Jarlath Quinn

A veteran of the predictive analytics industry working under the auspices of SAS, IBM and SPSS, Jarlath is pre-sales director at Smart Vision Europe. He is one of the most experienced SPSS trainers in the industry.

Predictive promoter part 4 – text mining and conclusions

In this final part of  our predictive promoter video series, Jarlath Quinn tackles the project’s most ambitious task: mining the open-ended guest comments to uncover important insights. Here you can discover how we can use text analytics to extract a series of concepts and sentiments from customer comments in order to categorise the guests’ responses.

Understanding what drives your net promoter score – how data science can help

The concept of the net promoter score was introduced to the world in Frederick Reichheld’s seminal Harvard Business Review article The One Number You Need to Grow in 2003. Reichheld’s research led him to believe that there was a deep and intrinsic link between profitable growth and customer loyalty. Two years of research revealed that, for most industries, …

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What If Analysis using IBM SPSS Modeler Premium

In this short video Jarlath Quinn demonstrates how to use the powerful simulation tools within IBM SPSS Modeler to perform What If analysis (also known as ‘Scenario Planning’). What if analysis allows business-focused analysts to go beyond simple predictive modelling to evaluate the impact of different choices and scenarios on predicted outcomes.

Predicting asset failure using IBM SPSS Modeler

This video shows you how organisations with substantial capital assets can use IBM SPSS Modeler to predict when asset failure is most likely. Predicting asset failure can prevent problems before they happen and enables organisations to save money, reduce asset downtime and increase efficiency.

Affinity analysis made easy

This short video shows how you can perform a simple affinity analysis using IBM SPSS Modeler. Affinity analysis can be used to understand interconnected relationships between key factors. For example, in retail it can be used to perform basket analysis, whereby retailers can identify which products are most commonly purchased together by customers in a single transaction or over a given period time.

Building a predictive model in SPSS Modeler

If you are considering making your first foray into predictive analytics or are interested in seeing the automated capabilities of IBM’s flagship analytical platform, this video will demonstrate the power and ease of building a predictive model in SPSS Modeler.

Unlocking IBM SPSS Statistics (part two)

Part two You will learn how to: Get your data ready for analysis in IBM SPSS Statistics Transform your data and create new fields e.g. creating an ‘age range’ field from date of birth information Ways in which you can transform different types of data

Unlocking IBM SPSS Statistics (part one)

You will learn how to: Read data into IBM SPSS Statistics from other sources such as Excel files Format your data and tidying it up for effective analysis Save time by using previously formatted files as templates for your new IBM SPSS Statistics data file 

Why not now? The barriers to adopting true predictive analytics.

While reading Jared Diamond’s excellent book on the rise and subsequent global dominance of Eurasian societies Guns, Germs and Steel, I was stopped in my tracks by his chapter on the evolution of technology entitled Necessity’s Mother. Diamond briskly demolishes the commonly-held view that necessity is the mother of invention. In fact he argues that many …

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6 ways to increase the value of your predictive analytics project

In my last blog post I talked about how it’s now possible to automate large parts of your predictive analytics projects, removing the need to get stuck into the complex statistics yourself. In this post I’ll suggest some ways to maximise the chances of your predictive analytics projects being successful. 1.     Use a proven analytics …

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Getting started with advanced analytics – advice for analytics virgins

What do you think of when you hear phrases like predictive analytics, data mining or machine learning? For many people these terms sound suspiciously like ‘statistics on steroids’ and unfortunately, even in the more data-centric and numerate industries, that isn’t likely to elicit the most enthusiastic of responses. To be fair that’s hardly surprising, as …

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