#Extract Real-Time Airfare Data

[iwebscrapingblogs]

Google Flight API - Easy Real-Time Data Extraction

In today's fast-paced travel industry, the ability to access real-time flight data is essential for businesses and travelers alike. Whether you’re a travel agent, an airline, a developer, or simply a tech enthusiast, Google Flight API provides a powerful tool to obtain real-time flight information. This blog will delve into how you can harness the Google Flight API for easy real-time data extraction, highlighting its benefits, applications, and a step-by-step guide to get you started.

What is Google Flight API?

Google Flight API is an interface that allows developers to access and manipulate flight data provided by Google Flights. This includes information about flight schedules, prices, availability, and more. The API enables seamless integration of this data into applications, websites, and other platforms, allowing for real-time updates and a more dynamic user experience.

Benefits of Using Google Flight API

Real-Time Data: The API provides up-to-the-minute information on flight schedules, delays, and cancellations. This is crucial for travel agencies and airlines to keep their customers informed and to make timely decisions.

Wide Coverage: Google Flights covers a vast number of airlines and routes worldwide. This extensive database ensures that users have access to comprehensive flight information.

Cost-Effective: Using the API can be more cost-effective than purchasing data from third-party vendors. It reduces the need for manual data collection and entry, saving time and resources.

Customizable: The API allows for customization to fit the specific needs of your application. You can filter results based on parameters such as price range, airlines, layovers, and more.

Enhanced User Experience: Integrating real-time flight data into your platform can significantly enhance the user experience. Users can search for flights, compare prices, and make bookings without leaving your site or app.

Applications of Google Flight API

Travel Booking Platforms: Websites and apps that facilitate flight bookings can use the API to display the latest flight options, prices, and availability to their users.

Travel Agencies: Agencies can automate the process of fetching flight data, providing clients with real-time information and recommendations.

Airlines: Airlines can use the API to update their own systems and customer interfaces with the latest flight schedules and status updates.

Travel Planning Tools: Apps that help users plan their trips can integrate the API to provide accurate flight information, helping users to make informed decisions.

Data Analysis: Researchers and analysts can use the API to gather flight data for various analytical purposes, such as studying travel trends, pricing strategies, and more.

Getting Started with Google Flight API

Accessing the API: Currently, Google Flight API is not publicly available as a standalone service. However, there are ways to access similar data through other means, such as third-party APIs or scraping Google Flights with web scraping tools. Alternatively, you can explore Google's QPX Express API, which provides flight search capabilities, though it has limited availability.

API Key: If you’re using an official API or a third-party service, you’ll need to obtain an API key. This key authenticates your requests and tracks your usage.

Setting Up Your Environment: Ensure you have a development environment set up with the necessary libraries and tools for making HTTP requests and handling JSON data. Popular choices include Python with libraries like requests and json, or JavaScript with axios and fetch.

Making Requests: Use the API documentation to understand the endpoints and parameters available. Construct your HTTP requests to fetch the data you need. For example, a typical request might look like this in Python:pythonCopy codeimport requests url = "https://api.example.com/flights" params = { "origin": "JFK", "destination": "LAX", "departure_date": "2024-07-20" } headers = { "Authorization": "Bearer YOUR_API_KEY" } response = requests.get(url, headers=headers, params=params) data = response.json() print(data)

Parsing the Data: Once you receive the response, parse the JSON data to extract the information you need. This could include flight numbers, departure and arrival times, prices, and more.

Integrating the Data: Integrate the parsed data into your application or website. This might involve updating the UI with the latest flight information, storing data in a database, or performing additional processing.

Conclusion

Google Flight API, or similar services, offers a robust solution for accessing real-time flight data. Whether you’re building a travel booking platform, enhancing an airline’s digital presence, or developing a travel planning tool, the ability to fetch and display accurate flight information is invaluable. By understanding the benefits, applications, and steps to get started, you can leverage this powerful tool to create more dynamic and user-friendly experiences in the travel industry.

[newstfionline]

How Online Shopping Makes Suckers of Us All

By Jerry Useem, The Atlantic, April 18, 2017

As Christmas approached in 2015, the price of pumpkin-pie spice went wild.

It didn’t soar, as an economics textbook might suggest. Nor did it crash. It just started vibrating between two quantum states. Amazon’s price for a one-ounce jar was either $4.49 or $8.99, depending on when you looked. Nearly a year later, as Thanksgiving 2016 approached, the price again began whipsawing between two different points, this time $3.36 and $4.69.

We live in the age of the variable airfare, the surge-priced ride, the pay-what-you-want Radiohead album, and other novel price developments. But what was this? Some weird computer glitch? More like a deliberate glitch, it seems. “It’s most likely a strategy to get more data and test the right price,” Guru Hariharan explained, after I had sketched the pattern on a whiteboard.

The right price--the one that will extract the most profit from consumers’ wallets--has become the fixation of a large and growing number of quantitative types, many of them economists who have left academia for Silicon Valley. It’s also the preoccupation of Boomerang Commerce, a five-year-old start-up founded by Hariharan, an Amazon alum. He says these sorts of price experiments have become a routine part of finding that right price--and refinding it, because the right price can change by the day or even by the hour. (Amazon says its price changes are not attempts to gather data on customers’ spending habits, but rather to give shoppers the lowest price out there.)

It may come as a surprise that, in buying a seasonal pie ingredient, you might be participating in a carefully designed social-science experiment. But this is what online comparison shopping hath wrought. Simply put: Our ability to know the price of anything, anytime, anywhere, has given us, the consumers, so much power that retailers--in a desperate effort to regain the upper hand, or at least avoid extinction--are now staring back through the screen. They are comparison shopping us.

They have ample means to do so: the immense data trail you leave behind whenever you place something in your online shopping cart or swipe your rewards card at a store register, top economists and data scientists capable of turning this information into useful price strategies, and what one tech economist calls “the ability to experiment on a scale that’s unparalleled in the history of economics.” In mid-March, Amazon alone had 59 listings for economists on its job site, and a website dedicated to recruiting them.

Not coincidentally, quaint pricing practices--an advertised discount off the “list price,” two for the price of one, or simply “everyday low prices”--are yielding to far more exotic strategies.

“I don’t think anyone could have predicted how sophisticated these algorithms have become,” says Robert Dolan, a marketing professor at Harvard. “I certainly didn’t.” The price of a can of soda in a vending machine can now vary with the temperature outside. The price of the headphones Google recommends may depend on how budget-conscious your web history shows you to be, one study found. For shoppers, that means price--not the one offered to you right now, but the one offered to you 20 minutes from now, or the one offered to me, or to your neighbor--may become an increasingly unknowable thing. “Many moons ago, there used to be one price for something,” Dolan notes. Now the simplest of questions--what’s the true price of pumpkin-pie spice?--is subject to a Heisenberg level of uncertainty.

Which raises a bigger question: Could the internet, whose transparency was supposed to empower consumers, be doing the opposite?

If the marketplace was a war between buyers and sellers, the 19th-century French sociologist Gabriel Tarde wrote, then price was a truce. And the practice of setting a fixed price for a good or a service--which took hold in the 1860s--meant, in effect, a cessation of the perpetual state of hostility known as haggling.

As in any truce, each party surrendered something in this bargain. Buyers were forced to accept, or not accept, the one price imposed by the price tag (an invention credited to the retail pioneer John Wanamaker). What retailers ceded--the ability to exploit customers’ varying willingness to pay--was arguably greater, as the extra money some people would have paid could no longer be captured as profit. But they made the bargain anyway, for a combination of moral and practical reasons.

The Quakers--including a New York merchant named Rowland H. Macy--had never believed in setting different prices for different people. Wanamaker, a Presbyterian operating in Quaker Philadelphia, opened his Grand Depot under the principle of “One price to all; no favoritism.” Other merchants saw the practical benefits of Macy’s and Wanamaker’s prix fixe policies. As they staffed up their new department stores, it was expensive to train hundreds of clerks in the art of haggling. Fixed prices offered a measure of predictability to bookkeeping, sped up the sales process, and made possible the proliferation of printed retail ads highlighting a given price for a given good.

Companies like General Motors found an up-front way of recovering some of the lost profit. In the 1920s, GM aligned its various car brands into a finely graduated price hierarchy: “Chevrolet for the hoi polloi,” Fortune magazine put it, “Pontiac … for the poor but proud, Oldsmobile for the comfortable but discreet, Buick for the striving, Cadillac for the rich.” The policy--”a car for every purse and purpose,” GM called it--was a means of customer sorting, but the customers did the sorting themselves. It kept the truce.

Customers, meanwhile, could recover some of their lost agency by clipping coupons--their chance to get a deal denied to casual shoppers. The new supermarket chains of the 1940s made coupons a staple of American life. What the big grocers knew--and what behavioral economists would later prove in detail--is that while consumers liked the assurance the truce afforded (that they would not be fleeced), they also retained the instinct to best their neighbors. They loved deals so much that, to make sense of their behavior, economists were forced to distinguish between two types of value: acquisition value (the perceived worth of a new car to the buyer) and transaction value (the feeling that one lost or won the negotiation at the dealership).

The idea that there was a legitimate “list price,” and that consumers would occasionally be offered a discount on this price--these were the terms of the truce. And the truce remained largely intact up to the turn of the present century. The reigning retail superpower, Walmart, enforced “everyday low prices” that did not shift around.

But in the 1990s, the internet began to erode the terms of the long peace. Savvy consumers could visit a Best Buy to eyeball merchandise they intended to buy elsewhere for a cheaper price, an exercise that became known as “showrooming.” In 1999, a Seattle-based digital bookseller called Amazon.com started expanding into a Grand Depot of its own.

The era of internet retailing had arrived, and with it, the resumption of hostilities.

In retrospect, retailers were slow to mobilize. Even as other corporate functions--logistics, sales-force management--were being given the “moneyball” treatment in the early 2000s with powerful predictive software (and even as airlines had fully weaponized airfares), retail pricing remained more art than science. In part, this was a function of internal company hierarchy. Prices were traditionally the purview of the second-most-powerful figure in a retail organization: the head merchant, whose intuitive knack for knowing what to sell, and for how much, was the source of a deep-seated mythos that she was not keen to dispel.

Two developments, though, loosened the head merchant’s hold.

The first was the arrival of data. Thomas Nagle was teaching economics at the University of Chicago in the early 1980s when, he recalls, the university acquired the data from the grocery chain Jewel’s newly installed checkout scanners. “Everyone was thrilled,” says Nagle, now a senior adviser specializing in pricing at Deloitte. “We’d been relying on all these contrived surveys: ‘Given these options at these prices, what would you do?’ But the real world is not a controlled experiment.”

The Jewel data overturned a lot of what he’d been teaching. For instance, he’d professed that ending prices with .99 or .98, instead of just rounding up to the next dollar, did not boost sales. The practice was merely an artifact, the existing literature said, of an age when owners wanted to force cashiers to open the register to make change, in order to prevent them from pocketing the money from a sale. “It turned out,” Nagle recollects, “that ending prices in .99 wasn’t big for cars and other big-ticket items where you pay a lot of attention. But in the grocery store, the effect was huge!”

The effect, now known as “left-digit bias,” had not shown up in lab experiments, because participants, presented with a limited number of decisions, were able to approach every hypothetical purchase like a math problem. But of course in real life, Nagle admits, “if you did that, it would take you all day to go to the grocery store.” Disregarding the digits to the right side of the decimal point lets you get home and make dinner.

By the early 2000s, the amount of data collected on retailers’ internet servers had become so massive that it started exerting a gravitational pull. That’s what triggered the second development: the arrival, en masse, of the practitioners of the dismal science.

This was, in some ways, a curious stampede. For decades, academic economists had generally been as indifferent to corporations as corporations were to them. (Indeed, most of their models barely acknowledged the existence of corporations at all.)

But that began to change in 2001, when the Berkeley economist Hal Varian--highly regarded for the 1999 book Information Rules--ran into Eric Schmidt. Varian knew him but, he says, was unaware that Schmidt had become the CEO of a little company called Google. Varian agreed to spend a sabbatical year at Google, figuring he’d write a book about the start-up experience.

At the time, the few serious economists who worked in industry focused on macroeconomic issues like, say, how demand for consumer durables might change in the next year. Varian, however, was immediately invited to look at a Google project that (he recalls Schmidt telling him) “might make us a little money”: the auction system that became Google AdWords. Varian never left.

Others followed. “eBay was Disneyland,” says Steve Tadelis, a Berkeley economist who went to work there for a time in 2011 and is currently on leave at Amazon. “You know, pricing, people, behavior, reputation”--the things that have always set economists aglow--plus the chance “to experiment at a scale that’s unparalleled.”

At first, the newcomers were mostly mining existing data for insights. At eBay, for instance, Tadelis used a log of buyer clicks to estimate how much money one hour of bargain-hunting saved shoppers. (Roughly $15 was the answer.)

Then economists realized that they could go a step further and design experiments that produced data. Carefully controlled experiments not only attempted to divine the shape of a demand curve--which shows just how much of a product people will buy as you keep raising the price, allowing retailers to find the optimal, profit-maximizing figure. They tried to map how the curve changed hour to hour. (Online purchases peak during weekday office hours, so retailers are commonly advised to raise prices in the morning and lower them in the early evening.)

By the mid-2000s, some economists began wondering whether Big Data could discern every individual’s own personal demand curve--thereby turning the classroom hypothetical of “perfect price discrimination” (a price that’s calibrated precisely to the maximum that you will pay) into an actual possibility.

As this new world began to take shape, the initial consumer experience of online shopping--so simple! and such deals!--was losing some of its sheen.

It’s not that consumers hadn’t benefited from the lower prices available online. They had. But some of the deals weren’t nearly as good as they seemed to be. And for some people, glee began to give way to a vague suspicion that maybe they were getting ripped off. In 2007, a California man named Marc Ecenbarger thought he had scored when he found a patio set--list price $999--selling on Overstock.com for $449.99. He bought two, unpacked them, then discovered--courtesy of a price tag left on the packaging--that Walmart’s normal price for the set was $247. His fury was profound. He complained to Overstock, which offered to refund him the cost of the furniture.

But his experience was later used as evidence in a case brought by consumer-protection attorneys against Overstock for false advertising, along with internal emails in which an Overstock employee claimed it was commonly known that list prices were “egregiously overstated.”

In 2014, a California judge ordered Overstock to pay $6.8 million in civil penalties. (Overstock has appealed the decision.) The past year has seen a wave of similar lawsuits over phony list prices, reports Bonnie Patten, the executive director of TruthinAdvertising.org. In 2016, Amazon began to drop most mentions of “list price,” and in some cases added a new reference point: its own past price.

This could be seen as the final stage of decay of the old one-price system. What’s replacing it is something that most closely resembles high-frequency trading on Wall Street. Prices are never “set” to begin with in this new world. They can fluctuate hour to hour and even minute to minute--a phenomenon familiar to anyone who has put something in his Amazon cart and been alerted to price changes while it sat there. A website called camelcamelcamel.com even tracks Amazon prices for specific products and alerts consumers when a price drops below a preset threshold. The price history for any given item--Classic Twister, for example--looks almost exactly like a stock chart. And as with financial markets, flash glitches happen. In 2011, Peter A. Lawrence’s The Making of a Fly (paperback edition) was briefly available on Amazon for $23,698,655.93, thanks to an algorithmic price war between two third-party sellers that had run amok.

[terrancedkennedy]

What I Think About When Thinking About Mexican Rates

Ok stick with me gang--I'm sorry to go deep on Mexico again but I think it is worth a drill down. Those of us that pay attention to these things can extract value by doing this type of analysis independently rather than taking the sell-side analysts at their word, given their incentives to not stray too far from the herd. You can come up with some fascinating conclusions and outliers with a little elbow grease--my next step here will be to run this past a few EM watchers and see what I might be missing.

As I noted last week, Mexican local rates rose dramatically in reaction to a spike in CPI, one which was driven in part by the depreciation of the peso.

Today, rates have rallied well off the highs, but are still sticky at levels above late 2016 levels, both in absolute terms and relative to US rates. For its part, MXN has come all the way back thanks to a weak dollar, EM inflows and higher local rates.

My thesis is that these rates can continue to come down because Banxico can, and will, cut the overnight rate aggressively in 2018. I believe markets are underestimating the magnitude of a rate cut in the same way they underestimated the size of the hiking cycle.  

First is this chart. There is an output gap, and one that I don’t think is going to be soaked up by tepid US demand in the manufacturing sector (note the channel cramming in the auto industry), or domestic demand when interest rates are high and there is uncertanty around the 2018 election.

Similarly, we see that real interest rates are high--Banxico hiked aggressively to stabilize the peso during the EM selloff and Trump-mania.

A more “neutral” real rate, as much as I hate that term, is likely to be around that 2% level--that is still high by historical standards and stands as a relatively conservative estimate given the 5y5y real rate in the US is still well below 1%.

This chart also shows that breakevens have been falling consistently since the US election as “Trump-flation” trade unwinds. While Mex breakevens have been falling too, they are still at elevated levels. As I noted last week I think there is at least 25bps in value in nominal curve built into the breakeven rate (roughly from 3.75% to 3.5%).

What makes me so sure that breakevens aren’t permanently higher given headline is currently above 6%? Among other things, wages haven’t shown any sign of secondary impact from that inflation shock--in fact, quite the opposite. This aruges that businesses are recognizing this spike in inflation as transitory and underlying inflation probably isn’t too far from Banxico’s 3% target.

Next is a similar chart from Banxico for inflation. They expect inflation to top out this quarter and come down relatively fast until it reaches the 3% inflation target in late 2018.

I agree with their path here, in fact I think it could come in on the low side. Let’s look under the hood of the Mexico CPI figures.

A big portion of the increase in inflation came from a hike in gas prices back in January. This was big enough to bring people in the streets in protest. The chart below shows the increase in gas prices alone--not counting second-round effects--has driven headline CPI roughly 1% higher (this is the “incidence”, the increase in the price times the weight in the index). Similar hikes in LP, natural gas, and electricity have had an effect too. Note that all four are near cyclical highs.

Another indirect impact from higher electricity prices is built into the producer price index: industrial, commercial and residential prices. Industrial prices have skyrocketed as the government passes along higher fuel prices and pressures heavy users to increase conservation measures. Just as importantly, commerical electric rates were up over 7%. More on that later.

Three big stories here:

After a history of gas subsidies, gas prices will be set by the market throughout the country by the end of the year, and international companies are now free to open service stations to compete with state-owned Pemex. There are some new independent stations that are already selling gas cheaper than Pemex stations, and several international service chains have big plans to expand. This effect combined with lower oil prices could result in 5-10% lower gas prices nationwide.  

The electric industry is in the process of deregulation. Outside capital is coming into the market, and the industry in is in the process of switching from dirty, expensive oil, to cheap, clean natural gas. The recent spike in electricity prices is likely to swing negative in the near future.

Natural gas is also undergoing a transformation. Thousands of miles of pipelines are going live this year and next to import cheap US natural gas. Again, the price increases here are unlikely to be structural.  

Government controled tarriffs are another big element of CPI. As the name implies, these are prices that are controled by the federal government. One of the big ones here is hikes in public transportation prices. This is a total one-off, and as the historical chart implies, is unlikely to be repeated.

Moving on to “core”--Merchandise inflation has contributed over 2% to headline inflation. I see this as a mean-reversion--the still slow economy and MXN coming back to mid-2016 levels will cause this component of CPI to head back towards the historical mean, if not below.

Similarly, the contribution of services inflation to headline CPI is also near historical highs. This is particularly unsual given the low wage hikes over the last year. No big outliers here but given the strength in MXN I think this is another sector that can mean revert, with downside in areas like education, restaurants, airfares, and tourist packages.

Putting it all together--I’ve noted below what I think are conservative estimates for how much inflation can revert from each sector.

Some of this analysis I owe to a paper written by Federico Kochen and Daniel Samano for Banxico. They estimate between 43bps and 73bps in “pass through” inflation from changes in the usd/mxn rate. My estimates for a reversion in CPI that “unwinds” the impact from the peso deval of 2016 and early 2017 is broadly consistent with their figures.

They also had data to show there was a 7.2bp “pass through” to headline CPI from a 1% change in the producers’ electricity price index. Given the 7.3% YoY increase in commercial electricity prices,  I’ve attributed a 50bp decrease in the headline CPI rate should electricity prices remain flat over the next twelve months--again, given the impact of reforms and lower natgas prices, I think this is a convervative estimate.

There you have it--what I believe is a plausible forecast that would get inflation back to 3% by mid-to-late 2018, and largely due to structural factors, while one-offs are unwinding. I would allow that the 50bps in electricity PPI might double count some of the factors I account for individually, but I estimated conservatively to allow for that.

What would 3% CPI in 2018 mean for rates? First breakevens would fall by at least 25bps. More importantly, I think Banxico would cut aggressively, consistent with the charts showing a stronger MXN, high current ex-ante real rate and negative output gap. If we were to assume a 2% real rate combined with 3-3.5% medium term inflation, it would not be out of the question to see the overnight rate cut to 5.5% from the current level of 6.75% (or 7%, if Banxico pulls the trigger on a 25bp hike tomorrow). With the entire TIIE curve gravitating around the 7% level, we might see another 50bp move lower if the market buys into the easing cycle.

Certainly the Fed and the presidential election are risks. The election is particularly tricky, because it is tough to see Banxico cutting aggressively in front of that event in July 2018.  But what I think is the real driver here is the reluctance of investors that got burned over the past year to buy into the idea that rates can come back down, especially with structural reforms bearing fruit, listless growth, and commodity prices grinding lower. We’ve seen it happen already in Brazil and Colombia--next it will be Mexico’s turn to cut.

[euro3plast-fr]

Use your data to build a better experience

Delayed flights. Lost luggage. Sketchy cab rides. Noisy hotel guests. For many years, Marriott has understood that the travel struggles are real. But it’s only recently that they’ve found ways to ease the pain with data. Their solution: Collecting, harnessing, and redeploying customer information from all along the travel journey—far beyond the hotel stay itself. By amassing a wide range of data from vacation searches, flight bookings, car rentals, cruises, hotel check-ins, room service, spa treatments, and more, Marriott has developed a series of “predictable data points” for each of its guests, whether they’re first-time visitors or loyal members of the rewards program.

Travel stress is introduced by unknowns. So Marriott extracts the “data points” inside each traveler’s head—the fight she always takes or the coffee shop he always visits—and preloads that information into its data platform. From there, Marriott finds ways to wrap each customer experience in the predictable data points that customers prefer. It could be something simple and delightful, like serving black coffee to a guest who always takes her coffee black—before she even has to ask. Or it could be something even more potentially stress-saving, like offering early check-in to a guest who always comes in on the red eye.

Customer insights, such as the predictable data points Marriott gathers, are at the heart of effective personalization. And the first step toward gaining this deep insight is to know who your customers and prospects are across every touchpoint in their unique travel journeys—not just at the points where they’ll encounter your brand. This means connecting with them from the moment they start imagining a trip all the way until they get back home and share their travel photos.

Online travel agencies like Expedia, Orbitz, and Travelocity, as well as meta-search sites like Kayak, already touch travelers at many points along their journeys. Because they’ve built a more holistic view of the traveler than most traditional travel brands, they’re able to offer solutions from airfare and hotels to car rentals and vacation activities, all from a single site. Traditional travel brands, on the other hand, have some catching up to do.

Creating a truly holistic view of the traveler requires gathering and linking data from disparate sources, including opt-in data from your own CRM system, data from your company’s call center interactions, booking flow metrics, purchase data, customer data from social applications, and comprehensive device usage data information from data co-ops.17 Equipped with all this customer intelligence, you’ll be better able to connect with travelers across their journeys—not only in the places you typically interact with them, but anywhere and everywhere they go.

But it only works if all your users understand the data and can act on it. “This is where our industry falls flat,” says Ahmed El-Emam, a digital strategist at WestJet. ˝The answer, he explains, is to shift from traditional “systems of record,” which focus on processes, to “systems of engagement,” which focus on people. By integrating customer data with new mobile and social technologies, systems of engagement help brands deliver experiences in the precise moment that travelers need them—like combining app and location context to know when a guest has entered your hotel and wants to check in, and even when she’s made it to her room and may want to order room service.

“This isn’t just a marketing function,” says El-Emam. “It’s about where data is stored, how you access it, how to integrate systems, and how to provide data transparency throughout the organization. It’s about how to surface information and make it actionable.”

Building a more complete view of the customer also allows travel brands to build models that can predict future customer behavior and responses—so you don’t have to guess which offer will resonate with which traveler. Use of new cognitive technologies takes this predictive marketing to all-new levels. With the IBM Watson platform, for instance, predictive insights aren’t just based on historical and transactional data, but also on psychographic insights about what customers are thinking, feeling, and saying.

Because Watson understands languages, learns as it processes information, and can actually reason a lot like humans do, the platform can make sense of unstructured data from the social sphere—like words a customer commonly uses on Facebook and Twitter. Correlating this social data with the National Psychiatric Index personality scale, Watson can help brands understand a traveler’s unique personality, from how agreeable a customer may be to how stressed out he’s likely to get. Equipped with insights like these, you can understand customers like never before and better imagine what a unique experience would look like for each of them—so you can provide one-to-one experiences that truly fit the way they think, feel, act, and live.

To learn how to build a complete view of travelers so you can find new ways to connect with them throughout their travel journeys, read “The Beauty of Integration.”

The post Use your data to build a better experience appeared first on Digital Marketing Blog by Adobe.

from Digital Marketing Blog by Adobe https://blogs.adobe.com/digitalmarketing/web-experience/use-your-data-to-build-a-better-experience/

[mccullytech]

Use your data to build a better experience

Delayed flights. Lost luggage. Sketchy cab rides. Noisy hotel guests. For many years, Marriott has understood that the travel struggles are real. But it’s only recently that they’ve found ways to ease the pain with data. Their solution: Collecting, harnessing, and redeploying customer information from all along the travel journey—far beyond the hotel stay itself. By amassing a wide range of data from vacation searches, flight bookings, car rentals, cruises, hotel check-ins, room service, spa treatments, and more, Marriott has developed a series of “predictable data points” for each of its guests, whether they’re first-time visitors or loyal members of the rewards program.

Travel stress is introduced by unknowns. So Marriott extracts the “data points” inside each traveler’s head—the fight she always takes or the coffee shop he always visits—and preloads that information into its data platform. From there, Marriott finds ways to wrap each customer experience in the predictable data points that customers prefer. It could be something simple and delightful, like serving black coffee to a guest who always takes her coffee black—before she even has to ask. Or it could be something even more potentially stress-saving, like offering early check-in to a guest who always comes in on the red eye.

Customer insights, such as the predictable data points Marriott gathers, are at the heart of effective personalization. And the first step toward gaining this deep insight is to know who your customers and prospects are across every touchpoint in their unique travel journeys—not just at the points where they’ll encounter your brand. This means connecting with them from the moment they start imagining a trip all the way until they get back home and share their travel photos.

Online travel agencies like Expedia, Orbitz, and Travelocity, as well as meta-search sites like Kayak, already touch travelers at many points along their journeys. Because they’ve built a more holistic view of the traveler than most traditional travel brands, they’re able to offer solutions from airfare and hotels to car rentals and vacation activities, all from a single site. Traditional travel brands, on the other hand, have some catching up to do.

Creating a truly holistic view of the traveler requires gathering and linking data from disparate sources, including opt-in data from your own CRM system, data from your company’s call center interactions, booking flow metrics, purchase data, customer data from social applications, and comprehensive device usage data information from data co-ops.17 Equipped with all this customer intelligence, you’ll be better able to connect with travelers across their journeys—not only in the places you typically interact with them, but anywhere and everywhere they go.

But it only works if all your users understand the data and can act on it. “This is where our industry falls flat,” says Ahmed El-Emam, a digital strategist at WestJet. ˝The answer, he explains, is to shift from traditional “systems of record,” which focus on processes, to “systems of engagement,” which focus on people. By integrating customer data with new mobile and social technologies, systems of engagement help brands deliver experiences in the precise moment that travelers need them—like combining app and location context to know when a guest has entered your hotel and wants to check in, and even when she’s made it to her room and may want to order room service.

“This isn’t just a marketing function,” says El-Emam. “It’s about where data is stored, how you access it, how to integrate systems, and how to provide data transparency throughout the organization. It’s about how to surface information and make it actionable.”

Building a more complete view of the customer also allows travel brands to build models that can predict future customer behavior and responses—so you don’t have to guess which offer will resonate with which traveler. Use of new cognitive technologies takes this predictive marketing to all-new levels. With the IBM Watson platform, for instance, predictive insights aren’t just based on historical and transactional data, but also on psychographic insights about what customers are thinking, feeling, and saying.

Because Watson understands languages, learns as it processes information, and can actually reason a lot like humans do, the platform can make sense of unstructured data from the social sphere—like words a customer commonly uses on Facebook and Twitter. Correlating this social data with the National Psychiatric Index personality scale, Watson can help brands understand a traveler’s unique personality, from how agreeable a customer may be to how stressed out he’s likely to get. Equipped with insights like these, you can understand customers like never before and better imagine what a unique experience would look like for each of them—so you can provide one-to-one experiences that truly fit the way they think, feel, act, and live.

To learn how to build a complete view of travelers so you can find new ways to connect with them throughout their travel journeys, read “The Beauty of Integration.”

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from Digital Marketing Blog by Adobe https://blogs.adobe.com/digitalmarketing/web-experience/use-your-data-to-build-a-better-experience/