Understanding of product analytics, metrics and A/B tests is a critical skill for data science consultants. While I have had some experience with these through former projects, it is a skill I am actively researching and studying to prepare for future engagements.

Before moving into data science consulting, I started my career in marketing. After university I was in advertising for a year, then moving into the market research industry for a further eight years. Looking back on the experience, I learnt a lot about consulting, marketing and market research techniques. I had the opportunity to leverage my data science and market research experience for a client while at Boston Consulting Group, and I hope to leverage these skills again in the future. As I progress in my career into new and exciting areas, I decided to write up a review of my experience. This could be a useful reference when working with clients in the future, or to go into further detail alongside my resume.

As I work through development upgrades for the wow_auctions repository, I thought it would be a good idea to quickly reflect on the development status. This will discuss the shape of the project after the first sprint of development, the initial upgrade stage, and plans for the future.

I have always been a keen video game player. Since moving to a new country, and especially during COVID times, video games have become a choice way to spend free time. I enjoy complex games with a lot of opportunity to analyse and optimise - no great surprise that I made a career of these skills!

A long time ago I heard about 'six degrees of separation', and the topic recently came up in a BCG training lecture. We were shown the 'Oracle of Bacon' site which shows actor's relationship to Kevin Bacon. I was curious what was the maximum relationship level, so I got to coding. I found this especially interesting as I have recently completed an algorithms course and this looked like a great use case for testing out a breadth-first-search algorithm. I had recently completed another toy project that needed some graphing algorithms so I saw this as a great chance to practice further.

Updated in mid-July 2020. This article is the looks to summarise the learnings from a semester studying Machine Learning. I reflect on the course, discuss the three branches of machine learning, and kick off with an example. We look at a 'decision tree' model on customer satisfaction data, and touch on the fundamental topic of overfitting and underfitting.

Over the past year, we have been developing survey chatbots as part of our Human Listening platform. We have noted the successes of this technology in many marketing applications, and feel there is great potential for this technology to fundamentally change how a lot of research is conducted.

We can't help but try to predict the future, especially when it comes to the application of new technologies. Who knew before the fact how radically our lives would transform due to the internet, computers, electricity and the industrial revolution? It seems the most recent development is the rise of AI and machine learning. Will this lead to a future like in Terminator and WALL-E, or something benign? We read about impressive advances such as game grandmasters beaten by AI, self-driving cars and using Alexa in the home. Naturally one might ask when we will have an AI that can beat chess grandmasters AND cook my pancakes? Most AI technology is quite specialised, and what we're referring to here is also known as 'General AI'. Unfortunately, this seems to be a long way off, but we can achieve a lot with focused effort in a single domain.

Weighting is an important - but often misunderstood - technique used to ensure research data is representative of a total population. We developed a vlog to explain the basics of weighting.

In market research, segmentation projects are some of the most rewarding projects. I can particularly remember one such project involving a workshop with more than 50 client staff to present a new segmentation. We had tables littered with props, butcher's paper and markers. The workshop was a great engagement exercise. It enabled connection to the research and formation of their own ideas about the segmentation. I have seen some projects follow segmentation analysis with focus groups. It is amazing to see a room full of 'segment A' and immediately see the contrasts to 'segment B' in the next group.

Business decision makers often rely on driver analysis to assess what is REALLY important to their customers. For example, customers often claim that price is the most important thing to them when asked. More often than not, however, driver analysis reveals service or other experiences are actually what drives overall satisfaction. This type of insight is what makes driver analysis able to sort the 'signal from the noise'. It means decision makers can more effectively allocate resources in a way that will yield the best return on limited resources to invest.

While performing some github repository cleaning in May 2020, I found a collection of blogs from my first blog site which I didn't stick with. This article explores ML classification of text data.

While performing some github repository cleaning in May 2020, I found a collection of blogs from my first blog site which I didn't stick with. This article further explores ML classification of text data.

While performing some github repository cleaning in May 2020, I found a collection of blogs from my first blog site which I didn't stick with. This article is a reflection on taking a big data with apache spark course.

While performing some github repository cleaning in May 2020, I found a collection of blogs from my first blog site which I didn't stick with. This article links to a presentation on automation - benefits and reasons not to.