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5 Shapes of Human-Centered Travel Analytics

April 22, 2019

Cirium’s own Steve Lappenbusch weighs in on how to make data analytics focused on the end users. In travel and […]

Cirium’s own Steve Lappenbusch weighs in on how to make data analytics focused on the end users.

In travel and aviation, the data analytics gold rush gains more velocity and complexity daily. Companies gather data in ever increasing piles trying to find differentiation, new products, or even new markets. Often lost is the reality that a human being needs to solve a problem after someone else solves all that data math. You must simplify exploding complexity for problems you never faced before without dumbing down the hard-won insights. You must make your analytics human-centered.

Spend five minutes reading this post and by the end you will have a simple way to ensure that no matter how complex your analytics task, you will never lose sight of your users.

Every analytics problem, especially in travel, can be broken into five basic dimensions, or shapes:

  1. Resolution
  2. Linking
  3. Proportion
  4. Probability
  5. Time

To start, let me give you two actual examples describing how I used these shapes to create human-centered analytics.

Consumer Identity Analytics

While the travel and aviation industries are just beginning to analyze specific data on individual consumers, other regulated industries, like tax and finance, have been doing so for decades.  Prior to focusing on travel and aviation analytics, I built an analytic product that prevented identity fraud across hundreds of millions of consumers using petabytes of data. Customers prevented over $600m in fraud. It was not easy, because human data is messy, changes rapidly, and is full of falsehoods.

To focus on users, we articulated the shapes of the problem. Specifically, we determined the problem had three of the five shapes layered together:

Consumer Identity Analytics Shapes

  • Resolution – understanding who the input was as a distinct entity amidst all the data and getting all his or her data in one correct entity
  • Linking – connecting that person to other resolved people who (suspiciously, in this case) had data in common
  • Probability – the likelihood of shared identity data being malicious based on proprietary historical ID fraud patterns

The shapes together help illustrate what analytics needed to be built and how the results needed to be exported for users. We resolved data to people, showed which other people shared their ID data, and showed a simple score indicating if it was suspicious.

I could just as easily use this proven shape-based approach to prevent travel fraud, discern consumers who travel together, or discover new travel consumer segments to target for new products.

QSI – Aviation Market Performance

Quality of Service Index (QSI) is a decades-old mathematical approach to understand an airline’s relative market performance. Understanding nuance and complexity is key to effective QSI – but that complexity can’t overwhelm users. At Cirium, our QSI is the global standard because we provide neutral, third-party assessment with unmatched data to simplify the complexity without dumbing down the solution.

While most airlines use proprietary in-house QSI, Travel Management Companies (TMCs) and other travel businesses now want to compare QSI to negotiate more effectively. For airlines, our global QSI will immediately make sense as a public-facing option. Other sectors, such as TMCs, need help learning these powerful analytics. To accomplish this, we broke down QSI and found it fits three of the five human-centered analytics shapes:

QSI Analytics Shapes

  • Linking – Connecting and continually updating all the flights worldwide
  • Time – Ensuring linked flights are chronologically correct to generate an accurate global airline schedule stretched backwards and forwards on the calendar
  • Proportion – Analyzing those schedules to show the fair share of a market and the share gap relative to competitors, and to allow customers to configure these proportions specific to their needs

We precisely linked over 120 million flight connections to create an unmatched worldwide schedule from over 930 airlines. Then we expertly generated a global QSI standard showing proportional market share for any origin/departure pair. We boiled down the complexity to basic shapes humans can easily understand. As a result, Cirium QSI is human-centered, not data-centered, for easy negotiation and collaboration across disparate companies like airlines and TMCs.

A Daunting Future Made Simpler

Travel and aviation are barreling head first into analyzing more data in more ways and more rapidly than ever before. Yet, making massive consumer data analytics human-centered is not the core capability of airlines, travel companies, or corporate travel departments. Without both deep travel expertise and veteran human-centered analytics experience, projects will easily fall into the age-old trap of being all about the data or the math instead of the travel users. Cirium has decades of human-centered analytics and travel expertise, unsurpassed and linked global travel data, the ability to fuse customer data to ours, and the simple human-centered framework to make sure our solutions make sense as we innovate with customers.

If you have a travel analytics problem you want help with solving, or are just curious about how the Five Human Centered Analytics Shapes can help you, please contact us. I’d love to work with you.

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