• BBA (Marketing), Specialist Qualification in Management
• (Digital) Analytics exprerience since 2005, lecturer at Aalto University 2017-2018
• Elements of Artificial Intelligence: https://www.elementsofai.com/
• Statistical thinking for Data Science and Analytics:
• Google Analytics Individual Qualification, Google Ads Fundamentals,
Google Tag Manager Fundamentals, Introduction to Data Studio
• MeasureCamp Amsterdam 2017: Predictive Conversion Modelling
• MeasureCamp Amsterdam 2018: From Digital Attribution to Marketing Mix Modelling
• MeasureCamp Amsterdam 2019: Linear Regression Analysis and Modelling
About classification modelling
• In Machine Learning and statistics, classification is the process of predicting the certain
output from input variables.
• For example, we can predict if the website user / visitor will convert or not?
• Classification is an example of pattern recognition.
• In Machine Learning, classification is considered an instance of supervised learning.
• Learning where correctly identified observations (outputs) are available.
• Analyzing which variables or features explain the certain output?
• For example, we can analyze which content, elements or features explain the conversions
• Variables can be categorical or numerical
• With case Sortter, we can predict and analyze things related to loan applications
• Decision tree is one of the used classification algorithms
Collecting and cleaning data
• By default, current Google Analytics tracking is crap…
• It doesn’t track outbound links, videos, email or phone clicks, forms, file downloads,
scrolling, transactions, unique sessions…or other elements / features in website or app.
• Luckily, we have Google Tag Manager…and yes, GA v2 is available
• If you want to know which content, elements and features are affecting on conversions,
and how much, you need to track these in the first place.
• Instead of aggregated data, save more detailed data with session, client or user IDs
• Useful custom dimensions for Google Analytics:
• For example, you can use Google Analytics APIs to pull the data out
• Make sure that your data is clean and in Machine Learning ready format
• Major “healthcare” player in Finland.
• 46 % of bookings come from online.
• Mostly used paid search (Google Ads), Facebook
and display as digital advertising channels.
• Paid search being clearly the biggest channel
according to money spent.
• Data collected during Q1/2019 on daily level.
• You can do the data cleaning manually in Excel.
• Or you can automate the data collection, for
example with Supermetrics (from Finland!).
Summary and key takeaways
• You don’t need to code in order to practice Machine Learning and Data Science
• But you have to understand Analytics and Statistics
• Classification modelling can be a solution if the output is kind of “yes / no”
• There are other modelling options as well
• You can analyze different kind of things which explain the output
• If you analyze website content, elements and features, you will get nice
insight on where to do A/B and Multivariate testing