Correlation Analysis

An integral part of the Zenoctus Correlation Analysis.

Correlation Analysis answers questions like: “Which items contribute most to the customer or employee overall satisfaction or loyalty?” Or “Which features contribute most to the overall revenue of a product or service?

In Digital Marketing, the questions could be as follows:

Do more blog articles lead to higher search rankings on Google? Do more tweets of a URL increase the conversion rates? Do emails with images have higher open rates?

These and hundreds of other questions are constantly challenging marketers and managers. The beauty is, Correlation Studies answers those questions mathematically through correlation data.

We use correlation-based observations in our everyday lives. Scientists use it even more often to explore potential hypotheses and conduct experiments to test them.

Correlation does not imply causation. However, it shows a relationship, which forms the basis of guesses and tests.

Here are a few ideas for correlation-based research in Online Marketing:

  • Correlation between a topic or phrase trending on Twitter and search volume spiking on Google
  • Correlation between Social Media shares for URLs across different industries (in which industries are networks stronger or weaker)
  • Correlation between share buttons on website and number of shares received
  • Correlation between positive, negative, or neutral content on various sites and their success in Social Media
  • Correlation between having a testimonial, email or phone number and rankings in Google’s search results

The degree to which a Correlation Analysis describes the relationship between two variables is shown by the coefficient. The coefficient may take on any value between + 1 and - 1. The sign (+, -) determines the relationship, that means either positive or negative.

When we have a positive correlation coefficient, it means that as the value of one variable or item increases, the value of the other variable increases as well. The same applies when the value of the one item decreases. Then the other decreases as well. On the other hand, a negative correlation coefficient shows that the other variable decreases when the first variable increases, and vice-versa.

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