Big Data Lessons from Orbitz

One of the week’s interesting technology stories was On Orbitz, Mac Users Steered to Pricier Hotels, an article by Dana Mattioli in The Wall Street Journal, about how online travel company Orbitz used data mining to discover significant spending differences between their Mac and PC customers (who were identified by the operating system of the computer used to book reservations).

Orbitz discovered that Mac users are 40% more likely to book a four- or five-star hotel, and tend to stay in more expensive rooms, spending on average $20 to $30 more a night on hotels.  Based on this discovery, Orbitz has been experimenting with showing different hotel offers to Mac and PC visitors, ranking the more expensive hotels on the first page of search results for Mac users.

This Orbitz story is interesting because I think it provides two important lessons about big data for businesses of all sizes.

The first lesson is, as Mattioli reported, “the sort of targeting undertaken by Orbitz is likely to become more commonplace as online retailers scramble to identify new ways in which people’s browsing data can be used to boost online sales.  Orbitz lost $37 million in 2011 and its stock has fallen by more than 74% since its 2007 IPO.  The effort underscores how retailers are becoming bigger users of so-called predictive analytics, crunching reams of data to guess the future shopping habits of customers.  The goal is to tailor offerings to people believed to have the highest lifetime value to the retailer.”

The second lesson is a good example of how word of mouth has become word of data.  Shortly after the article was published, Orbitz became a trending topic on Twitter — but not in a way that the company would have hoped.  A lot of negative sentiment was expressed by Mac users claiming that they would no longer use Orbitz since they charged Mac users more than PC users.

However, this commonly expressed misunderstanding was clarified by an Orbitz spokesperson in the article, who explained that Orbitz is not charging Mac users more money for the same hotels, but instead they are simply setting the default search rank to show Mac users the more expensive hotels first.  Mac users can always re-sort the results ascending by price in order to see the same less expensive hotels that would be displayed in the default search rank used for PC users.  Orbitz is attempting to offer a customized (albeit a generalized, not personalized) user experience, but some users see it as gaming the system against them.

This Orbitz story provides two lessons about the brave new business world brought to us by big data and data science, where more companies are using predictive analytics to discover business insights, and more customers are empowering themselves with data.

Business has always resembled a battlefield.  But nowadays, data is the weapon of choice for companies and customers alike, since, in our increasing data-constructed world, big data is no longer just for big companies, and everyone is a data geek now.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet.

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Comments

[Matteo]

Of course the big data are the fundamental point of the marketing of the future. To handle the increase in big data many giants of hi-tech already engaged in a cloud that brings the digital infrastructure, platforms, and services accessible through the internet. Amazon, Facebook, and Apple, for example, have launched in recent months initiatives to build new data centers in Europe.

Regarding the second lesson, the social media revolution has linked data and people, allowing people a two-way communication with enterprises with implications yet to be completely discovered.

[Steven Dupon]

The Orbitz example also clearly shows companies need to think about how they want to use their predictive analytics and how they will talk about it to their customers. More and more examples, like the Target one, pop up that show predictive analytics can be useful, but is used without empathy with the customers. It makes the shopping experience a bit creepy.

I don't think Orbitz is doing anything wrong with their modeling, but they should be much more open about the way they use it. What they do is the digital equivalent of proposing a higher-end product to a customer arriving in a luxury car than they would to a customer arriving in a budget family car.

[Dylan Jones]

Very interesting post Jim, thanks for sharing.

The fact is that companies have always done predictive marketing, they're just getting smarter at it. I remember living as a student in a fairly downtrodden area that because of post code analytics meant I was bombarded with letterbox mail advertising crisis loans to consolidate debts and so on. When I got my first job and moved to a new area all of a sudden I was getting loans to buy a bigger car. The companies were clearly analyzing my wealth based on post code lifestyle data.

Fast forward and companies can do way more as you say. Teresa Cottam (Global Telecoms Analyst) has cited the big telcos as a major driver in all this, they now consider themselves "data companies" so will start to offer more services to vendors to track our engagement across the entire communications infrastructure (read more here: http://bit.ly/xKkuX6).

I've just picked up a shiny new Mac this weekend after retiring my long suffering relationship with Windows so it will be interesting to see what ads I get served!