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.

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

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

It’s a new year and with a new year come new resolutions. Other than the usual New Year’s resolutions to eat less and to exercise more, I have been thinking about the consulting work we do with our clients and what resolutions we should be making in that area. I have come up with two that I thought I would share.

Think strategically, Act tactically

This notion has been playing on my mind for a while. A catalyst was listening to Jim Sterne speak at the Emetrics conference in Washington DC in October. Jim was talking about the need to solve people’s most immediate and pressing problems before you can then go on and show them all the amazing opportunities they might have in their online business. In our own work one of the things that we are conscious of is an organisation’s ability to execute from insight. So, if an organisation knows something, can they do anything about it?

We have seen occasions in the past where we have been engaged to work with organisations on developing strategic pieces of insight which the client then struggles to leverage due to functional or organisational constraints. I believe that it’s still necessary for analysts and consultants to “think the big thoughts” but they need to be translated into little actions that are digestible for the business in question. As consultants and analysts this is what we all try to do, my resolution for this year is to make this a core feature of the way to do business.

Segment, Segment, Segment

Segmentation has perhaps been one of the buzz words for 2007 and rightly so. In mature online economies, the ability to segment and target market to sub-groups is increasingly an important way to continue to grow and develop the business. However, not enough businesses are segmenting their online visitors and customers, and are still operating a “one size fits all” policy on their website. Larger and more complex websites will inevitably have a wide array of different visitor segments who are coming to the website for different reasons and who want to do different things.

More tools are available for understanding visitor segments from a behavioural, demographic and attitudinal perspective. In addition marketing technologies have developed to allow organisations to target different visitors with different messages and content using rules-based and automated profiling techniques. So, my second resolution is to continue to encourage businesses to invest and adopt these tools to allow them provide increased relevancy and improved customer experiences. I believe that those that do will stay up with the game, leaving behind those that don’t.

So those are a couple of my resolutions. What are yours?

Best wishes for a successful 2008.

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

In my last article I outlined my belief that what we call ‘web analytics’ is becoming a more diverse and complex field. What we have traditionally considered to be web analytics has been the analysis of site behavioural data captured, processed and reported on by a proprietary system designed to do just that. But as the online channel evolves and becomes more complex , the tools to help us understand what’s happening must also evolve and become more complex. In some areas, such as in the case social media, this may mean the development of new tools. In other areas it may mean the application of old tools to this new channel.
One of the areas that we work in a great deal is in the use of data mining and predictive analytical techniques. I first got started in this area about 15 years ago when at ACNielsen using these types of methodologies to help clients to try and figure out which half of their advertising money they were wasting. I have a book on my bookshelf that was published 25 years ago on the use of model building techniques in marketing. So the techniques aren’t new but what is relatively new is the systematic use of these techniques in the online marketing space.

I think that there are some reasons for this. Historically our main concern has been on managing the vast volumes of data and wrestling out of the web analytics systems a few numbers that told us how well we were doing and that we could do something about. Also, in the past, the natural organic growth in the channel has meant that we have not been faced with the need to scramble for market share and to fully optimise our business processes. And to some extent, we have not been asking the right questions. This is now changing. We understand our few numbers and we want to know more. The online world is far more competitive and we are beginning to ask questions that go beyond the limits of our traditional analytical tool set. Questions like:

• How do I understand the effects different marketing channels have on generating sales?
• What does the purchase lifecycle look like over multiple visits and how can I optimise it?
• How should I be segmenting my audience or customers, to improve the effectiveness of my marketing activity?

To answer these types of questions we are going to have to start to organise the data in different ways and we need to bring in some different tools. First of all we need to integrate our data so that we can see different aspects of the acquisition, conversion and retention processes in one place, Secondly we need to aggregate our data so that its focuses on the visitor or customer rather than the click or the visit. Thirdly we need to cut through the noise in the data using more sophisticated analytical techniques to get at the key insights. Let me give you an example of what I mean.

We all know that different types of people come to our websites for different reasons and to do different things. If I treat everyone the same, I am being sub-optimal in my decision making about how I allocate marketing funds and about how I manage the user experience. I need to segment my audience so that I can market to these different groups more effectively. However, I can’t do that on the basis on how they behave on the website alone, I need to also understand their demographics, their intentions, their aspirations and their opinions. So I need to integrate my hard core behavioural data with profiling and attitudinal data drawn from other data sources like surveys.

Next, I am interested in the behaviour of visitors over multiple visits rather than what they do in a single visit. So I need to aggregate the data so that I have a record of the behaviour of different visitors over a period of time. Also I probably need to summarise the data and create additional attributes which describe aspects of that behaviour over time such as number of visits made, number of conversions events, types of conversion events and so on.

Finally, I need to analyse the data to identify interesting and meaningful segments of visitors. In all likelihood I will probably have quite a large and noisy dataset where I won’t be able to see the forest for all the trees. Traditional querying and reporting techniques are unlikely to be an effective method of identifying the patterns, I need to use something that will find the patterns in the data for me. In this case I decide to use cluster analysis. The cluster analysis process looks for groups of visitors in the data, where the people within the groups have something in common but what they have in common is different from group to group. What I have to do then is interpret that data to understand what it is the visitor segments have been clustered on and decide whether these are meaningful and useful segments that I can do something with. This process may yield some surprising results and enable to think about the audience in a way that I had not previously thought of them before. I may find patterns and relationships in the data that I would never have found using traditional analysis techniques.

So using data mining and predictive analytical techniques will allow organisations to unlock more value from their data but it requires a different approach to managing your data, different tools and different skills. Next time I will look at another application of data mining and predictive analytics; to understand what are the important factors are that affect someone’s propensity to buy something during the purchase lifecycle.

Till then…

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

Last time in this series I looked at a number of different ways you might think about and measure customer loyalty. My view was that it’s not realistic to think about and measure customer loyalty as if it is a single entity but to create a loyalty measurement dashboard consisting of a number of appropriate and relevant indicators. These indicators might be behavioural, attitudinal or financial. To do this you will need to look at number of different data sources such as your web analytics data, surveys and other customer feedback data and any market or context data that may be available.

Following on from the tricky issue of looking to measure customer loyalty comes the issue of what to do about it. If you can look at the different aspects of customer loyalty through different metrics, then the question is: was do you do with this information? How do you act on it in a way that positively impacts on customers’ loyalty? How can you accelerate the building of loyalty when it’s in its ascendancy and how can you manage it when it’s beginning to decline?

On my customer loyalty dashboard I’m going to have a mixture of metrics. Some of them are going to be more strategic in nature, potentially even Key Performance Indicators (for example, a customer satisfaction index) and some of them are going to be more operational or tactical (such as recency or frequency measures). The strategic measures are going to be telling me how I am doing over the longer haul and the tactical measures are telling me what I need to do in the shorter term. The tactical measures are more likely to be behavioural metrics as, generally speaking, it’s easier to observe, react to and influence customer behaviour than customer attitudes.

RFM (Recency, Frequency, Monetary Value) analysis is often classically used to manage retention programmes. Customers are segmented according to how recently they have transacted, how frequently they have transacted and their value to the business. These segments can form the basis of differentiated retention and communication programmes depending on which segment the customer sites in. Customers who are in the top segment for recency, frequency and monetary value display loyal behaviour and are the ones that you don’t want to loose, and will probably deserve some special treatment.

A particular case of the RFM approach I think is the new customer, ie the customer who has just transacted for the first time. They’re a special case. It’s possible or even probable that you may not have made any money on them, you need to get them to transact again before you start to recoup your marketing costs. They are also at the steepest point on the “friction curve” which is the amount of effort required to get them to transact again. Retention is like momentum, once you get them started it’s easier to keep them going. In the case of the new customer, if you can get them to transact again, then they are more likely to transact a third time, and then a fourth and so on. So, customer retention, like conversion, is not one process but it’s a series of mini-events designed to move a customer from one state to the next.

The key advantage of RFM is its simplicity. It’s easy to do the analysis, create the segments and put together some specific customer communication. However, there are a couple of issues with it in my opinion. First of all, it’s assumes that people that behave the same on these dimensions will respond the same to particular communications. On it’s own it doesn’t help with the crafting of the retention marketing message. If you think of a multi-category retailer for example, different types of people will be buying different types of products. They may have similar shopping profiles but interested in completely different things. So, as well as knowing when to intervene, it’s also important to know how to intervene – what’s the trigger going to be?

The other issue is around recency. If you have a regular interaction in some way with your customers then by the time that you notice they’ve not been around for a while it may be too late. By the time they cancel the service, or stop visiting the site or whatever it is that means that they have stopped doing business with you, they could already be a lost cause. They might have stopped being attitudinally loyal some time earlier but it has taken a time to get to the point of being behaviourally disloyal.

So, we need to be able to anticipate changes in customer loyalty rather than just react to them. In many cases ,customers can give off signals or clues that their loyalty is shifting for the worse. They may change their patterns of behaviour, they may start calling customer services more often, and they may stop returning your calls. These are all indicators that changes are happening.

The role of predictive analytics in customer retention marketing is to give the marketer a heads up warning that something might be up with a customer. Predictive models look to identify customers who may be at risk based on the changes in other data. With all predictive models they will never be 100% accurate but if they are good enough they can at least reduce the risk of customers taking their business elsewhere. The inputs that go into these models will of course be specific to the individual business and the data that is available.

So, as markets become more competitive and retention becomes a more important facet of the digital marketer’s job description, it’s time to start thinking about customer loyalty seriously. What does loyalty mean in your business? Does it mean anything at all? If it does, how are you going to know if you’ve got it? What are the relevant measures? How can you impact those measures positively?

Lot’s of questions but they’re not necessarily difficult ones. The key thing I believe is to think them through carefully and build your customer loyalty dashboard accordingly. As the saying goes “Be careful what you measure, because what you measure is what you will get”.

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.

Step 3 – Data Preparation

Anyone who has ever analysed data knows what a nuisance it can be. Whenever we want to analyse it in a new style we often have to manipulate in some way before we can do so. The more “raw” the data, or the more fundamentally different the analysis, the more work we typically have to do to get into the shape we need for the analysis we want to perform.

As I mentioned in an earlier blog; if the primary data source is a data warehouse which contains well structured, rigorously cleaned and de-duped data, then this is usually the best starting point. But it is only that. The shape of the data tables in the warehouse will inevitably have been defined with a certain type of analysis in mind; most often to produce the standard business intelligence style of reports. You might get lucky and find you can use that data as-is for the kinds of predictive analysis you have in mind. The chances are that you won’t, and that you will have to re-structure the data in preparation for that analysis.
Furthermore it may well contain aggregated data, perhaps an OLAP structure of some sorts, which may allow you to produce time series forecasts but which will most likely contain data which is too summarised for most other kinds of predictive analysis. If this is the case then you’ll probably need to go back and locate the sources of the summary data. That might not be a trivial exercise.

How did we get here?

In the previous steps, discussed in other blog entries, we effectively designed the analyses which we intend to perform at the next step; Data Modelling. In Data Understanding we learnt all about the existing data structures, formats and sources and we started to look for patterns in those sources which are pertinent to the analytical objectives we defined at the start of the process. The truth is that, to perform the exploration, we would have had to prepare the data to some extent. But this is the point where we get serious and apply the necessary data management steps to get the data into the shape(s) required for the main task; predictive modelling.

At the top level this means we end up doing one or more of the following:

• Cleaning data
  This may not be necessary depending on how “clean” the original source is (though it is not unusual to find data problems when we start to analyse it in an unfamiliar way). Our previous exploration should have revealed any errors, or inconsistencies, which need to be corrected, or excluded.
• Merging data from multiple sources
  If you are lucky the data will be in a single data file, or a single table in a database. If you are unlucky it will be in a variety of disparate sources with different formats in various locations
• Shaping it for the analysis
  Often the most time consuming element. A classic example is where we have data with a sequence to it; typical if we are looking to predict the likelihood of a an event given a set of previous events. The starting point is typically data in a database which often contains all event transactions. In order to model it in a way which mimics how we will look to apply (deploy) the model we have to define an appropriate point in history as the baseline, e.g. if we are interested to know what will happen after March 2007 we might use March 2006 as that anchor point. We then have to restructure the incoming data to derive all the interesting predictors e.g. transaction frequency, transaction value in previous months, years, etc. from March 2006 backwards. We also need to have a separate data partition which contains the “what happened next” data for a period after March 2006 that corresponds to the period we want to predict into in 2007; so if we are interested to see which customers are likely to churn in April 2007, then April 2006 is likely to be the best month to look at it 2006. NB. Modelling and Evaluation (see later) will help test that hypothesis.
• Deriving new data elements
  Typically new fields(variables). In our exploration, for example, we may have found that there appears to be a strong relationship between the rate at which a customer buys products and the likelihood that they will churn. In many cases that rate will not exist as a separate measure in the current data, so we create it in this step.
• Describing it
  Labelling, formatting and generally documenting the data in a way which helps the analyst, or other viewers of the data, to understand its meaning.
  The outcome of the above is a set of tables, or data files, which are in the shape we believe we need for the modelling effort we have in mind.

You [almost always] never get it right first time

We’ve mentioned it before but it is worth re-stating that much of the CRISP process is iterative. Quite often we will get into the modelling step, for example, and discover a potential relationship that looks interesting but which we have to go back to the preparation process to derive. Frequently, because we are often building complex data handling processes from scratch, we just make mistakes which need to be corrected.

With large datasets the preparation time can be significant; It can take hours sometimes days, so mistakes and re-runs can be costly. Hence wherever possible it is a good idea to test the process using data subsets, ideally random, or at least representative, samples. Samples can also be used to boost productivity when we get into the analysis – more on that next time.

An example

Data collected in the web channel is a great illustration of this point. We work with a lot of this kind of data typically for web sites with large numbers of visitors; usually millions per week. These sites inevitably have a web analytics tool which they use to analyse key metrics of site performance. Most often we are interested to apply predictive and/or segmentation methods to the site data. This typically involves:

• Extracting behavioural data from the data warehouse (underlying the web analytics tool) or via a data feed that the analytics vendor provides. More often than not we extract this data to a number of text format files.
• For our Customer Journey Framework we usually have an additional data source in the form of on-line surveys. Depending on the analytics tool that the client is using we have developed a number of ways of linking the data that the visitor provides as a respondent in the survey to the behavioural data which maps that visitors journey through the site.
• The data we end up with can be at various levels but more often than not it is at the individual page or individual click level (remember these sites have millions of visitors so the number of records gets multiplied up). We take this data and aggregate it over a period of time to end up with tables for analysis which are at the visit and/or visitor level. Each of the resulting records will contain fields of interest; e.g. site content viewed, visit intentions and conversion goals which we will use for analysis.

For a typical site processing a weeks worth of data into the shape needed for analysis can take 4-6 hours.

Which tools?

As is often the case the choice of tool for data management comes down to those that the analyst/data is familiar with. Database tools are all about this type of work and often the best approach is to aim to construct data mining tables inside a relational database. This can be achieved using a combination of SQL, ETL tools and other database utilities.

Generally speaking; the more sophisticated the predictive tool itself the greater the data management capabilities which are built in. So SPSS, SPSS Clementine, SAS and SAS Enterprise Miner offer a broad range of data handling procedures.

So much for progress

Even though we have more and better tools, and faster hardware, with which to manipulate data these days this is offset by the increasing volume of data, complexity of structures and number of sources. Hence the old adage that data management consumes more of a data analysis effort than the analysis itself typically holds as much today as it ever did. But it is a necessary pain to get us to the point where we can get to the next step which is at the core of the predictive process; Data Modelling.

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

In my last article I started to take a look at the subject of segmentation. Something that is all the rage at the moment in the wacky world of web analytics. I outlined two key considerations in segmentation analyses; the approach you use to segment and the basis on which you segment. The approaches you use to segment may be deterministic or discovery based and I looked at those in the last article. This time we’re going to take a look at the basis on which you might segment your website visitors or customers.

There are three ways that you might choose to segment your users or customers. You might segment them on the basis of their demographics, their behaviour or their attitudes. Attitudinal segmentation is where people are segmented according to what they think about the brand or related issues. Often these attitudinal segments are developed from market research techniques and can be useful for brand development work. However, attitudinal segments are difficult to apply directly back into outbound marketing programmes. How do you recognise a “brand advocate” when they arrive on the site or from what they download or buy?

Demographics such as gender, age, household composition, income and the like may be a useful way to segment customers and visitors. Other lifestyle or geo-demographic classifications such as Mosaic in the UK may add a richer dimension to standard demographic segmentation. Demographic segments may be useful understanding the difference in browsing or shopping behaviour between men and women or between the young or the old. For business to business activities, demographic segmentation translates into business sector classification. This could be through industry standard definitions such as SIC codes or your own customer definitions such as SMEs, strategic accounts etc.

Whilst demographic segmentation can be interesting and sometimes quite useful it does assume similarities in underlying behaviour within the different segments. Do all 18 to 34 men think and behave the same when it comes to buying/using products and services? Probably not.

We often find that some of the most useful approaches to segmenting users and customers is by looking at their behaviour rather than just who they are. Behaviour can be easily observed either in your web analytics tool or in your customer database and those observations can then be used for the basis of outbound marketing programmes.

One of the classic behavioural segmentation approaches is Recency, Frequency, Monetary (RFM) analysis. RFM analysis is a deterministic approach where customers are dividing into segments on the basis of how recently they transacted, how frequently they have transacted in the past and what the value those transactions have been. Typically there are up 5 segments ranked from 1 (low) to 5 (high) on each dimension giving 125 segments in total. The top segment (number 5 on each dimension) is your most valuable customers, they transact a lot, they spend a lot and they have done it recently. They are the ones you don’t want to loose. For more information on RFM analysis you should check out Jim Novo’s site at www.jimnovo.com.

Whilst RFM analysis allows you to segment your customers based on their transactional behaviour in aggregate; it doesn’t give a perspective on what people are buying, downloading, reading and so on. Other segmentation approaches are based upon an analysis of what it that people are buying over time and look for commonalities and patterns in this behaviour. Are there groups of people that tend to buy the same sorts of products?

Discovery based techniques such as cluster analysis (which I discussed last time) are used for this type of segmentation approach. The purchasing behaviour of the individual customers is run through the algorithms to create distinct segments of customers with similar purchasing profiles. The segments are then profiled to understand what those commonalties are in the purchasing behaviour. These behavioural segments should then also be profiled with other data such as demographic and altitudinal data if possible. This additional data may come from the database, lifestyle profiling data or from survey work.

These purchasing segments can be used to improve the effectiveness of direct marketing programmes by adding insight into the type of message that might be relevant for each individual. We had an example of how it can add to a classic RFM approach in a project we worked on with a retail client. The client was using shopping behaviour as a way of segmenting their customer base using measures such as average order value, number of orders in the last year and so on. Using a product purchasing approach we identified two segments with very similar shopping behaviour but who were buying completely different products. Further profiling work showed they also had very different demographic profiles. One segment was older men and the other was younger females. The opportunity was now there to target and communicate to these two different groups in a much more relevant way.

So, segmentation encompasses a wide range of analytical approaches and techniques from the simple to the more complex. The trick is to start gently and build up your understanding of your customers by gradually breaking them down into meaningful and actionable segments, giving a sharper edge to your marketing communications.