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Classification Modelling with case Sortter

  1. Classification Modelling with case Sortter #mcsthlm19 – October 26, 2019 @mertanen
  2. Short history Petri Mertanen • • BBA (Marketing), Specialist Qualification in Management • (Digital) Analytics exprerience since 2005, lecturer at Aalto University 2017-2018 • Certifications: • Elements of Artificial Intelligence: • 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
  3. 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 •
  4. 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
  5. Finnish loan broker
  6. Data preparation • 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!).
  7. Platforms for AI and ML
  8. Different options to connect data source(s)
  9. 1-click dataset
  10. 1-click or configured Linear Regression
  11. 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
  12. Questions? Petri Mertanen Analytics Consultant Mobile: +358 400 792 616 Social media