BTN's annual answer book for business travel managers.
PredictX CEO Keesup Choe talks:
Managed travel has been a laggard in the business
intelligence space. Pi has had the advantage of working in other sectors that
have complex data, industries like retail and healthcare. That has informed how
it supports business travel data analysis. The company has just changed its name to
PredictX. CEO Keesup Choe spoke with BTN editor-in-chief Elizabeth West about
the change, what it says about the direction of the company and how that
direction is applied to travel analytics.
BTN: What's in a
really an announcement to ourselves as much as the market of what our focus is,
where we see the technology going and how we're going to leverage that to help
our clients. If you think of how corporate travel managers use data today, even
the sophisticated ones who've already gone through some of this work, it is: Analyze
and fix. They're looking at what already happened; It takes 10 times more time
and effort to fix something than to prevent it in the first place. That's
really our core focus now: helping our users predict and prevent these problems.
BTN: Pi, now
PredictX, has worked with companies in healthcare, retail and other industries
with complex data. How are you bringing that experience to bear on travel?
seeing massive adoption of data analysis and application of machine learning
algorithms in other areas like retail and healthcare. [Managed travel] is in
the very beginning stages of inquiring about this and understanding it. Our
advantage is that we're active in these other sectors where things are
happening much faster. We're getting a lot of traction and interest from our
clients wanting to apply these techniques to manage their [travel] programs.
BTN: Give me
a couple of examples of techniques used in other sectors and how PredictX is
applying them to travel.
concept of clustering individuals is good example. When Facebook and Amazon
target you with promotions, they're not really targeting you as an individual.
If Facebook maintained individual profiles of 600 million users or a billion or
whatever they have, it would be very difficult. [Instead], they take the data
they have on all their actions and cluster you into a cohort of people who are
kind of like you. Then, when they target you with a message or promotion, you
think it's completely individualized for you. In fact, it's targeting a whole
bunch of people with similar characteristics. They've been doing this in retail
service for ages. We're rolling this application out fairly soon [for travel].
kind of data are you using to arrive at these clusters, and what do you expect
travel managers to do with them?
talking about calculations that most [travel managers] don't even look at like
average time zones crossed and average ranking of hotels they stayed at, all
sorts of things that wouldn't naturally be part of the data that a TMC might
give you. We add a whole bunch of additional information. Then we apply this
clustering algorithm to it and then we classify your travelers into three,
four, five, six, seven distinct buckets. That allows travel managers to
fine-tune travel policies that both save money and improve the experience for
those cohorts. It also allows them to craft messages: policy announcements, new
programs or vendors or other things that resonate with each group. [The
traveler will think], "Oh wow, my travel managers know me." That's
gotten a lot of interest.
Corporate travel managers have a big challenge. There's no group of professionals anywhere in any sector that's managing so much spend with so few resources."
there another example that is more budget- or supplier-management oriented?
Choe: Sure. It's
in the early days, but again in retail, especially with area managers of stores
and who are on the road, you can't send them complicated reports or dashboards.
Literally, we send them text messages: "Go to this store, look at the cash
balances," or, "Go to this store and review the pricing for these
travel, that might translate into a text message about checking an airline
agreement because the program is missing targets, or some other issue?
And some of that isn't really predictive; some of that comes out of our
workflow engine, but it's still really, really useful. If you combine that text
message workflow engine with pre-trip data, real-time pre-trip data, we can
again avoid so many problems down the line. For instance, some countries have a
maximum number of days you can stay. If you go over that, you pay a fine, plus,
it's just a real hassle with the paperwork. We can text message the travel
manager, and maybe the traveler, that a booked trip will likely cause them to
overstay the allocation. Another application here is with safety and security
and following up with people booking [high-risk places]. The text message might
say, "OK, what are you doing there, and how can we help you make sure your
itinerary is safe?" These are some of the things you could just do with workflow
and pre-trip data. We're doing that now anyway for clients, taking real-time
data directly from the GDS and giving our clients that real-time visibility.
You combine that with predictive analytics and it could be incredibly powerful.
science and analytics isn't the No. 1 skill set of most travel managers. Can
you talk about the data demands being placed on them and the challenges
PredictX is trying to address?
travel managers have a big challenge. There's no group of professionals anywhere
in any sector that's managing so much spend with so few resources. The previous-generation
technologies were hard to use and [caused] a massive reliance on consultants to
get answers [for how to optimize travel programs]. The technology should be so intuitive and
suggest activities—sometimes workflow but even better when they can be
predictive and based on accurate forecasts. We don't want to say, "Here's
a complicated piece of software with a database and all sorts of stuff, and you
need to go write a query to do this, this and this." Nobody should have to
do that in this day and age.
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