Wednesday, April 19, 2017

Here's Why Airlines Treat Customers Poorly

Last week’s passenger-dragging incident at United Airlines left many marketers (and other humans) aghast that any company could purposely assault its own customer. As it happens, airline technology vendor Sabre published a survey of airline executives just before the event. It confirms what you probably suspected: airline managers think differently from other business people.  And not in a good way.

The chief finding of the study is that the executives rated technology as by far their largest obstacle to improving customer experience. This is very unusual: as I wrote in a recent post, most surveys place organizational and measurement issues at the top of the list, with technology much less of an issue. By contrast, the airline executives in the survey– who were about 1/3 from operations, 1/3 from marketing, sales, and service, and 1/3 from other areas including IT and finance – placed human resources in the middle and organizational structure, consensus, and lack of vision at the bottom.  The chart below compares the two sets of answers, matching categories as best I can.

It would be a cheap shot to point out that the low weight given to “lack of vision” actually illustrates airline managers’ lack of vision. Then again, like everyone else who flies, I’ve been on the receiving end of many cheap shots from the airlines. So I’ll say it anyway.

But I’ll also argue that the answers reflect a more objective reality: airlines are immensely complicated machines whose managers are inevitably dominated by operational challenges. This is not an excuse for treating customers poorly but it does explain how easily airline leaders can focus on other concerns. Indeed, when the survey explicitly asked about priorities, 51% rated improving operations as the top priority, compared with just 39% for aligning operations, marketing and IT, and only 35% for building customer loyalty.

There’s a brutal utilitarian logic in this: after all, planes that don’t run on time inconvenience everyone. The study quotes Muhammad Ali Albakri, a former executive vice president at Saudi Arabian Airlines, as saying, “Two aspects generally take precedence when we recover irregular operations [such as bad weather]: namely crew schedules and legality and aircraft serviceability. Passengers’ conveniences and connecting passengers are also taken into consideration, depending on the situation.” In context , it’s clear that by “situation” he means whether the affected passengers are high-revenue customers.

But as you may remember from that college philosophy course, most people reject pure utilitarianism because it ignores the worth of humans as individuals. Even if you believe businesses have no ethical obligations beyond seeking maximum profit, it’s bad practice to be perceived as heartless beasts because customers won’t want to do business with you. So airlines do need to make customer dignity a priority, even at the occasional cost of operational efficiency. Otherwise, as the United incident so clearly illustrates, the brand (and stock price) will suffer.

If you’re a truly world-class cynic, you might argue that airlines are an oligopoly, so customers will fly them regardless of treatment. But it’s interesting to note that the Sabre paper makes several references to government regulations that penalize airlines for late arrivals and long tarmac waits. These factors clearly influence airline behavior. There's even a (pitifully slim) chance that Congress will respond to United's behavior. So the balance between operational efficiency and customer experience isn’t fixed. Airlines will react to political pressures, social media, and even passenger behaviors. The fierce loyalty of customers to airlines that have prioritized customer experience, such as Southwest and Virgin America, should be lesson to the others about what’s possible. That those airlines have had very strong leaders who focused on creating customer-centric cultures highlights the critical importance of “vision” in producing these results.

In short, the operational challenges of the airline industry are extreme but they’re not an excuse for treating customers poorly. Visionary leaders have shown airlines can do better. Non-visionary leaders will follow only when consumers demand better service and citizens demand governments protect them.

Thursday, April 13, 2017

Monetate Adds Machine-Learning Based Real Time Ecommerce Personalization

Monetate is one of the oldest and largest Web testing and personalization vendors, founded in 2008 and now serving more than 350 brands. Its core clients have been mid-to-large ecommerce companies, originally in the U.S. and now also in Europe. I’ve been meaning to write about them for some time but when we finally connected late last year they had a major launch coming this April, so it made sense to hold off a little longer.

That day has come. Monetate last week announced its latest enhancement, a machine-learning-powered “intelligent personalization engine” that supplements its older, rules-based approach. Machine learning by itself isn’t very exciting today: pretty much everybody seems to have it in some form. What makes the launch so important for Monetate is they had to rebuild their system to support the kind of machine learning they’re doing, which is real-time learning that reacts to each visitor’s behaviors as they happen,

Montetate now holds its data in a “key-value store” (meaning, instead of placing data into predefined tables and fields, it stores each piece of information with one or more identifiers that specify its nature). This is a “big data” approach that lets the system add new types of information without creating a new table or field. In practical terms, it means Monetate can give each client a unique data structure, can rapidly add new data types and individual pieces of data, and can maintain a complete, up-to-the-moment profile for each customer. These are all essential for real-time machine learning. (Of course, the system still has some standard events shared by all clients, such as orders and customer service calls. These are needed to allow standard system functions.)

Important as these changes are, the basic operation of Monetate is still the same. First, it builds a database of customer information. Then, it draws on that database to help test and personalize customer experiences.

The database is built using Monetate’s own Javascript tags to capture behavior on the client’s ecommerce site. Users can also add other first- and third-party data through file uploads, by monitoring real-time data streams, or by querying external sources on demand. Monetate stitches together customer identities across sources and devices to create a complete profile. It can also build a product catalog either by scraping product information directly from the Web site or by importing batch files. Customer browsing and purchase behavior are matched against this catalog.

Testing and personalization rely on Monetate’s ability to modify each visitor’s Web experience without changing the underlying Web site. It achieves this magic through the previously-mentioned Javascript tag, which can superimpose Monetate-created components such as hero images, product blocks, and sign-up forms. Users manage this process by creating campaigns, each of which contains a user-specified target audience, actions to take, schedule, and metrics. Users can designate one metric as the campaign goal; this is what the system will target in testing and optimization. They can track additional metrics for reporting purposes.

The campaign audience can be based on Monetate’s 150 standard segments or draw on Web site behaviors, visitor demographics, local weather, imported lists, customer value, or other information derived from the database. Actions can virtually insert new objects on a Web page, or hide or edit existing objects. Users can build content with Monetate’s own tools or import content created in other systems. The content itself is dynamic so it can be personalized for each visitor. Actions can be reused across campaigns and campaigns can contain rules to select different actions in different situations. The new intelligent personalization engine automatically picks the best available content for each customer, drawing on both individual and group behaviors. Users can also embed split or multivariate tests within a campaign. The system will reallocate traffic to better-performing options while the test is running and switch all traffic to the winner when enough information is available.

In other words, this is a very powerful system.  The user interface is also remarkably, well, usable: some training is certainly required but no deep technical skills are needed.

Monetate’s intelligent personalization is currently limited selecting content for Web interactions. The company plans to add product recommendations later this year (finding the best product among thousands is a different challenge from finding the best content among dozens or hundreds). It will add support for other channels next year.

Pricing for Monetate has also changed with the new product. It was previously based on page views but is now based on unique visitors and number of channels. This reflects a desire to stress customer value over individual decisions. Fees start around $100,000 per year for a small to mid-size company.