Segmentation

While I have no doubt Albert Einstein was talking about digital marketing when he said, “Not everything that can be counted counts, and not everything that counts can be counted,” I have also come to the view that Mark Twain was an analytics guru. While possibly most famous for his “There are lies, damned lies and statistics” quote, I have discovered a number of other quotes that demonstrate his understanding of the world of analytics. Here is a selection of some of them:

“All generalisations are false, including this one.”

Here he was talking about segmentation. Generalised metrics such as overall conversion ratios, average time on site, and so on are largely useless. They only make sense when the data is segmented and the underlying patterns and differences can be understood. The same is for survey data as well. Metrics such as NPS can vary wildly among different customer segments and so these segments need to be identified and looked at separately.

“Few things are harder to put up with than the annoyance of a good example.”

A great insight into the power of evidence-based decision making. Solid facts, presented well, are hard to ignore. Twain was probably also thinking about the need to have an approach to testing and experimentation when he said this. There’s no better way to silence the HIPPO (highest paid person’s opinion) than to have concrete evidence from testing about why one approach is better than the other.

“It’s no wonder that truth is stranger than fiction. Fiction has to make sense.”

Sometimes the data doesn’t make sense and the skilled analyst needs to understand why. Is the data telling her something new? Is this real insight or is there a problem with the data? Often, if it doesn’t seem right, then it probably isn’t right, but occasionally there may be something that the business has missed, the competition has missed, and there is real opportunity to be exploited. Just make sure you’ve checked the data thoroughly before you tell the CEO!

Twain also had some great advice for budding analysts:

“The more you explain it, the more I don’t understand it.”

This is a great piece of advice. Basically, keep it simple, but don’t be simplistic. A good analyst will be able to present quite complex notions in ways that are easy for people to understand. This is the art of storytelling. Developing compelling narratives that engage the audience is quite a skill. There’s an important role for data visualisation here as well. It’s harder to create simple charts and graphics than to scatter lines and bars all over a PowerPoint slide. That’s why an analyst also has to be an artist.

“I was gratified to be able to answer promptly, and I did. I said I didn’t know.”

I think it’s OK to say that you don’t know; it’s better than bluffing…

“It is better to keep your mouth closed and let people think you are a fool than to open it and remove all doubt.”

…and being found out later, as long as you say that you’ll find out the answer and come back to them. And then make sure you do.

And finally:

“The most interesting information comes from children, for they tell all they know and then stop.”

Don’t be afraid of silence, particularly if you’re presenting to your audience. Deliver your insight and wait for a response. Maybe ask them a question, and then wait for a response. “Is that something you recognise?” and “Does that fit in with what you know?” are all ways of seeing whether what you’re saying is hitting the mark or missing the mark. It’s better to find out in the course of a conversation that what you’re saying is not falling on fertile ground rather than just to be ignored later. Maybe you need to get more evidence or maybe you need to explain the point in a different way.

So that’s why Mark Twain was an analytics guru!

This article was originally published on Clickz

This article, written by Neil Mason, was originally published on Clickz.com on 01/07/10 and is republished here with permission.

Last time I looked at some of the characteristics of data collected from surveys, particularly data collected from surveys run on websites where you have no control on who is answering the survey. Generally this lack of control can cause some bias in the data which can cause some issues if you are looking at the aggregated reports. For example the data on the profile of visitors (i.e. gender, age etc) that you collected from survey data may not actually reflect the true profile of visitors to your site because of the different propensities of different groups to respond to surveys. So, does that mean that survey data is useless? Not really but it does means that it needs to be handled with a bit of caution. 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…

This article, written by Neil Mason, was originally published on Clickz.com on 01/03/10 and is republished here with permission.

In my last column, I took a look at the meaning of segmentation and the different types of segmentation strategies available to digital marketers. There are three main types of segmentation; demographic segmentation, behavioural segmentation and attitudinal segmentation. But which one is best? The answer is that it really does depend on what problem you’re trying to solve. Read more…

This article, written by Neil Mason, was originally published on Clickz.com on 12/02/10 and is republished here with permission.

One of the things I like about my job working is a customer experience consultancy is that I’m surrounded by people with a very different outlook on life. Our user experience consultants tend to come from a behavioural psychology background and are great at using qualitative research techniques such as lab testing, eye tracking and ethnographic studies to get into the mind of users and to understand what makes for a good or bad experience. Read more…

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

A few weeks ago I took stock of the web analytics market, particularly looking at some of the key trends in Europe. This week to get a sense check from the position of someone in the US, I turn to a fellow WAA Board Director and friend Avinash Kaushik. Avinash is also an Author and one of the leading thinkers about web analytics and where it’s heading, having actually “been there and done it” previously at Intuit software.

This is what Avinash had to say.

Avinash, you had a busy year in 2007. What were some of the highlights for you?

It has indeed been a hectic year, becoming an Independent Consultant and Analytics Evangelist role at Google and publishing Web Analytics: An Hour A Day in June. Along they way speaking at conferences and running around the country became normal! Oh then there is the blog, Occam’s Razor, my baby (!), that took more time than I could ever have imagined.

I think the biggest professional highlight has to be the book. In five months sales have vastly exceeded my expectations. Since all of my proceeds go to charity (The Smile Train, Doctors Without Borders) it has meant a nice amount raised for them.

The book is a great primer and reference document for all things “web analytics”. But in this fast moving industry, isn’t it a risk writing a book? Are there some parts of the book that you think you might have to rewrite soon?

The core of the book I think will stand the test of time (and by that I mean five years at most! ). But there are many sections I would update. The book has been out only five months but I would add new things to the SEO section. Ditto for blogging metrics, I have slightly changed two of the six in the book and added a brand new one. I touch on Social Media but when I write the next version of the book I think things will be more settled and I can add more interesting things.

New tools will come with time, as will new sources of data and my book, or and those of others, will accommodate for that. But the biggest goal of Web Analytics: An Hour A Day is to teach you a new way of thinking, that I think will be relevant for quite some time to come.

All that said Willem from Wiley was over the other day asking me to start work on the next version!!

What do you consider to be some of the key industry developments to have been in 2007?

I get the distinct feeling that we will look back at 2007 and remember it as a turning point, a good one, for the industry.

Why is that?

Every site in the world seems to have Google Analytics – a leading indicator that even the most common person with tangential interest in data now has access to a great web analytics application. More interest translates into more mind share.

The industry has consolidated quite a bit. Omniture has built on top of its already impressive growth by acquiring Visual Science (/WebSideStory / HBX), in addition to Instadia (Analytics + Surveys), TouchClarity (Behaviour Targeting) and Offermatica (Multivariate Testing). This year all roads seemed to lead to Salt Lake City!

WebTrends is going through some temporary management turmoil, but with its excellent set of solutions I expect them to come back strong.

There were more web analytics consultancies launched, more than on you can count. Ditto for web analytics conferences. Actually a real interesting trend was how many non-analytics conferences had “web analytics day” or “web analytics pre-intensives” – a real sign of growing demands.

It was also a year of Web Analytics 2.0. An expansion of the core definition of what web analytics is, stretching is beyond just clickstream to include qualitative research, testing, competitive intelligence, multiple dimensions of outcomes etc.

So what are some of the key drivers?

Many, if not all, of the trends above were driven by a singular phenomenon: The web is becoming serious business.

It seems odd to say this in 2008 but in many companies web, and web analytics, have been a silo that someone else is taking care of. Websites are becoming the most important customer touch point and the most important revenue generator even for businesses that are not first of mind.

The consolidation in the industry, the increase in interest (tools or conferences) and expansion of the definition is a reaction to the demands now being placed driven by a desire to move beyond printing reports (to perhaps printing money!).

How would you assess where the web analytics industry is at the moment from the point of view of software vendors?

Full of opportunity.

Money and fame awaits all. Well at least those who are willing to work hard.

The vendors have done well thus far, mostly, but they are still scratching the surface of what is possible. Many big websites still don’t use web analytics. There are many growth opportunities in the Software market (aside from the current favourite child: hosted). We are not even scratching the surface of integration with data from other parts of the company and other tools should we decide that web analytics is not a silo but a part of “Business Analytics”? So there is a lot to do and appropriate financial rewards for companies that help accelerate the move beyond clickstream.

What about the people side, i.e. the end users and consultants?

There is a read dearth of skilled practitioners in our industry. And that has stunted the amount of progress that can be made (because the 10/90 rule still applies – spend $10 on software/services and $90 on people who can actually analyze data and produce insights). If you are a skilled person, you can name your own salary (but make sure you are on the web analytics 2.0 continuum and not 1.0), and if you are someone who wants a great Analytics career then now you know where to find it.

Consultants will thrive in any field where the rule is 10/90, because they can bring their expertise to bear on the $90 part of the equation. Additionally because of increase in the demand you are noticing many more consultancies (mom and pop and grandpa included), and an interest from the “big boys” for mature web analytics consultancies (example: our good friends Zaaz acquired by WPP). To make optimal amounts of money Consultancies, like other companies, are finding that they can’t be a one trick “let me parse your log files” pony. They are being forced to evolve into areas such as multivariate testing, competitive intelligence, usability etc.

What are some of the key trends that you see at moment? Where’s the market going?

The problem with Web Analytics 1.0 is that it is an exercise in data torture and reporting with long lags in taking action (if any). Data torture needs to get automated and expanded, decision making needs to get automated; people need to be left for smart hard things (vs. what happens today!). Smart companies will start to exploit more things like Multivariate Testing, Onsite Behaviour Targeting etc because in each case you are leaving humans to understand customers and create content and you are letting intelligent solutions create the right customer experience based on data. Won’t happen overnight, but are on this train for good.

I also believe that 2008 will see a more serious attempt to get Web Analytics to become a part of “Business Analytics”. We are still a silo in most companies (data and people!). We will see more collaboration and innovation in helping web data become a core part of the company data to truly give end to end visibility (and maybe the holy grail of multi channel analytics / impact). Won’t happen all in 2008, but we might get serious.

I am optimistic that we don’t have untouchable islands of data like we do today. Search Engine Optimization, RSS, Social Media, etc. They are all becoming mainstream yet the current generation of tools mostly stink at tracking them. You can track them, but if you are willing to row your leaky boat all by yourself to that island. I think this will change.

Oh and we are not done with consolidation in the industry.

It’s going to be fun!

I reckon so, thanks Avinash