Data Mining

Even if you’re not a Facebook addict or regular Twitter user, you’ll know how difficult it is to escape social media. Why? Because social media is revolutionising the way that people consume content.

Social media is opening new channels of communication between brands and customers and there is a lot of potential in the social web that marketers can tap into. For example, a study earlier in the year by Penn State University showed that 20% of all tweets mentioned a brand name. Sales and marketing professionals need to be aware of these significant media consumption trends so they can tailor and target their messages as effectively as possible across a changing landscape. Read more…

This article, written by Neil Mason, was originally published on Clickz.com on 12/03/10 and is republished here with permission.

In the previous two columns I have been looking at different types of segmentation strategies, mainly dealing with what segmentation is, the different types of segmentation strategies and the role each type can play in building up a core understanding of your customers or prospective customers. So once you’ve decided what to create the segments on, the question then becomes about how to create the segments. Remember with segmentation what we are trying to do is to create groups of people who have something in common. Read more…

Facebook turned six recently and celebrated the milestone by giving its homepage yet another makeover, this time to “improve navigation to and discovery of commonly used features”. Six years is a long time on the interweb but, even still, Facebook has made impressive and significant gains in that time. It currently sits at number four on the list of biggest names on the web (behind Google, Microsoft and Yahoo, respectively) and with over 350 million users (and growing fast) it is poised to very soon become number three. It’s become the “face”, as it were, of the social media space, if not the brain. Read more…

This post originally appeared on Applied Insights’ blog. Foviance acquired Applied Insights in November 2008, with Neil Mason joining us as Director of Analytical Consulting. As part of this acquisition, we’ve incorporated Applied Insights’ blog into our own.

When we think about how to evaluate a predictive model the first thing we typically think of is how accurately does that model predict against the (unseen) test data. More often than not though when we develop models our business/research customers want more than that. They want to know how the algorithm got to the predictions i.e. they want to understand the model.

The more transparent predictive methods don’t just predict they also reveal the patterns that underlie them. The two main benefits of this are that

1. Subject Matter Experts (SMEs) typically on the business/research side – can assess the model’s validity by viewing these patterns, for example as rules or formulae. This way they can see if the inherent relationships make sense. Do they see any potential anomalies in the data that we didn’t pick up when we previously explored it?
2. And of course the patterns themselves may reveal useful insights. We often find specific segments of interest; demographic groups who have a higher propensity to convert through a given channel, or re-purchasers who have short, but potentially interesting and valuable, buying cycles.

The bottom line is that when we can see what a model is doing we can glean much more from it than the likelihood that the outcome of interest (convert, attrite, default, etc.) will happen.

To be frank most of our projects are like this. This is where Decision Tree methods often win out because the output let’s us visually explore the data to both understand the model and to examine other potential patterns of interest. They may not necessarily give us the most accurate predictions but often the SMEs care more about understanding than predicting. This is a classic trade-off in PA.

There are exceptions to this. The alternative view is that accuracy is paramount and it could be that the winning model is opaque. Neural Network models are a case in point. Depending on the software you are using you might see a ranked list of fields which contribute to the prediction along with the prediction itself and perhaps an associated confidence level. Even if the final network is displayed it doesn’t necessarily explain much more.

For the most part these are the two most typical scenarios however we are currently designing a 3^rd type – where opaqueness is the main objective (together with an acceptable level of predictive accuracy of course). We’re talking to a government department who don’t want to have to send sensitive data out and who don’t want our models to reveal any of that information either. So the gist of our approach is that we’ll develop black-box models on our data and let them deploy them on their database. They’ll give us addresses and predictive scores in return but in so doing we won’t know why a particular address was selected.

Anyone living in the UK will understand the political backdrop to this as there have been various high profile cases of data going AWOL (here is the latest one). We are hoping that a somewhat unorthodox application of Predictive Analytics might help the UK government provide a valuable public service without further compromising the confidentiality of its citizens. There’s many a slip twixt the cup and the lip mind you – we’ll keep you posted…

This post originally appeared in Applied Insights’ events section. Foviance acquired Applied Insights in November 2008, with Neil Mason joining us as Director of Analytical Consulting. As part of this acquisition, we’ve incorporated Applied Insights’ events list into our own.

Applied Insights ran a one day workshop in Predictive Analytics in association with the Emetrics Marketing Optimisation summit on 22^nd May at the Hotel Russell in London. A course outline is below.

Please contact us if you would be interested in joining one of our courses or developing a customised in-house training session on predictive analytics.

Predictive Analytics – course outline

An Introduction to Data Mining and Predictive Analytics is a one day workshop covering the foundations of this innovation marketing analytics discipline. During the course of the day you will gain a thorough familiarisation with some of the key principles and methodologies of data mining and predictive analytics and learn how to apply them to common marketing problems such as:

• How can I predict campaign response?
• How do I segment my website visitors or customers?
• How can I anticipate possible customer defections?

In this one day interactive course we will cover the following topics:

Introduction:

• What is data mining and how is that different to predictive analytics?
• How organisations are currently using data mining and predictive analytics across their businesses and to solve particular marketing problems

Processes and implementation

• How to go about a data mining/predictive analytics project
• An overview of a standard industry process (CRISP-DM)

Methods and applications

• An overview of the main types of data mining and predictive analytics applications:
  • Forecasting
  • Segmentation
  • Classification
• An introduction to main methodologies such as:
  • Time-series forecasting
  • Regression analysis
  • Decision trees (CHAID, CART and so on)
  • Cluster analysis
  • Neural networks
• Case studies and examples of how these techniques are used and deployed in both online and offline marketing is areas such as:
  • Retention modelling
  • Conversion propensity modelling
  • Visitor segmentation

This post originally appeared in Applied Insights’ events section. Foviance acquired Applied Insights in November 2008, with Neil Mason joining us as Director of Analytical Consulting. As part of this acquisition, we’ve incorporated Applied Insights’ events list into our own.

At this year’s annual Web Analytics Congress in Holland, Neil delivered a keynote presentation on Marketing Optimisation and Predictive Analytics.

This post originally appeared in Applied Insights’ events section. Foviance acquired Applied Insights in November 2008, with Neil Mason joining us as Director of Analytical Consulting. As part of this acquisition, we’ve incorporated Applied Insights’ events list into our own.

At this year’s Emetrics Summit in San Fransisco, Neil will be presenting a session in the “Advanced Analytics Track” entitled ‘Cutting through the NOISE: Applications of data mining and predictive analytics’.

The presentation will be looking at the application of techniques such as segmentation and propensity modelling to better understand website visitor behaviour.

This post originally appeared in Applied Insights’ events section. Foviance acquired Applied Insights in November 2008, with Neil Mason joining us as Director of Analytical Consulting. As part of this acquisition, we’ve incorporated Applied Insights’ events list into our own.

A return visit to Stockholm by Applied Insights this year. This time we’ll be giving a presentation at the Internet Marketing Conference on “Predictive Analytics – Why Bother?”. We’ve also been asked to be on the panel on the subject of Testing and Analysis and have been roped in to moderating a panel session on Web Analytics. Should be interesting…