We’ve seen tremendous growth in data-integrated digital marketing, the use of data to drive digital marketing decision-making. No longer is data the domain of math majors and statisticians. Digital marketers everywhere are dusting off their old statistics books to relearn those skills. By applying these statistical methods, you can harness your data to improve sales and drive results.

Pearson Correlation Coefficient

Pearson Correlation Coefficient determines the relationship between two variables. It is a standard tool due to its ease-of-use allowing one to test many variables simultaneously and easily understand the results. The coefficient derived between two variables measures the strength of the relationship – how much one variable relates to the other. Positive variables imply the variables move in the same direction, while negative values indicate the two variables move in opposite directions. The significance of a correlation coefficient between two variables depends on the data; however, a general rule is that if the coefficient is above 0.6, the two variables are strongly correlated.

For the digital marketer, Pearson Correlation Coefficient is used for isolating and determining Key Performance Indicators (KPIs) and metrics. Correlation Coefficient can help assess the quality of customer experience or a project’s return on investment (ROI). You can quickly determine what factors influence success and focus your time and effort on those attributes.

Linear Regression

While the Pearson Correlation can tell you whether two variables are related, they cannot predict the result. Enter Linear Regression. Linear Regression is another popular tool that allows the user to determine the effect of one or more known variables on an unknown variable. This is useful when predicting future pageviews based on changes to ad placement, SEO, and design. It can also predict revenue, sales forecasts, and click-through-rate (CTR).

 For all its strengths, Linear Regression relies on four assumptions to be met for linear regression to apply: linearity, a variance of residuals, independent observations, and normality. While they sound more daunting than they are, these assumptions can be time-intensive to check. If the premises are not met, transformations can be used to make the regression viable.

Logistic Regression

Logistic regression might sound like linear regression’s sibling, but they are different tools that empower users in different ways. Linear regression is used for sales, revenue, and other variables with almost infinite results. But if you want to determine a binary objective – a yes/no outcome – then look no further than logistic regression.

Logistic regressions allow users to predict the odds of an event occurring based on other known events. It is similar to linear regression in that it uses known variables to predict an unknown variable. But that unknown variable is a yes/no question. Will the customer churn? Will the sale complete? Will a customer subscribe? Those are all questions logistic regression can answer.

Logistic regression is more versatile than linear regression, not requiring as many assumptions and more comfortable to interpret.

Takeaway

Fundamentally, statistics are about discovering and explaining new insights. Statistics-savvy digital marketers often use a few tools to generate more leads, more sales, and more success for their business by finding deeper insights faster than their competitors. With these methods above, you will be on your way to that same success. At Midas Exchange, we can help you with creative marketing solutions to build your business success.

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