Correlation and regression analysis are fundamental statistical tools used to measure the strength and direction of the relationship between two or more variables. They are widely used for prediction and forecasting, where their use has substantial overlap with the field of machine learning.
Though these tools can be built and run directly in many programming languages, such as Python, we tend to employ them more within Tableau workbooks, transforming the rather dry numerical outputs into more informative charts and dashboards for easier, visual analysis.
Case study
The client wanted to know what links, if any, may exist between their web traffic to particular service pages on their website, and their volume of leads and opportunities generated from all channels.
We joined traffic data from Google Analytics and sales data from Salesforce CRM into a set of Tableau workbooks. We analysed the traffic by source, by persona, and of course by destination, and tested the relationships against the volume and type of leads and opportunities on the pipeline over time.
The client was then able to view these relationships within the workbooks, using their knowledge of the market and their products to identify potential links between their marketing activities and their sales results.
