Introduction of Time Series Analysis For Digital Analytics

time series

I have an awesome textbook on statistics. It covers most statistical things, but one of the things you will not find in this awesome textbook is anything on time series. Time series are different and that makes them really interesting to me. This is because the x-axis is time, with the y-axis the thing (KPI) you are measuring. Because of that, you need to use different models to predict outcomes. 

In these models, the y-axis values are compared to other values on the y-axis (the lag values). For example, this month’s revenue is compared to last month’s revenue and the revenue of the month before, going all the way back to the beginning of the dataset. The chosen comparisons are based on the statistically significant lags. Last month’s revenue might be statistically significant to the current month’s revenue, but revenue two months ago may not be (but revenue from three months ago might be).

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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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