Companies spend an inordinate amount of money setting up complex IT systems to manage and optimize service offerings to customers.  Often these management systems only look at a single metric, like conversions or performance. What if both could be correlated?

Imagine you are a high-traffic on-line ticket vendor for major events nationwide.  As the head of IT operations, you want to maximize conversions by ensuring that you can handle the traffic volumes generated by a major concert event or marketing push and that the response times for customer queries remain optimal. In spite of your best efforts, however, the latest concert promotion brought your site to its knees and some of your systems went down for a short period of time.  The impact of that outage on your users might be unacceptably slow response times or actual system errors in their browser.  In either case, if they risk losing out on good seats for the concert while waiting for your systems to recover, they will abandon your site in favor of a competitor that is more responsive and you will have lost that revenue. On the assumption that you could have sold 1000 tickets at $100 apiece during that outage, you could lose up to $100k of revenue, depending on the number of users affected.  That’s a lot of crumpets, as they say.

Poor response times and unreachable sites are bad for revenue. But there is also a third reason why your site traffic could be impacted that is not caused by the performance issues described above.  It may be that your site content is unappealing to the average eye or that your offerings are priced out of reach for the average consumer.  This is the typical explanation that marketing gives to conversion dips, but it is clearly not the only root cause.

This is where Coradiant Analytics In A Box can help.  Our passive capture technology combined with the included Google Urchin software collects performance and availability metrics about your site while it is tracking user behavior. This allows you to look at conversions (e.g. ticket purchases) in the context of performance and availability, thus allowing you to categorize conversion dips into those that are infrastructure related and those that are marketing and content related.  For example, your analytics data tells you that the 22% of your users bought tickets between 12-2 PM on Monday afternoon. You also see that this is about 35% less than the norm for a corresponding period.  Coradiant allows you to correlate this event with an increase in page load time or availability problems.

End-user monitoring coupled with analytics provides a powerful solution that helps to maximize customer visits and conversions.  It places those responsible for business results and for performance in the driver’s seat by providing them with the data they need to understand the real business impact of system outages, thus enabling them to provide the best possible service to their Line-of-Business managers.

I feel real sympathy for Marketing departments.  They are saddled with the enormously difficult task of profiling users in order to achieve more effective marketing efforts.  In order to do that, they must understand who the user is, what they did on the site and how they reacted to various marketing efforts.  Up to now, they have been relying on traditional Web Analytics solutions to get that data.  However, the price for that data is the insertion of page tags and tracking cookies.  Cloud-based services add on an additional price – the exporting of analytic data out to the cloud.  All of that might seem like a small price, but it is actually a true Faustian deal.

To start with, Marketing must subject itself to the busy timelines of Development, QA and IT whenever it wants to change its tagging or cookies.  Since those departments are often busy rolling out new features, Marketing often has to wait weeks or even months to get their new data.  In many cases, Marketing discovers a problem with the page tagging during the course of a campaign and are unable to roll out a fix quickly enough. In companies where this problem is recognized, the problem is reversed and Marketing is allowed to hijack the roll-out process with an emergency patch to its analytics tagging, often negatively impacting the delivery of important new features.  Both Marketing and IT would benefit if this link could be severed.

The other problem that Marketing is facing comes from Europe, where a wave of privacy regulation is forcing existing Web Analytics solutions to run for cover, leaving Marketing departments with little to help them.  Germany has passed very strict laws prohibiting the use of page tagging and tracking cookies without the user’s consent.  Moreover, shipping analytics data to a hosted service for processing is specifically forbidden.  Although privacy is often talked about in every part of the world, Europe is the first to have passed these kinds of laws about it – a trend that could easily spread outside EMEA.

The irony is that most of the data that Marketing often requires is already contained within the traffic stream before any tagging or tracking takes place – where the user is from, what browser and OS they use, which ISP they used, where they came from, where they went, what they looked at, what they bought, etc.  It’s either embedded in the HTTP protocol or as part of a web server’s natural ability to maintain a stateful application or it is directly within the request or response content.

Consequently, as announced last week, Coradiant has teamed up with Google to create our Analytics In A Box (AIB) solution.  This revolutionary new product uses Coradiant’s existing technology to passively process all user traffic to and from the web site, producing full-featured web analytics data that remains in-house.  Instead of inserting page tags or using special cookies, Marketing can define AIB rules that will extract the information directly from the traffic stream. This approach will allow Marketing to define new metrics whenever they want to – even in the middle of a campaign during peak hours.  Moreover, the solution refreshes its data every hour, providing Marketing with immediate results from any changes.  No other department has to be involved.  Moreover, the data is 100% secure and kept in-house, which means no privacy violations.

As Bogey said, “this could be the start of a beautiful friendship”.

The holy grail of web monitoring – whether for real user experience or web analytics or any other purpose – is to be able to reliably recognize users.  You can only accomplish this goal by inspecting the traffic itself.   However, as I pointed out in a previous posting, you will always be as blind as your own applications.

Naturally, applications will only inject such identifiers when they are interested in identifying the user in some fashion.  Not all of them are.  Moreover, due to the undisciplined nature of web development, some of them are horrendously inconsistent in their intentions.There are really three levels of user awareness:

• Identity: an application is identity-aware if they require the user to authenticate themselves in some fashion.  This is typical of on-line banking, insurance sites, etc.
• User: an application is user-aware if they track the user’s session but cannot actually identify the user.  For example, most on-line stores allow anonymous users to buy items from a catalog, requiring a session state so that the site can remember the contents of the shopping cart.
• Anonymous: these applications do not track the user specifically and they do not place anything in the unique in the traffic.

Most sites that are interested in tracking users believe that they are either identity-aware or user-aware.  In fact this is frequently not the case.  Some applications appear to be user-aware because they use cookies to remember preferences or state.  However, none of those cookies are unique to a given user, so those applications are actually entirely anonymous.  Most other sites are at least partially anonymous, failing to place anything unique in the traffic unless absolutely required.  Thus, users are allowed to remain anonymous through large sections of those sites, only becoming traceable when they enter a transaction or log into the application.

Anonymous users cannot be tracked.  The HTTP protocol does not do it natively and the evolution of the internet is only making that more apparent.  If you are allowing traffic to be anonymous, then you must either accept that you cannot track that traffic reliably (you can make reasonable attempts using things like the client IP, but it will be seriously flawed) or you must alter your applications to build in the level of awareness that you need.

The Web Analytics industry is in the midst of a debate about how to identify and count Unique Users.  Some people are starting to suggest that we should abandon the idea of Unique Users in favor of counting something easier.  At the heart of that debate is the question of whether we will ever be able to uniquely identify users on the web.
 

Surely I can trust the client IP?

The problems with the client IP have been public knowledge for a long time.  This is an excerpt from a tutorial about Web Analytics, published by Summary.net (a log analysis tool) back in 2002:
“The majority of Internet users connect through dial-up services of some kind. In order to preserve IP numbers (there are a limited number available right now), the dial-up providers will assign each user a number when he connects and then reuse the number when he is done with it. So a dial-up service may have 100 IP numbers that they select from and use to serve 2000 users. This gets even more complicated with caches and proxies that many providers now use to improve performance …”
With the introduction of mega-proxies (like AOL), this problem gets even worse.  Mega-proxies will spray their traffic across multiple gateways.  Since the internet was designed to treat each hit as a stand-alone transaction, this means that every request making up a single page can be routed through a different client IP and port.  So, instead of a one-to-many relationship between the IP and the users, we have a many-to-many relationship.
Entities like corporate firewalls are rendering the client IP extremely weak and unreliable as a user identifier.  Mega-proxies completely destroy its reliability.
 

What about the user agent?

A lot of people choose to set aside the concern about mega-proxies and talk about combining the user agent with the client IP as a differentiator.  The problem with this is that there are a finite number of user agents in the world.  Admittedly, they number in the thousands.  However, these user agents are shared by millions of web users, which means that tons of users are being represented by the same user agents.  In fact, this understates the problem since most people are running the same browsers and plugins on the same basic operating systems, reducing the pool of popular user agents.  Combining IP and user agent will still result in users that are sharing the same combination.
If you are expecting to use this as a means for identity tracking – as in “this is Bob” – then you are going to be disappointed.  Since the user agent contains information about the browser and the OS, it can easily mutate over time as users upgrade their browser, download plugins, install service packs, etc.  Moreover, users are not limited to one browser – I use Firefox but am occasionally forced to use IE on specific sites – or one system.  I surf from my laptop, my wife’s computer and my iPod, so I’m using three different platforms as well as three different browsers.
 

Enter the plugin

At this point, you may be thinking that the user agent will at least improve your odds.  This would be true if it weren’t for plugins.  Plugins within a browser are allowed to request their own resources from the server.  When they do so, they send a user agent and it does not have to be the same one used by the browser.  The Java plugin is a classic example.
 

Grab your bootstraps and pull

This problem – as with all others – begins at home.  If you want to track users, do not expect the HTTP protocol to help you.  It was originally designed for anonymous traffic.  Deploy your own tracking IDs that are tailored to your needs.  Most web servers have mastered the art of injecting user awareness into the traffic (via cookies or URL-rewriting).  If you need identity awareness, then you need to take the next step and have your developers build that into your application.
There is no magic bullet.  You need to solve this problem for yourself.

Patrick Gardella, of Discovery Communications, recently spoke to Network World and said about Coradiant TrueSight, “Basically, it allows us to identify very rapidly what is happening with actual users on the site, and then it helps us debug those things.”

“With our huge online shopping site — and other Web systems that require major user interaction – users have problems. When that happens, we get e-mails saying, ‘Your site is broken’ and not much more. The reason I like Coradiant is that it offers a very simple, easy-to-use appliance that can find out what’s happening with those individual users, as well as how many other people are having those same problems.”

For the full article see: Network World 

Back in the dot-com heyday, everyone was terrified of customer churn.

The churn occurred, the theory went, when someone couldn’t buy a book from Amazon.com and switched over to Barnes and Noble (or vice-versa.) This led to shopping cart abandonment and costly acquisition of new customers.

The reality is a bit different. My Amazon account has all kinds of personalized features — from billing and payment information, to a wish list, to recommendations. I’m unlikely to switch unless they really upset me. I’ve changed providers for some things in the past, such as really bad airline procedures, abject failure, or the inability to provide a product or service. Sure, I went looking for a pair of shoes online and tried three or four places before finding them. But for relationship-based selling, where I return time after time, switching doesn’t happen much.

Or rather, switching happens a lot, but people don’t measure it right. Don’t get me wrong: Churn is a big problem. It’s just that traditional thinking about churn won’t work any more.

An often-quoted study conducted in the late nineties by Booz Allen & Hamilton compared the relative costs of a transaction in a bank:

• Internet: $0.01
• ATM: $0.27
• Automated call center: $0.44
• Call center personnel: $0.85
• Branch: $1.07

If I need to complete a transaction, and their website isn’t working, I’ll go to the branch. I hate that, and so, apparently, do their accountants. The first modern switching cost is channel switching: When I use an inefficient channel, I cost the company money and I get irritated.

A second switch occurs when I stop being productive and engaged. I recently presented at the Application Continuity Conference in San Jose, and looked at “user continuity.”

The gist of this is that, when performance degrades to horrible levels, it’s pretty clear to all involved that the application may as well be down. But what’s less clear is the cost of users switching their level of engagement. The second modern switching cost is engagement switching.

In 1968, Robert B. Miller published a study entitled “Response time in Man-Computer Conversational Transactions.” He looked at how the human brain behaves when the system it is using responds with different levels of delay.

Miller identified three main threshold levels of human attention:

• 100 ms or less and the person feels that response is instantaneous
• 1 second or less and the person feels they are “freely interacting” and can enter what Mihaly Csikszentmihalyi called a “Flow State” in which concentration and productivity climb while errors drop.
• 10 seconds or less and the person feels they are “attention focused,” meaning they are consistently engaged with the task at hand.
• For interactions that take more than 10 seconds, humans become distracted and will try to multitask, and productivity will drop dramatically.

What I like best about this study is that it predates the Internet; it’s about how we’re wired. We’d scan the grasses to look for the sabre-toothed tiger for about 10 seconds before returning to the task at hand. And according to an MIT thesis, signals travel about 90 meters per second along a sheathed neuron, so we pretty much treat things that happen in under a millisecond as “right now.”[1]

So when we look at switching costs, on many sites we’re not worried about visitor churn in the traditional sense. What’s a lot more relevant is the cost of channel switching and engagement switching that can drive up the cost of serving a customer or the disengagement of the user’s attention and productivity.

[1] Interestingly, our clock speed is between 500 milliseconds and 4 seconds, or 250-2,000 HZ, so those Pentium chips are catching up on us.