#Clustering #t-SNE #Customer Segmentation #Machine Learning

Segmenting with Mixed Type Data - A Case Study Using K-Medoids on Subscription Data

With the new year, I started to look for new employment opportunities and even managed to land a handful of final stage interviews before it all grounded to a halt following the corona-virus pandemic. Invariably, as part of the selection process I was asked to analyse a set of data and compile a number of data driven-recommendations to present in my final meeting. In this post I retrace the steps I took for one of the take home analysis I was tasked with and revisit clustering, one of my favourite analytic methods. ...

#Data Wrangling #Data Exploration

Segmenting with Mixed Type Data - Initial data inspection and manupulation

With the new year, I started to look for new employment opportunities and even managed to land a handful of final stage interviews before it all grounded to a halt following the coronavirus pandemic. Invariably, as part of the selection process I was asked to analyse a set of data and compile a number of data driven-recommendations to present in my final meeting. In this post I retrace the steps I took for one of the take home analysis I was tasked with and revisit clustering, one of my favourite analytic methods. ...

#Propensity Modelling #Machine Learning #Optimisation

Propensity Modelling - Using h2o and DALEX to Estimate the Likelihood of Purchasing a Financial Product - Optimise Profit With the Expected Value Framework

In this day and age, a business that leverages data to understand the drivers of its customers’ behaviour has a true competitive advantage. Organisations can dramatically improve their performance in the market by analysing customer level data in an effective way and focus their efforts towards those that are more likely to engage. One trialled and tested approach to tease out this type of insight is Propensity Modelling, which combines information such as a customers’ demographics (age, race, religion, gender, family size, ethnicity, income, education level), psycho-graphic (social class, lifestyle and personality characteristics), engagement (emails opened, emails clicked, searches on mobile app, webpage dwell time, etc. ...

#Machine Learning #Propensity Modelling #ML Interpretability #Model Selection

Propensity Modelling - Using h2o and DALEX to Estimate the Likelihood of Purchasing a Financial Product - Estimate Several Models and Compare Their Performance Using a Model-agnostic Methodology

In this day and age, a business that leverages data to understand the drivers of its customers’ behaviour has a true competitive advantage. Organisations can dramatically improve their performance in the market by analysing customer level data in an effective way and focus their efforts towards those that are more likely to engage. One trialled and tested approach to tease out this type of insight is Propensity Modelling, which combines information such as a customers’ demographics (age, race, religion, gender, family size, ethnicity, income, education level), psycho-graphic (social class, lifestyle and personality characteristics), engagement (emails opened, emails clicked, searches on mobile app, webpage dwell time, etc. ...

#Data Wrangling #Data Exploration #Propensity Modelling

Propensity Modelling - Using h2o and DALEX to Estimate the Likelihood of Purchasing a Financial Product - Data Preparation and Exploratory Data Analysis

In this day and age, a business that leverages data to understand the drivers of its customers’ behaviour has a true competitive advantage. Organisations can dramatically improve their performance in the market by analysing customer level data in an effective way and focus their efforts towards those that are more likely to engage. One trialled and tested approach to tease out this type of insight is Propensity Modelling, which combines information such as a customers’ demographics (age, race, religion, gender, family size, ethnicity, income, education level), psycho-graphic (social class, lifestyle and personality characteristics), engagement (emails opened, emails clicked, searches on mobile app, webpage dwell time, etc. ...