When asked how many web analytics tool they were using within their business over 50% of people said they were using two systems or more. Quite often the scenario was that a company was using a paid-for tool and then were using Google Analytics as well. Relatively few organisations were using Google Analytic exclusively. This is a trend that I have observed as well, an organisation has a system from one of the major vendors and then also deploys Google Analytics “to see what it is like” because it’s free. This result throws up some interesting questions like: “is this a good idea or not?”. One the one hand you can argue that since it’s not costing anything, then what’s the problem? Maybe Google Analytics does some things better than the system that you already have in place. On the other hand software like Google Analytics might be free to buy but it’s not free to implement (that takes time and effort) nor is it free to maintain (that takes time and effort too). Given that many organisations find it challenging to properly implement and configure one web analytics tool, does it make sense to try and manage two?

The other thing that struck me about this is that two systems will inevitably been giving different results. So which one do you believe? There’s a saying that a man with two watches can never tell the time. I can understand organisation wanting to try out different tools but at the end of the day I feel its best to stick with one and make sure that it’s giving you what you need.

There was some good news from this report about the adoption of other tools, particularly Voice of the Customer tools. Over 60% of organisations said that they looked at customer survey data. I think that if this survey had been done a couple of years ago the number would have been a lot lower. It’s good to see that businesses are beginning to realise that you can’t measure the effectiveness of the digital marketing strategy just by looking at data that comes out of a web analytics tools and that you need other data, particularly customer insight data to fully understand what is going on.

There are some worrying signs from the report. Organisations admit that they are still often not tying up their data collection strategy to their business objectives and relatively few said that they were definitely getting actionable insights from their web analytics. Quite a number thought that a lot of the data they had wasn’t particularly useful for decision making purposes and the clue to the reasons why came when you looked at the resourcing of their web analytics programme. 45% of respondents didn’t have a dedicated web analyst and when you look at where the money is being spent, the biggest chunk is usually on the technology rather than the resources to extract the value from the technology. So it’s hardly surprising that organisations are finding they are struggling to get insight from their web analytic programme that leads to better decision making.

The signs from the report suggest that there is progress being made in the UK but more vision is required at the right levels of organisations to tie their business strategy and the measurement strategy together. Reasons often cited as being a major barrier to having an effective online measurement strategy included lack of coordination, lack of senior level buy-in, budget and resources rather than problems with the technologies. And I think that if there is a difference between what is happening over here on this side of the Atlantic to what is happening in the US, it is probably more to do with those factors than anything else. For those of you in the UK and the rest of Europe, it’s worth taking a look at the report and seeing how you benchmark.

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

So what is an effective web measurement strategy for a small company doing business online? Well, it doesn’t look that much different to the strategy that a large organisation might employ just the scale and some of the tools might be different. A small business still needs to have a holistic approach to measuring their online channel and to have the right tools in the toolbox. A small business still needs to have clearly defined online goals and objectives which can be translated into a set of key performance indicators. A small business still needs to have in place the right processes to ensure the integrity of its data. Some of these aspects may in fact be easier for smaller businesses than larger ones. It may be easier to define the business goals and KPIs as there are less people involved in the process. It may be easier for a smaller business to manage their processes to ensure that pages are correctly tagged for example and that campaigns are properly tracked. It may be easier because it might just be one person doing everything.

Where it might be harder for smaller business is to take a holistic view of measuring their online channel by having multiple tools in their toolbox. In my view this holistic approach comprises of four main components:

An effective strategy for measuring and optimising website performance has four key components:

• Good market intelligence
• Sophisticated visitor behaviour analysis
• Excellent user profiling
• Effective site performance tracking

Market intelligence provides the context for the businesses own performance. Whilst the majority of a digital marketer’s time can be spent focussed on the brand and the site, it is important to remember that the neither the brand nor the site operate in a vacuum and that external factors and forces are at play. Larger businesses might buy into 3^rd party data providers such as ComScore, Nielsen NetRatings and Hitwise. These services are often out of the reach of small businesses and may mot even be suitable for sites with lower traffic levels. However, a small business can still uses online resources such as government statistics and sites such as ClickZ to keep a breast of trends in the industry.

Visitor behaviour analysis comes from web analytics tools and you can now get sophisticated reporting packages for free or at low cost. Obviously Google Analytics is free to use and will suit many businesses’ needs for a long time to come. Microsoft are launching their own service soon. For those willing to invest a little bit, there are other tools that are suitable for small businesses. One that I particularly like is Clicktracks for it’s simplicity of use combined with some powerful analysis features.

User profiling is the process of getting to know who is using your site and why. The basic principals of marketing are about understanding your customers and meeting their needs. In our online environment the basics that a business needs to know are:

• Who is visiting my site?
• What are they trying to achieve? What are their goals?
• Were they able to do what they wanted to do? If not, why not?

This sort of data can be collected from surveys and there are plenty of cost-effective web survey services around (such as SurveyMonkey, Zoomerang and so on) that allow you to create online surveys at a reasonably low cost. Just because a survey can be cheap to run, it doesn’t mean that it can be low quality. Attention needs to be paid to the type of information that you are asking for and the way

Finally, site performance measurement looks at the effectiveness of the site from a technical perspective. It concerns aspects of the site such as the speed of page delivery, site availability and the responsiveness of transactional processes. A Forrester report on this subject showed that users think that slow web sites are less interesting, less believable and less trustworthy. If you are small business trying to cut through the noise of the internet, you don’t want to burden yourself with these kinds of perceptions. So measuring and tracking your site’s speed is an important component of the mix. If you can’t afford to buy into continuous services such as Keynote or Gomez, then you can find sites where you test your site speed for free on an ad-hoc basis.

For small businesses the “price of light” may not be the actual price you need to pay for data services but it’s the time you need to spend managing, interpreting and understanding the data that you can get. But in this competitive environment doesn’t it make sense to work smarter?

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…

I imagine that e-tailers all over the world are now refining their campaigns, optimising their store fronts and adjusting their merchandising strategies to maximise their returns during the peak period. But there was a timely reminder earlier this month from the IMRG (Interactive Media in Retail Group) here in the UK (an equivalent to Shop.org) that e-commerce is still a relatively immature channel for most retailers and that some aspects of their operations still require significant refinement.

The IMRG highlighted in its report that e-tailers need to improve their delivery services or face a backlash from consumers. They asserted that too many basic mistakes are being made, for example putting small items in large packages which then don’t fit through letterboxes. This view form the IMRG highlighted to me the need to consider the whole end-to-end process when we think about optimising the customer experience rather than just what happens on the website.

A huge amount of time and effort goes into optimising the visitors experience on the website and that’s great. Quite often on websites there is still quote a lot to be put right once decent measurement systems have been put in place. However, all that effort comes to little if the value created by a positive website experience is destroyed by a poor post sales experience.

When online, sites often attempt to survey me to find out about who I am and whether I was satisfied with my experience. Whilst I don’t answer every single one I applaud the attempt to understand more about me and to find out how I feel about their website, assuming that they do anything with the data. However, I struggle to remember if anyone has ever contacted me after I have bought something online to find out what I thought about the overall experience and importantly what I would do in the future as a result. Just because someone is satisfied with the website experience, it doesn’t necessarily mean that they end up wanting to do business with you again.

I would therefore suggest that if you don’t have a programme in place that gives you input to your visitors overall opinion of your performance then you’re missing a vital component of your overall measurement strategy. As well as understanding satisfaction with the on-site elements of the transaction, look to understand satisfaction with the off-site components of the transaction. For example:

• Were there any problems with the payment processes?
• What was their opinion on the delivery times and prices?
• Did the order tracking capability work well?
• Did the delivery arrive on time?
• Did they use customer services/My Account etc?
• Was that a good experience?

I saw a good example of such a programme being put in place at Emetrics in Washington. StubHub.com presented on what they were doing to track the overall customer experience. In addition to phone surveys, website pop-up surveys and exit surveys, they also ran “post event surveys” two weeks after the transaction had occurred on the site to find out if everything went smoothly for both the buyer and the seller and if not, why not.

As conversion optimisation processes become increasingly tuned, other aspects of the customer experience are likely to be where retention battles might be won or lost. So, find out what impact the experience had on the likelihood of them doing business with you again. Propensity scores such as “likelihood to return” and “likelihood to recommend” strong indicators of satisfaction and customer lifetime value.

So when it comes to evaluating performance in January let’s not just focus on sales, orders, conversion rates and the like. Consider also whether the experience worked for the customer as well – all of it. Remember, a customer is for life, not just for Christmas!

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.

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.