Introduction of Time Series Analysis For Digital Analytics

time series

Let’s say you want to forecast future revenue. You could fill in a month-to-month or week-to-week spreadsheet template. However, these are very subjective methods of trying to predict the future and often wildly inaccurate.

A better method would be to create predictive models using time series analysis. Time series predictive models are a specific subset of statistical models that deal with data that are ordered by and dependent on time. On a graph, the x-axis would be time, with the y-axis the thing (revenue, in this case) you are measuring.

In this post, I will give an overview of one type of time series models and describe how they work in simple language. I will describe how to implement the models in BigQuery ML. 

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Multi-Channel Attribution R Shiny App For Google Analytics

multi-channel attribution

Not everyone needs to be concerned with multi-channel attribution. If it makes up a very small part of your business, it might not be worth it. However, if you want to get a better understanding of the value of your traffic channels and the transactions/revenue they bring in, you might want to give it a go.

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How to Analyze Your A/B Tests Using R

A/B Tests

A/B tests are one of the best ways to optimize your website. However, it is not a magical cure for websites that are:

  • Poorly Conceived – Website is off-brand or there is a major disconnect with users.
  • Poorly Designed – Website is designed in a way that is hard to maintain/confusing.
  • Poorly Developed – Website has a lot of code bloat or inefficient code.
  • Using Sub-Par/Inflexible Technology – Website is created with technology that allows for very little optimisation or growth.

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