Articles

In this day and age, a business that leverages data to understand the drivers of 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 this type of insight out of data 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. ...

Traditional approaches to time series analysis and forecasting, like Linear Regression, Holt-Winters Exponential Smoothing, ARMA/ARIMA/SARIMA and ARCH/GARCH, have been well-established for decades and find applications in fields as varied as business and finance (e.g. predict stock prices and analyse trends in financial markets), the energy sector (e.g. forecast electricity consumption) and academia (e.g. measure socio-political phenomena). In more recent times, the popularisation and wider availability of open source frameworks like Keras, TensorFlow and scikit-learn helped machine learning approaches like Random Forest, Extreme Gradient Boosting, Time Delay Neural Network and Recurrent Neural Network to gain momentum in time series applications. ...

This year has been rather rewarding for me! After completing some of the excellent Business Science University courses, I have worked on a number of Customer Analytics & Business Intelligence projects and summarised them into technical articles that I published on Medium’s Towards Data Science. This opened up an entirely new world to me and generated many new connections within the analytics and data science community the world over! ...

Clustering is one of my favourite analytic methods: it resonates well with clients as I’ve found from my consulting experience, and is a relatively straightforward concept to explain non technical audiences. Earlier this year I’ve used the popular K-Means clustering algorithm to segment customers based on their response to a series of marketing campaigns. For that post I’ve deliberately used a basic dataset to show that it is not only a relatively easy analysis to carry out but can also help unearthing interesting patterns of behaviour in your customer base even when using few customer attributes. ...

Recently I have completed the online course Business Analysis With R focused on applied data and business science with R, which introduced me to a couple of new modelling concepts and approaches. One that especially captured my attention is parsnip and its attempt to implement a unified modelling and analysis interface (similar to python’s scikit-learn) to seamlessly access several modelling platforms in R. parsnip is the brainchild of RStudio’s Max Khun (of caret fame) and Davis Vaughan and forms part of tidymodels, a growing ensemble of tools to explore and iterate modelling tasks that shares a common philosophy (and a few libraries) with the tidyverse. ...

Market segmentation refers to the process of dividing a consumer market of existing and/or potential customers into groups (or segments) based on shared attributes, interests, and behaviours. For this mini-project I will use the popular K-Means clustering algorithm to segment customers based on their response to a series of marketing campaigns. The basic concept is that consumers who share common traits would respond to marketing communication in a similar way so that companies can reach out for each group in a relevant and effective way. ...