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.

At the inaugural Web Analytics Wednesday event held in Berlin, Neil gave a presentation on “Integrated Web Analytics”. The first time he’s ever presented in a nightclub!

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…

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 ICT Forum event for small and medium enterprises in South Wales, Neil gave a presentation introducing web analytics called “If you can’t measure it, you can’t manage it”.

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

Wow, that was the week that was. As I write this I have just about recovered from a 3 day intensive immersion in the whole area of online marketing performance measurement and optimisation. I think we used to call it web analytics. Now I’m back in my own time zone I’m beginning to process some of what I saw and learnt at the Emetrics Summit in Washington a couple of weeks ago.

Before I went I was already picking up the vibes that it was going to be bigger and broader than anything I had been to before and that certainly proved the case. I think that there were over 600 people there over the course of the conference. OK, that may not be huge compared to some of the other US internet conferences you might attend but for the ugly duckling of online marketing I think it shows just how far the web analytics industry has come.

One of the challenges at these conferences now is deciding what to see. I probably spent as much time looking at the programme, trying to work out which session to see as I did wondering round the hotel trying to find the actual session. At various times during the conference there were six tracks running simultaneously ranging from topics such as behavioural targeting and testing through to public sector success, from search analytics to email metrics and from web 2.0 measurement to statistical analysis. A very eclectic mix of subject matter!

As I suspected, this industry is not only growing but diversifying. Some of the more interesting conversations I had over in DC were not with web analytics vendors talking about the latest features of their particular software but were with smaller companies tackling a particular problem in a different way. For example, new approaches to gathering and analysing customer feedback data through text mining or a methodology for media planning optimisation using predictive analytics.

Time and space doesn’t allow me to give a blow by blow account of what I saw and learnt at this conference and it’s already been documented in other columns and blogs. What I wanted to do is just share some of the key things I took out from the event. So for me, here are some of my highlights:

• Jim Sterne’s key note speech. I’ve heard Jim speak many times and I’m always impressed. This time round he challenged us to ‘think differently’. Getting us to ‘think’ full stop I think is often enough of a challenge but Jim’s presentation also reminded me that as an analyst you can’t realise strategic value unless you also deliver tactical benefit.
• Ronny Kohavi from Microsoft’s presentation on controlled experiments on the web. Everyone’s getting excited about multi-variate testing but Ronny showed what immense benefit you can get from running simple tests on a continuous basis. Also when evaluating those tests don’t look just at the short term gain but also look to understand the longer term value that you will gain
• Meeting a bunch of people for the first time. You read people’s blogs, you see their posts on the email groups but there’s no substitute for meeting them in flesh and discussing their ideas face to face.

So what next? As you read this I’m probably on my way back from the first Emetrics Summit in Sweden. This is continued sign of the growth and development of this industry, in particular outside of the US. Sure, the size and scope will be different to what I experienced in Washington DC but I expect the enthusiasm, inertest and lobby bar conversations to be very similar!

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 inaugural event in Sweden Neil presented on data integration.

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 Washington DC, Neil presented a paper entitled “Cutting through the NOISE: Applications of data mining and predictive analytics”.

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

In part one of this series, I examined visitor segmentation, a data-mining technique. Now, let’s look at how data mining can be used to understand important visitor behavior over time.

Quite often when we use Web analytics systems, we focus on what visitors do during a particular visit. The classic conversion funnel is a good example of this trendMost Web analytic systems look at the conversion funnel in the context of a single visit, that is, they report on how people got to page A, then B, then C, and so on within a single visit. This information is useful because it helps identify potential process areas that need improvement. But if we think about those times when a visitor might make multiple visits to a site before a conversion, the classic conversion funnel might not give you a true perspective on what’s happening.Take the example of buying car insurance online. In the U.K., it’s a very competitive business. Consumers typically shop around for quotes and go for the best value proposition. As a result, it’ s very unlikely people will arrive on a site and buy car insurance on their first visit. Maybe they’ll arrive from a search engine, check out the proposition, and bookmark the site for future reference. Maybe later they’ll come back, get a quote, and leave to compare it to other quotes. Hopefully they’ll return to complete the policy application process, and a sale is made.

A generic conversion funnel analysis will contain an amalgam of all three types of behavior: research, quote, purchase. As a result, you’re not seeing a true reflection of your ability to convert opportunity into value unless you analyze visitor behavior over sequences of visits, rather than just within the single visit.

If you work with Web analytics data, you know it’s hard enough to understand what’s going on when examining a person’s behavior in a single visit. Analyzing behavior over multiple visits adds complexity. Here, data mining and predictive analytical techniques come into play.

If we accept (as in the car insurance example) that conversion is often a multivisit process, we must understand the process’s key drivers over time if we are to influence that visitor’s behavior. We must find out what behaviors over multiple visits are most likely to lead to a successful outcome.

Using a decision-tree technique like CHAID can help you understand how different visitor behaviors over multiple visits may increase or decrease the likelihood of converting a browser into a buyer. CHAID, which is highly visual, shows factors that influence conversion in a tree diagram in the order they influence people.

) can help you understand how different visitor behaviors over multiple visits may increase or decrease the likelihood of converting a browser into a buyer. CHAID, which is highly visual, shows factors that influence conversion in a tree diagram in the order they influence people.) can help you understand how different visitor behaviors over multiple visits may increase or decrease the likelihood of converting a browser into a buyer. CHAID, which is highly visual, shows factors that influence conversion in a tree diagram in the order they influence people.) can help you understand how different visitor behaviors over multiple visits may increase or decrease the likelihood of converting a browser into a buyer. CHAID, which is highly visual, shows factors that influence conversion in a tree diagram in the order they influence people.As with the segmentation approach described in part one, data must be in the right shape before an analysis is started. That requires extracting and summarizing data to key activities and events in each visit of the visitor lifecycle. I often think that data mining and predictive analytics are part art, part science. The art requires possessing the right data in the right format for algorithms to provide meaningful and useful results. In these days of automated analytics, anyone can produce a model. It’s a question of whether the model is good or not.

In working with these techniques, we commonly find there are a small number of highly influential conversion drivers over multiple visits. Naturally those drivers vary from site to site, but the importance of time is usually one thing they share in common. The time between the first and second visit, and the second and third visit, and so on, are quite often a good predictor of the subsequent outcome.

As the need to tune the online marketing processes continues, organizations must add capabilities to their analytics tool kit. Data-mining and predictive analytical techniques are firmly established within other marketing disciplines. Perhaps their time is now coming in the online world.