• I'm Mark Edmondson (@HoloMarkeD)
• Englishman living in Copenhagen since 2010
• Data Insight Developer for IIH Nordic
• Google Developer Expert for Google Analytics
• RStudio Shiny Advocate

Source: oreilly.com

One reason is Google Chrome uses prefetch

Prefetch tag makes Chrome (and websites) faster by loading next page.

<link rel="dns-prefetch" href="//widget.com">
<link rel="preconnect" href="//cdn.example.com">
<link rel="prefetch" href="//example.com/next-page.html">
<link rel="prerender" href="//example.com/thankyou.html">

And we can use it too - can be inserted dynamically:

var hint = document.createElement("link")
hint.setAttribute("rel","prerender")
hint.setAttribute("href","next-page.html")
document.getElementsByTagName("head")[0].appendChild(hint)

var hint = document.createElement("link")
hint.setAttribute("rel","prerender")

hint.setAttribute("href", "next-page.html")

document.getElementsByTagName("head")[0].appendChild(hint)

Can we predict quick enough to dynamically add it to the page before a user clicks?

• Yes - using R, OpenCPU and Google Tag Manager

• By Jeroen Ooms, rOpenScientist at UC Berkeley @opencpu
• RaaS - R as a Service
• An API for embedded scientific computing
• Web server that turns R code into JSON
• Supporting JavaScript library
• A server that creates a RESTful web API
• Let R programmers create the models, front-end web developers handle the data
• See also: www.yhat.com, www.dominodatalab.com, Plumber, firey

• Free tag management system on Google infrastructure
• JavaScript container you can edit remotely
• DataLayer object to manage data in centralised manner
• Deploy analytics tracking, beacons or any JavaScript
• See also: DTM for Adobe Analytics, Tealium for paid features

• OpenCPU - used by your R team to come up with the models to a form a web developer can use
• Google Tag Manager lets you deploy OpenCPU output
• Only one method of deployment, a more stable execution would involve the client web development team working directly with the OpenCPU JSON
• But Google Tag Manager great for situations where you can't do that, or for proof of concept

Extracting data per user using googleAnalyticsR

library(googleAnalyticsR)
ga_auth()
gaId <- xxxx # Your View ID

## In this case, dimension3 contains userId in format:
## u={cid}&t={hit-timestamp}
raw <- google_analytics_4(gaId,
                          date_range = c("2016-02-01","2016-02-01"),
                          metrics = c("pageviews"),
                          dimensions = c("dimension3", "pagePath"),
                          max = -1)

Or extract via BigQuery if you have Google Analytics 360 via google_analytics_bq()

Our model library markovchain needs a vector of sequential pageviews per userId.

## for each cid, split pagePath in timestamp order
sequenceVD <- processed %>% select(cid, timestamp, pagePath) %>%
  group_by(cid) %>% arrange(timestamp) %>% 
  distinct(pagePath) %>%
  mutate(step = row_number(), n = n()) %>% arrange(cid) %>%
  filter(n > 1) %>% select(-n) %>%
  spread(step, pagePath)

Create a Markov chain model of first order

library(markovchain)

model <- markovchainListFit(sequenceVD[,2:10], name = "seq")

## save model for use on OpenCPU
save(model, file="./data/model.rda")

Predictions now we have built the model object.

library(markovchain)

## make predictions
predict(model$estimate, newdata = "/da/a-z/6236/2665")

## prediction output
Sequence: /example/251

OpenCPU allows webhooks to Github: updates the model everytime you push to Github

Create a small custom package with the model data and the function to predict pageviews

predictNextPage <- function(current_url){
  out <- try(predict(model, newdata = current_url), silent = TRUE)
  if(inherits(out, "try-error")){
    out <- "None"
  }
  out
}

An example library using the predict function and the model data is here:

https://github.com/MarkEdmondson1234/predictClickOpenCPU

This library is called on the OpenCPU server from Google Tag Manager

Test your OpenCPU calls here: https://public.opencpu.org/ocpu/test/

Google Tag Manager lets you deploy JavaScript to a website without needing IT resources.

Login via a web interface: https://www.google.com/analytics/tag-manager/

http://code.markedmondson.me/predictClickOpenCPU/example/107.html

In Chrome go to chrome://net-internals#prerender