Posts Tagged ‘analytics’

Recently I read Michael Notte’s excellent post on web analytics and the automotive industry in Europe, and then ended up getting drawn into a conversation about the tool Sophus3, which I used for a long time when I was working with a key automotive manufacturer. I ended up writing a bit of a review for it, so thought I would expand this out a bit on my own blog. I had a lot of problems with this tool and never really felt able to voice my issues about it due to client politics, but now I no longer work with this client I feel it is time my opinions were voiced. Hopefully this will assist with other client’s decision making processes.

Firstly, a disclaimer: these are my opinions alone and have nothing to do with any employer or client I have ever worked with – nor can I say that these are necessarily universal truths that others would have had as well; it’s simply a statement of my experiences working with the tool and supplier. Similarly, the client in question has an excellent relationship with Sophus3 and does get value from the tool; my beef is simply that they could do a hell of lot better and don’t realise it.

Furthermore, it really isn’t all bad – so just to avoid coming across too negative I will start with the positives:

Pros:

• They DO understand the automotive industry better than any other supplier. No other vendor targets vertical market segements in this way. It means that they have a lot of good insights on how the measurement framework for the sector should work and how reporting should be built.
• Their customer service is very good and their staff are very dedicated to client support (at least for their end clients, not so much if you’re an agency though).
• Their back end analytics interface, whilst extremely slow, is actually very flexible and feels more like querying a proper database than an OLAP set-up. Complex cross-tabs and tables can be built in a way which isn’t really possible in other tools.

Cons:

• Speed. The tool is very very slow to use in comparison to other tools, so much so that most of the time you just won’t bother. At times it is more like querying a huge SQL database than a web analytics tool.
• Accuracy of the competitive tool. The eData Exchange tool is supposed to provide benchmark data of all automotive suppliers. However, only key pages of the sites are tagged and non-standard stuff like microsites are ignored. This in my opinion makes the data too inaccurate to use. Some manufacturers rely heavily on campaign microsites and the customer never actually hits the main website. Other manufacturers do everything in the main site.
• Tagging. This does not work like any other web analytics tool. Tags are simply bits of code that are placed on the site – then Sophus3 themselves have to sort out all the meta-data and naming conventions at their end. This is a nightmare and removes vital control over how the tool is set up. It also creates a enormous possibilty for error that just doesn’t happen in other tools.
• ALL configuration has to be done by Sophus3. They have to set up all the dashboards, custom metrics etc etc. This wouldn’t be so bad, but personally I never really felt that this was done right and therefore wasn’t easy to use. No vendor, despite what they say, really has a proper handle on measurement strategy. This is something that needs to be handled by either the client or the agency; and they need the hands-on flexibilty to make the tool bend to this strategy.
• No proper page path analysis; no site overlay; no on-the-fly segmentation; and various other missing fundamental bits of functionality.

In summary, at this point in time I can honestly say that I would not recommend Sophus3 to any company, not even automotive manufacturers. The tool isn’t completely useless, but the point is that even free tools like GA are leaps and bounds ahead, not to mention the giants like Omniture and Webtrends. Sophus3 have a good organisational foundation, they just need to seriously update their tool to bring it in line with other players.

Is web analytics an art or a science? Is it primarily a creative or a methodical endeavour? Is it left-brain or right-brain?

Or, is it both? Does it rely on some kind of balance between the two?

I initially started pondering this after reading Steve Jackson’s Cult of Analytics. The book primarily describes an organisational structure and process that can be used to put web analytics at the heart of an organisation. However, it goes much further than this in that it attempts to create an intensely rigorous system of scorecards that can be used to police the delivery of this framework. I also noticed similar thinking in Akin Arikan’s recent call to create an expert system for web analytics, which argues that web analytics should operate much like the field of medicine or mechanics, with concrete processes followed to the letter.

I can certainly see where they are both coming from, but something about this whole thing just makes me feel really uncomfortable. It is undeniable that web analytics is a form of science and computing (it’s got the word ‘analytics’ in it for a start!), but something inside me constantly cries out “no, there’s more to it than that, web analytics is about creativity and intuition and sales and the passion for opportunity!”. You might need the science and the tech in order to understand what’s happening, but can this ever really tell you what to do next? Doesn’t this require a fundamental and intrinsic understanding of business strategy that can’t be reduced to statistics and data; or made into some kind of rules-based process?

But if this is true, and web analytics is a balance between science and art; analysis and intuition – then today the field seems woefully lacking in the art and intuition. But why is this?

Web analytics has a ‘tech’ heritage, but does this fit?

Web analytics originally ‘emerged’ from the field of IT, and was later integrated with the field of business intelligence. This produced a group of people with a huge amount of technical and analytical knowledge, but their role is to report things to other people. They don’t typically get involved with what that information is used for.

But web analytics IS the use of the information. You can’t divorce the information from what it needs to be used for. Anyone who has witnessed first hand an organisation where reporting is handled by IT and optimisation by marketing will know exactly how disastrous this can be. Separating the two creates an uncrossable chasm in the middle. You either need one person with both skill sets (unfortunately quite rare), or a well managed team with both camps working together.

Web analysis is not the same as traditional data analysis

The word ‘analysis’ in web analysis persuades most companies that they should fill their senior web analyst positions with hardcore data analysts. Some companies even go as far as employing people who have previously been analysing things like meteorological or geological data sets. However, whilst it is important to have at least some hardcore stats knowledge in a team, it isn’t necessary at the senior level.

Web analytics tools are easy to use, at least from a functionality perspective. The vast majority of the stats and number crunching has already been done by the software. Anyone who has crossed over from using something like SAS to something like Site Catalyst will understand this. It isn’t analysis in the same sense; it’s report viewing – so the ‘analysis’ is in fact the interpretation and translation of the reports into action, which starts to get much closer to marketing and general business performance than any kind of traditional analysis.

People only see the means, not the end

If someone came to your house to sell you double-glazing and spent an hour showing you the tools they planned to use, and talked about how the plastic was manufactured, you wouldn’t buy the windows! If, on the other hand, they talked to you about the reduction in noise, the increased warmth and the lower fuel bills that you would get, then you would be more interested, right?

For some reason, we have an endemic problem in this industry whereby people obsess over the analysis and data, and not the benefits of the analysis. This results in a perception of web analytics as boring and difficult to understand. If you are presenting recommendations of analysis to senior management, do you really even need to show the analysis? Web analytics is decision support, not a sleep-aid!

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The problem then it seems, is that you often have senior people who are too scientific in their approach and lack the spark of commercial intuition and business acumen that can drive truly actionable analytics. This was less of a problem in the old days of data analysis for direct marketing because there were clearly defined process frameworks through which marketing folk could receive standard reports and make decisions based on those reports – but, and here’s the crucial point, there isn’t really anything standard about digital marketing!

I have always said that, one day fairly soon, the word digital will cease to exist; it’s a term used to describe the fact that some things are analogue and other things are digital, but already there isn’t so much left that can reasonably be called analogue. Therefore, if digital marketing simply becomes marketing, and digital business simply becomes business, and these things are as data-driven as we all hope they will be, then ‘analytics’ is a huge central discipline to an entire business operation – to reduce it as a discipline to a fixed process is like trying to create a fixed process for all aspects of the management of a business. Is this possible and, even if it were, is it not suffocating to the organic growth and development of the business?

On a final note, I was curious to see that Avinash Kaushik’s new book, which I haven’t actually read yet, holds the strap-line “The Art of Online Accountability and Science of Customer Centricity” – I wonder what, if anything, he has in store for us on this question?

I have just been reading Jason Carmel’s post on Optimization and Analytics, which quite rightly argues that performance marketing may be a better and less ambiguous term to describe what we web analysts actually do on a day-to-day basis. I couldn’t agree more, but the issue obviously goes way beyond terminology; and the post actually reminded me of a recent discussion with a client on exactly the same topic, which might be worth sharing.

What is web analytics anyway though?

The real root of this issue is the fact that many companies fail to see what the real goal of web analytics is. They see it as something extra that might be useful, but only when they get around to it and when they don’t have anything more important to do. In the mean-time they carry on as normal; churning out emails, scheduling site updates, adding and removing things to the home page – all based on gut feel or, as Avinash Kaushik likes to put it, the HiPPO (Highest Paid Person’s Opinion). What they don’t understand is that all this stuff they do and web analytics are actually one and the same! Talking about performance marketing not only makes web analytics seem less geeky, it brings to light the fact that our ultimate output IS marketing.

Performance Marketing is a much better clarion call

The specific client I was talking to had exactly this problem; because they didn’t understand web analytics they just couldn’t connect it mentally to their own jobs. In this situation it’s no good talking to people about maturity models or measurement frameworks, or trying to train them on tools, because they still won’t get it. You need to educate them about why they should even listen to you and, more importantly, you need get them excited about why they need to be involved. This is how I went about it on this occasion:

Step 1 – Show them why they need it

The client was under immense pressure to deliver results with a reduced budget, and couldn’t see any way of doing it. They dismissed all notion of ‘web analytics’ because it sounded expensive, time-consuming and like something that wouldn’t deliver immediate results – i.e. they didn’t get it. The first step was to try and show them (without talking about analytics) that they needed to be cleverer about their marketing:

The Need for Data-Driven Marketing

This chart is specific to this client’s market and situation, but what it actually says isn’t so relevant. The key point is that mass marketing is no longer effective, even if you have got the cash for it. Customers are more individual than they use to be, and so you need to get closer to them and have more genuine conversations with them.

Step 2 – Make the connection

Nobody can really argue with what you’ve just said, and then the line of argument progresses in this fashion:

1. The ability to be pro-active and to successfully affect consumer decisions is reliant on the ability to listen, learn and to communicate genuine value through intelligent dialogue.
2. In an online environment listening and learning is achieved through web analysis, measurement and research; understanding how customers currently interact with us and how they want to interact with us.
3. Intelligent dialogue is achieved by optimising the customer experience in order to communicate our message in the most appropriate way, based on what we have learned by listening and understanding.
4. The process is only possible if the data, tools, capabilities and the methods for using them are available and tuned in to what we want to know, and so careful planning is required in order to ensure that insight can become actionable.

This describes holistically the whole process of marketing based on listening, which can also be called performance marketing. Then you can start talking about how to enable them with the ability to actually do it. At the heart of this is the ability to streamline and simplify the flow of data so that decisions can be made:

Enabling Performance Marketing

If data and tools are faster, easier, better and generally more efficient at providing meaningful insight, then your staff are able to spend more time generating action based on that insight and less time trying to work out what it means. This, in turn, means that more attention can be focused on optimisation and improvement initiatives that drive increased performance; and the final result of this is that dialogue and relationship with the customer becomes more tailored, more meaningful and more effective.

Now you can talk about analytics!

Only then can you start to have discussions about maturity models, vendors, internal or external consultants etc etc. It might still be a very long slog, but at least your client (or boss or whoever) can understand what the end game really is.

How do you know whether something is working if you don’t know what ‘working’ means? If your site gets 25,000 visitors a week, is that good or bad? You might reply that 6 months ago you only got 17,000 monthly visitors and therefore it must be fantastic, but how do you know that you haven’t simply moved from really terrible to slightly less terrible?

The way to get out of this is to set good, robust goals for your KPIs – ones which are achievable and based on sound insights and benchmarks. This isn’t just a ‘nice to have’, it is the foundation of a good web analytics measurement strategy or the downfall of a bad one if it’s missing!

Why goals are so important in web analytics

To illustrate the importance: imagine that your website drives offline sales of retail products. Recently you have gradually but consistently improved visitor numbers and congratulate yourself that this will mean more sales in stores. However, unknown to you the market for your particular product has just exploded and there are floods of eager new customers looking for ways to buy it, almost all of whom end up at your competitors. The ultimate effect of this is that you rapidly lose market share in the stores, but of course you never see any of this because you’re too busy congratulating yourself on your 2.3% increase in visitors.

To simplify this a bit – imagine if you owned a shop but never ever left the building or looked outside. You might think you’re doing well because your 10 average customers a day is more like 12 these days, but what if in reality the street outside was teeming with thousands of people and all the other shops had mile-long queues? Would you still think you’re a success?

So, goals – they’re really important! Here’s some tips on how to set them and use them:

How to create goals for your website KPIs

Creating a goal for a KPI is simply about asking first ‘where are we now?’ and then ‘where do we want to be?’ – there are a variety of data and insight sources that you can use to do this. In an ideal world you will use all in combination:

Corporate goals – This is so obvious that it shocks me how often it is ignored by web analysts and even marketing folk. Put very simply: how does your website relate to the overall goals of the business, and what does it need to do to help achieve them? For example, if you have a content site that makes money through ad revenue, how much ad revenue do your shareholders want/need in the next year? You can easily work backwards from this to understand what your goals should be: if you need an extra £85K revenue, how many additional visitors do you need and/or how many extra pages do you want people to view? Simple!

Competitive benchmarking - how do your competitors perform against the same KPIs? Whilst this can be difficult and sometimes impossible to find out, what information can be got is insanely useful. Ideally the data should be linked to corporate goals: if you know that your arch nemesis achieves 5 times the traffic that you do – but you also know that they target a larger and less profitable market segment than your strategy dictates – then you can use your market share estimates to work out what your traffic should be, rather than blindly trying to follow them on a pure number. Good sources of competitive data are commercial providers like Compete, Hitwise and Nielsen; as well as free tools like Google Trends. Failing that you could just ask them. You might be surprised how open they would be if you offered to share your stats in return.

Targeted improvements - Sometimes you just want to push yourself. Even if you have great competitive benchmarking, why stop at matching the competition? Push yourself further! This is about understanding what is achievable and stretching yourself slightly beyond it. Very useful if you have staff that work to bonus targets.

Common sense – if you know your business very well and have a good gut feel about where it can go, you might be brave enough to just use your intuition. Be very careful with this though, an unachievable and unrealistic target is often worse than having no targets – it will lead you on a wild goose chase!

How not to benchmark your performance

I have often seen people set up KPIs and then monitor them using a statistical method called standard deviation. In simple terms this is just a way of making sure that your KPI figures don’t fluctuate dramatically; i.e. they don’t deviate from the average in a given period. If the number improves you can learn from it and roll with it; if it declines you can understand why and rectify it. Whilst this has it’s uses on a day-to-day basis, this is a disastrous way to handle goals. Why would you not want to deviate from the average (mundane)? What else is business except the striving for improvement? No brainer!

Further reading

Avinash Kaushik on benchmarking, goals and more

Coremetrics on industry benchmarking

The Big Book of KPIs by Eric Peterson

The issue of data quality and accuracy in web analytics is something that most web analysts have no option but to learn and internalise very quickly, especially when people start asking why numbers don’t match. However, it is often easy for us to forget that our clients, business users and marketing teams don’t live and breath this data as we do. This post is therefore a reminder of the essential (by no means definitive) facts about why web analytics data can’t necessarily be taken as fact.

Why are the numbers different?

Most people first recognise a problem with web analytics data because they are trying to reconcile absolute numbers between two different systems, for example when comparing visits in Google Analytics with clicks as reported by Atlas (or some other ad tracking tool). The following are the key reasons why these numbers don’t match:

• The terminology used to calculate metrics usually differs slightly. For example, unique visitors must always be unique visitors within [a certain time frame]. Different vendors may use different time frames. Neither is right or wrong; they are just different. This same principle can also apply to lots of other metrics, and sometimes on a much more subtle level.
• Whilst advances are constantly being made, there are currently no agreed standards to these definitions. Analytics vendors often try to name-drop ABCe standards (at least in the UK), but these are generally considered to be outdated and were created for reporting on visits that derive from banner advertising and search; not for web analysis. Here is a good synopsis of the current state of standards.
• Tracking methodologies, such as cookies, packet sniffers and IP addresses all collect data in different ways and all have pros and cons to the way in which they do this. See example below for further info on this one.
• The Internet is composed of a huge array of different technologies, which are all constantly evolving and changing. These technologies play a big part in the accuracy of data collection.
• New browser versions invariably feature new types of technology that allow increasingly savvy web users to hide their on-line behaviour, or even block this behaviour by default.
• Robots and spiders crawl Internet pages in order to e.g. index what is in them for search engines. Data quality in web analytics is a race to keep up with these creatures!

Cookies and Unique Visitors – An Example

The issue of cookies is generally the biggest area of confusion. A client of mine was recently comparing Google Analytics to their incumbent provider, Sophus3. They noticed large differences in unique visitors and wanted to understand why. Whilst this issue is in some respect the product of all the points raised above, the main cause is the type of cookie used:

With Google Analytics, visitors are tracked using 1st party cookies. Estimates suggest that around 1% of users block these cookies and a further 4% block JavaScript. GA is therefore unable to track these users, so real visitors may be under-counted by about 5%.

Sophus3, on the other hand, uses 3rd party cookies. Many browsers block these by default, so estimates suggest that around 65% of traffic is lost due to the combination of this and JavaScript blocking.

Sophus3 then use IP address to track visitors who have blocked cookies. However, most broadband providers use dynamic IP addresses, which change periodically. In some cases, the IP address could change every time the person switches on their computer. Therefore, Sophus3 will register individual people as multiple visitors, and overall numbers will therefore be inflated.

The following chart illustrates this issue in a more visual way (numbers are rough estimates to illustrate a point, and are not meant to be accurate):

How cookies can affect data accuracy in web analytics

Whilst 1st party cookies are generally considered in the industry to be best practice, in truth neither is perfect. For more information, here is a more detailed overview of how cookies affect web analytics data.

Get over it!

The issue of data accuracy can cripple companies and cause vast amounts of wasted time. In truth there is no solution, it is much better to:

• Understand the limitations in as much detail as possible and ensure that all recipients of web reporting and analysis are familiar with what the numbers do and don’t tell them.
• Focus on trends and segments, and not on absolute numbers. This is easy to do when the focus is on analysis and not pure reporting; insight never comes from pure numbers.
• Where numbers such as unique visitors are required for decision making, confidence levels should be used to make reasonable judgements about those numbers.
• If we set a consistent base-line of data at the most accurate that we can get it, then we can use this data to make accurate trend assumptions and draw conclusions about time-series analyses.

The other day I was presenting the findings of some analysis to a client. The focus of this analysis was to discover the behavioural factors affecting checkout completion rates in order to shed some light on why people drop out. For example, within this analysis I was able to say fairly basic things such as:

• Visitors who spend a lot of time on the site before their purchase are less likely to drop out of the checkout process than those who’s session is shorter
• Visitors who land on the homepage are more likely to drop out of the process than those who land on a product page

Now, the client immediately got rather excited about this and began to say, regarding the first point, “Wow, so if we can increase the time on site then we can improve our drop-out rates. Excellent, how do we increase time on site?”. Had I allowed it, this person would no doubt have been rushing back to the marketing team with a new objective to get the time on site up!

So what’s wrong with this? Well, apart from the very numerous issues with dwell-time associated with this specific example, this represents a very common misunderstanding in web analysis. Put very simply:

Your customer didn’t complete their purchase because they were on your site for a long time. They were probably on your site for a long time because they are interested in your products and your site is relevant to them which, in turn, means they are more likely to complete their purchase. Increasing dwell-time per se doesn’t make any sense in this example because it isn’t the cause.

To provide a simpler example of this: you might notice that people who dress smartly often have quite tidy hair as well. Does this mean that dressing smartly causes tidy hair? If I put a suit on, will my hair instantly become much tidier because I’m wearing a suit? No, there is some other factor (the person’s need to look smart) that is driving both of these things.

This leads to all kinds of problems in web analysis, some of which are quite subtle. Furthermore, this problem is inextricably bound up with our obsession with click-stream; if we can’t see beyond the web analysis tool then we have to find our causes within it. The biggest danger is that we stop being able to see our visitors as real people with real needs, and instead just view them as lines of data or collections of behaviours.

A couple more examples of how this can cause problems:

• You notice that direct traffic is of a higher quality than other sources. Does this mean that you should simply get more people to come to you direct? You could do this by displaying your URL as a static image in non-clickable banners, meaning that people have to physically type it into the browser. Again, no. Real direct traffic is direct because of brand familiarity and relevance, which may have nothing to do with advertising. The pure fact that it’s direct is of little relevance. (by the way, be careful – direct traffic isn’t always what it seems)
• You notice a correlation between downloads of your latest white paper and calls to your salesteam. Excellent, the white paper is a succesful acquisition tool and is driving leads! Or is it? Which way round is it really? Are people calling you because they downloaded the white paper, or did they look at your site and dowload the white paper because they called you?

Remember, correlation does not imply causation! You can avoid this by remembering that your customers are real people with needs, desires, habits and lifestyles. They are not lines of data with dwell-times, page counts and completion rates. These things are only behavioural indications of something else more complex that is happening. Look beyond the click-stream and understand how your customers think and feel.

Drowning in Data

The concept of ‘drowning in data’ cannot be understated when it comes to web analytics. Apart from the sheer quantity of information available, the situation is worsened because the tools we use are so terribly fast and effective; it has never been easier to slice, dice and peel (?) your way through such huge mountains of click-stream data. But just because it’s there and easy to access certainly doesn’t mean it’s easy to make sense of. I believe most companies that fail in this arena do so because they simply don’t know what to look at, but rather flail around in the data following endless and infinite pathways that, whilst ‘interesting’, ultimately lead nowhere fast.

This post describes how to get your head above the water and start swimming in a straight line. The answer lies in what I call a ‘Measurement and Optimisation Framework’, which might sound complicated but is, in fact, simply a strategy for: what you should be measuring; how to do it; and what you should do with the information once you get it.

Developing a Measurement & Optimisation Framework

The process of developing a measurement and optimisation framework is simply about answering 3 key questions:

1. Why does my website exist?
2. How can I measure the success of that existence?
3. What can I do to make it more successful if I find it isn’t achieving what I want it to?

For very simple websites (such as a personal blog), you could probably get away with just spending an hour or so thinking about this. For more complex business sites it could take some time! Following is a brief summary of the top-level part of the process through which I would typically take a client in order to get this up and running:

1: Define your site’s KPIs

How can you fix something or make it better if you don’t know what it was meant to do in the first place? Not setting proper objectives and goals is the most serious and fundamental mistake anyone can make, and not just in web analytics!

Most companies fail to do this because they assume that they intrinsically know what their site is for and what needs to be done to improve it. Take the example of a site selling CDs – it’s for selling CDs, right? What could be more complicated than that?

But, think about it for a moment, who is it trying to sell CDs to? Is it trying to achieve the lowest price possible or is it selling at a premium because it caters to a niche? And where does the company want to be in 5 years time, and what does that mean in terms of the brand that needs to be built? Is it important that people tell their friends about it? Oh, and how does the profitability work? Do we need to reduce the cost per sale by increasing the number of repeat buyers and therefore reducing media spend? And what about our other sales channels? Sales on the web cost much less than those that go through the call centre, so do we need to persuade some of those customers to get on-line? etc etc etc…

The point is, what your website means strategically is not necessarily all that easy to articulate. You need to get a really firm grasp on what your companies corporate goals are and work downwards. For big companies this generally means using something like a Balanced Scorecard approach. The system you use isn’t necessarily important, the point is that you align the goals of your site with the strategic goals of the company or, better still, the strategic goals of your customers!

2: Set targets

Once you have defined how to measure success (your KPIs), you then need to determine what that success IS. Again, this goes back to your corporate objectives: if your site is there to generate advertising revenue, how much revenue do your shareholders need next year? And what does that mean in terms of the number of visitors you need and the number of pages they need to look at? This is how you set targets.

Even if you can’t get anyone in your company to give you these targets, you should make them up yourself! It is incredibly difficult to optimise something to work better if you don’t know what ‘better’ means. If you are not able to prioritise which areas of the site need the most attention at any one time, you will drown – and you cannot do this without a sense of the goal for each KPI. Just do it!

At this point in time you might be able to produce something like this:

Typical Web Analytics Measurement Framework

[Please note: I doctored this a lot to protect the identity of a client, so it won't necessarily make complete intuitive sense and is provided more as a visual example]

3: Guide your analysis with a KPI dashboard

Now you know what your KPIs are and how to measure them you can produce a dashboard report showing where they are against where they need to be. This is incredibly important because it is the guiding light of your analytics and tells you exactly what to look for. If, taking the example KPIs in the chart above, I produce my weekly or monthly dashboard only to find that my unique visitors are dangerously below target but that all other KPIs are OK, then all my analysis for that week/month will be guided by a very specific question: what drives unique visitors and how can I improve the volume?

By investigating this you might find, for example, that you have saturated your search market and therefore need to optimise the site for different, non-branded keywords – or that the TV campaign you tested sent lots of high quality traffic and should be repeated. The point is that, without the KPIs, targets and the dashboard, you have nothing to focus you and, more importantly, have no solid way of telling your marketing director why they need to spend more on TV!

4: Optimise, optimise, optimise!

Remember finally that web analysis is not about understanding, its about doing. If you think your job is to report figures to someone else so that they can make sense of them, then you are not an analyst. The output of everything you do is about making changes to your site, media strategy, internal processes or whatever. Analysis and optimisation are essentially the same thing!

Empower yourself!

So what’s the benefit of all this? If it isn’t already obvious think about these two possible scenarios in which you are presenting your ‘analysis’ to your wider team:

• You hold a meeting in which you present 30 charts of data from your analytics tool, moving through geography, time on site, hour of day, browsers, screen resolution and lots of other fascinating charts. At the end everyone agrees that it was really interesting and goes back to their jobs.
• You hold a meeting in which you state that you can make the company an additional £1.5m per year in sales revenue and then proceed to present a road-map for implementing changes to make it happen, with a full ROI justification of likely costs. You get promoted and paid more!

Which one would you prefer?

Analytics direct traffic reports are often viewed as both a highly insightful metric and, in itself, as a particularly valuable stream of visitors. These are people that typed your URL directly into their browser, right? They must have seen your TV ad or just been really engaged with your brand because they remembered your address and didn’t need to use search. Who could ask for better visitors? They are motivated and focused and really intended to come here.

This kind of language continues to dominate all kinds of discussions about web analytics, including blogs, forums, and articles – and even reaches into the field of the experts; just look at the way Google Analytics defines direct traffic. It’s even more worrying when I hear the way my clients talk about it!

The fact is, this definition of direct traffic in web analysis is extremely misleading. It’s true that the direct traffic bucket does include bookmark traffic and typed URLs, but these days (unless you are very strict about your campaign tracking parameters) it can and does include all kinds of other stuff. All it really means is that the session started without a referrer being passed by the user’s browser, and this can happen for lots of reasons as defined in this rather neat list. I have done some tests on some of my clients’ sites and estimate that in some cases up to 90% of ‘direct’ traffic is infact banner ad or PPC traffic!

Here’s an exercise you can perform that will demonstrate exactly how prolific this problem is: as you’re browsing the Internet and following links from one site to the next, you can check the referrer that is passed by typing the following snippet of code into the address bar of your browser:

javascript:alert(document.referrer)

For example, if you visit a site like AOL and click on one of the advertising banners, when you arrive at the destination page replace the URL with the code – a pop-up will appear showing you the referrer (or nothing if one wasn’t passed). Try this with different types of sites, banners and links. Also try it with different browsers. As you will see, quite a lot of the time the referrer is blank. This means that your visit would have been counted as direct traffic in the analytics reports of that site!

So, it’s time stop thinking of direct traffic as people typing in your URL, this isn’t necessarily the case. ‘Other’ or ‘unknown’ would be a more accurate description.

It’s also time to realise the importance of campaign tracking on your inbound links, as Avinash Kaushik points out in his definition of analytics direct traffic. If you always ensure that your links are passing source and campaign info, then you are forcing the referrer field to be populated even if the browser doesn’t pass it. Here’s an easy way to build campaign tracking URLs in Google Analytics.

I was driven to write this post after chatting to the online marketing manager of a large international company, who proudly told me that ‘dwell-time’ was now one of their most important KPIs; and that they had issued instructions to all local marketing teams that the primary focus for the coming year was to ‘increase dwell-time’, thereby getting customers ‘more engaged’. I suggested that they make the pages take longer to load. He didn’t get the joke!

In seriousness though, this is a very common example of the way many companies view their websites. Personally I think it might come from too many years dealing with traditional offline media – “if only we could find a way to get people to look at our bill-board for longer, and pay more attention to it!” But beware…

The danger of dwell-time

In most cases measuring dwell-time as ‘engagement’ (or even at all) is not only wrong, but is frankly dangerous. Just a few of the reasons for this are as follows:

• A lot of your visitors are at your site because they want to get something done, quickly: place an order for something they decided to buy last week; find your address; get help; and so on. Why do you want this to take longer? If you ran a supermarket you might want people to spend longer browsing the aisles, but would you want them to have to queue for longer at the check-out??
• I might spend 2 hours ‘engaging’ with every aspect of your site, but that might be because I despise you and am learning everything about you so I can destroy you! This is extreme, but the point is that engagement isn’t necessarily positive engagement.
• Most companies find, if they run the analysis, that people who buy things spent longer on the site than people who didn’t. This leads them to think that if they can get people to spend longer on the site then they will surely buy more stuff. This is one of the biggest errors I see in web analytics, and not just regarding this example. People who buy things don’t buy things because they were on the site longer, they were on the site longer because they were in the mood to buy something, or because your site was relevant to them. Simply getting people to stay on the site longer doesn’t change their state of mind, and by obsessing over it you ignore the real underlying drivers.
• If what you really want to do is get people more engaged with your content, and get them to think positively about it – why not just measure that? Do a survey or run some focus groups; ask them what they thought and, if they don’t like it, ask them why not and how you can improve it. This kind of brand engagement is a deeply emotional and qualitative thing – how on earth do you expect to correlate it to something so cold and bland as the time they spent on your site?

But there is something more fundamental underlying all this. I think in most of these cases companies (especially non-ecommerce sites) are unsure what their website IS; what it means to them strategically and, more importantly, the role it plays in the overall journeys taken by their different customer segments. How exactly do you want the content on your site to influence your customers’ behaviour? Do you even know how your customers are using the site at the moment? Until these questions are answered (quantitatively and qualitatively) you will never be able to meet them in relevant dialogue through your site. And if you really think this through, and then think back to the concept of pure dwell-time – how absurd does that sound now? It’s like locking the doors of the shop and not letting people out!

But we are trying to achieve something, so what is it and how do we go about it?

Nevertheless, websites do have a communicative role to play. Our visitors need to be influenced, motivated, persuaded, dazzled, awed – not just to make them buy something, but so that we become part of their lives in whatever way is relevant to them. So how do we do it? Well, unfortunately the answer to this question is deeply unique to every single business – you need to go on your own voyage of discovery in order to understand exactly what ’success’ and ‘performance’ mean to you and therefore how to influence them. However, here are some tips to set you off:

• Push the site itself (and especially anything to do with click-stream data) out of your mind temporarily. Work out who your customers are and why and how they want to interact with you as a business. Similarly, work out how you want them to think of you, and what role you want to play in their lives. Now, in the middle of all this – what does/might the website mean to them; how does it help them; what would make it important to them? If you have the budget I would strongly recommend this being a major research project.
• Remember that you don’t just have one type of customer, and even similar customers want different things at different times. Segment your customers by who they are and what they want to achieve, and make sure you understand the above question according to these different types of customers. What role does the site play for them at the current stage in their journey with you?
• Ensure that your objectives and KPIs reflect this understanding. If by engagement you really mean that all visitors successfully completed what they came to do, then ask them whether they did or not and use this as a KPI. If the journeys and tasks that people want to perform are totally different, then you need different KPIs.
• If things like dwell-time are still relevant to some of these journeys then use them, but remember and take heed: these are indicators of other behaviours or attitudes. You cannot influence this metric directly. Know what drives it!
• Never rely solely on click-stream data as your source of insight. Sometimes it is easier for continual reporting if all KPIs are based on click-stream, but if this is the case then you need to make sure you explain and drive these metrics using other, qualitative sources of data. Click-stream is the what, not the why!

Above all, remember that your website is not and will never be a ‘pamphlet on the web’. You might think of it like this, but your customers most certainly don’t. These days brands sink or swim based on how effectively they ‘engage’ with people through digital channels, but this ‘engagement’ is a million miles away from ‘dwell-time’!

With recent and continued advances to Google’s excellent and free analytics tool, one of the key questions that I seem to get asked these days is whether there is any real value in paying companies like Omniture and Webtrends for the commercial (and expensive!) services they provide. 

It’s probably already obvious that I’m a fan of Google Analytics (be prepared for gratuitous bias); for lots of clients I really don’t see how spending the money on something like Omniture would benefit them. However, this isn’t always the case, and I think a more systematic way of making this decision is often called for. 

This post is therefore an attempt to help make decisions about whether or not you should put your hand in your pocket, and I have chosen Omniture Site Catalyst as an example. 

Monetizing the Incremental Value of Site Catalyst

Now, it is undeniable that a tool like Site Catalyst does some more stuff than Google Analytics, and certainly that it has more dedicated and human support. However, it is very easy for clients to get blinded by the way sales people position these extra features; they don’t stop to think what they might actually use them for. Conversely, GA extremists will flatly deny that there is any use in these additional features (or sometimes that they even exist), likewise failing to provide adequate reasoning. 

It seems to me that there is a more simple way of stating the true question: 

Site Catalyst does various things that Google Analytics doesn’t. What benefit do these things provide on their own (i.e. in isolation from any of the things that both GA and SC can do)? And – can the entire cost of Site Catalyst therefore be justified based on these incremental benefits?

So what does Site Catalyst do that Google Analytics doesn’t?

Following is a list of the key things that I believe SC does that GA doesn’t. It isn’t meant to be completely definitive, but [in my honest opinion] everything else is pretty much cosmetic:

Weighing up the cost benefits

Real-time data – this basically means your stats update more-or-less straight away rather than after about 24 hours or at mid-night. Personally I find it hard to think of companies that could truly benefit from this, but if you think you might then you need to work out exactly what financial benefit it gives you over and above waiting half a day. Also check out Avinash Kaushik’s blog on real-time data.

Importing external data – at first glance, this is a fairly major thing that GA doesn’t do. In Omniture you could import a lookup table of postal codes and then use this to carve up sessions into sales territories. This can be pretty valuable, but what you really need to ask yourself is: ‘how much benefit does this give us over and above exporting the data to excel and making the table ourselves?’ How much extra work is it really to just do this outside the tool? This also applies to a lot of other stuff, such as the functionality that lets you add targets to KPIs – and also to most of the Genesis integrations.

Custom variables – you actually get 2 of these in GA, but then you get loads in Omniture. Yes, for some companies this is valuable, but are you one of them? Again, I’m not denying that these things are important; I’m saying that you need to make an actual financial calculation about the benefit you get from using them over not using them. ‘Nice to have’, ‘convenient’ and ‘handy’ are not good enough reasons! Another function with similar ramifications is the ability to link metrics with dimensions that are not available in the out-of-the-box package.

Creating paths and funnels on the fly – very nice, and I wish GA did this, but I would have a hard time selling it to a client and I also couldn’t say that it is critical. I’ve certainly never seen it as a barrier in GA. Monetize it if you need it!

And, seriously, that’s pretty much it! Like I said, everything else is cosmetic or falls into a similar category. The main point is that you don’t get swayed by the sales spiel, and you calculate the return on your investment not by asking what analytics per-se can do for you, but asking instead exactly what can commercial analytics do for you that the free stuff can’t?

But wait…

Having said all that, a big word of caution  – GA can do a lot of stuff that Site Catalyst can, but a lot of the time it isn’t necessarily easy or straight forward to do, so much so that you might not even know or believe that it is possible in GA. What I’m getting at here is, you may need specialist expertise (a decent analyst) to be able to match GA with Site Catalyst on some levels of functionality. Again though, monetize this properly – you would have to pay someone to use Site Catalyst, so how much more would you have to pay someone to get the most from GA and how does this weigh up against the cost of SC?

Finally, it is worth also noting that I haven’t even touched on what GA can do that SC (on its own, i.e. without Discover etc) can’t, and believe me there is plenty of stuff!