Predictive analytics for customer acquisition with MADE.com
By: Nicolas Lisch
Tags: Machine Learning, Google Analytics, Data Science, Conversion Optimization, Digital Marketing

As a Data Scientist at MADE.com, I am passionate about web analytics: I use Data and Machine Learning to improve customer conversion rates which in turn helps increase revenue.

If you are a marketer or just digital savvy, this article is made for you.

I want to demonstrate how we incorporated predictive revenue as the main metric to drive qualified traffic on our website and lower customer acquisition costs.

As a marketer, Return On Investment or Return On Ad Spend is probably the most important metric you use to judge the effectiveness of your digital campaigns and adjust your budget per channel accordingly.

Cumulated first transaction share for customers over 90 days since the first visit

However, in most cases you have to wait for 30–90 days to start to see which channel brings in high-value customers. Especially if you have a long sales cycle like at MADE.com (only 35% of transactions happened on the first session day of a new visitor).

That being said, predicting an accurate long term ROAS is a key challenge for marketing teams as they have to work against performance budget/target daily. Indeed, 45% of spending doesn’t lead to immediate revenue.

Of course, some proxy can be used to evaluate the quality of new visitors (eg: bounce rate or attribution modelling) but as we know “ time is precious ” and it’s already too late to make real-time accurate decisions.

This is why long term revenue needs to be predicted.

How the Analytics team tackled this challenge?

Start with your proof of concept …

Before you can test and justify to stakeholders whether your predictive revenue model is worth building, you might have to visualize or hypothesize beforehand the expected outcome of this model.

In that case, I strongly recommend starting with a proof of concept based on Google session quality metric.

This turnkey metric will allow you to predict overall revenue, but that accuracy declines as granularity level of analysis increases.

For example, session quality does not take into account important traffic features like the landing page, channel, markdown. This is why you will need to create your predictive model to predict at session-level.

When it comes to predict the SEM or Direct revenue, you need to learn in-depth patterns from the first session of the new visitors.

You will have to shift from using standard Google historical data (eg: bounce rate, time on site) to more advanced in-house visitors data (eg: cities, landing pages) and aggregate data to fill in gaps in your model.

We defined the predicted revenue as the conversion probability multiplied by the expected revenue of a new visitor ( Average Order Value) following the function below :

Proof of concept

If Google Analytics is your web analytics solution, as it is at MADE.com, you’re lucky. You already have a native KPI called ‘Session Quality’ available in Google Analytics & BigQuery.

This KPI provides you with a session quality indicator, with a score from 0 to 100.

Based on ML Google model, session quality seems built on most common web analytics metrics such as ‘hits’ or ‘time on site’ as you can observe on the correlation matrix below. It does not correlate with custom internal tracking points such as the one we developed (‘newsletter subscription’).

Correlation matrix to check session quality

This KPI which has, of course, its limits, is an ideal starting point to implement your baseline model to support your digital growth performance.

Predicted revenue for a given session will be calculated as follows :

Where

The adjustment coefficient to calibrate session quality to transactions in 90 days period is :

Session Quality is the session conversion probability KPI.

The average order value 90 days is :

Let’s now have a quick look at results from our first model!

The trend in Predicted Revenue Versus Measured Revenue Over Time

It seems promising as it catches pretty well the reality of the sales.

This model provides you with a better vision of predicted Return On Ad Spend:

But reach its limits when it comes to predict accurately revenue at session-level.