Areas of the day will cover

• The latest tools and techniques for implementing a successful SCRM strategy
• How companies are capturing and managing social media data
• Challenges of implementing social CRM
• Mapping your CRM system to the customer journey
• How social media can data be stored and managed between teams

Areas of the day will cover

• The latest tools and techniques for implementing a successful SCRM strategy
• How companies are capturing and managing social media data
• Challenges of implementing social CRM
• Mapping your CRM system to the customer journey
• How social media can data be stored and managed between teams

Despite the tough trading conditions a trend that I have observed here in the UK and I expect, is that organisations are continuing to build out their web analytics capabilities and grow their teams. A number of clients I work with are looking to bring on new people and the job market for web analysts remains reasonable healthy. In some cases this means that companies are looking to appoint their first person into a new role, but increasingly some organisations are looking to to expand the team to 2, 3 or more people.

An interesting challenge for organisations when building up an analytics team to determine the right mix of skills within the group, is manage the variety of tasks that a web analyst team has to handle. I believe that there are three main competencies that companies need to look for:
• Insight generation
• Data integrity management
• Data management and manipulation

One of the key requirements of any analytics team has to be to produce actionable insight that the organisation can use to make decisions and drive the business forward. To do this someone needs to extract the value from the investments that have been made in data and technology. This is the true role of the web analyst. The skills and competencies needed for insight generation are business orientated rather than technically orientated. In my view a good web analyst is an internal consultant with strong data pattern recognition skills that can communicate their findings to the business in terms it can understand. One of the inherent attributes of a good web analyst must be curiosity, a desire to understand why things are the way that they are and what can be done about it. For me, most analysis is about pattern recognition, the ability to identify trends and associations in the data and also by the same token things that don’t look right.

Most people don’t like making decisions on dodgy data. Getting the data integrity right is vital. Generating good quality data from a web analytics system requires continuous management and maintenance and is another competency required in a web analytics team. This requirement is more technical in nature and requires a different set of competencies and skills than the insight generation requirement. Most web data is collected using page tags these days and most data quality problems stem from data collection issues. Pages aren’t tagged in the first place or the tag is wrong and collects the wrong data or data isn’t collected at all. As organisations analytical requirements become more sophisticated, the data integrity issue becomes more complex. Deep level skills are required to ensure that the right data is being collected in the right way and that the system configuration is right to produce the right databases and reports for insight generation.

If organisations are building out their web analytics team it probably means that the web is becoming a more strategic and mainstream channel for them. At the same time it is coming out of the silo and the business wants to know how the digital channel interacts with other channels. At this point data integration becomes more of an issue and so good data integration and management skills are required in the team. The requirement often becomes to take data from one system and import it into another or to take data from two or sources and created a new data repository.

The question then becomes whether there competencies can be found in the same person or whether different types of people are needed in a team. In my experience it is rare that a single person has all the competencies that I’ve described. Someone who has strong insight generation skills may have a good understanding about data integrity issues but is probably not the person best suited to wiring a specification document for tags to be put on a new piece of functionality for the site. In the same way, someone with good data management skills may not feel comfortable presenting some findings to a group of executives. As digital analytical teams grow, organisations need to determine more carefully the competencies required within those teams and recruit accordingly.

Maturity models seem to be all the rage these days. These models help organisations to identify where they are on the roadmap of whatever discipline or capability the model is about and where they need to be. They help to describe the journey to world-class status or best practice. I have been using a simple “maturity model“ for a few years to help organisations identify where they are in terms of their development of their digital marketing optimisation capabilities. I’ve found it a useful device to help people understand the journey they are on and a sense of the ultimate destination.

At Emetrics in San Jose WebTrends launched their own maturity model called the Digital Marketing Maturity Model. Having taken a look at it, I think it’s a useful contribution to the debate. They are clearly hoping that it will be adopted as an industry standard and that, over time, organisations will be able to benchmark themselves against others. There’s a survey you can take based on the model that will help to build industry norms.

The model seeks to help organisations assess the development of their digital marketing measurement capability on six main dimensions:

• Measurement strategy
• Analytics resources and domain expertise
• Data integration and visualisation
• Data analysis and insight
• Adoption and governance
• Ongoing optimisation

These dimensions cover the bases and reflect the fact that an organisation’s maturity is not just based on what technologies they have invested in but also the infrastructure they have in place to support and the way that they are using them. The model recognises that one of the key criteria is that an organisation needs to have a coherent measurement strategy. Too often, still, we see organisations investing in measurement technologies without a clear idea of which problems they are trying to solve or with little recognition as to where the technology fits into an overall plan.

The other useful aspect of DM3 is the recognition that “adoption and governance” is also an important component. This section is essentially about business processes around the measurement technologies. Do organisations have procedures around some of the key processes such as campaign tracking, content tagging or user support. If they have those procedures, are they actually being followed? Again, too often, analytics and measurement technologies are implemented without the necessary rigour of the business processes around them leading to disappointment and poor quality data.

WebTrends stress that DM3 is a piece of work in progress and are looking for feedback. My own feedback is in some of the detail of the construct of the model in the different dimensions. For example, one of the components within the Data Analysis and Insight dimension is called “Social Media and Competitive Analysis”. For me these two things are completely different. You can have good benchmarking capabilities and spend a lot of time monitoring your competitors and at the same time do doing absolutely nothing about monitoring or measuring social media activity. These two elements need to be separated out.

One of the notable exclusion in DM3 is anything about mobile measurement. Given all the attention that mobile is getting at the moment and the challenges in getting decent mobile metrics, I think that it would be important to have it in the model somewhere or other. It probably highlights one of the challenges that the DM3 might face and that’s keeping current and up to date with emerging digital marketing channels such as mobile.

I also feel that the model is too orientated towards site centric measurement systems. Any digital measurement strategy needs to incorporate Voice of the Customer type programmes to a greater or lesser extent. Whilst it is implicit in some of the dimensions around Data Integration and Data Analysis, it needs perhaps to be a more explicit competency which an organisation needs to display.

It’s early days for the model and I’m sure it will evolve but I think that DM3 is a useful tool by which organisations can take a look at themselves and make an assessment about where they are on the learning curve. The model also gives some guidance about what needs to be done next to move to the next level. On the basis of this type of formalised process and scoring, organisations can be better informed about how and where they need to invest their efforts next.

Last week I was over in San Jose for the Emetrics Marketing Optimisation Summit for three days of input and output on all issues around digital analytics. As usual there was a wide variety of content to absorb and a breadth of issues to discuss. As I sit and write this at the airport on my way home (got to meet those ClickZ deadlines…) I get a chance to reflect on some of the key themes that came out of the conference.

This was my fifteenth Emetrics conference in the last six years on both sides of the Atlantic, so I have had the chance to see how the event has grown and developed over the years. Whilst some of the fundamentals remain the same (such as how to get analytics embedded into an organisation), the debate has moved on. I attended sessions covering mobile analytics, social media measurement, voice of the customer programmes, marketing mix modelling and data integration.

One of the sessions I found most interesting was from Joe Megibow from Expedia. Megibow outlined how they have invested huge amounts of time and effort into integrating their various sources of customer experience feedback, specifically OpinionLab and TeaLeaf. This gives them the ability to link comments left by users on the site with their actual sessions, so they can see the comment and then replay the user’s session to more fully understand the user’s experience that led to the comment. The integration is interesting but what was impressive was the detailed analysis that went into understanding and solving very specific customer experience issues. Megibow certainly washed some of his dirty laundry in public by taking us through a number of examples of how they had uncovered problems with the site through the voice of the customer, the impact that the problems were having on the customer experience and the steps they had then taken to solve them. What struck me was the fact that often these were very small specific problems affecting perhaps a relatively small number of people but when you added them all together they were having a significant impact on the user experience. In fact Megibow said that since they had embarked on this systematic programme that conversion rates were being steadily improved but that the major success had been “winning the cultural shift of listening to customers, institutionalising analytics in the business and executing against the outcomes”.

Putting customers at the centre of the company’s strategy was a theme taken up by Greg Dowling from Nokia as well. He took us through the challenges of developing and implementing a global measurement strategy for a business that is looking to develop consumer data as a strategic asset. These challenges that had needed to be addressed included the lack of a common language around metrics, the fragmentation and quality of the data, the lack of competencies in certain areas and the fact that data and insights were not part of the business processes. Anybody who works in a large global organisation will probably recognise one or more of these challenges. Over a two year period Dowling and his team had worked to address the challenges and is his words “behavioural data is at the heart of our relationship with customers”.

One of the tough parts of implementing the Nokia measurement strategy had been around getting the mobile analytics strategy sorted. This was taken up in more detail in a further session by Dowling and Gary Angel from Semphonic. I touched on the issues around mobile analytics in my last column but in this session I really discovered how tough it is at the moment to get decent data on user behaviour on mobile devices, let along integrate that with the same users’ behaviour on the fixed web. All the issues that impact our ability to measure on the fixed web 10 years or so ago plague the mobile web such as the data collection methodologies and browser standards. There are challenges around visitor identification and measuring mobile applications usage. However given that it’s expected that by 2012 more mobile phones in the world will be accessing the internet than PCs it’s clearly an area that organisations need to start to address and given the evidence I saw in San Jose, the sooner they start to think about their mobile measurement strategy the better.

Having said that, there is still a ways to go for many organisations in term of sorting out their fixed web measurement strategy and WebTrends contribution to the debate was the release and publication of their Digital Marketing Maturity Model (DM3). This model provides a framework against which organisations can assess the maturity of the measurement capabilities on a number of different dimensions. I haven’t had the chance yet to look at it in detail, so something for next time. Till then…

There is a generally accepted view that an organisation’s multi-channel customers are its best customers. The theory is that if a customer buys from an organisation over more than one channel, for example in the store, from a catalogue and over the web, then they are more likely to be of higher value than if they just purchase through one or two channels. I can see there is a natural inclination to believe that if a customer does business with an organisation over more than one channel that it is probable that the customer has a higher degree of loyalty and hence value. However, the mathematics of the analysis state that a multi-channel customer is also more likely to be of higher value anyway by the simple virtue of having bought more than once rather than necessarily because they bought across different channels. So understanding the value of multi-channel strategies requires a bit more careful consideration than simply looking at the average customer value.

There is another dimension I think as well to evaluating the impact of multi-channels strategies. In the example above the focus is on the result and the channel in which the transaction occurred. From a customer perspective that is fine but to fully understand how multi-channel strategies are working (or not) it’s also important to understand the dynamics between the channel that the customer was acquired in and the channel in which the transaction takes place. This is particularly important for understanding the role of the online channel in driving offline transactions and there are two important ingredients to achieving this. The first important thing is to have the tracking mechanisms in place to be able see multi-channel behaviour. I admit this can be easier said than done. The second important thing is to understand why multi-channel behaviours are happening the way that they are and then to evaluate whether some of these behaviours are desirable or not.

The type of industry an organisation is in and the type of channels it uses to do business will determine the appropriate methods it can use to track multi-channel behaviour. For example, the use of a specific telephone number on the website for the call centre or using source codes or reference numbers to identify customers. Some of these methods will be more accurate and reliable than others but the initial solution to understanding the multi-channel puzzle is to have at least some mechanisms in place to track behaviours.

The next issue is then to understand the behaviours that are being tracked. It’s likely that first challenge will be to integrate the data from the different channels. Data may need to come from web analytics systems, call centre systems, customer databases and so on. Data will need to be cleaned, integrated and then analysed. This may require some different data analysis tools. The type of analysis you need to do will depend on the type of problem you are trying to solve. Let me give you an example based upon work we have done in the travel industry.

A company sells holidays to an older target market. The main channel historically has been telephone sales through a call centre though the web channel now makes up a significant proportion of their business. The website also allows visitors to download a brochure and it also gives the number for the call centre. Although web site traffic is growing steadily, the conversion rate was not increasing. Increased sales were a function of increased traffic.

The company wanted to increase the conversion rate to get more bookings transacted online as opposed to through the more costly call centre.

The website already had its own special number for the call centre so the number of calls that originated online could be tracked. The next stage was to understand how many of these calls turned into bookings. In this instance the call centre system didn’t allow bookings to be tracked against specific inbound numbers, so for a period of time call centre operatives receiving “web calls” were asked to track how many of them resulted in a sale. In this way a conversion rate could be calculated.

The other aspect was to understand what happened when people ordered a brochure from the website. The approach here was to match the names and addresses of people who had ordered the brochure online and to cross-reference them against bookings received in subsequent months and to look at what channel they had booked through. Although perhaps not perfect it seemed to be good enough. From this analysis we could determine how many of those people who had ordered a brochure online had subsequently booked and which channel they had used to make the booking (via the call centre or via the website).

This analysis allowed us to do two things. First of all we were able to estimate the total value being delivered to the organisation. This was not just the value of the online bookings but also the value of the bookings that came through to the call centre on the special website number and even those who had ordered a brochure from the website and had subsequently booked via the normal call centre number. In this case a significant proportion of the internet channel’s total value to the organisation came from its delivery of business into the offline channels and highlighted that the way that the organisation had been historically measuring the value had been underestimating the true Return on Investment.

The second thing that the analysis allowed us to do was to explore the dynamics of the interaction between the online and offline channels and to understand why some of these behaviours were happening. I’ll go into that in more detail next time. Till then…

A few weeks ago I took stock of the web analytics market, particularly looking at some of the key trends in Europe. This week to get a sense check from the position of someone in the US, I turn to a fellow WAA Board Director and friend Avinash Kaushik. Avinash is also an Author and one of the leading thinkers about web analytics and where it’s heading, having actually “been there and done it” previously at Intuit software.

This is what Avinash had to say.

Avinash, you had a busy year in 2007. What were some of the highlights for you?

It has indeed been a hectic year, becoming an Independent Consultant and Analytics Evangelist role at Google and publishing Web Analytics: An Hour A Day in June. Along they way speaking at conferences and running around the country became normal! Oh then there is the blog, Occam’s Razor, my baby (!), that took more time than I could ever have imagined.

I think the biggest professional highlight has to be the book. In five months sales have vastly exceeded my expectations. Since all of my proceeds go to charity (The Smile Train, Doctors Without Borders) it has meant a nice amount raised for them.

The book is a great primer and reference document for all things “web analytics”. But in this fast moving industry, isn’t it a risk writing a book? Are there some parts of the book that you think you might have to rewrite soon?

The core of the book I think will stand the test of time (and by that I mean five years at most! ). But there are many sections I would update. The book has been out only five months but I would add new things to the SEO section. Ditto for blogging metrics, I have slightly changed two of the six in the book and added a brand new one. I touch on Social Media but when I write the next version of the book I think things will be more settled and I can add more interesting things.

New tools will come with time, as will new sources of data and my book, or and those of others, will accommodate for that. But the biggest goal of Web Analytics: An Hour A Day is to teach you a new way of thinking, that I think will be relevant for quite some time to come.

All that said Willem from Wiley was over the other day asking me to start work on the next version!!

What do you consider to be some of the key industry developments to have been in 2007?

I get the distinct feeling that we will look back at 2007 and remember it as a turning point, a good one, for the industry.

Why is that?

Every site in the world seems to have Google Analytics – a leading indicator that even the most common person with tangential interest in data now has access to a great web analytics application. More interest translates into more mind share.

The industry has consolidated quite a bit. Omniture has built on top of its already impressive growth by acquiring Visual Science (/WebSideStory / HBX), in addition to Instadia (Analytics + Surveys), TouchClarity (Behaviour Targeting) and Offermatica (Multivariate Testing). This year all roads seemed to lead to Salt Lake City!

WebTrends is going through some temporary management turmoil, but with its excellent set of solutions I expect them to come back strong.

There were more web analytics consultancies launched, more than on you can count. Ditto for web analytics conferences. Actually a real interesting trend was how many non-analytics conferences had “web analytics day” or “web analytics pre-intensives” – a real sign of growing demands.

It was also a year of Web Analytics 2.0. An expansion of the core definition of what web analytics is, stretching is beyond just clickstream to include qualitative research, testing, competitive intelligence, multiple dimensions of outcomes etc.

So what are some of the key drivers?

Many, if not all, of the trends above were driven by a singular phenomenon: The web is becoming serious business.

It seems odd to say this in 2008 but in many companies web, and web analytics, have been a silo that someone else is taking care of. Websites are becoming the most important customer touch point and the most important revenue generator even for businesses that are not first of mind.

The consolidation in the industry, the increase in interest (tools or conferences) and expansion of the definition is a reaction to the demands now being placed driven by a desire to move beyond printing reports (to perhaps printing money!).

How would you assess where the web analytics industry is at the moment from the point of view of software vendors?

Full of opportunity.

Money and fame awaits all. Well at least those who are willing to work hard.

The vendors have done well thus far, mostly, but they are still scratching the surface of what is possible. Many big websites still don’t use web analytics. There are many growth opportunities in the Software market (aside from the current favourite child: hosted). We are not even scratching the surface of integration with data from other parts of the company and other tools should we decide that web analytics is not a silo but a part of “Business Analytics”? So there is a lot to do and appropriate financial rewards for companies that help accelerate the move beyond clickstream.

What about the people side, i.e. the end users and consultants?

There is a read dearth of skilled practitioners in our industry. And that has stunted the amount of progress that can be made (because the 10/90 rule still applies – spend $10 on software/services and $90 on people who can actually analyze data and produce insights). If you are a skilled person, you can name your own salary (but make sure you are on the web analytics 2.0 continuum and not 1.0), and if you are someone who wants a great Analytics career then now you know where to find it.

Consultants will thrive in any field where the rule is 10/90, because they can bring their expertise to bear on the $90 part of the equation. Additionally because of increase in the demand you are noticing many more consultancies (mom and pop and grandpa included), and an interest from the “big boys” for mature web analytics consultancies (example: our good friends Zaaz acquired by WPP). To make optimal amounts of money Consultancies, like other companies, are finding that they can’t be a one trick “let me parse your log files” pony. They are being forced to evolve into areas such as multivariate testing, competitive intelligence, usability etc.

What are some of the key trends that you see at moment? Where’s the market going?

The problem with Web Analytics 1.0 is that it is an exercise in data torture and reporting with long lags in taking action (if any). Data torture needs to get automated and expanded, decision making needs to get automated; people need to be left for smart hard things (vs. what happens today!). Smart companies will start to exploit more things like Multivariate Testing, Onsite Behaviour Targeting etc because in each case you are leaving humans to understand customers and create content and you are letting intelligent solutions create the right customer experience based on data. Won’t happen overnight, but are on this train for good.

I also believe that 2008 will see a more serious attempt to get Web Analytics to become a part of “Business Analytics”. We are still a silo in most companies (data and people!). We will see more collaboration and innovation in helping web data become a core part of the company data to truly give end to end visibility (and maybe the holy grail of multi channel analytics / impact). Won’t happen all in 2008, but we might get serious.

I am optimistic that we don’t have untouchable islands of data like we do today. Search Engine Optimization, RSS, Social Media, etc. They are all becoming mainstream yet the current generation of tools mostly stink at tracking them. You can track them, but if you are willing to row your leaky boat all by yourself to that island. I think this will change.

Oh and we are not done with consolidation in the industry.

It’s going to be fun!

I reckon so, thanks Avinash

This post originally appeared in Applied Insights’ events section. Foviance acquired Applied Insights in November 2008, with Neil Mason joining us as Director of Analytical Consulting. As part of this acquisition, we’ve incorporated Applied Insights’ events list into our own.

At this inaugural event in Sweden Neil presented on data integration.

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”.

Yet again another year whizzes by and we end up another year older and hopefully another year wiser. At this time of year there is only one thing for it; a spot of reflection and prediction.

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.