevant sources as possible. Once the data is charted patterns should emerge driven by the influence of each data set. Using these patterns, management can optimize areas such as sales prices, inventory, and manpower.

    Another unconventional use of regression analytics is to correct errors in judgment. If the company implements a change, analysis of the affected data sets may reveal an adverse effect on profit, risk, or operations. Using the beverage seller as an example, the choice to switch to lower-cost generic beverages to reduce the cost of goods sold may not offset the loss in profit when sales drop. Conversely, an analysis prior to making a change might prevent such errors.

    Marketing Research

    An often-overlooked source of independent data is marketing statistics. Businesses commit a significant mistake when forgetting to include data from marketing efforts when analyzing trends. What appears to be a record cold beverage sales quarter driven by a heatwave could be the result of a social media ad campaign blitz. Using a regression analysis that incorporates marketing data can shape strategic marketing efforts. Furthermore, such an analysis can determine the success of campaigns and uncover fresh target markets. Taking advantage of marketing data for analysis is especially critical when a large portion of a company’s budget is spent on advertising. Advertising then does double-duty which increases the return on investment.

    Conclusion

    Both linear regression and logistic regression methods of analysis are invaluable decision-making tools for business. Using these tools effectively and skillfully not only helps businesses move in the right direction but also correct misguided efforts. Businesses can chart a path for growth while endorsing that path with sound, scientific proof.

    Author Bio:

    Dawn is a budding entrepreneur. After graduating with her MBA, she spent a few years working in the CPG industry and a few more working in the business tech industry before she set off to start her own business. She has been consulting with businesses, large and small, on the side ever since.

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