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”.

A lot of the answers to these types of questions will depend on what industry or vertical you happen to in. The notion of loyalty is different if you are selling biscuits (or cookies) than if you are selling cars. The frequency of the decision and the purchase decision process itself are very different. Some commentators believe that loyalty is essentially a behavioural phenomenon. Certainly in terms of managing retention, it’s primarily the behavioural drivers that are used to trigger marketing events such as promotions or emails. But that’s not necessarily because the altitudinal components to loyalty are not important, it’s just that they are harder to work with.

My own view is that the notion of customer loyalty is often nebulous, difficult to define and hard to measure. But we shouldn’t let that put us off! Often in the work that we have done for clients we see the disproportionate value of repeat customers to the overall business. So, how do we define and measure loyalty?

In my ideal world I wouldn’t have a loyalty measure, I would have a loyalty dashboard. I don’t think it’s really possible to measure and manage customer loyalty using a single metric, I think you need a number of different indicators giving you different perspectives on how visitors and customers are thinking about their relationship with your brand. It’s not just about how they behave but it’s also about what they think and the emphasis between the two will be dependent on the kind of business that you are in.

In our online world we’re pretty good at tracking behaviours and so it doesn’t come as a big surprise that behavioural data is often used to describe customer loyalty. In a quick survey of various web analytics tools, most of those that have a “visitor loyalty” metric base it on the frequency of visits or perhaps the number of “conversion” events. What they generally don’t do, however, is take into account what the visitor does when they get to the site. So, someone who visits 3 times and spends 5 minutes on the site each time is considered to be more loyal than someone who visits one and spends half an hour on the site. So, a frequency metric may be interesting but may not necessarily be very useful when it comes to thinking about loyalty.

Then there is the issue of recency. Does recency have anything to do with loyalty? Does the fact that someone visited my website yesterday make them more “loyal” than someone who last visited last month? Probably not. But if they have visited more frequently in the past and have visited more recently, then they are displaying characteristics of “loyal” behaviour. Recency and frequency analysis in conjunction are better than looking at them individually but we’re probably still not getting the full picture.

I think that customer loyalty also needs context. We all generally live in a competitive world. We are fighting for our share of the wallet, the budget or just someone’s attention. We want our visitors and customers to spend more time or money with us than with the other guys. To be able to measure this context I need some other data, I’m not going to get that from a web analytics system.

Other data sources that I can add to my loyalty dashboard to give me this context include 3rd party sources such as audience panels or my own surveys. Not everyone is going to have access to panel data such as Nielsen NetRatings or Comscore but if you do have that data, you can use it to add to context to your web analytics data. As a simple level you can measure the duplication or overlap between your audience and that of your closest competitors or you can drill into more depth and look at the amount of time visitors spend on your site compared your competitors.

If you don’t have access to these types of services, you can get at some competitive context by asking your own visitors through the use of surveys. You can ask your own visitors which other sites they visit and if relevant how much time or money they tend to spend on these others sites. The data can then be analysed to produce some loyalty metrics that can be tracked over time or across different visitor segments.

Surveys can also be the vehicle to give you a wealth of powerful attitudinal information for your loyalty dashboard. Measures such as “propensity to return” and “propensity to recommend” have been shown in the past to be strong predictors of loyalty and customer lifetime value. Satisfaction can also be used as a leading indictor for changes in loyalty and the benefit of these types of measures is that they can give you an opportunity to act before it’s too late. Often customers can become attitudinally disloyal before they actually change their behaviour.

There isn’t a “one size fits all” approach to measuring customer loyalty and I would encourage you to think about measuring customer loyalty using a composite approach of different metrics drawn from different data sources. Create your own customer loyalty dashboard.

From insight needs to come action and next time I will have a look at how we can utilise data-driven insights in our retention marketing activities. Till then…

At the same time, conversion optimisation is gaining momentum. There has been a lot more focus on what actually happens when someone gets to the site over the past couple of years, spawning the growth of web analytics and multi-variate testing services. There is still probably a lot of mileage to be gained for most organisations through site improvements but generally the process is underway.

However, from the work we have done with a number of organisations it seems to me that companies generally heave a sigh of relief when they make the sale as if the job is done. Having been through the effort of creating awareness, acquiring the traffic and converting it on the site, the important asset that has been created, ie the customer, is then neglected and the organisation sets off in pursuit of new ones. The result is most customer databases are littered with customers who have only transacted once. For more, the definition of retention marketing is converting someone twice without having to acquire them twice. You don’t want to have to go through all that heavy lifting again!

So as part of this increased focus on retention marketing there’s going to be an increased emphasis on customer loyalty. Over the next few weeks I’m going to be taking a look at issues around customer loyalty such as “what is customer loyalty?”, how do you measure loyalty and how can you use data and insights to manage the retention marketing process more effectively.

What do we mean by loyalty?
What do we mean by customer loyalty? For example is it a state of mind or it is a set of behaviours? How can we really establish whether a customer is loyal or not? In our multi-channel world is the notion of loyalty even valid or useful?

These are tough questions that have been debated for many years and, of course, there are arguments on all sides. If you look up the definition of loyalty is usually describes loyalty as being a “quality” which suggests it’s more attitudinal than behavioural. However in our marketing world we’re generally interested in outcomes, like someone buying something. But are behaviours actually always the best indicators of loyalty.

As part of my MBA I did a lot of research into store loyalty amongst supermarkets in the UK. I measured how much people spent in each of the grocery stores that they shopped in over a 12 week period and created a metric to measure how loyal they were to various retail brands. What I found was that most people spent most of their weekly shop in one supermarket. So you could argue that most people seemed to be “loyal”. But what is the context to this behaviour? Are they only loyal to that store because it happens to be the closest or the most convenient? Would they in fact rather shop somewhere else if it was more convenient?

Another example can be found in financial services. People can appear to be behaviourally very loyal to their bank. If I look at my own behaviour I have had my main account with my bank since I was a student. So I must be very loyal, right? Well if I was asked whether I considered myself to be a loyal supporter of my bank I would say that I wasn’t. I’m behaviourally loyal but I don’t consider myself to be a loyal customer. The problem is that the pain of switching accounts to another bank is just too high at the moment. There are inertia effects at work and there is a great deal of inertia in financial services.

Of course, the internet is a technology that can break down inertia. There is the famous saying that “your competitor is only one click away”. I can now choose to get my groceries delivered by whichever supermarket will deliver to my area. I am actively encouraged to use shopping comparison sites to get the best deal from a selection of stores.

However the technology can also create barriers to switching which means for example, that our household generally get’s all it’s online groceries from one supermarket chain because all our orders are stored there. Similarly Amazon get’s a significant chunk of my expenditure in certain categories mainly because it’s so easy to buy, not because they are necessarily the cheapest.

So how do we measure customer loyalty? Do we look at behavioural indicators or should we be concerned about what our customers think of us? Or both? This is something I’ll be looking at next time. Till then…

Jim’s developed the approach for this Emetrics summit into a multi-track format. I’ve been given the job of moderating a track on “The Voice of the Customer” and there’s a great line up of speakers including my fellow ClickZ columnist Jason Burby from Zaaz.

I’m really pleased that there’s this track at the conference. If you have looked at any of my other contributions to this column, you will know that I believe that the “web analytics” world for too long has been too “site centric” and not “customer centric” enough. In other words people tend to focus on too much on the analysis of the site, rather than on the people who are trying to use the site.

The inclusion of this track in the Emetrics summit means that we are going to get exposure on some of the issues, challenges and opportunities of working with surveys and other customer data sources alongside the data collected from web analytics systems. I’m looking forward to it.

Jim’s email giving the speakers their instructions for the event made me start thinking about the whole area again and just what some of those challenges and opportunities are. The opportunities are plenty and pretty obvious. Augmenting your understanding of behaviour on the site by adding additional insights into who your visitors or customers are and what they think gives you both sides of the story. I often say to clients that web analytics data tells you what is going on on your website and survey data often tells you why.

However, despite the many benefits many organisations still think of their data in silos. So what are the challenges to getting a more holistic approach to thinking about how the effectiveness of your online marketing programmes?

I think they probably fall into three main areas:

• Technical challenges
• Competency challenges
• Organisational challenges

The technical challenges are around getting the different data sources to sit next to each other in a way that makes it easier to analyse. This is the data integration challenge and I’ve written about macro data integration and micro data integration in previous articles in this column. The web analytics systems vendors are making it easier for us to be able to integrate survey data in with the site data and this is a good trend. Most of the major vendors now say that they can integrate survey data into their sytems, but do look closely at exactly what they mean by “integration”. One Scandinavian web analytics company, Instadia, has gone as far as making customer surveys an integral part of their product with the ability to write, launch and analyse surveys from within the system. The survey data that is collected is stored in the same database as the visitor’s behavioural data. That’s what I call integration.

Competency and organisational challenges are probably two sides of the same coin. Analysis and reporting of continuous marketing data and the development and analysis of customer surveys are different skill sets. The web analytics industry is probably not mature enough yet for individuals to have had the opportunity to be exposed to survey work before getting involved in web analytics and vice-versa. Typically these may also be separate functions within an organisation. The web analytics data may be owned by the online marketing function and surveys may be owned by the marketing research or consumer insight function. Each function may not be familiar with the other sort of data and so it’s rare that they are brought together.

So lots of challenges and opportunities but things are definitely moving in the right direction. I’m looking forward to hearing the speakers at the Emetrics summit in Washington talking about how they have met those challenges, I’m sure it will be fascinating. I’d also be interested in hearing from you if you have some good examples of how you have successfully integrated web analytics and customer data.

Download our Customer Journey Framework white paper here.

Typically we may have numbers coming in from our web analytic systems, our PPC consoles, our affiliate marketing systems, our ad-serving data and so on. On average a marketer may have at least five or six different sources of data, if not more. So, the challenge is to make sense of it all and last time I looked at what I call “macro data integration”. Macro integration is about pulling together data at the summarised level in order to be able to relate different data sets together and spot trends and exceptions.

However, as you want to be able to act far more tactically on your data, you may need to think about “micro data integration”. Micro data integration is where different data sources are integrated at a much more atomic or granular level. Often this is done at the customer level.

Why would you want to think about micro data integration and what are the benefits?

You might want to think about data integration at the micro-level for a number of reasons:

1. To enhance the value of the data that you hold on a customer or a product
2. To enable better diagnostic analysis of marketing activity
3. To be able to execute personalised or event driven marketing programmes

For example, you may want to be able to combine data from a web analytics system on browsing behaviour with online and offline shopping data so that you can be more specific and targeted in your direct marketing activity. Or you may want to look at the long term value of customers brought in by different types of acquisition channel.

Often the question is asked about the best place to integrate the data. Should data be imported into your web analytics tool or should data be exported from the web analytics tool into a CRM system or something similar? The answer is driven by what the objectives are and may involve both activities.

If the objectives are to improve the customer marketing processes then it is likely that the best route will be to export certain data from the web analytics system into the CRM system, as it is usually the CRM system that drives the operation of the outbound marketing activity. The customer database or CRM system provides the total customer view and the data from the web analytics system will be just one component that total customer view along side other data gathered from other systems.

Another reason why you might want to export the data from a web analytics system into another database is because you might want to analyse the data using other tools. Web analytic systems can report data in a variety of ways but there may be occasions when you want to do some more sophisticated statistical analysis using tools such as SAS, Clememtine, SPSS and the like. In some of the work that we do, we process data using a web analytics system to generate visitor level records which we then look at using data mining tools to look for interesting patterns of behaviour.

At other times, it may be useful to enhance the data in a web analytics tool by importing data in from other sources such as the marketing database, customer database or the product database. This is likely to be more useful when you maybe need a site centric view rather than a customer centric view, eg:

• Which type of people look at what type of content?
• Which acquisition channels given the greatest return on investment?
• Which campaigns tend to acquire the least loyal customers?

Last time I discussed the data management challenges around data integration and this is true of micro data integration as well. Thinking about what data to move will require some careful planning. Different data sources have different data structures and they won’t necessarily fit easily together. Often this may mean that the data may need to be manipulated or transformed in some way in order to be able to lay it along side the other data.

The volume of data being exported or imported is an issue as well. This will also impact on how often you do the data integration. Monthly, weekly, daily? We all know that web sites generate huge volumes of data and it is often impractical and unwieldy to extract the data in its rawest format. Think about what you want to do with the data and create summarised variables if possible. For example, if you want to have visit based recency and frequency data in the customer database, then it’s preferable to create a couple of summary variables such as date of last visit and number of total visits, rather than import the whole customer’s visit history.

The good news is that web analytics systems are becoming increasingly open and able to interoperate with other systems. The launch of WebTrends 8 and WebTrends Marketing Warehouse last month is another example of steps in the right direction for making it easier for users to “micro-integrate” their data.

Till next time.

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.

First of all, there are many different forms of surveys that an online business might run and they can vary on a number of dimensions. For example you might be surveying visitors on your site as opposed to customers, you might be collecting some general background information or you might be asking about a specific issue. The survey might be a once off survey or it may be run on a continuous basis. Indeed some of the different dimensions that might be involved and considered in the development of an online survey include:

• The purpose of the survey
• The target audience for the survey
• The type of survey and how the respondents are recruited
• The number of responses needed
• The expected response rate
• The purpose of the survey

Purpose of the survey
Any survey should have clear objectives; there must be a reason why you want to do it. There could be more than one research objective in a survey, but it is important that they are clearly stated, easily understood and are not contradictory. From the objectives everything else flows, ie the type of survey needed, the target audience and so on.
The target audience for the survey
It should be apparent from the objectives who you want to talk to in the survey. You may not want to invest time and effort understanding everything about everybody who visits your site. Your primary interest will be about finding out the right information about the types of visitors who are of most interest to you, like customers, subscribers and so on.

The type of survey
Having determined what the survey’s objectives are and who you want to survey, you are in a better position to decide on the type of survey that is most likely to meet your needs

On the whole, there are two main types of online surveys:

• Pop-up surveys
• Site based surveys

Pop up surveys (as the name implies) pop up in a window on your site. They must generally be short and easy to answer. Site based surveys are potentially more extensive surveys that people are directed to on a separate part of the site or on another site altogether. Some of the key differences between pop-up surveys and site-based surveys are highlighted below.

The number of responses needed
Another key consideration for your survey is the number of completed responses you need. This can vary enormously with the type of work you are carrying out and the target audience for the survey itself. In general terms for consumer analysis, you would ideally be looking for about 400 respondents to allow you to be able to do any meaningful analysis.

Response rates
Having determined how many respondents you think you need, you then need to think about how you are going to get them. For a pop-up survey, visitors are typically randomly selected on the site and presented with the pop-up survey invitation. For a site-based survey, people will either be invited by e-mail or via an invitation on the site.

In either case, only a proportion of those who are invited to participate in the survey will actually do so and complete it. This proportion is known as the response rate. This response rate can vary from survey to survey and it has been found that response rates to surveys are influenced by:

• The style and quality of the survey’s first page
• Relationship with the web site and/or the brand
• The level of interest and relevance of the survey to the potential respondent

If you are inviting people to participate using an e-mail, then the style of the e-mail and the subject line will also be an important factor affecting the response rate. You should try and make the call to action as interesting and as engaging as possible so that it cuts through the noise in their Inbox. You should use language be appropriate to the type of business that you are and also the relationship that you have with the potential respondent. Many e-mail and survey systems allow you to personalise the invite and this can be used to good effect to improve the chances that someone opens the e-mail and then acts on it.

Survey frequency
The final consideration will be on how often you are going to run the survey. A great many surveys are only run once to get some insight into a particular issue, eg the effects of a new site design, but some surveys such as a customer satisfaction monitor might be run more than once or on a continuous basis.

Certainly, a more interactive experience for many businesses with their customers means that they had much more access to information on what their customers were doing (or not doing) than ever before. The web was perceived to be totally measurable through analysing behaviour on the site and that the use of traditional practices like market research was no longer appropriate to doing business in the digital age.

As we all know enormous amounts of data can be generated from online businesses about what visitors and customers are doing. It is possible to track their every movement through the website, you can tell how many people put something in their shopping basket and then either take it out again or fail to complete the check out process. It⿿s also possible to track exactly which creative of which ad was clicked on and which site it was clicked on to attract them to the site in the first place. We also all know of the challenges involved is generating meaningful insight from the vast quantities of web analytics data.

However, whilst site-centric data is very good at telling you what is happening it is generally very poor at telling you why it is happening. In addition, web analytics data can tell you all about what has happened in the past but doesn⿿t necessarily help you understand what might happen in the future.

Relying totally on analysis of web analytics data can be likened to diving down a motorway at full speed but only looking in your rear-view mirror. Whilst you can tell where you⿿ve been, you can’t tell what’s about to happen. Getting beyond the “what” and more into the “why” enables you to get beyond taking a purely historical perspective on the business and to form a view on where the business is going. To do this you need to get under the skin of your users and customers and understand why they do what they do and how they feel about it.

Surveys are one of the most common methods of understanding what customers think and how they feel and developments in the usability and affordability of online research tools means that conducting research amongst the customer base is now easier than ever before. However, there is a huge difference between deploying surveys that generate useful insight and pulling together a few questions or doing a poll on a website.

Concerns that have existed in the general market research world about possible biases by conducting research using online methods are not relevant to the online business. If a customer is doing business online then they are generally likely to be able to be researched using online methods.

Conducting research online can have many advantages over the more traditional approaches such as using face to face interviews or telephone research:

First of all, costs can be dramatically reduced as there is a much lower cost associated with actually collecting the data in the first place. Typically the actual costs of just collecting the data can be 50% of the overall costs of a market research study using face-to-face or telephone data collection.

Secondly, project times can be reduced. With some of the tools available for conducting online research it’s possible to write your questionnaire into a system and have it “in the field” within a day or so.

However, just because online research can be cheaper and faster, it doesn’t mean that it doesn’t deserve the same kind of rigour in its design. A badly designed survey is still a badly designed survey and it doesn’t matter whether the data is being collected face to face, over the telephone or on the web.

A badly designed survey can not only have an impact on response rates but can also have an impact on people’s perceptions of you as a brand. So whilst the increased accessibility of online research means that potentially many more businesses can use surveys as part of the marketing intelligence tool kit, they still need to ensure that those surveys have some degree of expertise applied to them as well.

Next time, some thoughts on how to improve the effectiveness of your online surveys.

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