Finding a Marketing Mix with Google Analytics Multi Channel Funnels and R

Google Analytics Multi Channel Analysis

Online marketing channels such as Paid Search and Display Advertising are used by scores of organizations to improve outreach.  Having the ability to improve your visibility by simply purchasing traffic is very useful.  What is not useful, at least for most marketing organizations is having to come to grips around whether the money for said traffic was spent as efficiently as possible.  Most organizations will simply use the Acquisition reporting in Google Analytics to learn of how much traffic their marketing campaigns generate (bad).  Some will even venture to see how many conversions or revenue they produce (better but still bad).

Only the savvy organization will employ the technique known as “multi-session marketing analytics.”  This technique uses user and session data in order to analyze the activity of users across multiple sessions.  This improves on the simplistic “last click” attribution model used for the regular Google Analytics Acquisition reporting.  Google has provided some reporting tools for this type of analysis in the Multi-Channel Funnels and Attribution reporting found under the “Conversions” tab in Google Analytics.  The Model Comparison Tool report can even be used to compare different models (i.e. “last click”, “first click”, etc.).

multi session marketing analysis
Model Comparison Tool in Google Analytics

The unfortunate thing about using these models is that there is no such thing as a one-size fits all model for analyzing a site’s marketing channels.  Each site has it’s own flavor and it’s own user base with it’s own marketing behavior.

In order to deal with this issue, an organization could either pay for Google Premium (which uses machine learning algorithm to predict the best model to use) or it could run a probabilistic model on its GA data. Markov Chain is one of the easier models to use. It is also a model that is used by a number of marketing attribution analytics consultancies.

I’m no statistician, but I believe the simplest way to explain Markov Chain is that is way of describing the probability of events based on the most previous event state. In this case each event is a session with an assigned medium and the result is the re-assigning of values based on the highest probabilities of conversion.  Read more on Markov Chain here.

Markov Chain for Marketing Analysis
Markov Chain Illustration

Good thing for those of us that have no advanced math degree, there is a package for R called ChannelAttribution which allows us to run the Markov Chain model on data direct from the Google Analytics API. It also compares the Markov Chain model to other models such as last touch, first touch and linear without much fuss.  There is a great tutorial on using ChannelAttribution on the Lunametrics Blog.  Read below to see how this technique can be used with data direct from the Google Analytics API in R.  The script seems a bit verbose, but it works very well, thanks to Kaelin Harmon!

This produces a dataframe and a plot which compares each of the heuristic models (last touch, first touch, linear) and the Markov Model.

Multi Session Analysis in R
Heuristic Models vs Markov Model

If you liked this post, you might want to take a look at my last post on using the GA API and R as an alternative to Google Analytics Premium or just leave me a comment below.

3 Ways To Analyze Google Analytics Data in R with RGA and ggplot2

In my opinion, Google Analytics is the single most influential development in marketing analytics ever.  Quantcast estimates that 70% of its top 10,000 website have GA installed.  Google has shown a relentless drive to improve the product over the years and it’s free price tag insures access to most anyone that runs a website.  With that said, Google Analytics is a service and no service (great or lacking) is without flaws.  One of the hidden advantages GA possesses is a robust API and this advantage allows users to build some of the features that are missing from the standard interface.  I wanted to cover some of the ways a user could use R to deal with some of the features not available in GA.

In order to use any of these techniques, you will have to install R as well as the rga package and dplyr package which available on CRAN.  Other packages used include ggplot2 for visualization, scales, lubridate and zoo.  Use the script below to install.

  1. Event Conversion Rate Script

    One of my gripes with Google Analytics is that the Top Events report includes total event counts but does not include a conversion metric.  If you are using the Google Tag Manger click listening technique to add events to your site by listening for click elements, a you could add a bit of custom Javascript to pass an impression for the same element, however, in many cases, just a simple total event count over the pageview count would suffice.  Here’s a script that grabs that simple metric:

    eventLabelpagePathtotalEventscontentGroup1pageviewspageconv
    Social Link/3Homepage9.3333

    This gives all event parameters (Category, Action and Label) as well as the page URL and content group 1, allowing the user to easily aggregate pages if they are passing content groups.  I strongly encourage using content groupings.

  2. Analyze Acquisition Mediums with ggplot2

    Google Analytics has some good embedded graphs for analyzing traffic mediums and the advent of Google Data Studio gives users even more flexibility, however, sites with high numbers of marketing mediums (10+) will pose issues for these tools.  Using ggplot2 in R allows a user to create what analysts call “small multiples” or a series of similar graphs or charts using the same scale and axes, allowing them to be easily compared.  Below is a script that returns small multiples for a year over year comparison of marketing mediums.

    small multiples using ggplot2 and r
    Small Multiples using ggplot2 and R
  3. Analyze Product Performance, Content Groups or Other Categories with ggplot2

    A user could also use the previous script for small multiples to learn about other categorical data like revenue by product:

    If you haven’t had a chance yet, please read my post on why an analyst should learn R.

    Have any questions or comments???  Let me know in the comments section.