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We take a look at the Top Ten blog posts which received the greatest number of views in 2015.

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10. Data Analytics 101: Turning Credit Risk Managers into Information Addicts

In the field of credit risk management, few would challenge data’s role in financial forecasting, lender analysis, credit-modelling and risk aversion. In short, credit risk managers are no strangers to data. But it could be argued that the value and volume of data they have access to largely determine the quality of their decision making. The shift towards more technology and data-centric business models has created new opportunities for those in the credit risk landscape to play more collaborative roles and engage other business divisions to produce positive outcomes in shorter time-spans.

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9. How Mobile and Social Data are changing the Face of Credit Scoring

In emerging or developing markets where formal credit history collection infrastructure or credit bureaus are lacking, the majority of people without reference data would struggle to achieve anything resembling an impressive credit score. As a large contingent of previously unbanked individuals join their respective middle classes in increasing numbers worldwide, lenders are finding creative ways of profiling and welcoming newcomers to the world of financial services.

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8. Three Things successful Loyalty Programs are doing right

Simply put, loyalty programs exist for two simple reasons: to motivate increased engagement with your brand and to collect data in order to build deep customer understanding. But they also exist for a third reason: customers want them. In a study by Nielsen, 84% of respondents said they were more likely to choose retailers that offered a loyalty program. Forrester Research have found that 64% of consumers agree that loyalty programs influence where they make purchases, and 50% agree that loyalty programs influence what they buy.

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7. How Netflix and Data Analytics are making Movie Magic

Kevin Spacey may not have given data analytics the nod at his acceptance speech last year at the Golden Globes, but that doesn’t mean the star doesn’t understand the depth of data’s role in the success of his hit show, House of Cards. Since the early days of its DVD-mailing business model, Netflix has been aware of the potential that lay dormant in consumer data. In 2006, the company offered $1 million to developers to create an algorithm that could best predict how viewers would rate movies based on their previous ratings. With the arrival of video streaming, thanks to internet maturity and consumer buy-in, data became abundant and Netflix were poised to capitalise on it based on the success of their initial foray into analytics. Today, House of Cards is a multi-award-winning TV-series that is conceptualised, cast, produced and written in partnership with the insight gained from massive volumes of consumer data. 

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6. How Big Data Analysis is driving Business Transformation

Corporate silos may have been necessary in a burgeoning industrial era some decades ago, but times have changed. The  era of social, mobile, analytics and cloud (SMAC) technologies has been fuelling a new wave of business transformation. Virtually every industry is being affected by the SMAC phenomenon and we’re currently only scraping the surface of what is possible.

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5. Four cool ways Data Analytics is changing the World of Sports

After the success which Data Analytics had in predicting the outcome of the Rugby World Cup games, Principa had a look into other ways data analytics is being used in the sports world today. There are indeed many ways, but for the sake of brevity, we looked at four of the more interesting ways that data analytics is changing the world of sports.

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4. Football had the Octopus, Rugby has Data Science

Many of us remember the hoopla around the predicting ability of the now deceased FIFA World Cup predictor, Paul the Octopus. For those who don’t recall, Paul was an Octopus at a German aquarium that famously predicted with 100% accuracy the results of team Germany’s six matches and final match of the 2010 Soccer World Cup. Although, Goldman Sachs defending their 37.5% success rate back then contested that Paul would have only been 33% accurate had he had to predict the results of all 48 games, including draws. Be that as it may, given the option of breaking into Cape Town Aquarium and kidnapping an octopus to help us predict the outcome of the Rugby World Cup or rather relying on data science and machine learning, we stuck to what we know and believe in and it has paid off.

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3. Can Machines predict the Outcome of the Rugby World Cup?

To kick off the SuperBru Challenge on predicting the results of the Rugby world cup we explained the methodology and data used to make the predictions for the rugby world cup.

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2. Out-predicting 99.68% of Humans during the Rugby World Cup

Another favourite from the Rugby World Cup challenge. Half way through the challenge we thought we’d share some of the key lessons learned so far and the challenges we face predicting the results of the up-coming knock-out stage Rugby World Cup matches.

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1. Using Predictive Analytics to beat the Bookies

It was Man vs. the Machine at the Principa HQ's as our data scientists applied predictive analytics and machine learning to predict the winners and spread of each match during the Rugby World Cup. We signed up two internal teams of data scientists onto sports prediction site SuperBru.com as an exercise to put theory into play in last year’s Rugby World Cup. By applying the same principles used to predict customer behaviour for our financial services and retail clients, our two teams are vying against each other to develop algorithms and predictive models that can predict the outcome of the matches with the highest accuracy.

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