Poor models are killing the travel loyalty star, says Booz Allen Hamilton

Airline, hotel, and rental car brands used to be the leaders among consumer industries when it came to using technology to understand their best customers.

Think of the success of loyalty programs, for instance. American Airlines’ pioneered the concept with its frequent flier program. Loyalty is now a major profit center for all types of travel companies.

Yet in recent years, travel companies have fallen behind retailers when it comes to leveraging customer relationship management data. The culprit is out-dated analytical models.

That’s the opinion of Alex Cosmas, chief scientist responsible for transportation and hospitality at Booz Allen Hamilton, a US-based consultancy.

Booz Allen Hamilton has done two case studies that analyze the “breadcrumbs” that travelers drop in the course of their digital shopping.

The studies are intended to drum up business, of course. Travel brands hire the consultancy to create custom algorithms for their live systems.

Yet even if you’re not a potential client, his viewpoint may intrigue you. Cosmas’s sales pitch casts light on some of the strategic mistakes most travel brands are making around Big Data.

He challenges travel executives to ponder whether their brand is asking the right questions about its most valuable customers.

Loyalty is an underachiever

Loyalty programs once had such potential, such promise. But they have become stagnant, failing to engage the highest value customers.

Right now loyalty is just a revenue model. The more you spend with a company, the more rewards you receive. This model drives additional revenue, creates profit centers, and limits market share erosion.

But loyalty programs could deliver much more if companies better understood which factors drive repeat business and market share gains. Says Cosmas:

“Loyalty, at the end of the day, is supposed to drive an incremental willingness to make a decision that may not be considered rational in a strict cost-benefit analysis.

The key question is this: When is someone being loyal rather than booking out of convenience or for the lowest price? As a travel company, I want to reward loyalty but not the other reasons people book.

The current analytics models being used by airlines, hotel chains, and rental car companies generally can’t determine when, say, a traveler books in a particular instance.

But newer models are out there. These models can help determine what that traveler was driven by: convenience, price, stickiness, or some true affinity for the brand.

The most provocative thing we found in our case studies is that the highest travel spenders tend to be more promiscuous than the lowest spenders.

To talk in broad terms, high-spenders tend to be high-spenders de facto. They spend the same or sometimes even more on your competitors. Is that a behavior you want your program to reward equivalently?

Better to try to entice someone who may spend 50% per transaction as your highest spending customer but for whom you have 100% of wallet share. That’s the one you want to target.”

Imagine there’s a traveler named Alice, who rents a minivan from Avis. The company checks its records and sees that Alice doesn’t rent with it often, and usually only rents minivans.

Avis could then compare Alice’s booking history against its database of competitor availability. It could discover Alice is only booking Avis when Hertz is sold out of minivans. What should Avis do?

Loyalty programs present the intersection of these massive beautiful gorgeous data sets. The opportunity is to get more granular.

Booz Allen Hamilton

Following a trail of breadcrumbs

Cosmas argues that travel companies need to adopt next-generation analytics models that are capable of analyzing and learning from why an individual booking, not just a group of bookings, took place.

“Retailers have embraced information theory. But travel companies haven’t. This is not the Statistics 101 that many travel executives still have in their heads from college. It’s more the computer scientists’ domain.

In traditional statistics, you’re designing models by weighting things to generate a statistically significant sample. But information theory turns it on its head by saying every incremental real-life data can improve personalization.

Every single time a customer books a stay, each of those instances, should fit or reinforce my model of that customer. But travel systems aren’t collecting and analyzing that data yet.

None of the major airlines in the US is building customer-level networks or schedules. Everyone is still very much analyzing aggregated historical data. They’re looking at distributions of things rather than individual decisions.”

Be less ambitious

Cosmas says one of the biggest pitfalls he continuously sees when data science tries to make inroads into hospitality and airlines is that the companies are too ambitious.

Some travel companies have spent millions trying to get up to speed. But their efforts flop.

A more effective alternative would be for companies to launch a handful of quick and cheap — think: three-month — projects that tackle bite-sized problems first.

The peril is that executives may set lofty goals, taking on major overhauls of their data sets, technology stacks, and internal decision-making processes. This can require tens of millions of dollars of investment upfront, and more than a year may pass before seeing a return to the bottom line.

But companies may succeed more efficiently if they adapt the lean startup ethos of Silicon Valley. Says Cosmas:

“Look for quick wins. Try small pilot tests that can result in incrementally more sustainable and operationalizable gains. Use the Agile development model: try for a 12-week proof of concept, at the end of which you decide if you’ll invest another 12 weeks and additional resources.

A comedy of vendors

Simplicity sounds obvious. So why doesn’t it happen? There are some incentives in place that tend to distort decision-making.

“Everyone is full of good ideas about how Big Data can change the world. And some of the most provocative ideas come from strategists who have never done it, they’ve never scripted or coded or had to deal with a messy, sparse data set from a travel company.

I’d hesitate to trust or necessarily make an investment of millions based on their ideas. The devil is in the details with big data for airlines, hotel chains, rental car companies or cruise lines.

It’s a little cat-and-mouse. It takes a C-suite initiative to revolutionize a company’s approach to data analytics. But the c-suite tends to talk to the idea generators and evangelists and not the data science community, writ large.”

If Cosmas is right, the travel industry is moving into era where it can squeeze out of the data how much decision was made by true passion and not necessarily out of convenience. That insight could have a mammoth effect on how business is done.

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Sean O'Neill

About the Writer :: Sean O'Neill

Sean O’Neill had roles as a reporter and editor-in-chief at Tnooz between July 2012 and January 2017.



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  1. macy marvel

    What is needed is more immediate rewards in travel & hospitality. In any case, we can’t get away from the fact that most hotel chains and airlines are chasing a limited number of frequent business travellers who will typically be the member of multiple programmes and will book according to convenience at any given time.

  2. RobertKCole

    I wholeheartedly agree with the sentiment, but with two key caveats.

    Particularly for hotels, there is a huge issue with the quality of the data. Success of even the most rudimentary algorithms is primarily dependent on the reliability of the underlying data. Given the minivan example, what if Alice regularly rented midsize cars for business, but those rentals were not identified and matched to Alice?

    True, that is less likely to happen with car rentals, where a drivers license number offers a huge advantage for matching rental records. However, in hotels, where most OTA-originated bookings are not eligible for frequent guest benefits, the guest’s membership number is not included int he booking, and is rarely requested upon arrival. Suddenly, hotels can make poor decisions based on only corporate bookings and be oblivious to the traveler’s leisure bookings (and getting back to the key share of wallet issue, the fact that they have dramatically different leisure travel personas.)

    The second point is that the major travel intermediaries (OTAs, meta-search & marketplaces) are putting significant focus on Big Data, user experience and predictive analytics. They have not gotten answers to all of the burning questions involving demand aggregation and conversion, but they are learnign a lot from asking the right questions.

    This is the key issue for travel brands moving forward – do travelers ultimately become more loyal to the travel suppliers or the travel sellers? The answer probably is that it depends on the individual traveler, which is where Alex is dead-on. And why the travel suppliers, in particular, need to get their underlying data in order before putting marketing dollars at risk based on bad assumptions.

    As one OTA CIO told me “Good data beats good analytics every day of the week.”

    • Sean O'Neill

      Sean O'Neill

      Thanks, Robert!

    • Ray Mason

      Great topic, and as always, good to read your thoughts, Robert. As already mentioned, loyalty programmes have traditionally revolved around spending money in return for collecting points; thus the points became the currency that was valued by the customer, which ironically, often did not need to be redeemed (spent) with the original supplier.

      But loyalty does not have to be all about swiping membership cards and accruing points, particularly in the hotel sector. Recognising a guest as an individual, with personal interests and preferences can be even more powerful in building a feeling of loyalty and encouraging them to make subsequent bookings direct, rather than through a third party or OTA. Within their property management system, a hotel has access to far more information about the guest than an OTA is able to collect at the time of booking, such as details about what hotel services were used (restaurant, spa, etc.), and full postal address. A hotel can also see ALL past and future bookings, no matter what the source of booking was.

      This level of understanding who your customer is enables the hotel to implement a valuable guest engagement programme, sending personalised and relevant offers by email or SMS. These offers can even take into account whether they have bookings pending (to avoid possible dilution of revenue, for example), or where they live (why do I keep getting invitations to enjoy an afternoon tea at a hotel that is over 100 miles away?). The technology to combine PMS data with existing guest databases and utilise it in this way is available today and already being used by several independent hotels that I know of, with impressive results.
      So in the independent hotel sector in particular, I think we will increasingly see loyalty programmes that are built upon the principle of great guest engagement. When this happens, I believe that it will be the guest experience (rather than points), that becomes the currency that is valued by the guest, which in turn supports the hotel’s brand and encourages direct bookings.


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