Why Data Predicting Trumps Data Reporting

Predict What Matters Quick – name a travel metric that your CFO would pay a lot of money to predict reasonably well.

Next year’s travel savings? Maybe. Next year’s travel spend? Maybe.  But he won’t pick your firm’s future travel policy compliance rate, or next year’s average airfare, or your traveler’s future satisfaction with your online booking tool.

My point is that while firms spend a lot of money on travel data reporting, the core value that those pretty dashboards deliver is not very high – not in the grand scheme of your firm’s business.

Here’s why: Data reports are the result of the 20th-century management dictum “Measure what matters”.

Within the boundaries of a travel program there are dozens of things that matter.  And so we’ve figured out how to measure them. In order to report them.  In order to manage them.

But all those dials and gauges and stop lights really do is simply give you signals.  Signals that you have to interpret to keep your travel program  between the white lines of your side of the road.

What those travel dashboards don’t do is tell you how to get to a better travel program.

They can’t, because they have two big flaws:

  • Data reports are stuck in the past
  • Travel data reports are stuck in the world of travel

Gas Gauge vs. GPSSo that’s the data-driven solution to getting better travel programs – deal with the future, and deal with data well outside of travel.

Enter Predictive Analytics

The key is linking travel’s impact to business outcomes.  Outcomes that matter on a much bigger scale, like sales, customer satisfaction, employee attrition, health/safety costs, etc.

This will fundamentally change the way you view and manage a travel program.

Instead of seeking to minimize travel costs, you’ll be trying to maximize sales, or perhaps minimize employee turnover – by putting a travel program in place that clearly contributes to those goals.

If sales are trending down, or employee turnover is trending up, what should you, the travel manager do to help fix these problems?

Obviously, you’ll need some new lights on your dashboard – lights driven by data from Sales and HR.  More importantly, you’ll need to know how to impact those non-travel metrics.

That’s where predictive analytics comes in.  You need to have a data-driven understanding of how things like cabin policy and hotel tiers impact bigger, non-travel metrics like employee productivity, health and safety, and attrition.

You need to predict with confidence that by changing a variable in the travel policy, it will cost $X and improve the non-travel metric by Y%.

You’ll do this in one of two ways.  If your travel program is big enough, you’ll be able to mine your own data and build these models.  If your program is too small to offer enough data, you’ll depend on benchmarks and case studies from the larger firms.

Data Reporting vs. Predictive AnalyticsEither way, you’ll find yourself importing non-travel data into your travel dashboards, and exporting pro-active, fact-based advice on how to drive to your firm’s bigger goals.

Management theories evolve.  Dictums change.  It’s time to move on from measuring what matters to predicting what matters.

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3 thoughts on “Why Data Predicting Trumps Data Reporting

  1. Very interesting article. I once sat down with the head of Inside Edge who provided metrics and reports to many World Series champs. Championship teams did not pay for historic analytics, but based on trends and patterns wanted to predict what was coming next. Did the catcher usually repeat his calls the second time a batter came through a lineup so if he started him with fastballs high and away, how often would he repeat the location, etc. Another interesting conversation I had was with the Packers head of analytics. They broke things down into overt and covert tenancies. Overt is tenancies that the opposing team would be aware of while covert would be things that they likely wouldn’t alter (when a QB goes into a 2 minute drill and calls the plays, what are his crutches, etc).

    For travel, I would offer up that it isn’t so much “what-if I move a percent here” as predictive (though still valuable). Instead I would say that predictive has more to do with recentness and consistency. For example, if a hotel has 1000 nights for a year but 900 of them came within a 2 week span, that was a meeting – there was no consistency to that property and they likely shouldn’t be solicited for inclusion in a transient program. Also, which destinations receive consistent volumes? At what rate do your travelers support program carriers when it isn’t natural to take them (i.e. taking Delta exit Chicago to Atlanta as opposed to hub based UA or AA)? That could help determine likelihood to make changes in your program. What has been the uptick in volume for routes or destinations the past quarter? Where is your Mergers and Acquisitions accounts traveling? Where did spikes happen last quarter that weren’t consistent with either the quarter before or the same quarter last year?

    Thanks so much for opening this discussion up. Would love to hear more!

  2. Pingback: Why TMCs Need a Dramatically Different Sales Approach | Gillespie's Guide to Travel+Procurement

  3. Pingback: Why TMCs Need a Dramatically Different Sales Approach | Resource Center

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