Cards have become more important than cash in moving around London. In July 2014,Transport for London (TfL) stopped accepting cash on its buses, telling commuters they had to use Oyster cards, or debit or credit cards with a contactless payment chip. It also began to accept cards on its Underground and rail services.
TfL flagged up the customer convenience angle and acknowledged the change would make big savings over time – but the move to card payments will also provide more data it can use for long-term planning.
It demonstrates the potential to tap data from near field communication (NFC) cards and radio frequency identification (RFID) tags for analytics at macro and micro levels. The current norm is for RFID middleware to filter the data it reads into an enterprise system to monitor activity; but it is also possible to direct it towards a business intelligence system for analysis. There is even a basic analytical capability in some middleware, such as Checkpoint’s OAT Foundation Suite and Xterprise Clarity.
It is still early days but, as people make more small payments by swipe card and companies attach RFID tags to more goods, there will be a torrent of information on customer behaviour. This is going to prompt more organisations to look at how they use that data.
TfL has been gleaning information from Oyster cards for 12 years. Sashi Verma, its director of customer experience, says the cards provide data for “demand profiles”, showing how many people enter and exit the transport network at specific points, and where the peak load develops on any route. They can tell TfL where people get on buses, and where they get on and off trains.
This makes it possible to construct a picture of individual journeys and how the bus and train networks are used by the public.
“The Oyster Card advantage over survey data is that the information is much more granular,” Verma says. “I can conduct any kind of demand analysis for any day of the year.
“If you want an analysis for yesterday I can give it to you today. If you want analysis for unusual events we can study those across the network.”
He says it is possible to create any number of metrics.
“Every time you touch your card we get the card number and time and location of use, from which you can construct any metric – the demand profile of a particular bus stop or station, the load profile of sections of train lines or bus routes, the off-peak profile of demand, the day-by-day profile over a week, seasonal profiles, and so on. There’s no shortage of what you can construct.
This article has been extracted from http://www.computerweekly.com, please click on this link to read the article in full http://www.computerweekly.com/feature/Transport-for-London-builds-customer-profiles-using-RFID-data-analytics
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