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Web analytics tools can track the user or consumer journey from user origin and profile, through to on-site activity, to goals achieved and the exit point. This allows analysts to measure and understand user interactions and any valuable actions on the website using data to inspire hypotheses and insights into why these behaviors are occurring. It also allows analysts to try to recreate the conditions to drive more valuable actions or reduce the instances of poorer performing interactions.
Gaining a return from the metrics in programs such as Google Analytics is an ongoing process of monitoring, analysis, and testing enhancements from data-driven hypotheses.
Testing allows analysts to optimize campaign performance by altering single or multiple variables at a time in a controlled way. When data insights point towards an opportunity to improve performance, analysts can create a data-driven hypothesis to test it.
Testing involves a control and a test. The control is the original set of variables; the ‘test’ or ‘challenger’ is the new set of variables. When testing two or more hypotheses, in a 50/50 test, one option will be worse than the other. Testing reduces the risk of choosing the poorer performing option for the duration of the test and once complete. The results allow for a more informed decision on whether or not to proceed with the hypothesis.
Here are some suggested steps for testing hypotheses:
A by-product of testing is the possibility of false positives or false negatives.
A false result is an error in the data where the result wrongly points to an outcome or condition. In testing, this is known as a type one error (false positive – where the data shows a positive result for the test when the true result was a negative), or a type two error (false negative –where the data shows a negative result when a positive result was the true outcome of the test).
So, how can analysts test for false positives? An analyst can:
Jack Preston is a Data Scientist working within marketing analytics, with a particular focus on strategic customer loyalty. Jack has experience working in both small-scale startups and large corporates, including dunnhumby and Notonthehighstreet. He also holds an MSc in Business Analytics from UCL where he graduated with distinction.
ABOUT THIS DIGITAL MARKETING MODULE
This short course covers the principles of analytics and demonstrates techniques and useful tools that you can use to develop and refine your knowledge of data analytics.
You will learn:
Approximate learning time: 3 hours
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