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Mapping the Consumer Journey and Testing Hypotheses

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Digital Marketing - Study Notes:

The role of web analytics

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:

  1. Choose one variable to test based on your insights or data.
  2. Identify the goal for the test.
  3. Create your control (existing set of variables) and test (new set of variables).
  4. Set your test split (for example, 50/50).
  5. Set your sample size, length of test, and channel or campaign to test.
  6. Begin testing.

False results

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:

  • Use common sense when interpreting results.
  • Trust but verify.
  • Review historical performance and look out for anomalies in the trends.
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Jack Preston

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.


Jack Preston
Skills Expert

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:

  • The fundamentals of data, collecting data, and processing data, including best practices, techniques, and challenges
  • The principles of web analytics, the benefits and limitations of Google Analytics, terminology for reporting, and the legalities around consent and data privacy
  • The concepts of Big Data, the processes around data, including mining, scraping, cleansing, and de-duping, and the various languages and programs for testing your data
  • The importance of AI, Machine Learning, analysis types, the value of testing hypotheses, and forecasting based on the data available
  • How best to report and present data findings to management and the different tools available to you

Approximate learning time: 3 hours