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 your self 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

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”.

I always say to people that building an online marketing analysis and measurement capability is a journey and not an event. You don’t, one day, suddenly start “doing web analytics”, you build up a capability that develops over time. I describe that journey in three stages.

1. Building a performance tracking capability
This is the process of getting the right numbers right, counting the things that count, developing KPIs, distributing key reports that tell you how you doing and whether you are meeting your goals or not.

2. Process analysis and optimisation
At this stage, organisations are using their measurement systems to understand and optimise key business processes, such as acquisition and conversion. They are using additional tools such as A/B and multivariate testing. They are thinking more strategically about site design, developing key customer journeys and segmenting their visitor base.

3. User centricity
In the final stage the focus of the organisation shifts from the site to the user or customer. Retention as a process becomes more important and as a result site behavioural data needs to be integrated with other data sources and other marketing technologies. Metrics for customer loyalty and lifetime value become KPIs. The online channel is an integrated part of the multi-channel customer strategy.

Here in the UK, a lot of companies are still in the first stage. Many organisations that we speak to and work with are in the process of more clearly defining online goals and objectives and putting in place the metrics and systems to measure them. For large, complex and global organisations this is a process that can take months. Many companies are upgrading their web analytics systems to give them the sound tracking capabilities that they need to and to give them a foundation for the next stage of growth.

That growth will come from ironing out the inefficiencies in their marketing processes and working smarter. This is the second stage of the journey and my perspective is that this is where the US market is at the moment. Investments have been made in systems and people and those resources are being put to work. It’s been a strong year in the US for marketing technologies such as behavioural targeting and multi-variate testing.

My view is that in 2007 we will start to see adoption of these types of capabilities by more forward thinking companies in the UK. Obviously there are already many European companies who are already pushing the envelope when it comes to using data, insights and technology to make significant improvements to their online marketing processes but they are relatively small in number. I think that in the UK in 2007 we will see more evidence of more companies looking to do the same. US vendors in that space are already looking to these shores.

I think that in 2007 we will also see more focus on retention as a marketing process with organisations looking to understand and manage concepts like customer loyalty and what that means in today’s multi-channel world. This will result in more focus on “data interoperability” i.e. the need to have various marketing technologies speak to one another and for data on customer activity to be shared. 2007 could be the year for many organisations when the world of online marketing emerges from its silo and takes up its role as a vital component of the total customer marketing mix.

I was at Ad-Tech last Wednesday running a couple of workshop sessions. One of the workshop sessions was entitled “Data integration: Why does it have to be so difficult?” where I moderated a panel made up of representatives from Tangozebra and Logan Todd  here in the UK and from Sitebrand giving a North American perspective on the issue.

We didn’t really know what to expect from the audience and we were worried that we would fail to keep them engaged for long on what might be considered somewhat dry and turgid subject matter.

We needn’t have worried. After a cautious start (after all we are talking about Britain here) the audience soon warmed up to the theme which was basically this: “How do I make sense of all these numbers? Especially when I have different tools, supposedly showing measuring the same thing showing me different numbers!”

A show of hands revealed that the vast majority of end users were using at least two web analytics systems to track their online businesses as well as having other data coming in from search engine marketing systems, ad-server reports, affiliate systems and so on. Probably fairly typical of most online businesses today. So, how do you deal with it?

Probably the most consistent piece of advice that came back from the panel was that it’s messy and difficult, so don’t spend loads of time and effort trying to get it all absolutely right. But at the same time be absolutely clear about it is your measuring in the first place and so understand the difference between a click recorded on an advertising system and a visit recorded on a web analytics system. The two are not the same thing.

One of the biggest fallacies in online marketing is that’s totally measurable and accountable. I think we all know that it just isn’t true. Measuring online marketing performance is fuzzy and complicated. For example, how do you attribute sales properly? Typically you may have someone first visiting your site on a search referral, followed by a subsequent visit from an affiliate before converting on a later visit by coming to the site direct. Each of the acquisition marketing channel vendors may have their own rules claiming the sale. How do you avoid paying two or three times over?

The panels’ answer was to set your own rules and decide how you are going to pay for traffic and conversions. But to do this you also need good data, so you need to spend some time and effort getting your tracking systems sorted out so that you fully understand the dynamics of customer acquisition on your site.

As with a lot of things in life there isn’t a “silver bullet” to solving the problems of data integration and data validation. One key aspect that came through from the session was the importance of looking at relative performance rather than absolute performance all the time. We used to have a saying when I was at ACNielsen many years ago that “a trend is a friend”. If the data shows consistent in a certain direction, then it’s probably true. If there is a sudden change, then it’s either as a result of something happening or there’s an issue with the data. Either way, you will want to find out why is happening.

The same principal can be applied to online marketing data. Rather than worry too much about why “System A” is showing a different level of traffic then “System B”, look to make sure that the trends are at least in the same direction. Also, rather than focus to much on is it 10,000 visits or 12,000 visits, look at what’s happening between different visitor segments for example.

With such a data rich environment it’s potentially easy to get consumed by the numbers. The feeling from the session in London last week, that you just need to accept that it’s messy and horrible, get over it and move on. There’s a cost to perfect information that’s probably not worth everyone paying.

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.

It depends on what you are trying to do.

One of the challenges faced by the typical digital marketer is the number of different data sources they are dealing with at any one point in time. These might include for example data from:

• Ad-serving systems
• Pay per click bid management systems
• A web analytics system
• Affiliate management systems
• Transactional systems
• Customer management systems

The list goes on. How do you pull all this stuff together to get a sense of what is really going on?

I tend to think about data integration on two levels:

• Macro integration
• Micro integration

Macro integration is where you pull the outputs of the various data systems together in to one place so that you can see relationships between different data sources. Excel is often the ultimate macro data integration tool and legions of analysts all over the country spend hours and hours pulling together spreadsheets from multiple data sources to provide a summarised view of what is going on.

This is not necessarily a bad thing as the process of pulling the data together means that the analysts is looking at it and is likely to spot any data quality problems. However, the problem is that 90% of the analyst’s time is spent in data production and only 10% of it is spent in data analysis. The value equation here is all wrong.

One approach is to try and automate the process as much as possible and get the various data sources into a single database. The data is likely to be processed or summarised, rather than in its original format, but at least it’s in the same place and makes it easier for you to be able to report out across the range of KPIs and key metrics. Inevitably this requires a degree of investment upfront to set up the data feeds correct and the reporting system set up. Standard databases and Business Intelligence reporting tools could be used for this type of work.

At a company that I worked at in the past, we took this approach to reporting on our key metrics. We extracted summarised visit, unique visitor and page view data from our web analytic system and stored it along side other key company data in a datmart designed specifically for that purpose. The extracts were done every night so that the next morning the key site metrics was available and reported against some of our other key transactional data using a Business Intelligence tool (Business Objects in our case).

After the inevitable initial teething problems, the process worked quite well and the questions from management and colleagues changed from asking about when the reports were going to be ready, to what the reports actually telling them in terms of business impact. We were able to go from spending 90% of an analyst’s time on data production, to 90% of an analyst’s time on… Analysis!

Obviously, this doesn’t come without a cost. Managing multiple data sources is a messy business. Significant resources may be required from your IT team and they may or may not have the necessary experience in dealing with this particular type of data. An alternative route may be to outsource the process to companies such as Blackfoot Inc and others who specialise in this type of data integration activity.

Macro integration is the process of pulling the outputs of different data sources together, so that you can more easily and effectively report them side by side. Next time I will be taking a look at the challenges and opportunities in pulling together and integrating data at a much more granular level - the customer. Micro integration as I call it.

Till then⿦

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.