What’s the next application for your intelligence?

I had a great conversation last night with the receptionist at my dentist about the not-so-new Citi Bike program in NY (yes, it’s odd that an Irishman travels from Florida to NY to see an English dentist, but I’m a big fan).

Citi Bike Station

Michael was raving about the scheme – spending $100 on it will save him $1400 annually in subway spend; he’ll get at least 30 minutes of cardio a day, and so on. As he explained the scheme, it seems to have been really cleverly-thought out. Participants can see on a website or app which ‘stations’ have available bikes (to pick up) or bays (for dropping off – and if a bay is full they give you additional time to go to another bay without charge). They can report mechanical issues and flat tires at the touch of a button when docking the bikes. And, Citi is getting phenomenal branding by being ridden all over the City, while including some clever tie-ins like discounts for Citi card holders.

Then when I went to the website, what got me really excited was a tab for “System Data“. They use an open source tool to provide simple but slick visualization of information ranging from the number of annual members that have signed up to how many miles have been ridden to date.

But, if that’s the information they’re sharing, what about the information that they have behind the curtain. The City can begin to understand which neighborhoods are greater adopters – so that they might provide additional stations – but also to examine factors that might be holding other neighborhoods back. In other words, they can use intelligence to improve the system over all.

And, because this is a New York City initiative, they can presumably use the intelligence across multiple departments:

  • when planning for bike lanes – by understanding common routes/paths;
  • to adjust police feet on the street – if there’s an especially high or unusual spike of people in a given area;
  • to prioritize road repairs – if a given neighborhood has an unusually high number of punctures or bent forks, maybe the pot holes are to blame;
  • to plan other transportation system loads – the MTA might staff up subway booths in preparation for an influx of regular cyclists if there was a big dip in normal usage on a give morning.

There are so many ways the City can use the data – but its the same in every company. It’s always worth pausing when accessing insight about your customers for one specific reason, to consider how else the intelligence can be applied. Brainstorm non-obvious uses of the intelligence that might deliver further utility and value for the customer, and hopefully further insights and revenue for the business.

Cheers,
Dave

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