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.

First of all there is the need to get the basics right. This means getting the right numbers right and having Key Performance Indicators that are aligned the business goals and that are strategic, action able, easy to understand and based on valid data. Once this has been done businesses can start to optimise their digital marketing processes but must ensure that they are organisationally ready to do this. I’ve talked before in this column about the need for organisations to have “the ability to execute”. There is no point generating all the insight required for optimisation if you can’t do anything about it. Finally there is the need for “customer centricity”, which means getting beyond the “one size fits all” approach to digital marketing and developing an understanding of your customer segments and the role that digital channels play within the relationship between them and your brand.

The comforting thing for me was to see some of the themes reinforced by other speakers from big brands in the UK and Europe. Julian Brewer from Barclays Bank talked about the journey they had been on from “Activity Counting” through to “Customer Intimacy” and how now they wanted to deploy their web analytics fare more operationally to move towards the concept of Customer Tailoring.

Dell talked about their Voice of Customer programme which not only included the organisational-wide deployment of a customer satisfaction tool but also the work that they are now beginning to do in monitoring and understanding the impact of social media and “Web 2.0″ activity on their brand. Angus Crombie from Dell explained why Dell felt they needed “better listening skills” and that comment reminded me that it’s not just about having the “Voice of the Customer” but actually listening to it. Another interesting aspect of the talk from Dell was the work that they had done to try and measure the impact that their Voice of the Customer programme had had on the business in terms of Return on Investment. Understanding and quantifying the ROI on the investment in measurement systems and analytics is a notoriously difficult thing to do. However Dell estimated that there had been a very short payback on the investments that they had made.

As at in San Francisco a couple of weeks ago, I took the opportunity to catch up on the latest thinking and discussions around the social media space. Listening to the various contributors in a panel session on the subject made me think that the discussions around social media analytics is sort of where the debate around web analytics was five years ago. There is a lot of discussion around the technology of data capture and reporting and the accuracy of measurement. There is also the obvious need to start to develop some standards in this particular part of the industry so that people know what these measurements are and how they are defined. I know this is something that the Web Analytics Association is beginning to address and it is definitely needed.

We also got an insight into how media companies are using analytics these days from Channel 4 on this side of the Atlantic and from the New York Times on the other. Channel 4 talked about some of the challenges involved in terms of tying up web metrics with business metrics. New York Times showed us some of the work that they have been doing in cross-channel analytics, tying up web data with circulation data and understanding the impact of online activity on circulations sales and vice-versa.

Perhaps one of the more interesting presentations was in the work that Econsultancy (www.econsultancy.com) had been doing to understand how to measure the effects of online PR. Having been saying that companies should be looking to measure the effects of their online PR activities, they decided to put their money where their mouth was and carry out their own study. They set up an online PR campaign and measured to the best of their ability the impact of that campaign in monetary terms. What was interesting was whilst they saw a positive ROI from the online PR campaign itself, there was also an additional benefit from improved search engine optimisation performance.

All in all, having been to two Emetrics conferences in the past few weeks (San Francisco and London), I can’t say that there was a massive difference between the two in terms of the issues being talked about and discussed. Core themes revolve around the need to move to a more analytical framework, to develop the tight kind of analytics “eco-system”, the fact thata we need to listen more and the challenges of social media measurement. The scale and the emphasis may be different but the main issues were the same.

So what did I take out of the conference? One theme that came through was there was a lot more evidence of organisations using integrated measurement strategies than I have seen before. More organisations were showing how they use a wide array of tools and techniques to understand the effectiveness of their digital marketing programmes. Voice of the Customer methodologies such as onsite feedback and surveys are the norm, most people are using testing and experimentation approaches and the use data mining and predictive analytical techniques is on the increase. Text mining tools are being used on verbatim comments from onsite surveys to extract the core essence of what is being said.

The stage was set on the first day with a keynote presentation “Competing with Analytics” from author Tom Davenport. There were some interesting things that Davenport said that set the tone for the conference. “The planets are aligned for analytics” he said, meaning that all the necessary components for organisations to adopt and deploy analytical capabilities are being put in place: Data, Enterprise, Leadership, Targets and Analysts. “Using analysis is good, competing on analysis is better” summed up the need to be able to move from insight to action. There is no point knowing stuff if you don’t do anything about it. He described the five stages of an organisations analytics capability from being “Analytical impaired” at the low end of the scale to being “Analytical competitors” at the other end. Organisations such as Harrahs and Marriott ion the US and Tesco in the UK use analytics as a source of competitive advantage.

Another stand out presentation was from Tim Goudie from The Coca Cola Company. Tim described Coke’s journey from the early implementation of their web analytics platform through to the development of their whole measurement framework. Goudie told us that “Metrics are ridiculously political; there is no such thing as a neutral metric”. Once you begin to measure things, then your are likely to start to change behaviour.

Other sessions I attended confirmed my belief that measuring and understanding the impact of social media is still in its infancy. Metrics and measurement frameworks are still in development, debates still stage about the meanings of terms like “engagement” and so on. Fellow columnist Jason Burby reminded us that when it comes to social media measurement of the importance of defining what success looks like in terms of key behaviours and that whilst the activities may be different the underlying measurement processes are the same.

A great take out from the presentation by Ebay was the use of “home visits” to better understand the user experience. I have seen this technique used by consumer packaged goods companies where people from the company visit consumers in their own homes to see them, using their products in real life. This was the first time that I had heard of this approached being used by an internet company. Executives from Ebay would visit users in their homes to understand the context within which the site is being used, revealing more insight into what really goes on than a standard usability test would.

Jacob Nielsen though showed what you can get out of usability testing in the laboratory. He asserts from the many tests they’ve completed over the years that task completion rates are going up and stressed the value of using testing early on in the development process. Oh and by the way “most people tend to ignore junk on websites”.

So this industry is now more than just what comes out of a web analytics tool. It’s about having a range of tools and technologies embedded within strong business processes. As Avinash Kaushik told us it’s about “multiplicity, flexibility and agility”. The planets are indeed aligned for analytics.

Looking at the programme for Emetrics in San Francisco next month gives some clues as to how the market is developing anyway. The size of the conference has increased since last time with more tracks covering more areas. This shows how the industry is broadening and, to some extent, fragmenting into a number of separate sub-disciplines such as campaign optimization, search analytics and so on. Also there is an increased depth to the conference with more sessions in some of the tracks. Last October in Washington I gave one of three presentations in the “Advanced Analytics Track”. This year in San Francisco I will be giving one of nine sessions in the same track.

Certainly when comparing the conferences in the US with the ones in Europe they are poles apart when it comes to scale. The summit in London this year (which I’m involved in) is shorter and has fewer tracks. But is this a reflection of the “sophistication” of the UK and European markets or is it just a reflection of scale? I suspect that it has more to do with scale than it does to do with sophistication. There are some really interesting things being done in Europe by some really interesting people. There just aren’t as many of them over here, particularly when you look at it on a market by market basis.

However, scale can be a driver of progress. For example, I observed this when the market for multi-variate testing and experimentation really began to take off in the US two to three years ago. It was easy for US companies to justify the investment in these services and technologies because they could see the returns. It’s taken the UK market until now to show the same growth in adoption. I think the main reason for that is the potential returns on investments weren’t there for many organisations because their online businesses weren’t big enough, not because they didn’t “get it”.

So it will be interesting to see whether what is talked about at Emetrics in San Francisco next week in the presentations and the lobby bar conversations is radically different to the conversations that I will be having in London two weeks later. And if they are different why is that? Is it because the market development is at different levels or is because the markets are just different. I think this will help inform the debate as to whether we need a radically different approach to developing and growing the industry over here.

It would be great to catch up with as many of you as possible if you’re travelling to San Francisco as well next week. Next time I’ll be writing this column on my way home and be giving my perspective on the conference. Till then….

My own definition of retention marketing is:

“Retention marketing is the art and science of converting someone twice without the pain and cost of acquiring them twice”

It’s not just about getting someone to buy again (or whatever the conversion action is) but using what you know about them to improve the chances of converting them again without having to go through the whole acquisition process again.

But somehow “retention optimisation” doesn’t quite sound right and I prefer to think of it in different terms. What we are trying to do once we have acquired a customer is to optimise their lifetime value and so I tend to think about this process as “customer optimisation”. How can I optimise the return on the investment that I have already made in acquiring that customer in the first place? What data, tool, technologies and processes do I need?

Classically we tend to think of email when it comes to retention marketing channels and used well it can be a powerful retention tool. However once you have transacted with a customer there are multiple touch points that can be used to increase the chances of them doing business with you again; the call centre, the store, the site etc and what is required is a view of the customer that straddles these multiple channels. This much easier said that done, especially for organisations with legacy systems that have been developed over the years. Often data on customers can sit in a number of disparate systems and it can take a huge data cleaning and integration effort to get the data into shape and fit for purpose.

So having good quality data is important but what you then do with it is pretty important as well. As I said earlier, what we are trying to do here is to increase the likelihood that the customer will transact with us again without the cost of repeated acquisition. What we want to improve is the “expected customer lifetime value”. The way to do that is to be in the right place at the right time by being relevant and timely.

Being relevant is about sending out the right kind of messages, whether it is in an email or on the site. Segmentation is a way of increasing relevance. Personalisation is a way of increasing relevance. These techniques, which may be manual or automated, are leveraging the insight that you have about someone to present them with more appropriate and relevant. These techniques do not necessarily have to be sophisticated to be effective, and in the early days of your customer optimisation programme being over elaborate can undermine the process. Remember, a key component of any optimisation programme is to have the ability to execute. That means that you need to ensure you have the processes and tools in place which allow you to act, measure and react.

For example, if you decide that you want to improve relevancy by having a segmented email marketing programme instead of having a single email that goes out every month then you’re probably on the right track. However, if your resources and processes are geared up around just sending out on version of one email every month, then it’s going to be a major step up to implement a segmented email marketing programme whereby different groups of customers will get different versions of different emails at possibly different times. You will need to have a more sophisticated email systems that can handle segmented email marketing programmes, your database will need to be more extensive and robust, you will need to invest in more copy and creative material and your processes will need to be more rigorous.

So it will pay to walk before you start to run and to look for the low hanging fruit. In terms of customer optimisation, I think that the most critical point is getting someone to make the second transaction. Generally there is a “friction curve” that needs to be managed. The steepest part of the curve is in the early days of your customer relationship. The more times that someone has transacted with you the more likely they are to do it again in the future; the friction isn’t as high. Getting them to repeat for the first time is the hardest part. So this is a special case in the programme and is when timing can be vitally important. The time when people are most likely to transact with you is just after they last transacted with you, so for this special group all about “recency”. The time to get them thinking about the next transaction is just after the first.

Ultimately profitable businesses are built on profitable customers and repeat customers tend to deliver the majority of the profit. Acquisition and conversion optimisation are essentially components of a success marketing programme but optimising long term customer value is key.

To start of let’s be clear about what we mean by conversion optimisation. To be more precise what do we mean by conversion? To optimise effectively you need to optimise some defined outcome and it’s important to clear about what the desired outcome is. For example, if you have a site where people can either complete the transaction online or pick up the phone and call the call centre, which one of those outcomes do you want to optimise? It may not be possible to optimise both. If you optimise the site to increase the online bookings you may find that the process doesn’t work for those people who prefer to research online but transact over the phone. If you optimise the site to generate call centre volume, it may mean that you have to incur extra costs in the call centre to deal with the extra demand. So, clarity of purpose is an important ingredient in the conversion optimisation process.

The conversion process is not one process; it’s a series of mini-conversion processes. Each of these processes needs to be optimised. You need to chunk the problem into bits. What is right for you will depend on your site and what you are trying to achieve. Fairly generically, the main micro-processes are: Land, Browse/Search, Engage/Transact. Part of the analytical approach to conversion optimisation is to identify where it hurts most. Which part of the conversion process seems to be causing the most problems? However, this also has to be balanced against the “ability to execute”. Which parts of the website can you change or impact in which timescales? It may be that the checkout process is identified as requiring optimisation and tuning but it may be that it will take 3 months before you can get the development resource to make the changes. In the meantime you be able to change some other parts of the site more tactically.

In an ideal world it would be great it is was possible to make changes to all parts of the site with relative ease and to measure the changes to get to the optimal result. There are technologies and platforms out there that are making this easier to do but in many cases the reality is that real choices need to be made in terms of impact versus time to effect. In these cases my instinct is usually to start at the beginning and look at landing page optimisation. Landing page optimisation is something that increasingly is becoming easier to do and it’s an area where there is always the potential of a high impact.

For me one of the biggest challenges in digital marketing is getting someone to do something twice. A lot of people who visit a website only visit it once, they only look at one page, and they stay for less than a minute. If they buy or transact, they only do it once. So how do you get them to do it twice? Look at that second page? Stay for that second minute? Make that second visit? A lot of subsequent behaviour is determined by the user experience on the first page of the first visit. The first page of the first visit needs to generate the momentum that ultimately leads to a successful outcome.

The growth of testing and experiment systems such as those provided by brands such as Optimost and Offermatica have made page optimisation processes a lot easier. They enable some of the hurdles associated with enacting change to be overcome and provide a systematic way to understand how to improve con version. They are not the only tool in the toolbox and conversion optimisation is an area where a wide array of data and services can be used in a holistic way to understand and optimise the user experience. Good site analytics gives an insight into the effectiveness of the micro-conversion processes, surveys help you understand the level of satisfaction with the user experience and usability testing tells you (warts and all) what processes works, which ones don’t and why.

Having gone through the process of acquiring prospects and converting them into customers, do you want to go through the pain and cost of doing it all again? I thought not. Next time we’ll take a look at the process of managing your investments in acquisition and conversion through optimising your retention marketing processes.

Till then…

Campaign optimisation is probably the most established optimisation practice. Acquisition marketing has been the corner stone of digital marketing for many years and a number of us can probably remember the mantra of the dot-com days when all the focus was on “eyeballs” and getting as many of them to the site as possible, often with little regards to what happened when they got there. Things have moved on a bit since then. Thankfully we now take a more sophisticated approach to measuring and optimising our acquisition marketing efforts. However, I think that there is still a way to go as campaign optimisation is a multi-level problem and once you have cracked one level then it’s time to move on to the next.

I think campaign optimisation operates on three levels:

• In-channel optimisation
• Cross-channel optimisation
• Multi-channel optimisation

In-channel optimisation

This is the process of optimisation within a single digital marketing channel (ie PPC search, display advertising etc) and is in effect what we have been doing for years. Over that time we have become more sophisticated and we have seen the development of solid processes and the deployment of analytics and technology to help automate the optimisation processes. Within PPC search for example, there have been bid management tools around for years and a whole industry discipline has grown up around the optimisation of PPC and organic search marketing activity.

We have also seen the technology evolve to the extent where campaigns can be optimised in almost real time through automated algorithmic approaches like of SearchRev in the case of PPC marketing or DART Adapt for display advertising. We’re almost at the point of “fire and forget” where once the initial deployment has been made and the system is calibrated, little manual intervention is really required.

That’s all great, as long as you are optimising against the tight thing and that you’re correctly attributing value to the right behaviours. A good case in point again is PPC search marketing. Classically PPC optimisation is done at the keyword level looking at the clicks that generated the conversions and adjusting bid-management strategies accordingly. However, we know that in a lot of cases it takes multiple touch points to generate a conversion, and so value needs to be attributed all along the acquisition value chain rather than just against what happened at the end.

Cross-channel optimisation

“In-channel” optimisation is relatively straightforward. It usually involves a single piece of technology and the data to drive the optimisation process is generally sitting in one place. The problem is that marketers rarely market using a single digital channel. They use a combination of different channels often at the same time to achieve their goals. Display advertising may be used for generating awareness; search marketing may be used for generating response and so on. Each of these channels might be optimised in their own right but what about the impacts of one digital channel on another?

A classic example of this was when one of my clients went through a process of optimising their search marketing and display marketing independently from each other. They came to the conclusion that their display advertising wasn’t working hard enough for them, whereas their search marketing was. They switched investment out of display marketing into more search marketing and then promptly saw their search volumes drop. Up until this point they hadn’t appreciated how much display advertising was driving search volumes because they were optimising within the channel and in the case of display they were optimising against the wrong thing. They hurriedly reverted to their previous approach and saw search volumes recover. But they were none the wiser. They knew there’s a relationship between search and display (and their other channels) but they didn’t know how to optimise it and that was because their data was all over the place.

The key to cross-channel optimisation is data integration. It’s necessary to have the different channels activity and responses in the same database. This might be achieved by using a single campaign management system, by deploying a universal tag or by integrating the data in a separate system. Either way, once the data is in the same database, it’s possible to look at the effect of different channels on each other and on conversions and then to be able to optimise accordingly.

Multi-channel optimisation

Increasingly digital or online marketing doesn’t sit in glorious isolation from an organisation’s other marketing efforts. It’s a multi-channel world and businesses are looking to integrate their online and offline marketing strategies and tactics. So the problem becomes broader. It’s not just about how do I optimise my search marketing, or how do I manage all my digital channels, it’s about how do I optimise the total marketing mix? Online and offline? Search, display, TC, radio, print – the lot? This is the strategic marketing optimisation problem that companies are beginning to wrestle with. Again one of the main challenges is data integration.

Online and offline marketing data generally have quite different characteristics which means that it’s not necessarily easy to integrate them. Online marketing data is usually at the cookie level, tracking an individual user (or more precisely, an individual device). Offline marketing data is usually at the “market” level ie starting at a country or a regional level and down to individual stations, publications and so on. User level data and market level data can be difficult to marry up and relate to each other. With the development of geographical profiling from IP addresses it is getting easier but the analysis techniques will then also be different and are more likely to resemble the inferential modelling techniques of offline marketing rather than the “direct cause and effect” techniques used in the online world.

So, campaign optimisation is a multi-tiered problem. It’s like going on a hike up a mountain. When you get to the top of one summit, another one comes into view and on you go. If you’ve just about got your search engine marketing cracked, it’s time to move on to the next problem!

Next time I’ll be taking a look at conversion optimisation. Till then…

• Philosophy
• Processes
• Data
• Technology

At its heart data driven marketing underpins the drive towards optimisation but optimisation is one of those words that’s used a lot but often means different things to different people. A bit like segmentation, engagement and so on. So in this series of columns I thought I would offer my take on optimisation, what is means and how it can be achieved.

I first came across “optimisation” as part of my college degree course. It was presented as a mathematical problem; how do I maximise a desired outcome given a set of certain constraints? This is the way I have tended to think about optimisation ever since, particularly when it comes to marketing optimisation. My desired outcome will be things like orders, revenue or profit and my constraints will be things like money, time and resources. The challenge is to maximise the return on investment in marketing.

The problem with this purely mathematical approach to optimisation is that often the problem is too large or complex to solve easily. You need to understand and define the relationship between all the variables (such as sales and advertising) and then use the mathematics to determine the optimal allocation across the various inputs. The challenge is that there are often too many variables and the relationships are too complex to be solved easily. The approach then is to chunk the problem down and iterate towards a solution. This is where the components of data driven marketing come into play.

In the digital world the ultimate goal of many marketers is to maximise customer lifetime value and to allocate the resources available in such a way to achieve that. The issue is that things like customer lifetime value can be difficult to define and to measure and marketers may not have the ability to efficiently manage all the resources appropriately. The intent may be there but the ability to execute may not. As a result we break the overall process into smaller processes and we need to begin to think about how we can optimise individual separate processes rather than the complete value chain in one go.

We already think about digital marketing as three separate processes, namely:

• Acquisition
• Conversion
• Retention

Sometimes these processes can be too separated with little joined up thinking between the three. Having said that, for the purposes of optimisation and given the constraints of data and technology, it probably makes sense still to use them separately as the basis for our optimisation strategy. However, it is useful to bear in mind that what we are trying to so ultimately is to maximise the allocation of resources and investment across the whole customer lifecycle.

So what problems are we trying to solve? In acquisition we are trying to optimise our campaigns to increase the propensity of people to visit a website and engage. This is an area where there has been a lot of focus over the years and where technology has made a significant impact in either allowing marketers to iterate through the cycles more quickly or where the technology effectively automated the optimisation process. However, I think that the goalposts are moving and the problem set is changing and increasingly marketers need to be looking to optimise in a different way.

When it comes to conversion what we are trying to do is to increase the likelihood of some desired outcomes, whether that is an order, a registration and download or telephone call. Here there has been a lot more attention given over the past couple of years and where analytical technology has evolved to help marketers understand how to improve site architecture and design. There is more work to be done in this area though and the technologies and the processes to manage them need to be more widely adopted.

With retention marketing what we are looking to do is to increase customer value. Here we are talking about maximising on the investments that have already been made in acquisition and conversion so that we don’t have to make those investments again. With a few notable exceptions I don’t think that many organisations are focused on this area at the moment.

Over the next few columns I’ll take a look at optimisation is more detail, looking at acquisition, conversion and retention and examining the philosophy, processes required and data and techniques available to maximise the effectiveness these individual processes. Till then…

The four components are:

• Philosophy
• Processes
• Data
• Technology

Philosophy

This is the most important component I believe. To be successful at data driven marketing, an organisation needs to have the right culture and philosophy. At its hear t, data driven marketing is about continuous optimisation and iterative improvement. It’s the deployment of a “test, learn and adjust” philosophy. However, you can have the best data and technology in the world (see later) but if there is not the desire to act and to change, then the data and technology are only providing interest as opposed to insight. Organisations must have the “desire to act”.

At a Web Analytics Wednesday I attended in Berlin last week a lot of the talk in the networking session was not about metrics and systems but about how do you embed analytics within organisations? The biggest challenge often facing analysts is getting support for the development of their programmes because culturally the organisation doesn’t have a philosophy of measurement and accountability.

Processes

If “philosophy” is about the desire to act, then “processes” is about the ability to act. More specifically it’s about the ability to execute and then to react. These processes involve the management of the technologies and also the management of the decision making. Processes will include building “measurement” into the marketing development process for example, so that there is no question that new campaign won’t be tracked properly or that new content on the website won’t be tagged. It also involves ensuring that a feedback mechanism is in place that enables trends to be identified and changes to be made in the appropriate timescales.

One example I had in the past demonstrated to me where a potential desire to act was inhibited by an inability to execute. We did a piece of segmentation analysis for a retailer to feed into their email marketing programme. In the analysis we identified a number of distinct groups of customer with different purchasing behaviour that could be marketed to in a more customised way. We also identified some key timing mechanisms that could potentially double the customer’s propensity to buy again. Despite this insights the segments were never deployed operationally because the retailer didn’t have the resources and processes in place to develop and deliver more targeted email marketing programmes.

Data

Data is of course a vital ingredient in the mix, but it is the organisational culture and processes that provide the recipe for success. Good quality data is important and attention must be paid to getting the numbers right. People are reluctant to make decisions if they don’t have any faith in the data.

Also data driven marketing needs integrated data rather than data sitting in silos. Often within organisations different types of data sit in databases and different functions may have ownership of different data. For data driven marketing activities to be effective, the different data sources need to relate to each other. To understand and optimise marketing across channels, the data from different channels (PPC search, display ads, email etc) needs to be in the same place, whether that be in a web analytics systems, a campaign management system or both. In addition data needs to be managed across the life cycle of the customer, for example by ensuing that data on how customers are acquired can be analysed with the customers’ long term value or profitability.

Technology

Finally the technology is the enabling component. It is the technology that allows you to execute and react either over the duration of a planning cycle or even in real time. I don’t think that technology can make up for deficiencies in the philosophy and processes, though if you have the right apporach and procedures, you can make progress even if your technology is not the most effective. Good technology enables you to cycle through the processes faster to the point where real time optimisation is possible. Like the data, the technologies should be integrated and allow the loop to be closed between insight and action.

So, the core ingredient of data driven marketing is good quality, integrated data. The technologies are the tools but it is the combination of the organisational philosophy and strong processes that will provide the recipe for success.

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