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Principa's Top 10 Data Analytics Blog Posts For 2016

January 5, 2017 at 4:33 PM

We take pride in our ability to predict - from the results of the 2015 Rugby World Cup and the 2016 Oscars to predicting profitable customers and customer churn. However, there is no denying that 2016 was a year full of shocking, unexpected events - from Brexit and the US election results to the acrimonious break-up of "Brangelina" (shocking!) and the sad loss of some very talented artists.

Whether you saw 2016 as a good or bad year, it's now behind us as we begin this month with optimism and great anticipation of what 2017 holds for us. But before we let go of 2016 completely and "wipe away the tears, pick up the pieces and move on," we've had a look at which of our data analytics blog posts generated the most interest in 2016 and listed them below by order of popularity in case you missed any of them:

10. The Top Predictive Analytics Pitfalls to avoid

Predictive models are not bullet proof. The commoditising of Machine Learning is making data science a lot more accessible to the non data scientists of the world than ever before. With this in mind, my colleague and I sat and pondered, and we devised the following list of top predictive analytics pitfalls to avoid in order to keep your models performing as expected. Read more...

9. Using Big Data Analytics to prevent crimes the "Minority Report" way

It’s been almost 15 years since we saw the future of crime prevention in “Minority Report” – but today, we are beginning to see those then fictitious yet fantastical methods of predicting and preventing crime being implemented in various parts of the world. I’ll briefly mention three examples below of how analytics is already being used to prevent crime today before going into more detail on a fourth example: using analytics to prevent a criminal from re-offending. Read more...

8. How to get started with Machine Learning

The benefits have been recounted many times, but now that Machine Learning has the business world’s attention, how does one get started?  Moving into the machine learning space can be somewhat daunting, but we hope this blog post provides some guidance that you will find helpful. Read more...  

7. How mobile and social data are changing the face of Credit Scoring

Thanks to the prevalent usage of mobile phones and social networks in developing markets, fresh – albeit non-conventional - sources of consumer data are available in abundance for financial concerns to tap in to, and some companies aren’t waiting to get left behind in the race to be the first to shake hands with this new customer. Read more...

6. 3 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...

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5. You want fries with that? Using machine learning to cross-sell and up-sell

Today, the generation and tracking of customer data, transaction data and purchase behaviour data are enabling companies to move away from a generic upsell and cross-sell to a personalised one, and machine learning is ensuring data-driven recommendations reach the right customer at the right time. Read more...

4. What is Machine Learning?

Here's a blog post covering some of the most frequently asked questions we get on Machine Learning and Artificial Intelligence, or Cognitive Computing. We start off with "What is Machine Learing?" and finish off with addressing some of the fears and misconceptions of Artificial Intelligence. Read more...

3. Data Scientists Predict Oscar Winners

Following a highly successful initiative of using Machine Learning to predict last year’s Rugby World Cup results, we're trying our hand again at predicting the future and revealing some interesting insights along the way about another major event: The Academy Awards, or the Oscars. Read more...

2. How Machine Learning is helping Call Centres improve 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...

1. How Marketers use Machine Learning in Retail

When trends and insights are used to develop a campaign or an entire marketing strategy, there’s considerably less guesswork and a greater chance of success. To get a better idea of machine learning in practice, let’s have a look at how two of the world’s top retailers are using machine learning to improve marketing ROI. Read more...

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Julian Diaz
Julian Diaz
Julian Diaz was Head of Marketing for Principa until 2017, after which he became Head of Marketing for Honeybee CRM. American born and raised, Julian has worked in the IT industry for over 20 years. Having begun his career at a major software company in Germany, Julian made the move to South Africa in 1998 when he joined Dimension Data and later MWEB (leading South African ISP). Since then, Julian has helped launch various South African technology brands into international markets, including Principa.

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