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While on my way back from New York, for some odd reason I started playing around with Foursquare and plotting my check-in data using a handful of apps. Very quickly I realized two things: the amount of time I spend in airplanes has doubled every year since 2009, and when I am in San Francisco, I lead a very predictable life and go to only a handful of places — a lot.
Except for one small thing: While the data shows that I lead a pretty boring life, it doesn’t reflect the “emotions” behind the data. Why, you might ask, is this important? The answer is that as we move towards a quantified society, one shaped by data, we start to dismiss things that aren’t easily quantifiable. Empathy, emotion and storytelling — these are as much a part of the business as they are of life. Without these, we might as well residents of starliner Axiom.
The problem with data is that the way it is used today, it lacks empathy and emotion. Data is used like a blunt instrument, a scythe trying to cut and tailor a cashmere sweater. Some folks do a better job of making data interesting — like the fine folks at Foursquare. They use cutesy phrases to remind me of my coffee addiction and occasionally point out that Jared Kim and I are besties when it comes to eating ramen noodles or visiting Hakkasan, but they don’t really tell the whole story and they need to do more. I will get to that in a minute.
The idea of combining data, emotion and empathy as part of a narrative is something every company — old, new, young and mature — has to internalize. If they don’t, they will find themselves on the wrong side of history.
The Uber-side Story
Let’s explore this idea further with what is my most used service: San Francisco-based local transportation startup Uber. As someone who doesn’t drive and doesn’t own a car, Uber has been a godsend. And that is why I am hoping that they stay in business — for a long time. Selfish, much? Of course! That is why I am perhaps harder on them than any other startup, poking holes in their strategies, ranting on Twitter and occasionally praising them for their awesomeness. Time and again, Uber finds itself in the eye of the (public relations) storm, and to me the reasons are pretty obvious.
If you look at the relatively young history of Uber, you would see that it is a company that has done many things right and a couple of things very wrong. It has figured out how to remove friction between a traveler and transportation by reducing it to mere minutes. In doing so, Uber has also become the first next-generation commerce company to use connectedness to its advantage. It has also figured out how to organize what is a disorganized and poorly managed business: local transportation.
Uber’s secret weapon is the data it is collecting, but what it has failed to do — use that data in the most powerful kinds of ways. Data tells the service that there are fewer cars on the road and massive demand, and out comes surge pricing. This makes perfect business sense — except when it is in New York City battered by Hurricane Sandy. Ooops! The company failed to factor in the “emotion” and “humanness” in its data.
A better approach would have been to give people discounts based on the time they had to wait, and make up the difference to the drivers (or even give them bonuses for working around the clock). Yes, it would have been costly, but it would have cemented the “Uber cares” sentiment.
The whale theory
Uber (and I don’t mean to pick on them, because it applies to all companies) should be thinking about using data to create positive experiences. A good way to start is to take a cue from the casinos (or Zynga): make the “whales” happy.
If someone is a big spender on their service, then they should get to the front of the line and get an Uber car before everyone else. Sure, they can create loyalty discounts, but time saved (and a clean experience) is what’s more important to an Uber customer. (This shoe store in Manhattan knows how to use data to make customers happy, and perhaps others should take a hint from them.)
Uber should give the whales an experience that puts a higher premium on their time than that of occasional users. Match up these whales with the best cars and the top-ranked drivers so they will keep spending. In other words, use data to shape experiences.
Some might say that this is yet another example of data darwinism at work — and in one sense it is — but big spenders get big perks from casinos and hotels (and some airlines). Uber wouldn’t be doing anything wrong if it followed suit, as I don’t think of them as an essential service. I am and will remain wary of the idea of data darwinism creeping into essential services.
Using data to shape experiences has to become the default for all startups, regardless of whether their focus is on consumers or large companies. Almost every day I come across an app that has an astonishingly beautiful interface, only to find it incredibly vapid and unintelligent. A lovely interface comes alive when married to data and the insights and context it brings.
Coffee and empathy
What will it take to build emotive-and-empathic data experiences? Less data science and more data art — which, in other words, means that data wranglers have to develop correlations between data much like the human brain finds context. It is actually not about building the fanciest machine, but instead about the ability to ask the human questions. It is not about just being data informed, but being data aware and data intelligent. Sean Gourley, co-founder of Quid, in his keynote speech at Structure: Data, noted:
Data scientists are presented with a set of parameters to optimize over, yet they don’t take the time to step back and say, should I even be optimizing this at all. Data science, I believe, we need to re-imagine it, because data is incredibly powerful. We need to step back from the scientific notations and start thinking of it as data intelligence. Data intelligence has a slightly different philosophy that embraces some of the messy and unstructured nature of the world that we do live in.
Let me explain by using my favorite coffee shop as an example. The raw number of check-ins at the coffee shop tells me that I am boring and predictable. Anyone who doesn’t know me well will draw the same inference. But parse the data in more granular manner and correlate it with other information and you start to see a picture that understands the emotional value of that data.
Let’s run with this example. The service needs to realize that I visit said coffee shop first thing in the morning (something that can be inferred from the time of the check-in.) It also needs to learn that the coffee shop is a few miles from my home, which tells you that I go out of my way to go there.
The service also knows that I check-in with a handful of people, whose relationship to me can be inferred from the social graph. Add to the mix the fact that I have left tips and taken photos at that spot. Now, compare it to all other coffee shops I have checked into and how they rank against this one location. Add all of them up, and you end up with a rudimentary conclusion: I don’t go to this coffee shop simply because it’s an interchangeable part of my daily routine, or because it’s on my way to work. I visit it because it is my happy place, my one cup (or dozen) of zen. And a company like Foursquare could use that fact to package even more compelling experiences for me.
Data needs stories
The key to getting the context is to think in terms of stories. Sean put it best when he said:
“Data needs stories, but stories also need data. Data, when its put up in front of you as a number, it gets stripped of the context of where the data came from, the biases inherent in it, and the assumptions of the models that created it.”
The symbiotic relationship between data and storytelling is going to be one of the more prevalent themes for the next the few years, starting perhaps inside some apps and in the news media. I was reminded of the future filled with data narratives when I saw this visualization — Out of Sight, Out of Mind, by Pitch Interactive. It takes data about drone attacks and makes them visual and easy to understand, and in doing so, elicits a strong reaction.
But it merely scratches the surface — presenting a slight improvement on an infographic that might have appeared in the pages of a magazine. In a future where we have tablets and phones, packed with sensors, the data-driven narratives could take on an entirely different and emotional hue.
As for Uber and Foursquare, they should start by thinking, how do we make our customers feel special?
Post and thumbnail image courtesy of Shutterstock / Chepko Danil Vitalevich