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.