Data analytics doesn’t have to be complicated. By taking small steps to connect your data, you can begin to alleviate members’ pain points.
The daily commute can be a grind. Here in Washington, DC, Metro riders know that they might be late to work for multiple reasons—mechanical issues, offloading a sick passenger, or the dreaded “single tracking,” which delays an entire rail line.
For years, the Washington Metropolitan Area Transit Authority was seen as tone deaf to commuter frustrations. It’s why there’s a popular Twitter account called Unsuck DC Metro.
But thanks to a system-wide effort to prioritize safety and track improvements, there have been some positive developments for commuters. These days, 87 percent of Metro riders arrive on time, a big improvement from last year’s 66 percent rate.
Still, delays are a fact of life, and WMATA understood that it needed to make them less annoying. In January, Metro debuted Rush Hour Promise, a data-driven solution to anticipate lateness and refund riders to say, essentially, “We’re sorry.”
As we noted in the January/February issue of Associations Now, the use of anticipatory intelligence is predicted to grow rapidly in the coming years, especially with the development of new technologies like artificial intelligence and machine learning, which are already helping to improve member experiences.
But let’s say you can’t make an investment in these technologies just yet. You can still improve the member experience by making smart connections among data points you already have.
Just look at how WMATA does it to quickly refund customers for service disruptions. Personal rider data is connected to SmarTrip cards. Separately, WMATA has data indicating how long a trip should take, including the time it takes a typical rider to walk to a train, wait on a platform, transfer, or walk to the exit. To qualify for the refund, users must be registered in the database, and their trip must take at least 15 minutes longer than it should ordinarily take during rush hour.
Riders outside the estimated time are granted the refund, which is then credited back to the SmarTrip card within five days and communicated to the customer via an automated email.
Of course, riders may try to game the system by simply waiting at a station platform. But WMATA can spot irregularities by analyzing aggregate data showing the duration of passenger trips and time of train arrivals.
And as it turns out, refund-eligible trips are only a small fraction of total passenger volume.
Thus, Rush Hour Promise is not only a data strategy to alleviate a common customer annoyance; it’s also a marketing tactic, something WMATA is actively promoting right now in TV commercials.
Easing Attendee Congestion
What can associations learn from commuter woes? The team at EDUCAUSE, for example, is using data connections to help ease traffic flow and room congestion at its annual conference via an app [member login required].
“After members of our program committee identified one of their conference pet peeves—walking across a huge convention center only to find a session already full—we decided to solve the problem,” Thad Lurie, CAE, who at the time was vice president of operations and chief information officer at EDUCAUSE, wrote recently in an ASAE article [member login required]. “Using existing room capacity information and real-time beacon data for the number of attendees in that room, we created an app that lists rooms, session titles, and capacity status for each session.”
The room and session name turned yellow when the room was 80 percent full and red when it was 90 percent full, so that fewer members were annoyed by a “long-walk-followed-by-disappointment scenario,” Lurie wrote.
Lurie suggests that other associations, in the same way, can pick a specific problem and use data analytics to solve it. For example, the same data about attendee traffic at a conference session might help to inform a targeted marketing campaign that results in more member sign-ups for a paid webinar series on a related topic.
This approach of collecting and analyzing data to predict the future doesn’t require extreme computing power. It just requires a targeted focus, Lurie says.
“It’s always helpful to start with your end goal in mind,” he says. “And I would suggest two—using technology to grow net revenues and making your attendees happy.”
Have you used data to alleviate a common member annoyance? How did you go about using data to solve the problem? Post your experiences in the comment thread below.