A scientist was conducting an experiment with a fly. He pulled off one of its legs and set it down to see if it could fly. Conclusion: a fly without one leg can still fly. He pared off a second leg and set it down, saying "Fly!" Conclusion: a fly without
two legs can still fly. He removed all the legs and set the fly on the palm of his hand, shouting "Fly!" Conclusion: a fly without legs can still fly, briefly, before crashing to the floor. He pulled off all the fly's wings and set the fly on the palm of his
hand, yelling "Fly!" Nothing. "Fly!" Nothing. Conclusion: a fly without wings is deaf.
This was an old, lousy and a bit vicious joke even when I was a kid. It does, however, effectively demonstrate a long lasting truth: it is not the collected data, but rather how we interpret it, that renders its effectiveness in decision making. Errors range
from confusing cause and effect (is it that customers who experienced fraud are more active, on average, or that active customers are, in average, more prone to experience fraud?) to gross segmentation causing severe false positives; a lot of these cases are
triggered by analysts sticking to high level, big numbers rather than complementing their analysis with case-by-case review and customer engagement. Business intelligence is a very important practice, and we must use our tools wisely to reach the best possible
conclusions to guide our decisions.
One interesting case of interpretation I found was regarding Javelin's
2010 Identity Fraud Survey Report. Here's an excerpt from the link:
"18 to 24 Year Olds are Slowest to Detect Fraud – Millennials (consumers aged 18 to 24 years old) take nearly twice as many days to detect fraud, compared to other age groups, and thus are fraud victims for longer periods of time. Millennials were found to
be the less likely to monitor accounts regularly and the least likely group to take advantage of monitoring programs offered by financial institutions. However, Millennials were the most likely group to take action such as switching primary banks or switching
forms of payment."
Why is that? Well, looking for interesting opinions I came across
this blog post. It suggests that Millennials are optimistic about the economy and feel invincible, being young, not imagining that fraud could happen to them. Interesting, but I don't buy into this kind of explanation, for two reasons:
one, is that it's over simplistic in its description of Millennials' psych, but the second is that it puts a cap on our ability to engage with a group of users about their financials. It's just too important to let go: being able to engage with your user community
to deter fraud will be a growing need for payment services in 2010 and beyond, and I claim that
they expect this to happen. It just doesn't resonate with me that social networks and games can get you engaged but your bank or eWallet, the place where all your
money is, can't. It's just a question of the right engagement model. What is the difference between those that work and those that fail? As a user myself, I don't feel like I have compelling interfaces that help me monitor my financials - and I log in to my
online banking interface on a daily basis. There's just too much information, too many buttons and graphs to make sense. To add insult to injury, many monitoring programs (such as the lately advertized
Chase debit card program) require users and parents to set their own monitoring rules. This reminds me of another area, online predator monitoring, which poses the same challenge to parents - you set the rules to monitor suspicious words
in your child's IM. Seriously? We force the laymen to do our job for us? Can we really not provide a compelling, interactive, machine learning interface that provides an appealing user experience? I think we can. Especially if the alternative is accusing Millennials
of being too optimistic.
Looping back to the beginning of the post, I'm just hypothesizing (or pulling the fly's leg, if you'd like). It's now a question of actually engaging with users and examining behavior to validate basic assumptions; something that we must do to make sure we
understand the data we are getting. But this is my own hunch on Javelin's results. What do you think?