Conversion Attribution: a Markov model (using Google Analytics and the R ChannelAttribution package)

Hello, analyst! This post is about Conversion Attribution Modeling using Google Analytics (multi channel funnel report data) and the R programming language. Quite a mouthful.

It’s a follow-up post on an earlier blog I wrote on multichannel conversion rate.

I’m not going to tell you long stories on why you need this.

In short: use this script..

• If you want to know the conversion rate of each path (multi-touchpoint path)
• If you want to model the value of each point in the path.

The path I’ve chosen to get from Google Analytics is the source/medium path. You can choose the Campaignpath or similar quite easily, it depends on what you want to know. Just change the configuration of the script.

What you need before running this script

You need Google Analytics (gasp!) but, with a twist. You should create a view where you configure a Goal that is counted for every visit to the site. For more details on how to do this, read my blogpost on multichannel conversion rate.

You also need to download and install the R programming language. It’s freely available, open source, and there’s a nice GUI available (RStudio) as well. You’ll love it (it has a quite steep learning curve (challenge!) and an awesome community that can help you out (love!))

After setting things up, fire up R, and install the two packages you need: GoogleAnalyticsR and ChannelAttribution. We’re going to use these libraries to do all the hard work for us.

install.packages('googleAnalyticsR')
install.packages('ChannelAttribution')

After that, you’re ready to go!

The Script and the script configuation: a 1-minute setup

Let’s first paste the script here, so we can discuss it. It’s also on Github!

Configuration lines are line 17 to 28, and the script has plenty of comments and is not very complex.

# multi channel attribution and path conversion rate calculation.
#
# see https://stuifbergen.com/2016/11/conversion-attribution-markov-model-r

# install.packages('googleAnalyticsR')
# install.packages('ChannelAttribution')

library(googleAnalyticsR)
library(ChannelAttribution)

# auth first - this opens a browser (first time)
# ga_auth(new_user = TRUE)
# this one doesn't (refreshes token)
ga_auth()

# view id of your Google Analytics view where 1 conversion = visit
vid <- "115546653"

# transaction filter
trans_filter <- "mcf:conversionType==Transaction" # the conversion you're interested in
visit_filter <- "mcf:conversionGoalNumber==012"   # "visit" conversion

# date range
from <- "2016-09-01"
to   <- "2016-09-30"

# what do you want to know the attribution of?
dim = "sourceMediumPath"

# the function
get_data <- function(vid, from, to, filters = "",
                     dim = "sourceMediumPath", max = 5000) {
  df <- google_analytics_3(id = vid, 
                         start = from, end = to, 
                         metrics = c("totalConversions"), 
                         dimensions = dim,
                         filters = filters,
                         type="mcf",
                         max_results = max)
  # clean up and set class
  df[,1] <- gsub(" / ", "/", df[,1])              # remove spacing
  df[,1] <- gsub(":?(NA|CLICK|NA):?", "", df[,1]) # remove CLICK and NA
  df[,2] <- as.numeric(df[,2])                    # conversion column is character :-/
  
  # return the dataframe
  df
}

# get transactions
transactions <- get_data(vid=vid, from=from, to=to, dim=dim, filters=trans_filter)
colnames(transactions) <- c("path", "transactions")

# get visits (remember: this view has a goal where a visit = a conversion)
visits       <- get_data(vid=vid, from=from, to=to, dim=dim, filters=visit_filter)
colnames(visits) <- c("path", "visits")

# merge dataframes, based on path
alldata <- merge.data.frame(visits, transactions, by = "path", all=T)
# not all visit paths have conversions. Change those NA's to zero.
alldata[is.na(alldata$transactions), "transactions"] <- 0

# calculate conversion rate
alldata$rate <- alldata$transactions / alldata$visits
# null = visits without transaction
alldata$null  <- alldata$visits - alldata$transactions

# run the markov model
mm <- markov_model(alldata, var_path = "path",
                   var_conv = "transactions",
                   #var_value = "value", #use this if you have conversion values
                   var_null = "null",
                   order=1, nsim=NULL, max_step=NULL, out_more=FALSE)


# run the heuristic model
hm <- heuristic_models(alldata, var_path = "path",
                       #var_value = "value",
                       var_conv = "transactions")

# merge mm + hm, and voila.. your modeled acquisition sources
modeled <- merge.data.frame(hm, mm, all=T, by="channel_name")

# and View it (Rstudio)
View(alldata) # the conversion rate of each path
View(modeled) # the attribution table per channel

So: you need to input the following:

• The ID of your Google Analytics View (in GA under Admin > View > View Settings)
• The Goal that you care for (In this script, I user Transactions, since I run a little webshop)
• The Goal you configured where every visit counts as Goal. In my case I use Goal 12: mcf:conversionGoalNumber==012
• Your date selection (from and to)
• Optionally, you can choose something else than sourceMediumPath to use as referrer indicator.

Running the script and interpreting the output

When the script runs, you’re first redirected to a browser to allow you to give this script read-access to your Google Analytics data via the API.

After that, the data is pulled in via the API, cleaned up a bit and the calculations can be made.

Conversion rate per path

This is stored in the alldata table. It simply shows the visits, the transactions, and the conversion rate (as well as a “null” column, which is the number of non-converting visits).

Attributed conversion rates, modeled in 3 ways

This is stored in the modeled table. It shows each channel (in the sourceMediumPath) individually, along with the assigned number of conversions according to 3 models.

Please note: if your transactions have different goal values, you can use this as well. See the documentation on the ChannelAttribution package for details.

This table has 4 metrics in it:

• First Touch – Last Touch: assign all conversions to the channel of the first/last visit
• Linear Touch: assign equal value to all touchpoints
• total_conversions: use a Markov Model

Markov Model (Markov Chains)

A Markov Chain model deals with probabilities of switching from one state (touchpoint in this case) to the next state. Our freshly installed R package does all this wonderful calculations for us, and provides us with the number of conversions that can be attributed to each touchpoint, as well as the value of each touchpoint.

Please read the documentation on the ChannelAttribution package for more info.

Next step..

Save your data-output to Excel or a text file, and adjust your campaign budget to be more efficient. But note: since we’re tracking users here, PLEASE consider implementing unique user tracking, and use proper device (mobile, desktop, table) segmenting to be sure you know what’s going on.

Now go ahead, and be awesome!

Questions / Feedback? Leave a comment, or tweet me @zjuul. Thanks!

Related posts:

Multi Channel Funnel Conversion Rate Properly Analyze A/B Test results with Advanced Segments Analyse Web Site Click Paths as Processes GTM Monitoring in Google Analytics