“Insight”. It’s a word that most of us probably use every day. Client companies demand “insight”, agencies and consultancies strive to deliver it. But what is it and how do you know when you’ve got it? Or have created it? Is it one of those words these days that we use glibly like “analytics” when we really mean “reporting” because it sounds better or more sophisticated?

It’s interesting how the vernacular and language of the marketing research and information services business has changed over the years. When I started out (sometime in the previous century) at the end of the data presentation we would present a “summary”, then we started to present our “conclusions” which was a shift from a statement of the key facts to an opinion as to their significance. We then strove to deliver “more value” by providing “recommendations” as well, which was an additional opinion as to what we believed the client should do about our conclusions. The trouble was that the recommendations as such were not often very well informed ones as you were often only addressing at a problem with a single set of data and therefore only looking at it through a particular lens.

Round about the turn of this century the word “insight” suddenly burst onto the scene. Everyone got really excited about and started to change the name of their departments and their jobs. Market Research Departments became Customer Insight departments but apart from that nothing else much changed. The same job was being done, pretty much in the same way. So what exactly is insight, particularly when we are talking about marketing or customer insight?

The standard definitions of insight talk about the ability to discern the true nature of a situation or to understand a complex problem. I think we think that there’s more to it than that though. For me these definitions don’t pass the “so what?” test. The “so what?” test is one that I coach my analysts and clients to use. When faced with a piece of data as yourself “so what?”. If you can’t answer the question, then the data isn’t telling you anything useful. The test can also be used to understand the ability to turn information into action. When faced with a request for information, ask: “So if I tell this information, what are you going to do about it?” No answer means no value.

We all inherently believe that insight is powerful and potentially game changing. Insight is more active than passive and is more than just a really interesting finding from a piece of analysis that no one ever knew before. It might be really interesting; it might be new but…so what? Unless something is done with it or about it, then it’s nothing more than an interesting fact.

This view of insight was reinforced to me a couple of weeks ago when attending a meeting set up by a client with all their research agencies and consultancies. The head of the marketing services function spent 40 minutes talking about “insight”. He talked about what insight was in his opinion and why it was hard to deliver. He talked about insight as being “contextualised information that changes behaviour”.

That definition rang true for me as I’ve often talked about “analysis without action is not insight”. Real insight leads to change either in the way that a company or its customers behave and that’s why clients demand it and that why it’s hard to deliver. It’s hard to deliver because information that changes behaviour I think rarely comes from a single source. Insight is the compound effect of the analysis and interpretation of multiple sources of data. That’s why our recommendations all those years ago were pretty facile most of the time; it was because we didn’t have enough of the facts. If you think about it in the digital marketing industry, how much “insight” comes from a web analytics system? Probably not a lot but when combined with other data from voice of the customer programmes, customer databases, user testing and so on, the cumulative impact can lead to insights that change behaviour.

I’m not advocating that we should stop using the word “insight” and start using something else instead. I do think it’s worth stopping every now and then and asking ourselves “Well if that’s insight, what’s going to change as a result?”

So how are businesses coping with the need to deliver a multi-channel, integrated customer experience? Well the evidence from a recent report conducted by Foviance in association with Econsultancy here in the UK, suggests that whilst organisations recognise that is something they need to do, they are a long way from delivering on that need.

The report is based on a survey of over 500 businesses. It conforms that the majority of organisations do recognise the link between business performance and customer experience but are struggling to develop a strategy for multi-channel customer experience management and then delivering on that strategy…

A more in-depth version of this article has  been republished with permission by ClickZ on the Foviance website if you’d like to read more. Also, please download your own copy of the Multi-channel Customer Experience Report today.

This article was written as part of the Foviance December 2010 newsletter

Even if you’re not a Facebook addict or regular Twitter user, you’ll know how difficult it is to escape social media. Why? Because social media is revolutionising the way that people consume content.

Social media is opening new channels of communication between brands and customers and there is a lot of potential in the social web that marketers can tap into. For example, a study earlier in the year by Penn State University showed that 20% of all tweets mentioned a brand name. Sales and marketing professionals need to be aware of these significant media consumption trends so they can tailor and target their messages as effectively as possible across a changing landscape.

However, with all the hype around social media it can be difficult to understand where to start. So here is an outline plan of action to dip your toes into the world of social media:

• Set clear aims and objectives.
• Listen to the social world; understand what is going on out there and find out who your audience is.
• Use all the information you have at your fingertips to build up a picture of the social landscape.
• This information will show you how to work with your audience to achieve your goals.
• Then simply measure, refine, repeat.

More businesses should focus on developing social media campaigns relevant to their customers. The days of pure brand ‘broadcasting’ are long behind us. Successful campaigns are now being supported by an online social media component, or taking place exclusively in social media.

As you get to know more about your customers and their social media presence, you will develop a strategy that not only enables you to influence conversations about your brand and win more brand advocates, it will also recruit them as willing foot soldiers in your battle for brand supremacy.

Note: *A Report detailing this subject was written by Jonathan Culling and Billie Andersen for Evaluation Centre. To read this article please go to the crm software, contact centre software and marketing software section of the Evaluation Centre.

It’s hard at this time of year not to get a bit reflective at the year that’s gone by and to think ahead to the year that’s about to be. But it only struck me though as I sat down to write this column that I am just about to complete my first decade working in digital marketing analytics. I got started when I moved to work at an online auctions business in 2000 having spent a (large) number of years working in “offline” marketing analytics and consumer insight. I remember that when I got to this online business that the head of marketing told me to forget everything that I had learned in the offline world as this was “new media” and that “things were different” now.

Things were different for sure. There were different marketing channels to manage and to understand, there were different technologies to deal with to collect and analyse the data. Things were happening phenomenally fast and it did seem that the normal rules didn’t seem to apply. But I quickly came to the view (and one to which I still subscribe) that underneath all the noise and the excitement that the normal rules did still apply and that the core principles and marketing and marketing analytics were still valid. The methods of execution may change and the methods of analysis may be different but at the end of the day it’s just another marketing channel.

So how far has digital analytics come in the last 10 years?
In some ways it has come on incredibly well but in some areas there is still a way to go. First of all, web analytics exists as an industry in way that it didn’t at the beginning of the decade. There is an established Web Analytics Association which continues to grow and shape the industry. Soon, there will be certification which will allow analysts to have official recognition of their competence and expertise. So, in that sense the web analytics industry is displaying more of the characteristics of its older cousins such as brand (advertising) marketing and direct marketing.

There have also obviously been significant developments in the technology behind web analytics. The costs of data collection have fallen dramatically enabling more organisations to have access to data on what is happening on their website and the effect of their digital marketing campaigns. The page tag has become a universal method of collecting that data giving the potential of real time or near real time results. The reporting interfaces on web analytics systems have (on the whole) got better allowing easier access to that data and the ability to generate new or customised reports has improved.

If I have a frustration about the development of the industry over the last 10 years, it is this: I don’t think that we have got much wiser about how digital marketing works and to truly understand the dynamics of marketing across multiple channels. In this sense I wonder whether as digital marketers we’re any better off than our offline colleagues. There’s the famous saying, attributed to John Wannamaker “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” That was said over a hundred years ago but how many digital marketers today really believe that they are any better off today than John Wannamaker was back then, even with all the technology and data at our disposal? If so, then why is this?

One thought is that we have become the victims of the data. Because we have so much data available to us we have become paralysed by the numbers. In John Wannamaker’s time they didn’t have the data, these days a lot of offline marketing analytics is supported by data that can be expensive to collect, whereas digital data is cheap and widely available. Does this wide availability of data mean that we are not as efficient and as effective in our analysis of that data as we should be? It’s a thought that crosses my mind often. I might be wrong but having had the opportunity to work on both sides of the online/offline fence my sense is that over the past 10 years the online marketing world has been “data rich” but “analytically poor”. We still have constraints and conventions, such as last click attribution, that make it easier to report historical activity but not to measure marketing effectiveness. I have no doubt that this industry will continue to evolve, adapt and flourish. We have new challenges to address such as measuring the newer digital channels such as mobile but my hope is that we don’t get too wrapped up in the technology and that now the focus is more on the “analytics” in web analytics rather the “web”.

My best wishes for a successful, prosperous and satisfying 2010.

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

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

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

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

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

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

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

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

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

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

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

In my last article I started to explore the notion of loyalty. What do we mean by loyalty? Is loyalty about the way we behave or is it about the way that we think? And if even we can get a definition of what it is, how easily can we track it, measure it and manage it?

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…

One of my predictions for this year is that retention marketing will increase it’s importance in the online marketing mix. My thinking is that acquisition marketing activities through channels such as affiliates and search is becoming increasingly sophistacted. The tools are out there, the skills are out there and the improvements in efficiencies are beginning to tail off. That’s not to say that there aren’t improvements to be made. It’s just not the low hanging fruit for most organisations these days.

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 got a bit of a wake-up call this week. An event which I thought was a long off is getting closer. The Emetrics summit in Washington DC in October is now only a matter of a few weeks away and the event organiser, Jim Sterne, has been marshalling his speakers.

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 systems, 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.

There is one thing about the digital marketing world – we are not short of numbers!

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.

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

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

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

The Customer Journey Framework comprises of three key components:

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

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

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

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

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

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

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

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

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

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