The Data Analytics Blog

Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.

Luke Turnbull

Luke Turnbull

Luke Turnbull was the Head of Customer and Lead Analytics at Principa, until the end of 2017, after which he returned to his home country of New Zealand. He worked in the financial services industry since 1995, during which time he worked in process, strategy and operational design across a range of organisations in New Zealand, the United Kingdom and South Africa. Luke had been with Principa for 9 years and led consulting engagements with Principa’s local retail clients across the customer lifecycle, with a particular focus on customer engagement and lead generation.

Recent Posts:

Infographic: How To Create A Data-Driven Customer Loyalty Strategy

Here's a great visual overview of what you need to get started with a data-driven customer loyalty programme: the questions to ask before getting started and an overview of all the possible data sources to consider.

How Marketers Use Machine Learning In Retail

Machine learning is revolutionising how companies are capitalising on Big Data to develop their marketing strategies. While the term encompasses a broad spectrum of technologies and approaches, in a marketing context it can be used to improve targeting, response rates and overall marketing ROI. To put it simply, machine learning involves the automated analysis of large volumes of data – such as consumer spending habits and purchasing behaviour, as well as demographic information – and using a mathematical algorithm and a computer to identify patterns and trends. The algorithm then tests predictions based on historical campaign data and learns from the predictions it gets right. With time, these algorithms become highly accurate as more data from campaign results is added.

How Marketers Can Use Machine Learning To Boost Customer Loyalty

Thanks to mobile technology, wearable devices, social media and the general pervasiveness of the internet, an abundance of new customer information is now available to marketers. This data, if leveraged optimally, can create opportunities for companies to better align their products and services to the fluctuating needs of a demanding market space.

Data Analytics Now Fuels Customer Loyalty In Banking

As the banking industry pursues improved customer engagement, unlocking the value of data becomes critical in designing a successful loyalty programme. The balance of power in banking has changed. What customers expect, how they want to be serviced, what information they are prepared to share, and how loyal they are prepared to be, have all changed radically. According to leading industry analysts, Forrester Research, we are in the age of the customer, in which the only sustainable competitive advantage is knowledge of and engagement with customers.

How To Moneyball Your Customer Loyalty Strategy

If you at all follow the on-goings of Hollywood, you’ve probably heard of a baseball movie starring Brad Pitt that came out a few years ago. Its name is Moneyball, and it relates an important lesson that is revolutionising customer engagement strategies. The movie tells the true story of Billy Beane, general manager of baseball team the Oakland Athletics, who’s sick and tired of their lacklustre performance. After a key loss to the New York Yankees, he’s forced to rebuild the team on a limited budget. Instead of going with the obvious picks, he enlists the help of a Yale economics graduate to crunch the numbers and pick a team of statistically strong yet undervalued players. After a few losses, the data-driven approach is proven effective when the Athletics go on to have a 20 game winning streak – the longest in the history of the game.

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