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Forecasting techniques have become one of businesses’ main tools. It allows a company to generate more sales by predicting future customer behavior, building marketing strategies, analyzing data, formulating strategies to increase profits, determining the standing of a company, and deducting what factors are more closely related to a product’s sale. Forecasts are usually one of the main drivers of companies’ success, while lack of predictive techniques oftentimes leads to failure. Because predictions are essential for a corporation’s decision making, it is also a fundamental part of data analysis. There are many types of data analysis tools that allow us to make relatively accurate predictions, one of them being regression analysis. Regression analysis allows us to filter out data by highlighting the relation that exists between variables and sales. Dependent variables that are proved to be strongly related to the independent variable are usually a big predictor of future sales, and hence, taken into account when forecasting. At the same time, variations on dependent variables weakly related to the independent variable have less impact and are taken out of consideration when estimating future sales. Regression analysis therefore allows us to narrow down the variables a company should be focusing on to increase profitability.

Three main issues

Lack of data is one of the biggest issues. Data collection is key for a business to establish performance goals and influence decisions. It allows us to conduct studies through observations, measurements, analysis, and forecasts. Because predictions are based on data gathered about past behavior and patterns that have been built with the passage of time, lack of access to this historical information can hinder accurate and complete predictions of the future. For example, a company that is relatively young doesn’t have sufficient data and therefore lacks clear patterns in which to base its predictions. Forecasts would be erroneous and incomplete if they were based on wrong or insufficient data. Another constraint would be inaccessibility to customers demographics data. Customers information regarding age, gender, geographical location, and economic and social status are important factors influencing sales. Knowledge about customers demographics is highly necessary to market products and analyze customers’ preference according to their personal data.

Invalid assumptions are another issue. Every model has an underlying assumption that given an x factor, something else will happen. For example, forecasting tools rely on the assumption that future sales will follow a pattern that has been created based on past occurrences. However, time-based assumptions are not always reliable since nothing is static, and times change. Due to changing times, the behavior and preferences of customers also change. The variables that used to influence products sales today may not be as influential a year from now. If a model was created several years ago, due to the time elapsed time, customers’ likings most probably changed; the old predictive model is no longer valid ore reflective of our current times. Another important factor that makes an assumption invalid relies on the possibility that the variables used in the development of a model do not entirely reflect possible outcomes. This happens when there are missing variables that were not taken into consideration when creating the model, and that are important for forecasting.

The last issue I wanted to mention is that managers sometimes can mistakenly think of a dependent variable strongly related to the independent variable as being the cause to a certain outcome. However, correlation does not imply causation. This means that although a variable may have a statistical significance in the result of an independent variable, it doesn’t necessarily constitute the cause of those results. The risk of confusing these two is that management can make the error of making important product decisions entirely based on a variable and obtaining unexpected, unwanted results. This often happens when assumptions are made based on regression analysis results only and not taking into consideration other factors.

Three lessons learned

Regression analysis allows companies to evaluate the relationship that exists between two or more variables. Regression analysis is important in the prediction of what variables have a bigger influence on sales, and what variables do not impact sales as much. Having knowledge about the elements that are highly related to sales gives companies an advantage that they didn’t use to have before. The results provide the company with information about what factors matter most, which can be ignored, and how they interact with one another. Based on these results, management can understand better supply and demand, choose one marketing strategy over another, predict what sales would look like over the following months, implement different tactics on how to increase sales, and expand its business operations.

Forecasting tools help us predict things about the future, which is key to understand the market, formulate strategies based on those predictions, arrive to conclusions, and influence decisions based on what’s best for the company. Thanks to forecasts, a company can estimate when business is going well and when it is necessary to take action. Predictions are helpful when determining a company’s performance. They will let us know when a company is not performing well, or if a decline in sales is forecasted for the next 12 months; and therefore, it allows us to make a plan of action early to avoid future declines. Businesses rely on predictive tools to strategize for the future, to see what they are lacking on, and to see what areas need improvement.

Something else that I learned from this reading is that practice companies should take into account the error term. It sometimes happens that analysis or models ignore or overlook the error term. It is important to remember that results are not always certain, therefore there should be room for errors. Probable outcomes are determined when analyzing patterns based on past behavior; however, even though we can make a possible prediction of future behavior we cannot foresee the future. Estimates claiming a 100% accurate reflection of what future outcomes will look like are not real. In real life, forecasting measures are never perfect predictors; there is always a margin of error. The error term within a statistical model allows us to understand and consider the differences that may exist between a theoretical value of the model and the actual observed results.

Three best practices

Data collection is critical. It is necessary to be careful about the data gathered and the source of that data since it may not be accurate or may not reflect actual customer behavior. The sources where we get our data from play an important role. Sometimes we get our data from internal sources such as accounting resources, sales force reports, or internal experts. On other occasions we get data from external sources such as government publications, industrial firms, whole sellers, retailers, and other corporations. It is important to ask questions regarding this topic. Where did you collect the data? How? Is it reliable? Can it be trusted? Is it an accurate measure of reality? Do we put more value on data collected internally or externally?

Because we have no control over the independent variable, it’s best if we focus on what is in the reach of our hands, what we can actually do. Instead of wasting efforts on a variable that cannot be changed or influenced, we could instead try to monitor it, study it, and perform different analysis to evaluate every possible outcome and be prepared to overcome any changes that might occur. Some examples of independent variables that could affect sales and ultimately profits are weather, customer demographics, and demand. In turn, if some changes were to occur in these independent variables, the company should try to implement new tactics such as advertising to reach new customers with different demographics or adapting our products and services to meet the needs of the customers we already have.

The last practice I think companies should follow when arriving to conclusions based on data is to not ignore intuition. Even though data is key aspect of decision making, it is also important to not just focus on the data but also on critical thinking. Factors to consider when arriving to decisions are what’s happening in the actual world. Management should not just look at predictive variables as numbers in a computer, but as real factors that influence people. An advisable way to understand customers and the market in general is to talk to people and ask them why they think they’re influenced to act in a certain manner. Ultimately, it is the customers who decide what products they want. Asking for feedback trough surveys, customer reports, satisfaction levels, five-star range reviews in online websites, and so on are good ways to determine what the public thinks about a company’s product and what they think the company could do better. These factors are as important as a forecasting analysis and management should take them into consideration when elaborating the company’s plan.

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