Believe it or not, we are halfway through 2017 and if you’re feeling like you’re no where near achieving what you set out to achieve this year, I’m sure you’re not alone.
But fear not! If one of your resolutions this year was to research how to apply data analytics or machine learning to your area of specialisation – be it Marketing, Customer Experience, Debt Collection or Risk Management – you still have time! And our Data Analytics Blog is a good place to start.
I’ve looked at the stats and compiled our Top 10 list of most-read blog posts for the first half of 2017. Check out our list of blog posts below and see what topics your colleagues and industry counterparts are researching this year:
10. How does Data Analytics help Debt Collection?
Credit lenders use data analytics to assess potential clients and determine affordability. However, many credit lenders and debt collection companies fail to apply the same practice when dealing with defaulting clients. In my first blog post, I’ll cover the important role that data analytics can play in collections operations. Read more…
9. What is Customer Segmentation?
Effective communication helps us better understand and connect with those around us. It allows us to build trust and respect, and to foster good, long-lasting relationships. Imagine having this ability to connect with every customer (or potential customer) you interact with through communication that addresses their motivators and desires. In this blog post, I take a brief look at ‘customer segmentation and how it can foster the type of communication that leads to greater customer retention and conversion rates. Read more…
8. The 7 Logical Fallacies to avoid in Data Analysis
“Lies, damned lies and statistics” is the frequently quoted adage attributed to former British Prime Minister Benjamin Disraeli. The manipulation of data to fit a narrative is a very common occurrence from politics, economics to business and beyond. In this blog post, we’ll touch on the more common logical fallacies that can be encountered and should be avoided in data analysis. Read more…
7. Three ways Credit Risk Managers should be using Big Data
In order to survive and thrive in this economic climate, credit risk professionals need to consider innovative means of decreasing default rates and improving the accuracy with which credit is issued. One such way is applying data analytics to Big Data. Read more…
6. How Machine Learning is helping call centres improve the Customer Experience
The call centre world, unsurprisingly, ranks as one of the highest adopters of data analytics platforms year on year. This is largely due to the invaluable insights we gain through the analysis of thousands of calls received each day by the typical call centre. With speed being of the essence in making the right decision at the right time for each caller many call centres are turning to machine learning to automate their data analysis and make crucial customer experience decisions within seconds. Read more…
5. Hostage negotiation tips and techniques for Debt Collection
I contacted a former hostage negotiator from the South African Police Service (SAPS) and had the pleasure of spending the morning with him a few weeks ago. In this blog post, I’ll outline some of the key techniques learned in negotiation from a hostage negotiator and how they can be applied to achieve a significant lift in your debt collection outcomes. Read more…
4. How Marketers are using Machine Learning to upsell and cross-sell
McDonalds mastered the upsell with one simple question at the time of purchase: “You want fries with that?” A simple and relevant question at the right time that has likely generated millions of extra dollars in revenue through the years for the company. Ever since then, companies have tried to emulate their success by identifying complementary products in their offering and training sales staff to ask customers the right question at the right time. Read more…
3. Making the move from Predictive Modelling to Machine Learning
The move from predictive modelling to machine learning can be easier than you think. However, before making that move you need to keep two key considerations in mind to ensure that you benefit from all that machine learning has to offer and that your predictive analytics system remains a trustworthy tool that lifts your business, rather than harming it: the Consequence of Failure and Retaining Frequency. Read more…
2. 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. Read more…
1. The 4 types of Data Analytics
We’ve covered a few fundamentals and pitfalls of data analytics in our past blog posts. In this blog post, we focus on the four types of data analytics we encounter in data science: Descriptive, Diagnostic, Predictive and Prescriptive. Read more…
There you have it: the topics our visitors found most compelling for their business. If one of your new year’s resolutions was indeed to apply Machine Learning and Data Science to your business area, please visit our home page to learn about our new subscription-based Machine Learning and Artificial Intelligence applications-as-a-service for Credit Risk, Marketing, Call Centres, Customer Acquisition, Customer Engagement and Collections & Recoveries.
