Machine learning can be a gateway to improved pattern recognition in your data. But to walk through that gateway, marketers must first establish if correlations exist in their data. Regression analysis can help here.
What Is Regression Analysis?
Regression analysis is a statistical method that allows you to examine the relationship between two or more variables. It allows businesses to compare technical parameters, which can seem nebulous on their own, against traditional business activities like costs and sales.
Linear regressions are the simplest regression models, as they deal with a limited number of data points and assume a linear relationship between the outcome and the predictor variables. A relationship, in this case, results when you find a statistical correlation among a given dataset.
But analysis can get tricky when multiple variables are introduced or data correlates in a nonlinear fashion. At this stage increased computational muscle is needed for quick and accurate calculations. That’s where machine learning comes into play.
The line between machine learning and regression analysis has been debated ad nauseam and the lines become particularly blurry when looking at prediction and forecasting. Machine learning establishes data relationships through induction — the process of bringing about or giving rise to the relationship. In plain language, it means data is transformed into a model to predict what output data is likely. This approach differs from statistical treatment of a regression, where weights are set to output based on inputs.
The process starts with data mining to make sure the data is not introducing error into the models. Any errors introduced at this point will of course ruin the quality of the results.
How Can Regression Models Help Marketing?
So how can marketers use regressions for better success with machine learning initiatives?
Marketers can use regressions to discover what business questions they can answer within the context of data. Framing the questions with data-driven descriptions in mind can reveal when a deeper dive with machine learning is called for. Good starter questions to inform machine learning planning can include:
- How many business questions do the conclusions from a basic regression answer? Does more exploration with machine learning seem necessary to answer the questions not covered with a basic regression? If so, are the answers potentially beneficial to long-term objectives?
- Are there any significant data concerns related to the analysis? Do we have conditions such as outliers and non-available data fields that should be addressed? How do these concerns impact the training data and test data?
Planning regressions can highlight the hypothesis you are looking to prove and establish what support you'll need. The effort transforms the business questions into a framework which can help you decide if a basic regression will suffice or a deeper machine learning initiative is needed to answer the question.
Data alone doesn't give questions and answers. Regressions can provide a framework to explore your data which results in meaningful analysis for your business.