Understanding Nothing

It used to be that when I returned from a week away, I’d have thousands of emails to work my way through – even after deleting spam. I’d also have a few web sites to visit to catch up on news.

These days it’s reversed. I was pretty much done with my mail in an hour or so, but it took me all morning to catch up on my weblog reading.

There’s two main reasons why this seems to have happened.

Firstly, I had virtually no work related mail to read. There’s only 4 of us working for Kasei, and we were all at the same conference.

Secondly, I don’t read anywhere near as many mailing lists as I used to. I used to keep up with technical topics via mailing lists. This took a lot of time, even on lists where I delete entire threads without reading them, based on the subject line.

These days, I tend to get the same information (or at least the highlights) from the weblogs I read. I still have to skip over information I’m not so interested in – but not anywhere near as much of it. And I also get a lot of pointers to stuff that would never have appeared on tightly-focussed mailing lists.

I can’t help but fear, however, that unless I refine this approach some more, this time next year I’d take a full day to catch up (even in skim mode). I’m adding 2 or 3 people to my blogroll every week on average, and only dropping people at the rate of 1 every couple of weeks.

When I read them “in real time”, via my regularly refreshed “last updated” blogroll, it’s not too hard to keep on top off. But when you don’t read for a week, almost everyone has updated, and it’s not easy to handle.

I have no idea what the solution is.

There was an interesting article in April’s issue of Management Science (the paper linked to has a 2000 date, but seems to be the same paper), on how customers behave in invisible queues (such as when they email on-line retailers, or have to wait for a call-center call to be answered).

The authors note research that show that customers adapt their behaviour to their perceptions of what the wait time should be, formed through accumulated expecience, offset by how important their query is to them.

Contrary to common belief, they show that customers can and do change their expectations and accepted delay time based on perceived system performance – in one study of abandoned calls to a call center, they discovered that 38% of calls were abandoned across all delays at peak times between 100 seconds and 240 seconds.

And so they spend a lot of time with graphs and formulae (and circles and arrows and a paragraph on the back of each one…) showing how to model customers’ patience levels.

This is all well and good of course, but seems to miss the more important question of how to actually set customers’ expectations properly in the first place.

At BlackStar we implemented an auto-response system to incoming emails that said something like “Your email is 24th in our queue, and so we should be able to answer it within 50 minutes.”

It was a fairly simple approximation, with no complex predictive behaviour at all – it merely averaged the time taken to answer each of the last 10 emails. But it was good enough. At peak times, or when we were flooded with emails, the time people were told they’d have to wait rose, and we got a lot less customers deciding we hadn’t answered quickly enough and sending another email, or even telephoning (setting off a terrible vicious cycle that could sometimes take days to work our way back out from).

Loads of customers commented on how wonderful it was, several competitors approached us asking how they could buy the technology, and it was mentioned in at least 3 or 4 press reviews as a sign of how customer friendly we were.

We didn’t need or do any fancy impatience modelling – we just knew that the best way to keep the customers happy was to tell them what you were going to do, and then do it.