Forecasting sales using predictive analysis with regression to predict future outcomes means utilizing a method based on historical performance. According to Richardson et al. (2023), regression analysis is a mathematical method to understand the relationship between dependent and independent variables. The results of this analysis demonstrate the strength of the relationship between the two variables and if the independent variable significantly impacts the dependent variable. The formula for a linear equation from algebra or other math classes, Y=mx + b, is also used to calculate sales regression analysis when only one explanatory variable is at play (Richardson et al., 2023). M represents the slope of the exploratory variable, and b represents the y-intercept.

Sales regression acknowledges how some factors in your sales process affect sales performance and predicts how sales would change over time if you continued the same strategy or pivoted to a different method. In sale forecasting, the dependent variable is always the same: sale performance. The independent variable is the factor we examine that will change sales performance, such as the number of salespeople we have or how much money is spent on advertising. An example of predictive analysis with regression running sales forecast to understand if having salespeople make more sales calls will mean that they close more deals. To conduct this forecast, we need historical data that depicts the number of sales calls made over a certain period. So mathematically, the number of sales calls is the independent variable or m value, and the dependent variable is the number of deals closed per month or Y value.

There are some procedures for addressing the discrepancies between the projected budgeted sales and the actual amount. First, contingent upon unfavorable variance, an analysis is critical to understanding the causes and what we can do to fix the issues. For example, the cost of maintaining internal operations increased for whatever reason, or the sales dropped significantly. Knowing why is the essential first step in determining the next step. The second step is to prepare a budget vs. actual report to identify where a significant discrepancy variance occurs and determine where to start developing solutions. The third step is to tweak forecasting and adjust the data to make more accurate budget predictions in the future. The last one is to repeat the process every month; continuous analyses will ensure that we catch financial issues early so that handling the problem can be performed right away to avoid problem build-up.

Communicate your finding clearly and timely with the necessary party. Inform them of the issues, the causes, the impacts, and the actions taken or planned. Use appropriate channels and formats to convey the information, such as emails, meetings, dashboards, or memos. You should also be respectful, transparent, and constructive in your communication.