Segmentation

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

Segmentation. There’s a word. It’s a word that quite often means different things to different people and it’s all the rage in web analytics. Everybody is doing segmentation; all the web analytic tools are offering segmentation. But what it is it what does it mean and how can it be used? In simplest terms segmentation is the process of dividing a group into sub-groups. The idea in marketing segmentation is that there are some meaningful differences between the sub-groups which can be useful for marketing purposes.

I think that there are probably two main things to think about when doing segmentation. The approach you use to segment and the basis upon which you segment. There are two main approaches to segmentation I believe:

• Deterministic approaches
• Discovery approaches

The basis on which you segment might be along the lines of:

• Demographics and lifestyle
• Behaviour
• Attitudes

Deterministic approaches are where you create your segments based on some pre-defined or pre-determined classification. It might be a relatively simple classification like “Male vs Female” or they may be more complex like “First time visitors with abandoned shopping carts containing yellow socks”. With deterministic approaches you have some hypothesis that the segment is interesting, important or valuable and you maybe then test that hypothesis. Most web analytic tools now offer what I call this deterministic approach to segmentation. They offer the ability (to varying degrees) to divide or extract visitors into different groups and run reports comparing different groups against each other. In addition you may be able to extract email lists and other details from the segments for outbound marketing purposes.

The ability to segment and analyse different sub-groups of the visitor base is increasingly important. You can’t continue to run the site as a “one size fits all” business. Deterministic approaches are useful to try and identify some meaningful differences or to understand underlying behaviour in more detail. However, you have to go hunting and you may not always go hunting in the right direction. This is where discovery based techniques can come in to play.

By discovery based techniques I mean statistical and other data mining techniques such as cluster analysis and neural networks. Having spent some time in the past in the market research industry, I often think of the use of these techniques when talking about segmentation. Cluster analysis is a statistical technique that segments the population into sub-groups that display some commonality. There are many different cluster analysis algorithms that vary in their application and complexity. The overall objective of cluster analysis though remains the same: to maximise the similarity of the members within each of the sub-groups and to also maximise the differences between the sub-groups. In other words, you want each member of the sub-group to look as similar to each other as possible (all part of the same club) and for each sub-group to have distinct and meaningful differences from each other (all the clubs are different).

Cluster analysis is an iterative statistical process and therein lies the rub. The statistical process can create segments that are distinct but it doesn’t necessarily result in segments that are meaningful! So, the use of these types of techniques is as much an art as it is a science. Just because the analysis software reaches a result that is statistically correct, it’s not necessarily a useful result and these techniques also are dependent on the data that you start with. As the old saying goes “Garbage in, garbage out”.

Neural networks are a more “black box” kind of technique, based on the way that the brain works. They use artificial intelligence algorithms, such as Kohonen Networks, to find relationships or patterns in data. With classical statistical analysis techniques such as cluster analysis, the analyst has more control over the analysis process and can more easily interpret the findings and the outputs. Data mining techniques such as neural networks can be more powerful but also can be more difficult to handle (bit like driving a Ferrari or so I imagine).

In either case, getting some segments out is only half the battle. The other half is about understanding what they mean and what can be done with them. Typically the output of a cluster analysis will tell you that this person belongs to this segment. You then have to work out what it is that characterises the individual segments and what the differences are between the various segments. This is the profiling stage. The way the segments are constructed will be based upon the data that goes into the analysis. So if you use some behavioural data to create the segments, then the differences will be based on those behaviours and that’s the first place to look. However, you will also usually want to pull in other data to help explain what the segments mean. This can be demographic or attitudinal data for example.

So, segmentation can mean different things to different people, from simple classification through to more complex pattern discovery approaches. In this article we’ve looked at some of the approaches and techniques that can be used. In the next article in this series I will take a look at the different types of data that you may want to segment on and how they may be useful to the internet marketer. Till then…

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

There is a tendency when we talk about analysing web data that we focus on the use of so-called web analytics tools such as Google Analytics, Omniture, Coremetrics and the like. These analysis tools were developed specifically to manage the challenges of managing the reporting and analysis of data collected from web sites but they aren’t necessarily the only tools we might have in our toolbox.

There are a variety of other reporting and analysis tools that we might want to use on the data from our web sites to get a better understanding of online business performance and customer behaviour. It is fair to say that web analytic systems have significantly improved their analytic capabilities over the past few years and will no doubt continue to do so. These days there is a far greater ability in a number of the systems to be able to filter and segment data on the fly to look at the behaviour or characteristics of particular groups.

However, as the needs of the organisation continue to develop so too might the need for different or specialist reporting or analysis tools. Other systems for reporting and analysing web and customer data can be grouped into three broad categories:

• Business Intelligence (BI) or OLAP tools
• Visualisation tools
• Statistical analysis and data mining tools

BI or OLAP tools are often found in the corporate reporting environment and this class of tools includes systems such as Business Objects, Microstrategy and Cognos. Databases such as Oracle and SQL Server also either come with BI functionality or it can be bolted on. Underpinning many of these tools is the concept of a data cube that allows the analysts to drill through the data in a hierarchical manner. In a commerce environment I might start looking at say at total sales for a year and then drill down into product categories, then into sub-categories and then down to the product level.

Some web analytics systems do have the ability to drill through data in this way but a feature of the family of BI tools is the ability to handle multiple hierarchies across multiple dimensions. So, in addition to being able to drill through on the product dimension, you can also drill through the data say in terms of geography and also time. BI tools could also be used to report on web data in the context of other channels, for example comparing the profile of leads or enquiries generated online against those generated in the call centre.

As the saying goes, a picture tells a thousand words and visualisation tools can be a valuable weapon in your analytical arsenal. Again some web analytical tools such as Visual Sciences and Site Intelligence have some powerful visualisation capabilities. Whilst many web analytics systems have improved the visual reporting of web data through developments of click overlays for example, for the analyst a visualisation tool might add another dimension.

Visualisation tools can range from add-ins or add-ons for Excel through to complex applications that are commonly integrated in with data mining tools. At the desktop level, Excel add-ons such as MM4XL extend the scope of the charting abilities of Excel and allow the analyst to present data in different ways. More sophisticated tools can produce three dimensional rotating images that allow the analyst to explore and look for patterns in the data. The human brain is still one of the most powerful tools available for spotting patterns and trends in data when presented in the right way!

The final set of tools that might be useful for analysing web and customer data are statistical analysis and data mining tools. What’s the difference between statistical analysis and data mining? The way that I tend to view it is that statistical analysis is predominantly about exploration and data mining is about discovery. With statistical analysis you are often looking to test an assumption or a hypothesis. For example, you may be looking to prove that one group of customers rate your product or service more highly than others. With data mining, you are looking for patterns or relationships in the data that you may not know about.

Statistical analysis and data mining covers a wide variety of approaches, methodologies and techniques that might be useful for the web analyst. The can be broadly be classified as follows:

• Statistical analysis
• Classification techniques
• Clustering and segmentation methodologies
• Forecasting
• Text analysis

Increasingly many of these techniques are being used for making predictions and so the phrase “predictive analytics” is a term that is often used as well to describe these various methodologies.

Some of this stuff may seem like a long way from the current day to day analysis of conversion funnels and the like. But as the market continues to mature and growth comes from optimisation and improvements in marketing efficiencies, some of these techniques will have a place on the analyst’s workbench. Over the next couple of weeks, I will take a look at some these techniques in more detail and how they be used in the context of analysing online visitor and customer behaviour.

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

Over the past few weeks I have been taking a look at the variety of data sources available for evaluating e-business performance in addition to the data that comes from your site. These additional sources include audience panels, surveys and focus groups. I’ve also been making the point that purely focussing on web analytics data rarely gives you the full picture.

To talk about this on a practical level let’s take a look at how multiple data sources can be used together to look at a specific business issue such as optimising conversion rates on the site. The simple premise is that if you know who is coming to your site, why they are there and what they are trying to do then you can develop the site to optimise these key customer journeys.

To help digital property owners understand how visitors are interacting with their site we use something called a Customer Journey Framework. This framework is an approach to understanding which visitors are trying to use the site, how they are using it and whether they are being successful in their goals or not. There isn’t a single source of data that will give you the answer to these questions. You need to draw the answers from a suite of different places.

The Customer Journey Framework comprises of three key components:

• Understanding the different types of visitors (Audience segments)
• Understanding why people visit the site (Intentions)
• Understanding usage of the site and the consumption of different content (Content)

Different people come to your site for different reasons and there are bound to be different segments of visitors. The challenge is to work out what the most meaningful segments are for your business that you can use for your marketing and site development activities. This is something we’ll take a look at in the future.

Working out who is coming to your site is where you might use audience panel data, surveys or internal data from customer of registration databases. The reality is that you might need to use all three to build up a true profile of the different types of users that you might have. Audience panels can give you a demographic profile (if your site is large enough) but they may not help you to segment your audience in a meaningful way.

Surveys can help you understand if different types of visitors are coming to your site for different reasons. We call these “intention modes”. What is the visitor’s intention when they arrive on the site? What is their goal? To use an e-commerce example, a visitor may come to a site in one of these modes:

• To browse for something and buy it they find something they like
• To do research for price comparison purposes
• To buy a specific product that they have already researched
• To browse around with no intention of buying anything

Visitors in each of these modes will have different goals and will also exhibit different behaviours on the site.

By linking intentions to visitor segments you may find that some modes are more pronounced in certain groups of visitors. For example, in some work for a high street retailer in the UK we found distinct differences in these modes were evident when we looked at it along age and gender lines. In this particular case, older females were tending to arrive at the site with higher levels of purchase intent than younger females. The younger females were looking to be inspired by the site to make a purchase whereas the older females were more likely to already have in their mind what they wanted to buy.

The final link is then to layer these visitor segments and their modes onto the actual content of the site. This is where web analytics data is important and the linking the behaviours that you see on the site back to what you know about visitors and what they are trying to do. So, in our example above are the younger females looking at different types of products than the older females and so do those products need to be merchandised differently on the site to maximise conversion?

Linking behavioural data and profiling data can be tricky. It’s certainly easier if you can identify at least some of the site’s visitors through say a registration process or a transaction. You can match the profiling data captured in the process with the actual behaviour on the site and use that information to generalise for all traffic. It is also possible to link survey response and site behaviour data as well, though certainly here in Europe you need to be mindful of privacy concerns about identifying individuals.

The framework we’ve looked at here is one example of bringing together data from different sources to get a holistic view of what is happening on the site. It’s also just that – a framework, which can be adapted to suit the circumstances of your own site and information sources.

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

One of the things that you hear being talked about a lot more about these days in the wacky world of web analytics is “segmentation”. But I sometimes wonder what people mean when they talk about segmentation. I think it’s one of those words that is used more often than it is necessarily understood. Understood in the marketing sense of the word anyway.

I’ll take one example. One of the largest and most successful web analytics systems vendors has a section in their report menu called “Segmentation”. What we actually find there are reports on the most popular pages and sections of the site. I’m not too sure what that has to do with segmentation. Other vendors talk about segmentation as well but mean different things. Sometimes they talk about the ability to filter along different dimensions or the ability to analyse the data by combining different variables. So, segmentation could mean reporting particular data, filtering data or analysing data. All of these things are good things, and potentially even useful things, but are they segmentation?

I dug out some of my marketing text books to see if there was a consensus view in them about what segmentation actually is. I found that what they tend to talk about is that segmentation is a means of identifying different groups of people in order to develop different strategies for each group. So, segmentation is a purpose rather than an outcome and I think that’s the difference between classification (which is what a lot of analysis tools do) and segmentation which is what marketers or marketing analysts do.

The point of segmentation is that you do something as a result of having it. For example:

• You target different groups of people with different messages in your acquisition campaigns
• You present a different site experience dependent on your understanding of who that person is
• You interact with different people differently dependent on where they are in a customer lifecycle

In one of the books that I looked at that was actually written 20 years ago, the authors described three conditions of a good segmentation*. They are:

Homogeneity – the degree to which people in the segment are similar in ways that is interesting to you

Parsimony – the degree to which the segmentation would make every person a unique target

Accessibility – the degree to which you can describe the segments in ways that help you deploy differentiated marketing strategies

That all sounds pretty theoretical (well, it was a text book), so what does this mean in practice?

My interpretation of this is that a good segmentation has to be robust, useful and actionable. There are many ways that you might segment say a site’s visitors or your customer base from simple classification approaches through to complex statistical techniques but they have to pass the sense check of being robust, useful and actionable.

You might simply classify according to some demographic or geographic variables. For example classifying the customer base between male vs female is a form of segmentation but it is only robust and useful if men and women exhibits differences that are potentially useful to you and only actionable if you can realistically target them in different ways.

Alternatively, you might develop a segmentation based on some attitudinal variables. Many years ago I was involved in a project where we segmented the visitors across the number of different sites we had in Europe according to their attitudes to online shopping and their motivations for visiting the site. Whilst the results were certainly interesting and highlighted some interesting differences in the visitor profile of the different sites, we had to question how useful it was to us. How were we going to action the insight? We couldn’t identify and classify people arriving on the site by their attitudes nor could we easily use it in our retention marketing activities as we didn’t have people’s attitudes stored on our customer database.

So, I think that there is always a balancing act in satisfying those three conditions of homogeneity, parsimony and accessibility in a good segmentation. In our own work, we tend to use behavioural segmentation approaches as it makes it easier to act on the outcomes. This may often involve using statistical methods such as cluster analysis to segment customers into groups that are distinct from each other in a meaningful way like their browsing behaviour or their purchasing behaviour.

However, we are also mindful of the ability to the client to be able to act on the results. There is no point in developing a sophisticated methodology that identifies some really meaningful segments if there is neither the skills nor the tools available to realise the opportunity. For example if your email tool is not easily integrated into your customer database then it’s going to be difficult to execute improved target marketing initiatives. It is best to start with something simple and develop the capabilities to act in line with the development of the insight itself.

As it’s getting to that time of the year, in my next article I will be taking a personal look back at 2005 and reflecting of the some of the key events from my perspective and trying to get a sense of where we may be heading in 2006.

* “Marketing Decision Making – A model-building approach” by Gary Lilien and Philip Kotler