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This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

Over the past few weeks I have had the opportunity to try out the new Advanced Segmentation features that are being rolled out in Google Analytics. Although it’s a beta version of the new capability it allows Google Analytics users to take a deeper dive into their data and to discover underlying patterns in visitor behaviour.

In the past couple of years since I first started writing about segmentation in this column (www.clickz.com/3615916) it has become easier for digital marketers to look at different groups of website users. Developments in the technologies mean that there is almost no excuse for not beginning to segment your website visitors and to begin to understand how different types of visitors behave on your site. Having said that there are still many organizations that only look at the top line numbers and treat all visitors with a “one size fits all” approach to marketing.

So what is segmentation and how can it be used? In marketing terms segmentation is the process of identifying groups of individuals that have something in common. Those individuals then belong to the same segment. Importantly, what those individuals in that segment have in common is different to what other individuals in other segments have in common. So a simple example would be to segment users by the number of times they have visited the website. You could classify them into “buckets” such as new users (the first time they have visted), light users (i.e. have visited 2 to 3 times) and heavy users (i.e. have visited 4 times or more). The number of buckets in this instance would be determined by looking at the distribution of visits per visitor on the site and making an appropriate decision.

The point of making segmentation such as the one described above would be to understand whether there are any differences in the behaviour of these different groups which could then lead to some kind of differentiated marketing message or user experience being developed. If there is, then there is the opportunity to improve the expected return on investment. Let’s imagine that you have just run some kind of email marketing campaign and experienced a 10% uplift in sales. If the campaign was a generic campaign that you sent to all your users on your database, then what probably happened was that some segments of users really responded to your campaign and others didn’t. The reality is that you might have experienced a 20% sales uplift in some segments and 0% uplift in others. The idea behind segmentation is that if you can design a series of different campaigns that are relevant to each of the different segments then you should achieve say a 20% uplift in sales across all segments.

With most web analytics tools these days it’s possible to carry out basic segmentation of the data. It’s up to the analyst or the user to determine and decide what are the useful segments to look at. In GA (Google Analytics) there are some segments that are already set up that cover some of the obvious behaviours that might be of interest, like new vs returning visitors, paid for vs non-paid for traffic but the analysts will want to explore and create their own segments based on their own understanding of the website, and also the kinds of issues that are being faced in the business. For example it might be that there is an issue in the business about how many people look at a quote for a hotel room but then don’t book. In that instance the analyst may set up three different visitors segments as follows:

• Browsers (people who visited the website but didn’t reach the quote process)
• Quoters (people who got a quote but didn’t book a room)
• Bookers (people who booked online)

Once the segments have been created, the analyst can look for differences between these segments in terms of how they get to the website and what they do when they get there. Are there differences in the type of channel they come in on or the keywords they use? Do they look at different types of content? Do they use different tools and applications on the site such as the on-site search? If so what are the kinds of keywords do they use on the on-site search engine?

As you can see, once the ability to create segments becomes a reality, the analysis possibilities become endless. Different tools offer different capabilities but the principles remain the same for the curious analyst. The challenge though with segmentation is not so much in doing the analysis but taking action as a result. The benefits from taking insight into action, are building and deploying more targeted marketing programmes as a result.

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

It’s been a busy week since I started a new job at Foviance. The reaction from people I know in the industry about Foviance and Applied Insights coming together has been very positive. People tell me that it makes sense, which is good news because it certainly made sense to me.

The reason why it made sense to me is that I passionately believe that analysis and insight lies at the heart of improving the online user experience. Working with one of the leading user experience consultancies in the UK as the person responsible for driving their analytical services capabilities forward is going to present some great opportunities to grow and to learn. It’s never too late to stop learning!

For me marketing has always been a blend of art and science and in the digital marketing space there is more science available to those who want to take advantage of it. For many organisations though it has taken some time for them to adopt the ability to improve the online customer experience through measurement and analysis. At times there is often a tension between the “creatives” and the “analysts”, whereas the reality is that both is needed and both need to be blended.

I think that the position where many organisations have got to know is that they have “adopted” the measurement and analysis side of things. They have plumbed in a web analytics system. They may be regularly measuring customer satisfaction. They may be routinely doing testing. They now have access to the science. However, what they haven’t managed to do is to integrate the science into the way that they do business.

Often decisions are made on the basis of judgement even when the data is available to them. But the trouble is, as someone once said, “Good judgement comes from experience. Experience comes from bad judgement”. Or as an old boss told me after I had made some cock-up: “Neil, all experiences are learning experiences. It’s just some are more pleasant than others”. One of the key roles of data, analytics and insights is to help us avoid having too many “unpleasant learning experiences”.

So the opportunity going forward is to blend the art and the science in a seamless approach to improve the user experience. Creative designers working alongside analysts to understand the impact of their design changes in a collaborative fashion.
Quantitative analysts such as web analysts working alongside qualitative researchers such as usability consultants to understand the user experience form all the angles. Not just looking at what users did but also understanding why they did it and what they felt about the outcome. This kind of integrated approach will need integrated thinking based on integrated data. Integrated thinking will come from the recognition from all the players that they only have a part of the solution and their instinct should be to go and seek out the other parts.

The difference between adoption and integration will come down to organisational culture and processes. This is a theme that I keep returning to in my consulting activities as well as in this column as I think this is one of the biggest challenges in the industry at the moment. People still worry too much about the technologies rather than worry about what they are going to do with the technologies. Organisations and their agencies will need to start thinking about how to build the science into the creative process in a systematic way and how to view the creative process as an iterative, cyclical process rather than just a linear process. The physical manifestation of this vision might be a roomful of designers, information architects, analysts, usability experts and brand marketers coming together to throw ideas around about what the user experience should look like. Each contributing their perspective and each contributing to the final outcome. Not on an ad-hoc basis for big projects, but on a regular basis constantly iterating the solution week after week. It might take a while to get there but I’m looking forward to the journey.

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

As I write this, the Emetrics Marketing Optimisation Summit in Washington DC is drawing to a close and I am trying to process all the inputs and turn them into some outputs of the core themes and takeaways. Tough job, there’s been a lot of stuff to process.

One of the key messages that I picked up from a number of the sessions I attended was that people had moved on talking about the “what” to the “how”. So the talks focussed less on “What is web analytics?”, “What is optimisation?”, “What is multi-variate testing?” and more on “How do I embed web analytics into the organisation?”, “How do I create a testing culture?, “How do we move towards a performance driven organisation”. It’s like we know what the tools in the toolbox are now; we just need to figure out how to use them better and to get other people in the organisation on board. There were still some presentations that covered the “what” type questions but they seemed to mostly revolve around the newer technologies and the emerging measurement practices such as social media and mobile analytics.

A parallel theme that came through was the sense that some organisations or people within these organisations were hitting a glass ceiling. They had deployed the tools, generating the data, created the reports but were struggling to take it to the next level. They could see the opportunity but were not able to make the break through. Bill Gasman from Gartner outlined a number of requirements to move an organisation’s analytics capability forward and the first one of these was to have senior “C” level sponsorship. I’ve just finished reading Tom Davenport’s book “Competing on Analytics” and time and time again he also makes the point that companies that successfully deploy an enterprise wide approach to analytics usually have someone at the top making it happen. The question then becomes how you go about getting that support? I described one approach that worked for me in my last article. (www.clickz.com/showPage.html?page=3631145) and that seemed to be a view endorsed by Bill Gassman when asked the same question by someone in the audience. His answer was to start small and build momentum. It was interesting to observe that some of the issues we are encountering in Europe are not all that different to some of the issues being raised here in the US.

One of the highlights for me was watching Avinash Kaushik unveil the latest enhancements to Google Analytics. You sensed it was what the crowd had been waiting for. It’s not often you see a vendor being applauded for announcing feature releases. Of the various developments announced there were two that caught my attention. The first one is the new advanced segmentation feature. I’m a big fan of the ability to filter and segment your data and so any developments in this area are welcome. Providing a segmentation capability in a tool like Google Analytics will encourage property owners to look beyond the topline numbers and to start to think about their site in terms of different groups of visitors behaving in different ways. So hopefully people will start to look beyond teh bland averages of topline reports and start to drill down into their data.

The other feature that caught my eye was the announcement of a Google Analytics API to allow access to the underlying data. Details are still a bit sketchy at the moment but for me one of the features of a more enterprise level tool is the data integration capabilities. There are many hacks out there for getting data out of Google Analytics and hopefully the API will make this easier in the future. This seems to be recognition by Google that web analytics data can’t operate in a silo if it is to survive.

Finally the presentation of the conference for me was by Jason Carmel from ZAAZ. Jason’s presentation went by the title of “Effectively using kittens for optimisation and usability” (Go figure!) and in it he looked at how site optimisation using tools such as Optimost and Google Website Optimiser is complementary to user-centric design processes and usability based optimisation. He outlined the process by which the two can work together in site optimisation projects with the site optimisation tools basically telling you what’s working and the usability analysis showing you why it’s working and how to use usability experts to improve the quality of the site optimisation tests. It reinforced to me that you always will need more than one tool, in the toolbox to get the job done properly.

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

In my last article I looked at the measurement aspects of understanding multi-channel dynamics. This week I’m going to look at some of the analytical approaches. Having put in place the mechanisms to track cross-channel behaviour, it’s important to explore the observed dynamics of the interaction between the online and offline channels and to understand why some of these behaviours are happening and whether they are desirable or not.

A point of focus for an organisation might be to understand why a customer who starts a transaction online then ends up completing it offline. Many organisations would usually prefer those transaction to be completed online as its cheaper to process the transaction. The reasons why this channel shift occurs could be down to the way that an organisation does business, down to traits of consumer behaviour or because of the complexities of the product.

A while back we worked with a bank to map out the channel dynamics and to try and measure the channel shift from online to offline. This was complicated by the fact that the bank had installed internet terminals into its branches to allow prospective customers to fill in applications for some of the simpler products online but in the branch. The idea was that it would reduce the need for customers to wait until branch personnel were available and that one branch person could help many customers at the same time. Branch personnel would also be freed up to sell more complex and higher value products. However, what the bank found was that the branch personnel would often lure people away from the branch terminals to do the transaction on their own systems. The reason was simple and that was the branch personnel didn’t get commissioned on sales that were made on the terminals in their branches. In order to get the desired behaviour the bank needed to capture the IP addresses of the terminals in the branches, link them to the sales made on the terminals and then allocate those sales back to the branches. In that way the branch personnel were much happier about allowing people to “self serve” in the branch.

Last time I talked about a holiday company serving an older target market. Having set up the measurement tracking capability to look at cross channel behaviour, we set about analysing why channel shift as happening. We looked at the bookings that had been made on the website and compared them against bookings where someone started the process online and then had completed the process in the call centre. Across of the things that we looked at the gender of the person making the booking was the biggest factor. Men were more likely to do their research on the internet and book online. Even if they had ordered a brochure they were more likely to go back online to make the actual booking rather than call the call centre. Women on the other hand we4re far more likely to use the site for research only and to order the brochure but would then call the call centre to make the actual booking. Focus groups confirmed that this was the preferred apporach for women and so in this case channel shift was down to gender differences.

In some cases channel shift might be down to website issues. We conducted a similar piece of analysis for an insurance company looking at channel dynamics on their car insurance products. Once again we assembled the data to look at the bookings that were made online and compared them to those bookings that end up in the call centre. We looked at a number of different characteristics including the type of insurance cover, the car being insured as well as the demographics of the policy holder. In this instance given the breadth of the data we used Chaid analysis to identify those characteristics which were the most important in predicting channel shift. The results were somewhat surprising. Rather than demographics being the most influential factor as I had had suspected, it was actually whether someone had bought a particular optional extra on the policy. If they had, they were far more likely to have completed the transaction in the call centre. Armed with this information, the company went back and reviewed the site processes for buying this particular optional extra on the policy and could see where the process could be improved to help reduce the need for people to call the call centre.

Channel shift may be down to organisational issues or site issues. These issues can be addressed. Other factors may be more ingrained in the way that customers want to do business and so in these cases channel shifting should be embraced as long as it’s recognised accordingly.

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

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…

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

Here in the UK recent statistics have confirmed that the economy has stopped expanding and that it’s possible that we may head into recession. We have had continuous economic growth for the last 16 years or so and so for many people operating in a recessionary environment is going to be new. If it’s like the last recession we had in 1991/1992 then it could be tough. So, when it comes to marketing there’s probably two ways that organisations and businesses might react.

The dumb way to react will be to slash sales and marketing costs across the board, batten down the hatches and hope to ride out the storm. Marketing services costs like investments in measurement, analytics and research will be some of the first causalities as they are seen as “discretionary” costs and not core to the business operations. Also each channel or division will take a similar hit.

The smart way to react will also be to reduce sales and marketing costs. After all, if you are selling less, you have to react accordingly to maintain profitability. However, the smart organisation will look at how they can significantly increase the efficiency and effectiveness of their marketing expenditure and what are the important activities and tools they need to be able to do that.

In a recessionary environment it may be that the online channel is a winner. Smart organisations will look to see how they can acquire or service customers more cheaply through the e-channel than through other channels. Even with the digital channels, I believe the marketing emphasis is likely to shift with three possible trends:

• An increased focus on multi-channel acquisition optimisation
• Greater deployment of conversion optimisation tools and applications
• Development of more robust and sophisticated retention marketing programmes

As acquisition budgets come under pressure, digital marketers will need to focus on how they get more bang for their buck. Classic single channel optimisation techniques such as PPC bid optimisation will only work to a certain extent as all organisations will be looking to improve channel productivity. However single channel optimisation will essentially remain sub-optimal. Smart organisations will allow investment into the tools and analytics necessary to understand how to optimise budgets across digital acquisition channels such as display, affiliates and PPC. They will ensure that they have improved attribution models that enable them to understand how channels work alongside each other (or not) and which channels are delivering value. They will also ensure that they are able to reduce the costs of Cost Per Acquisition (CPA) programmes not only through better channel optimisation but also through correct attribution of sales or conversions to the correct channel. To do this, organisations will need to look at how they collect, manage and analyse their campaign related data. Joined up marketing is difficult to achieve without joined up data. They will also need to have the right tools and skills sets to allow them to analyse that data to understand that data. Improved effectiveness will come from improved analytics.

Having persuaded someone to visit the website, the trick is to get them to do something of value. Conversion optimisation has come of age in the past couple of years but is still a nascent practice in many organisations. To leverage the investments in acquisition, organisations will need to ensure that conversion rates increase. Site designs need to continue to improve and the customer experience enhanced. To do this will require a greater understanding of what’s working and what isn’t. Good site tracking will be vital not optional. Also testing and experimental tools as well as behavioural targeting platforms can be viewed as investments that have a measurable ROI. Therefore despite a potential squeeze on budgets these types of capabilities can pay for themselves inj a relatively short period of time if they are deployed correctly. Organisations should look to improve the effectiveness and efficiency of their processes and procedures around the tools to save money rather than reduce the investments in the tools themselves.

Finally, the other trend will be the development of more robust and accountable retention marketing programmes. I often think of the digital world as a “world of ones”. Most people who visit your website only ever visit it once. A lot of them only ever look at one page or stay for one minute. If they convert, they only do that once. Most of the challenge in digital marketing seems to be to get people to do something twice. Visit twice; make the second click; place the second order and so on.

The classic saying is that it’s far cheaper to retain a customer than to acquire a new one. In recessionary times it makes sense then to focus on extracting more value from the investments already make in customer acquisition and conversion than spending more on the same. For me the definition of retention marketing is the process of converting someone twice or more without paying the costs of acquisition and conversion twice. At the point of initial conversion there is usually an exchange of value. You sell them something; they tell you their name and address. They download something, you get their email address. You also know what they bought or downloaded and so that insight forms the basis of improving their propensity to transact with you again with relevant communication at the right time. Using tools and techniques such as segmentation and predictive analytics will help with both relevancy and timeliness.

If there are stormy waters ahead what are you going to do? Batten down the hatches and hope for the best? Or invest in the right navigation equipment, learn how to use it and plot the smoothest possible course to keep ahead of the pack?

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

A couple of weeks ago I met up with executives from Omniture here in the UK to get a bit of an update on the products and the product roadmap. After all the acquisition activity over the past 18 months and the subsequent integration of the various operations, a number of product launches and the rebranding of some of the businesses, it was useful to get a perspective of the Omniture business as it stands and where it’s headed.

After having talked for a couple of hours it seemed to me that Omniture have got most of the bases covered. SiteCatalyst offers core web reporting and analysis capabilities and data integration requirements are managed through the Genesis programme with dozens of partners covering most digital marketing disciplines. Optimisation capabilities are offered through the integration of the Offermatica and Touch Clarity into the “Test and Target” service. High end analytical requirements are covered by the Discover product including the Visual Sciences product rebranded as Discover onPremise. And at last we see one of the benefits of the Instadia acquisition through the launch of Omniture Survey which allows survey response data to be integrated with web analytics data.

Having been through a number of company and product integrations in the past in the marketing services industry, I understand the challenges involved in bringing together a mishmash of different services and cultures into something that looks like a coherent product line up. From what I have seen I think Omniture have done a pretty good job. The presentation of the services makes sense and you can see how they can deliver against an organisation’s needs as they evolve and grow.

This is not meant to be an ad for Omniture but I did come away from that meeting thinking that if Omniture can be used as a proxy for the web analytics industry then the industry is at an interesting point in its development. Over the past couple of years a number of the vendors have broadened from the core application of web reporting, either through acquisition (as in the case of Omniture) or by being acquired themselves (as in the case of NetTracker). The question is: where next? Will web analytic tools develop into enterprise level systems capable of supporting multi-channel analytics or will they remain a “point application” for the digital marketing channel?

The model for hosted web analytic systems is that everything is fine if you want to analyse and manipulate the data within the system itself. Generally the tools are getting better at analysing and reporting web data and the systems are getting easier to use. The challenge comes when you want to either report or analyse the data in a different way or even using a different tool. Let’s take an example.

Everyone knows that the “last click” attribution model for measuring campaigns is naïve. Advertisers want to understand better the relationships between different digital channels and their impact on an eventual conversion. The standard model in most tools is to use the last click with an option of using the first click to allocate a conversion to a channel. In some cases you can also allocate the conversion equally across all channels involved. If the advertiser wants to look at different ways of attributing conversion to marketing channels in a typical hosted environment then they would need to get the data out and analyse it separately. This then presents another challenge.

Hosted web analytics systems generally offer an “all or nothing” approach when it comes to exporting the data. You can export the topline reports into Excel or similar and that’s it, or you can also have all the raw clickstream data. It’s like saying you can have the drips from the tap or you can stand in front of the hose and get soaked. Few organisations are equipped to handle raw clickstream data which is why they opt for a hosted service in the first place. There is too much noise in the data and part of the value of a web analytics system is that it manages and processes the data into something that is analysable and reportable. But in the process sometimes it dampens the noise too much so that it’s hard to see what’s going on. The example of the campaign attribution is one example.

What organisations increasingly need from their web analytics systems is the ability to have access to clean, summarised but granular data. WebTrends have made progress in this direction with the Visitor History File. It allows you to export on a regular basis a series of attributes against each visitor that comes to the website. It includes for example the first campaign that attracted the visitor, the last campaign, the total number of visits and so on. It doesn’t solve all the problems but it is a step in the right direction. Increasingly organisations will be looking for tools that allow them to integrate the data more easily into other marketing or corporate systems so that they can understand all the customer touch points. It will be interesting to see how the industry responds. Will it see itself as a solution for digital marketing only or will it be an important component of the broader mix?

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

In this series I have been looking at some of the basics of setting up a web analytics programme. Often when organisations come unstuck with getting their web analytics programme working effectively it’s because of issues around planning and processes rather than the technology itself. Last time I looked at campaign tracking and this week it’s the turn of understanding content consumption.

Typically when web analytics systems are deployed “out of the box” the reporting of what content is being looked at is at the page level. We are all familiar with reports such as the “Top Pages” report which shows which were the most popular pages on the website, but the problem with these types of reports is that they rarely change and from looking at these reports it’s difficult to understand overall patterns of content consumption. The data is too granular. Often it’s more useful to know what types of content are being consumed the most (or the least) rather than which individual pages.

The solution to this problem is to assign pages into “Content Groups”. A content group will represent pages that have something in common. For example, all news items might belong to a content group called “News” and there may also be sub-groups or a hierarchy such as “News: Domestic, News: International” for example. Once all the pages are assigned to the various content groups, it’s possible to take a look at how many people looked at a group of content, how long they looked at it, where they came from to reach that content and where they went to afterwards. For sites that are content reach and maybe don’t have much transactional activity, this is more useful and more important.

Content grouping is fine in theory but how does it work in practice? Different web analytics systems tackle content grouping in different ways and some have more flexibility than others. If you are looking at different systems, this might be relevant to your decision making. Content grouping in some systems is dependent on the URL and folder structure and is usually fixed in the reporting interface. Google Analytics has an example of this approach where it is possible to use the Content Drilldown report to look at content consumption at each level in the folder structure. This approach can work well for sites where the content is organised with a neat folder structure but for many sites this isn’t the case and a different approach is required.

An alterative approach to content grouping is to assign pages to groups in the data collection tag. This approach is more flexible. Content groups can be defined independently from the folder structure of the website and in some cases a different hierarchy can be developed as well. Pages can then be assigned to content groups by customising the page tag. But flexibility comes at a cost and that cost is in development and maintenance.

First of all at the point of implementation, a plan is needed of what the content group structure is going to look like and how it is going to be implemented. For a large site with lots of content, this can be quite a significant exercise and require a good deal of planning. It’s also something that needs to be considered for site refreshes or rebuilds. The implementation approach will depend on the technology behind the site such as the content management system being used but ideally there will be some rules based approach which will help with the ease of implementation. At the end of the day there may be trade offs that need to be made between what the content groupings might look like in an ideal world and those that can be achieved in practice.

After implementation there is also the matter of maintenance. Most sites are dynamic in the sense that content is regularly being updated and changed. Pages are added, changed or deleted. In order to maintain the integrity of the data processes will need to be put in place to ensure that as pages or sections of content are added that the content grouping is managed at the same time. So, it needs to be worked out who is going to own the process, who’s going to manage it and who’s going to be responsible for doing it.

As with campaign tracking (which I looked at last time) the success of measuring content consumption on the website is not just down to what technology you’ve got but also down the planning skills and the maintenance resources that you put behind it.