Marketing attribution with markov chains in R

In the world of e-commerce a customer has often seen more than just one marketing channel before they buy a product. We call this a customer journey. Marketing attribution has the goal to find out the importance of each channel over all customers. This information can then be used to optimize your marketing strategy and allocate your budget perfectly but also gives you valuable insights into your customers.

shutterstock_181286708devices-touchpoint-attribution

There are a lot of different models to allocate your conversions (or sales) to the different marketing channels. Most of the wider known models (e.g. last click) work on a heuristic manner and are fairly simple to implement but with huge restrictions. I am not going to explain these models in this blog post as you can find tons of articles on the web about this topic.

Today we want to focus on a more sophisticated algorithmic approach of marketing attribution which works on the basis of markov chains. In this model each customer journey is represented in a directed graph where each vertex is channel and the edges represent the probability of transition between the channels. As we are going to focus on how to use this model in R, I totally recommend checking out the research by Eva Anderl and her colleagues. There is another research paper by Olli Rentola which gives a great overview of different algorithmic models for marketing attribution.

There is a great package in R called ChannelAttribution by Davide Altomare which provides you with the right functions to build a markov based attribution model. But let’s start with creating some data. With the code below we are going to create customer journeys of different length with userid and their touchpoints to a channel on a specific date.

# load packages
require(dplyr)
require(reshape2)
require(ggplot2)
require(ChannelAttribution)
require(viridis)

# simulate some customer journeys
mydata = data.frame(userid = sample(c(1:1000), 5000, replace = TRUE),
                    date = sample(c(1:32), 5000, replace = TRUE),
                    channel = sample(c(0:9), 5000, replace = TRUE,
                              prob = c(0.1, 0.15, 0.05, 0.07, 0.11, 0.07, 0.13, 0.1, 0.06, 0.16)))
mydata$date = as.Date(mydata$date, origin = "2017-01-01")
mydata$channel = paste0('channel_', mydata$channel)

To feed our model with data we need to transform out table from long format to sequences with the code below. I used some simple dplyr commands to get this done and cleaned up the data with the gsub function.

# create sequence per user
seq = mydata %>%
 group_by(userid) %>%
 summarise(path = as.character(list(channel)))

# group identical paths and add up conversions
seq = seq %>%
 group_by(path) %>%
 summarise(total_conversions = n())

# clean paths
seq$path = gsub("c\\(|)|\"|([\n])","", seq$path)
seq$path = gsub(",","\\1 \\2>", seq$path)

Now we are good to go and run our models. The cool thing about the ChannelAttribution package is that it not just allows us to perform the markov chain but also has a function to compute some basic heuristic models (e.g. last touch, first touch, linear touch). There are a lot more parameters to specify your model but for our example this going to be it. Use the help function from the console to check out your possibilities.

# run models
basic_model = heuristic_models(seq, "path", "total_conversions")
dynamic_model = markov_model(seq, "path", "total_conversions")

# build barplot
result = merge(basic_model,dynamic_model, by = "channel_name")
names(result) = c("channel","first","last","linear","markov")

result = melt(result, id.vars="channel")

ggplot(result, aes(channel, value)) +
 geom_bar(aes(fill = variable), position = "dodge", stat="identity") +
 scale_fill_viridis(discrete=TRUE) +
 xlab("") + ylab("Conversions") +
 guides(fill = guide_legend(title = "Model"))

# build another barplot to see deviations
result = merge(basic_model,dynamic_model, by = "channel_name")
names(result) = c("channel","first","last","linear","markov")

result$first = ((result$first - result$markov)/result$markov)
result$last = ((result$last - result$markov)/result$markov)
result$linear = ((result$linear- result$markov)/result$markov)

result = melt(result[1:4], id.vars="channel")

ggplot(result, aes(channel, value)) +
 geom_bar(aes(fill = variable), position = "dodge", stat="identity") +
 scale_fill_viridis(discrete=TRUE) +
 xlab("") + ylab("Deviation from markov") +
 guides(fill = guide_legend(title = "Model"))

Now we would like to display in a simple barplot (see code above) to see which channels are generating the most conversions and which needs to catch up. I am using ggplot for this with the awesome viridis package for a neat coloring.

rplot

We can go even further and use another barplot to see how the basic heuristic models perform compared to your fancy markov model. Now we can perfectly see some real difference between all these models. If you making serious decisions on which channels you spend your marketing budget you should definitely compare different models to get the full picture.

rplot01

You can get the whole code on my Github along with other data driven projects.

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