Category Archives: Strategy

Why Do Gas Station Prices Constantly Change? Blame the Algorithm

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Retailers are using artificial-intelligence software to set optimal prices, testing textbook theories of competition; antitrust officials worry such systems raise prices for consumers

The Knaap Tankstation gas station in Rotterdam, Netherlands, uses a2i Systems artificial-intelligence pricing software.
The Knaap Tankstation gas station in Rotterdam, Netherlands, uses a2i Systems artificial-intelligence pricing software. PHOTO: KNAAP TANKSTATION BV

ROTTERDAM, the Netherlands—One recent afternoon at a Shell-branded station on the outskirts of this Dutch city, the price of a gallon of unleaded gas started ticking higher, rising more than 3½ cents by closing time. A little later, a competing station 3 miles down the road raised its price about the same amount.

The two stations are among thousands of companies that use artificial-intelligence software to set prices. In doing so, they are testing a fundamental precept of the market economy.

In economics textbooks, open competition between companies selling a similar product, like gasoline, tends to push prices lower. These kinds of algorithms determine the optimal price sometimes dozens of times a day. As they get better at predicting what competitors are charging and what consumers are willing to pay, there are signs they sometimes end up boosting prices together.

Advances in A.I. are allowing retail and wholesale firms to move beyond “dynamic pricing” software, which has for years helped set prices for fast-moving goods, like airline tickets or flat-screen televisions. Older pricing software often used simple rules, such as always keeping prices lower than a competitor.

On the Same Track

Two competing gas stations in the Rotterdam area both using a2i Systems pricing software roughly mirrored each other’s price moves during a selected week.

These new systems crunch mountains of historical and real-time data to predict how customers and competitors will react to any price change under different scenarios, giving them an almost superhuman insight into market dynamics. Programmed to meet a certain goal—such as boosting sales—the algorithms constantly update tactics after learning from experience.

Ulrik Blichfeldt, chief executive of Denmark-based a2i Systems A/S, whose technology powers the Rotterdam gas stations, said his software is focused primarily on modeling consumer behavior and leads to benefits for consumers as well as gas stations. The software learns when raising prices drives away customers and when it doesn’t, leading to lower prices at times when price-sensitive customers are likely to drive by, he said.

“This is not a matter of stealing more money from your customer. It’s about making margin on people who don’t care, and giving away margin to people who do care,” he said.

Driving the popularity of A.I. pricing is the pain rippling through most retail industries, long a low-margin business that’s now suffering from increased competition from online competitors.

“The problem we’re solving is that retailers are going through a bloodbath,” said Guru Hariharan, chief executive of Mountain View, Calif.-based Boomerang Commerce Inc., whose A.I.-enabled software is used by StaplesInc. and other companies.

Staples uses A.I.-enabled software to change prices on 30,000 products a day on its website.
Staples uses A.I.-enabled software to change prices on 30,000 products a day on its website. PHOTO: RICHARD B. LEVINE/ZUMA PRESS

The rise of A.I. pricing poses a challenge to antitrust law. Authorities in the EU and U.S. haven’t opened probes or accused retailers of impropriety for using A.I. to set prices. Antitrust experts say it could be difficult to prove illegal intent as is often required in collusion cases; so far, algorithmic-pricing prosecutions have involved allegations of humans explicitly designing machines to manipulate markets.

Officials say they are looking at whether they need new rules. The Organization for Economic Cooperation and Development said it plans to discuss in June at a round table how such software could make collusion easier “without any formal agreement or human interaction.”

“If professional poker players are having difficulty playing against an algorithm, imagine the difficulty a consumer might have,” said Maurice Stucke, a former antitrust attorney for the U.S. Justice Department and now a law professor at the University of Tennessee, who has written about the competition issues posed by A.I. “In all likelihood, consumers are going to end up paying a higher price.”

In one example of what can happen when prices are widely known, Germany required all gas stations to provide live fuel prices that it shared with consumer price-comparison apps. The effort appears to have boosted prices between 1.2 to 3.3 euro cents per liter, or about 5 to 13 U.S. cents per gallon, according to a discussion paper published in 2016 by the Düsseldorf Institute for Competition Economics.

Makers and users of A.I. pricing said humans remain in control and that retailers’ strategic goals vary widely, which should promote competition and lower prices.

“If you completely let the software rule, then I could see [collusion] happening,” said Faisal Masud, chief technology officer for Staples, which uses A.I.-enabled software to change prices on 30,000 products a day on its website. “But let’s be clear, whatever tools we use, the business logic remains human.”

Online retailers in the U.S., such as Amazon.com Inc. and its third-party sellers, were among the first to adopt dynamic pricing. Amazon.com declined to comment.

Since then, sectors with fast-moving goods, frequent price changes and thin margins—such as the grocery, electronics and gasoline markets—have been the quickest to adopt the latest algorithmic pricing, because they are the most keen for extra pennies of margin, analysts and executives say.

The pricing-software industry has grown in tandem with the amount of data available to—and generated by—retailers. Stores keep information on transactions, as well as information about store traffic, product location and buyer demographics. They also can buy access to databases that monitor competitors’ product assortments, availability and prices—both on the web and in stores.

A.I. is used to make sense of all that information. International Business Machines Corp. said its price-optimization business uses capabilities from its Watson cognitive-computing engine to advise retailers on pricing. Germany’s Blue Yonder GmbH, a price-optimization outfit that serves clients in the grocery, electronics and fashion industries, said it uses neural networks based on those its physicist founder built to analyze data from a particle collider.

Neural networks are a type of A.I. computer system inspired by the interconnected structure of the human brain. They are good at matching new information to old patterns in vast databases, which allows them to use real-time signals such as purchases to predict from experience how consumers and competitors will behave.

Algorithms can also figure out what products are usually purchased together, allowing them to optimize the price of a whole shopping cart. If customers tend to be sensitive to milk prices, but less so to cereal prices, the software might beat a competitor’s price on milk, and make up margin on cereal.

“They’re getting really smart,” said Nik Subramanian, chief technology officer of Brussels-based Kantify, who said its pricing software has figured out how to raise prices after it sees on a competitor’s website that it has run out of a certain product.

Algorithmic pricing works well in the retail gasoline market, because it is a high-volume commodity that is relatively uniform, leading station owners in competitive markets to squeeze every penny.

For years, price wars in cutthroat markets have followed a typical pattern. A retailer would cut prices to lure customers, then competitors would follow suit, each cutting a little more than the others, eventually pushing prices down close to the wholesale cost. Finally one seller would reach a breaking point and raise prices. Everyone would follow, and the cycle started all over.

Some economists say the price wars helped consumers with overall lower prices, but led to very thin margins for station owners.

Danish oil and energy company OK hired a2i Systems in 2011 because its network of gas stations was suffering from a decade-old price war. It changed what it charged as many as 10 times a day, enlisting a team of people to drive around the country and call in competitors’ prices, said Gert Johansen, the company’s head of concept development.

A2i Systems—the name means applied artificial intelligence—was started by Alireza Derakhshan and Frodi Hammer, both engineering graduates of the University of Southern Denmark, in Odense. Before focusing on fuel, they built other A.I. systems, including a game displayed on interactive playground floor tiles that adapted to the speed and skill level of the children running around on top.

For OK, a2i created thousands of neural networks—one for each fuel at each station—and trained them to compare live sales data to years of historical company data to predict how customers would react to price changes. Then it ran those predictions through algorithms built to pick the optimal prices and learn from their mistakes.

In a pilot study, OK split 30 stations into two sets, a control group and an a2i group. The group using the software averaged 5% higher margins, according to a paper Mr. Derakhshan presented last June at an A.I. conference in Seville, Spain.

Scandinavian supermarket chain REMA 1000 says it will roll out price-optimization software made by Texas-based Revionics Inc. in coming months.
Scandinavian supermarket chain REMA 1000 says it will roll out price-optimization software made by Texas-based Revionics Inc. in coming months. PHOTO: JOSEPH DEAN/NEWSCOM/ZUMA PRESS

The new system could make complex decisions that weren’t simply based on a competitor’s prices, Mr. Derakhshan said in an interview.

One client called to complain the software was malfunctioning. A competitor across the street had slashed prices in a promotion, but the algorithm responded by raising prices. There wasn’t a bug. Instead, the software was monitoring the real-time data and saw an influx of customers, presumably because of the long wait across the street.

“It could tell that no matter how it increased prices, people kept coming in,” said Mr. Derakhshan.

On the outskirts of Rotterdam, Koen van der Knaap began running the system on his family-owned Shell station in recent months. Down the road, a station owned by Tamoil, a gasoline retailer owned by Libya’s Oilinvest Group, uses it too.

During a late-March week for which both Tamoil and Mr. van der Knaap provided hourly data, the costs for unleaded gas at the two stations—which vary in opening hours and services—bounced around independently much of the time, and generally declined, reflecting falling oil prices that week.

During some periods, however, the stations’ price changes paralleled each other, going up or down by more than 2 U.S. cents per gallon within a few hours of each other. Often, prices dropped early in the morning and increased toward the end of the day, implying that the A.I. software may have been identifying common market-demand signals through the local noise.

The station owners say their systems frequently lower prices to gain volume when there are customers to be won.

“It can be frustrating,” said Erwin Ralan, an electronics-store manager who was filling up at the Tamoil station that week. “Prices usually go up at the end of the day. But when you’re empty and you’re in a rush, there’s not much you can do.”

The coming revolution in insurance

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Technological change and competition disrupt a complacent industry

IN THE stormy and ever-changing world of global finance, insurance has remained a relatively placid backwater. With the notable exception of AIG, an American insurer bailed out by the taxpayer in 2008, the industry rode out the financial crisis largely unscathed. Now, however, insurers face unprecedented competitive pressure owing to technological change. This pressure is demanding not just adaptation, but transformation.

The essential product of insurance—protection, usually in the form of money, when things go wrong—has few obvious substitutes. Insurers have built huge customer bases as a result. Investment revenue has provided a reliable boost to profits. This easy life led to a complacent refusal to modernise. The industry is still astonishingly reliant on human labour. Underwriters look at data but plenty still rely on human judgment to evaluate risks and set premiums. Claims are often reviewed manually.

The march of automation and technology is an opportunity for new entrants. Although starting a new soup-to-nuts insurer from scratch is rare (see article), many companies are taking aim at parts of the insurance process. Two Sigma, a large American “quant” hedge fund, for example, is betting its number-crunching algorithms can gauge risks and set prices for insurance better and faster than any human could. Other upstarts have developed alternative sales channels. Simplesurance, a German firm, for example, has integrated product-warranty insurance into e-commerce sites.

Insurers are responding to technological disruption in a variety of ways. Two Sigma contributes its analytical prowess to a joint venture with Hamilton, a Bermudian insurer, and AIG, which actually issues the policies (currently only for small-business insurance in America). Allianz, a German insurer, simply bought into Simplesurance; many insurers have internal venture-capital arms for this purpose. A third approach is to try to foster internal innovation, as Aviva, a British insurer, has done by building a “digital garage” in Hoxton, a trendy part of London.

The biggest threat that incumbents face is to their bottom line. Life insurers, reliant on investment returns to meet guaranteed payouts, have been stung by a prolonged period of low interest rates. The tough environment has accelerated a shift in life insurance towards products that pass more of the risk to investors. Standard Life, a British firm, made the transition earlier than most, for example, and has long been primarily an asset manager (see article).

Meanwhile, providers of property-and-casualty (P&C) insurance, such as policies to protect cars or homes, have seen their pricing power come under relentless pressure, notably from price-comparison websites. In combination with the stubbornly high costs of maintaining their old systems, this has meant that profitability has steadily deteriorated. The American P&C industry, for instance, has seen its “combined ratio”, which expresses claims and costs as a percentage of premium revenue, steadily creep up from 96.2% in 2013 to 97.8% in 2015, and to an estimated 100.3% for 2016 (ie, a net underwriting loss). Henrik Naujoks of Bain & Company, a consultancy, says this has left such insurers facing a stark choice: become low-cost providers, or differentiate themselves through the services they provide.

One fairly simple way to offer distinctive services is to use existing data in new ways. Insurers have long drawn up worst-case scenarios to estimate the losses they would incur from, say, a natural catastrophe. But some have started working with clients and local authorities on preparing for such events; they are becoming, in effect, risk-prevention consultants. AXA, a French insurer, has recently started using its models on the flooding of the Seine to prepare contingency plans. Gaëlle Olivier of AXA’s P&C unit says the plans proved helpful in responding to floods in June 2016, reducing the damage.

Damage control

Tech-savvy insurers are going one step further, exploiting entirely new sources of data. Some are using sensors to track everything from boiler temperatures to health data to driving styles, and then offering policies with pricing and coverage calibrated accordingly. Data from sensors also open the door to offering new kinds of risk-prevention services. As part of Aviva’s partnership with HomeServe, a British home-services company, the insurer pays to have a sensor (“LeakBot”) installed on its customers’ incoming water pipes that can detect even minuscule leaks. HomeServe can then repair these before a pipe floods a home, causing serious damage.

The shift towards providing more services fosters competition on factors beyond price. Porto Seguros, a Brazilian insurer, offers services ranging from roadside assistance to scheduling doctor’s appointments. In France AXA provides coverage for users of BlaBlaCar, a long-distance ride-sharing app. The main aim of the policy is to guarantee that customers can still reach their destination. If, say, the car breaks down, it offers services ranging from roadside car-repair to alternative transport (eg, calling a taxi).

Insurers face many hurdles, however, to becoming service providers and risk consultants. Maurice Tulloch, head of the general-insurance arm of Aviva, admits that such services are yet to catch on with most customers. So far, his firm, like its peers, has focused on enticing them to adopt the new offerings by cutting insurance premiums, rather than on making money directly from them. It reckons it can recoup the cost of, say, the HomeServe sensors and repairs from the reduction in claims.

One example of what the future may hold comes from the car industry. Carmakers have traditionally bought product-liability insurance to cover manufacturing defects. But Volvo and Mercedes are so confident of their self-driving cars that last year they said they will not buy insurance at all. They will “self-insure”—ie, directly bear any losses from crashes.

Some think that such trends threaten the very existence of insurance. Even if they do not, Bain’s Mr Naujoks is not alone in expecting the next five years to bring more change to the insurance industry than he has seen in the past 20.

This article appeared in the Finance and economics section of the print edition under the headline “Counsel of protection”

YouTube Tops 1 Billion Hours of Video a Day, on Pace to Eclipse TV

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Google unit posts 10-fold increase in viewership since 2012, boosted by algorithms personalizing user lineups

YouTube viewers world-wide are now watching more than 1 billion hours of videos a day, threatening to eclipse U.S. television viewership, a milestone fueled by the Google unit’s aggressive embrace of artificial intelligence to recommend videos.

YouTube surpassed the figure, which is far larger than previously reported, late last year. It represents a 10-fold increase since 2012, YouTube said, when it started building algorithms that tap user data to give each user personalized video lineups designed to keep them watching longer.

Feeding those recommendations is an unmatched collection of content: 400 hours of video are uploaded to YouTube each minute, or 65 years of video a day.

“The corpus of content continues to get richer and richer by the minute, and machine-learning algorithms do a better and better job of surfacing the content that an individual user likes,” YouTube Chief Product Officer Neal Mohan said.

MERRILL SHERMAN

YouTube’s billion-hour mark underscores the wide lead of the 12-year-old platform in online video—threatening traditional television, which lacks similarly sophisticated tools.

Facebook Inc. and Netflix Inc. said in January 2016 that users watch 100 million hours and 116 million hours, respectively, of video daily on their platforms. Nielsen data suggest Americans watch on average roughly 1.25 billion hours of live and recorded TV a day, a figure steadily dropping in recent years.

Despite its size, it is unclear if YouTube is making money. Google parent Alphabet Inc. doesn’t disclose YouTube’s performance, but people familiar with its financials said it took in about $4 billion in revenue in 2014 and roughly broke even.

YouTube makes most of its money on running ads before videos but it also spends big on technology and rights to content, including deals with TV networks for a planned web-TV service. When asked about profits last year, YouTube Chief Executive Susan Wojcicki said, “Growth is the priority.”

YouTube’s success using tailor-made video lineups illustrates how technology companies can reshape media consumption into narrow categories of interests, a trend some observers find worrying.

“If I only watch heavy-metal videos, of course it’s serving me more of those. But then I’m missing out on the diversity of culture that exists,” said Christo Wilson, a Northeastern University computer-science professor who studies the impact of algorithms. “The blessing and curse of cable and broadcast TV is it was a shared experience.…But that goes away if we each have personalized ecosystems.”

YouTube benefits from the enormous reach of Google, which handles about 93% of internet searches, according to market researcher StatCounter. Google embeds YouTube videos in search results and pre-installs the YouTube app on its Android software, which runs 88% of smartphones, according to Strategy Analytics.

That has helped drive new users to its platform. About 2 billion unique users now watch a YouTube video every 90 days, according to a former manager. In 2013, the last time YouTube disclosed its user base, it said it surpassed 1 billion monthly users. YouTube is now likely larger than the world’s biggest TV network, China Central Television, which has more than 1.2 billion viewers.

YouTube long configured video recommendations to boost total views, but that approach rewarded videos with misleading titles or preview images. To increase user engagement and retention, the company in early 2012 changed its algorithms to boost watch time instead. Immediately, clicks dropped nearly 20% partly because users stuck with videos longer. Some executives and video creators objected.

Months later, YouTube executives unveiled a goal of 1 billion hours of watch time daily by the end of 2016. At the time, optimistic forecasts projected it would reach 400 million hours by then.

YouTube retooled its algorithms using a field of artificial intelligence called machine learning to parse massive databases of user history to improve video recommendations.

If I only watch heavy-metal videos, of course it’s serving me more of those. But then I’m missing out on the diversity of culture that exists.

—Northeastern University computer-science professor Christo Wilson

Previously, the algorithms recommended content largely based on what other users clicked after watching a particular video, the former manager said. Now their “understanding of what is in a video [and] what a person or group of people would like to watch has grown dramatically,” he said.

Engineers tested each change on a control group, and only kept the change if those users spent more time on YouTube.

One strategy was to find new areas of user interest. For instance, YouTube could suggest a soccer video to users watching a lot of football, and then flood the lineup with more soccer if the first clip was a hit. “Once you realize there’s an additional preference, exploit that,” the former manager said.

But the algorithm didn’t always deliver. For instance, when Ms. Wojcicki joined as CEO in 2014, YouTube recommended videos to her about eczema because she had recently watched a clip about skin rashes after suspecting one of her children had one, said Cristos Goodrow, YouTube’s video-recommendation chief.

That made the video-recommendation team realize there were certain “single-use videos” to ignore as signals of user interest. But to mark them, they had to think of each example, such as certain health and how-to videos.

Then last year YouTube partnered with Google Brain, which develops advanced machine-learning software called deep neural networks, which have led to dramatic improvements in other fields, such as language translation. The Google Brain system was able to identify single-use video categories on its own.

The age of analytics: Competing in a data-driven world

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McKinsey Global Institute

By Nicolaus Henke, Jacques Bughin, Michael Chui, James Manyika, Tamim Saleh, Bill Wiseman, and Guru Sethupathy

Big data’s potential just keeps growing. Taking full advantage means companies must incorporate analytics into their strategic vision and use it to make better, faster decisions.

Is big data all hype? To the contrary: earlier research may have given only a partial view of the ultimate impact. A new report from the McKinsey Global Institute (MGI), The age of analytics: Competing in a data-driven world, suggests that the range of applications and opportunities has grown and will continue to expand. Given rapid technological advances, the question for companies now is how to integrate new capabilities into their operations and strategies—and position themselves in a world where analytics can upend entire industries.

The age of analytics
The age of analytics
Big data continues to grow; if anything, earlier estimates understated its potential.

A 2011 MGI report highlighted the transformational potential of big data. Five years later, we remain convinced that this potential has not been oversold. In fact, the convergence of several technology trends is accelerating progress. The volume of data continues to double every three years as information pours in from digital platforms, wireless sensors, virtual-reality applications, and billions of mobile phones. Data-storage capacity has increased, while its cost has plummeted. Data scientists now have unprecedented computing power at their disposal, and they are devising algorithms that are ever more sophisticated.

Earlier, we estimated the potential for big data and analytics to create value in five specific domains. Revisiting them today shows uneven progress and a great deal of that value still on the table (exhibit). The greatest advances have occurred in location-based services and in US retail, both areas with competitors that are digital natives. In contrast, manufacturing, the EU public sector, and healthcare have captured less than 30 percent of the potential value we highlighted five years ago. And new opportunities have arisen since 2011, further widening the gap between the leaders and laggards.

Progress in capturing value from data and analytics has been uneven.

Leading companies are using their capabilities not only to improve their core operations but also to launch entirely new business models. The network effects of digital platforms are creating a winner-take-most situation in some markets. The leading firms have remarkably deep analytical talent taking on various problems—and they are actively looking for ways to enter other industries. These companies can take advantage of their scale and data insights to add new business lines, and those expansions are increasingly blurring traditional sector boundaries.

Where digital natives were built for analytics, legacy companies have to do the hard work of overhauling or changing existing systems. Adapting to an era of data-driven decision making is not always a simple proposition. Some companies have invested heavily in technology but have not yet changed their organizations so they can make the most of these investments. Many are struggling to develop the talent, business processes, and organizational muscle to capture real value from analytics.

The first challenge is incorporating data and analytics into a core strategic vision. The next step is developing the right business processes and building capabilities, including both data infrastructure and talent. It is not enough simply to layer powerful technology systems on top of existing business operations. All these aspects of transformation need to come together to realize the full potential of data and analytics. The challenges incumbents face in pulling this off are precisely why much of the value we highlighted in 2011 is still unclaimed.

The urgency for incumbents is growing, since leaders are staking out large advantages, and hesitating increases the risk of being disrupted. Disruption is already happening, and it takes multiple forms. Introducing new types of data sets (“orthogonal data”) can confer a competitive advantage, for instance, while massive integration capabilities can break through organizational silos, enabling new insights and models. Hyperscale digital platforms can match buyers and sellers in real time, transforming inefficient markets. Granular data can be used to personalize products and services—including, most intriguingly, healthcare. New analytical techniques can fuel discovery and innovation. Above all, businesses no longer have to go on gut instinct; they can use data and analytics to make faster decisions and more accurate forecasts supported by a mountain of evidence.

The next generation of tools could unleash even bigger changes. New machine-learning and deep-learning capabilities have an enormous variety of applications that stretch into many sectors of the economy. Systems enabled by machine learning can provide customer service, manage logistics, analyze medical records, or even write news stories.

These technologies could generate productivity gains and an improved quality of life, but they carry the risk of causing job losses and dislocations. Previous MGI research found that 45 percent of work activities could be automated using current technologies; some 80 percent of that is attributable to existing machine-learning capabilities. Breakthroughs in natural-language processing could expand that impact.

Data and analytics are already shaking up multiple industries, and the effects will only become more pronounced as adoption reaches critical mass—and as machines gain unprecedented capabilities to solve problems and understand language. Organizations that can harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage.

A survival plan for bricks-and-mortar Starbucks

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Food Business News

by Jeff Gelski

Starbucks Coffee store
The Starbucks stores need to remain a place where people go to seek out human interaction.

SEATTLE — Web sites, e-commerce and what he described as the Amazon effect could lead both large and small companies to close retail stores in the coming years, said Howard Schultz, chairman and chief executive officer for the Starbucks Coffee Co.

Howard Schultz, Starbucks
Howard Schultz, chairman and c.e.o. of Starbucks

“There is no doubt that over the next five years or so we are going to see a dramatic level of retailers not be able to sustain their level of core business as a traditional bricks-and-mortar retailer, and their omni-channel approach is not going to be sustainable to maintain their cost of their infrastructure, and as a result of that, there is going to be a tremendous amount of changes with regard to the retail landscape,” he said in a Nov. 3 earnings call.

Mr. Schultz said Starbucks stores have an advantage in that they maintain a special place in terms of a sense of community, an environment where people go to seek out human interaction. Starbucks could be in a unique position 5 to 10 years from now. Other retail stores closing could mean fewer stores competing for Starbucks’ customers, he said.

“I’m not talking about the coffee category,” Mr. Schultz said. “I am talking overall, but we are in the very, very early stages of a tremendous change in the bricks-and-mortar footprint of retailers domestically and internationally as a result of the sea-change in how people are buying things, and that is going to have I think a negative effect on all of retail, but we believe that it is going to have ultimately a positive effect on the position that we occupy and the environment that we create in our stores.”

Starbucks Roastery in Seattle
New Starbucks roastery stores will be designed to enhance the consumer experience.

New Starbucks roastery stores will be designed to enhance the consumer experience. The Seattle roastery, the only one in operation right now, delivered a comp sales increase of 24% in the fiscal year ended Oct. 2, Mr. Schultz said. A roastery in Shanghai, China, should begin operations next year.

“Opening in late 2017 on Nanjing Road among the busiest shopping destinations in the world, the Starbucks Shanghai roastery will be a stunning two-level, 30,000-square-foot experiential destination showcasing the newest coffee brewing methods and offering customers the finest assortment of exclusive micro-lot coffees from around the world in an immersive all-sensory experience emblematic of our Seattle roastery, respectfully curated through a unique lens that will make it highly impactful and relevant to our Chinese customers,” he said.

Starbucks plans to open roasteries in New York and Tokyo in 2018. A roastery should open in Europe in 2019, but Starbucks has yet to select a city.

Starbucks mobile ordering
Mobile orders now represent 6% of Starbucks transactions.

Starbucks has a digital presence as well. Mobile orders now represent 6% of transactions, said Kevin Johnson, president and chief operating officer.

“We are continuously improving the mobile order and pay experience with newly released functionality that presents our personalized offer directly on the front screen of the mobile app and allows the customer to save favorite stores, favorite customers’ beverages, and we have new features in the pipeline to be released shortly, including real-time personalized product suggestions and the ability to save favorite orders, and there is more coming,” he said.

Starbucks executives discussed results of the 2016 fiscal year in the Nov. 3 earnings call. Net earnings attributable to Starbucks in the year ended Oct. 2 were $2,817.7 million, equal to $1.90 per share on the common stock, which was up 2.2% from $2,757.4 million, or $1.82 per share, in the previous fiscal year. Consolidated net revenues grew 11% to $21,315.9 million from $19,162.7 million in the previous fiscal year. The 2016 fiscal year contained 53 weeks compared to 52 weeks for the previous fiscal year.

THE 21ST CENTURY CORPORATION DISRUPTED How to Master Change

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Here’s what chipmaker Nvidia can teach us.

Go to any business conference and you’ll hear people talking about transformation. For a lot of companies it’s a matter of life and death. Their legacy business is fading, and they need to become something new. The problem is that most companies, like most people, aren’t good at change.

Some, though, are amazing at it. Like those Hasbro toys that start out as a robot but can be bent into the shape of a car, these companies start out doing one thing, then—poof!—flip a few switches and become something else.

Consider Nvidia NVDA -0.05% , a chip company in Santa Clara, Calif., that started out 23 years ago and originally became successful in the somewhat humble business of making graphics boards that videogame fans used to soup up the performance of their PCs. Then, in 2006, Nvidia figured out that its graphics chips could be hooked together to make a supercomputer. Today its graphics processors power many of the brawniest computers in the world, and they will be used in two next-generation supercomputers being designed by U.S. Department of Energy labs. That line of business generates $150 million a year for Nvidia.

But now the chipmaker has spotted a market that could be its biggest opportunity yet: self-driving cars. To make a vehicle autonomous, you need to gather massive streams of data from loads of sensors and cameras and process that data on the fly so that the car can “see” what’s around it. Turns out Nvidia’s graphics chips are great for that. So far, 80 companies, including Volvo, Audi, and Tesla TSLA -0.27% , are using Nvidia technology in their research around autonomous vehicles. “We’re transforming into an artificial-intelligence company,” says Danny Shapiro, a senior director in Nvidia’s automotive group.

Why has Nvidia been such a natural quick-change artist? Well, it turns out that it and other “transformers” have a few traits in common:

Big ears. They listen to customers. Nvidia’s self-driving-car business grew out of a long-standing relationship with auto companies. Car guys used Nvidia chips for computer-aided design, then used Nvidia supercomputer chips to do crash simulations. When the car guys started thinking about autonomous vehicles, Nvidia leaped at the chance to help them solve the problem.

▸ An impatient boss. Transformer CEOs like change and will drive it down throughout the organization. Nvidia’s CEO, Jen-Hsun Huang, is an engineer and a chip designer. He ­cofounded Nvidia and still runs it like a startup.

▸ Active imaginations. Conventional companies try to find new uses for capabilities they already have. Transformers look at what the market needs and then go build it, hiring new people and/or taking people off other jobs.

▸ A brush with death. That’s not the case at ­Nvidia, but a close call with the corporate undertaker can sometimes provide a necessary spark. Think of Apple’s AAPL -0.20% multiple rebirths under Steve Jobs.

▸ Finally: Yes, transformation is hard—but not changing can sometimes be fatal.

From Audi To Zoox, 17 Companies That Are Driving Change In The Car Industry

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Along with GM, these innovators are finding ways to improve the basics of modern transportation.

From Audi To Zoox, 17 Companies That Are Driving Change In The Car Industry
Building a Tesla Model S
 

OLD-GUARD CAR MANUFACTURERS

Audi: In 2015, started test-driving an AI-laden prototype nicknamed “Jack” that lets drivers easily switch to autonomous mode via buttons on the wheel

BMW: Has promised an entirely autonomous car called iNext by 2021; BMW’s ReachNow car-sharing service launched in April in Seattle and expanded to Portland, Oregon, in September

Ford: Announced plans for fully autonomous car with no pedals or steering wheel by 2021; recently invested $75 million in California laser-sensor company Velodyne; bought San Francisco–based private bus service Chariot and plans to expand it

Volvo: Forged partnerships with Microsoft (will incorporate HoloLens augmented-reality technology into its cars) and Uber (which is planning to use Volvos as part of its self-driving test fleet in Pittsburgh); teamed up with safety-system-maker Autoliv to set up a new company focused on autonomous-driving software

TECH GIANTS

Alphabet: Launched self-piloting-car project back in 2009; testing retrofitted Lexus SUVs and its own adorable prototype vehicles in several locations; recently partnered with Fiat Chrysler to build self-driving minivans

Google’s self-driving prototype.[Photo: Brooks Kraft LLC/Getty Images]

Apple: Has invested $1 billion in Chinese ride-share company Didi Chuxing; reportedly rebooting its efforts to develop an Apple car; might also build a system to add autonomous features to preexisting vehicles

Baidu: Chinese search-engine company has teamed up with digital-graphics pioneer Nvidia to create a self-driving-vehicle system that uses 3-D maps in the cloud; is in the testing stage with several different self-driving-car prototypes, including one built with BMW

Tesla: After revolutionizing electric vehicles with the semi-autonomous Model S, will release more-affordable all-electric Model 3, possibly in late 2017; Model S’s involved in a pair of high-profile fatal accidents

IN-DEMAND PROVIDERS

Didi Chuxing: Acquired Uber’s Chinese operations in August, ending a fierce rivalry for Chinese market

Lyft: Partnered with GM to start testing autonomous Chevy Bolt taxis within the next year

Uber: In September, began testing autonomous Ford Fusions in Pittsburgh—the first self-driving fleet available to the public in the U.S.

A fleet of Uber’s autonomous cars.[Photo: Angelo Merendino/Getty Images]

STARTUPS

Comma.ai: Andreessen Horowitz–backed company making an inexpensive kit that turns regular cars into semi-autonomous ones

Mobileye: Israeli software maker that had partnered with Tesla to provide chips and software, but the two companies ended their collaboration in the wake of a fatal accident in May (Tesla cars currently still use Mobileye chips); has teamed up with Delphi Automotive to build a self-driving system by 2019

NextEV: Shanghai-based electric-car innovator headed in the U.S. by former Cisco exec Padmasree Warrior; set to show off a high-performance all-electric sports-car prototype this year

Nutonomy: Born at MIT and backed by Ford, makes self-driving cars, software, and autonomous robots; started testing driverless taxis in Singapore this summer

Quanergy: Silicon Valley–based company developing light- and object-sensing technology for self-driving cars; boasts $1.59 billion valuation thanks to investors Samsung and Delphi

Zoox: Palo Alto startup behind the Boz, a fully autonomous concept vehicle (still in the design phase) with inward-facing seats similar to a train car; company valued at around $1 billion

A version of this article appeared in the November 2016 issue of Fast Company magazine.

How the Best Business Leaders Disrupt Themselves

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And how doing so helps them keep up with technological change.

Why isn’t Intuit INTU -0.94% dead? Its peers from the Pleistocene epoch of PC software (VisiCalc, WordStar) are long gone; only Intuit survives as a significant independent business. The reason is easy to state, hard to emulate: The company has continually disrupted itself, most recently scrapping its desktop-driven business model of the previous 30 years and switching to one based on the cloud. Revenues went down before they went up, but Intuit’s stock recently hit an all-time high.

Such stories are extremely rare. Successful incumbent firms are more likely to follow the trajectory of Kodak, Sears SHLD 2.39% , Bethlehem Steel, and many newspapers, dead or diminished after technology transformed their industries. Little wonder that for the past two years, when we have asked Fortune 500 CEOs to name their single biggest challenge, their No. 1 answer has been “the rapid pace of technological change.”

Yet a few incumbents have defied the odds and succeeded at self-disruption. How they do it is becoming clear.

They see their business as disrupters would see it. This challenge is psychological and requires escaping the aura of headquarters. At the dawn of the web, American Airlines’ AAL -2.31% Sabre subsidiary assembled a team and sent it to another building with orders to disrupt the industry’s travel-agent-based business model. The result was Travelocity. Charles Schwab responded to the rise of “robo-advisers” like Betterment and Wealthfront by forming a full-time team that ignored the company’s corporate playbook. The team developed Schwab Intelligent Portfolios, a robo-product that now manages more assets than any of its disrupter startup rivals.

They find the courage to leap. Netflix NFLX -0.44% CEO Reed Hastings knew that online streaming would disrupt his successful DVDs-by-mail model. He committed to streaming in 2011—and Netflix’s stock plunged 76%. Wall Street called for his head. But Hastings pushed on, and today DVDs are just 7% of the company’s business, while the stock is up 150% from its pre-plunge peak

They never stop. Self-­disruption isn’t something you do just once. Every successful disrupter becomes an incumbent in its transformed industry, and digital business models don’t last long. Amazon AMZN 0.82% disrupted bookstores 20 years ago, then disrupted its own books-by-mail model with Kindle e-readers. Digital evolution is merciless: Intelwas a champion self-disrupter until it missed the mobile revolution; in April it announced 12,000 layoffs.

Leaders can glean these lessons from the first industries to be disrupted by digital tech. But the hardest step for incumbents is the first one, best expressed by Peter Drucker: “If leaders are unable to slough off yesterday, to abandon yesterday, they simply will not be able to create tomorrow.” 

Shoppers Flock to Apps, Shaking Up Retail

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By GREG BENSINGER

Wall Street Journal
April 13, 2016

Hana Pugh, a 29-year-old event planner and new mother, buys most of her household items and baby goods online. But she doesn’t use her computer to shop. Instead, Ms. Pugh taps away at her iPhone.

“It’s quicker to pull out my phone and click ‘buy’ than to log on to my computer,” said Ms. Pugh, of Bowie, Md., who relies on Amazon.com Inc.’s app for essentials such as diapers and wipes.

Shopping on a small screen used to be a pain. But as consumers spend more of their days glued to smartphones, retailers are getting savvier with apps that ease browsing, offer rewards, suggest the right products and simplify the purchase to one click.

The so-called appification of shopping promises to radically transform the retail industry by creating new shopping habits, reshaping sales tactics and carving out winners and losers. Instead of placing one big order from a computer, people are increasingly making smaller purchases in short bursts throughout the day on their phones, a phenomenon retailers call “snacking.”

Mobile sales are booming, especially compared with sales gains from desktop computers. Last year, U.S. sales from mobile devices jumped 56% to $49.2 billion, doubling the previous year’s growth, according to comScore. Desktop sales still dwarf mobile, reaching $256.1 billion last year, but annual growth slowed to 8.1% from 12.5%.

The retailers that are succeeding are training customers to think of their smartphones like an all-day impulse aisle. Apps are able to capture data available on handsets and push consumers to buy when they have a spare moment, whether in line for a morning coffee, or, as in Ms. Pugh’s case, nursing her child.

But merchants say many shoppers on phones still shy away from buying big-ticket items such as sofas, preferring larger photos, expanded reviews and product descriptions, as well as price comparisons available on a desktop computer.

And retailers have to be careful not to seem invasive. Amazon’s shoe retailer, Zappos.com, is testing technology that highlights different products based on a phone’s operating system. Target Corp. pushes coupons to customers who have the app open in its stores. Fashion retailer ModCloth Inc. suggests some products based on a customer’s location.

Amazon’s dominance on the Web extends to mobile, and the online retailer has the top-ranked app, according to Apple. Among the top-ranked apps on both Apple and Android devices are two, young mobile-centric marketplaces, OfferUp Inc. and ContextLogic Inc.’s Wish, which last year were valued at $800 million and $3 billion, respectively, by investors.

“Mobile devices are driving demand,” said Andrew Lipsman, a comScore vice president, who has studied mobile shopping. “They can create an impulsive buying moment at any point in the day because they are with you all the time, right in your pocket.”

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Selling merchandise on smartphones still poses challenges. The ease of buying single items instead of building a shopping cart can drive up retailers’ shipping costs. And consumers are more likely to window shop for products but not necessarily buy them.

People still make most of their online transactions on desktop computers. Ryan McIntyre, chief marketing officer of men’s fashion retailer JackThreads Inc., said the average cart is about $5 higher on his company’s website than its app. But he says customers spend about 10% more on average on mobile devices in a given six-month period.

Mobile devices also drive Web sales: Nearly 40% of desktop transactions in the fourth quarter took place after a customer visited the retailer’s app or mobile site, according to consulting firm Criteo.

Olivia Bryant, a 19-year-old Starbucks barista in Bakersfield, Calif., said she spends up to two hours a day shopping on her iPhone through apps such as marketplace Etsy Inc. and fashion retailer Poshmark Inc. “It’s much simpler to shop on my phone,” she said. “There aren’t a lot of distractions.”

The average U.S. consumer last year spent 3 hours and 5 minutes a day using apps, compared with 51 minutes surfing the mobile Web, according to eMarketer. Such devotion to apps isn’t lost on retailers. Target has shifted resources and staff away from Web development to its app—which it hopes to make central to all of its digital design.
“With smartphones we’re able to reach you all the time” through texts or lock-screen messages, known as push notifications, said Wish CEO Peter Szulczewski. The San Francisco startup has created an experience that mimics the mall, with a seemingly endless inventory to scroll through and bargain-basement prices.

Some apps, such as those from Zappos and online auction site Tophatter Inc., identify a user’s device to judge a customer’s possible buying power. “Our data shows that the more expensive device you have, the more you might spend,” said Tophatter CEO Ashvin Kumar.

“Millions of Amazon customers shop exclusively on a mobile device all year,” said an Amazon spokeswoman.

Retailers may be most excited to cater results based on a customer’s location, whether it is knowing a user has entered their store or is vacationing in warmer climes.

“If you’re in Australia, we might serve you swimwear when it’s winter in the Northern Hemisphere,” said Matt Kaness, CEO of San Francisco fashion retailer ModCloth, which has about 1.2 million users. EBay Inc. will highlight generators to customers shopping in areas affected by a big storm, said Hal Lawton, the company’s North America senior vice president.
Other retailers such as Wal-Mart Stores Inc. and Nordstrom Inc. have explored ways to tie in mobile shopping with stores. Customers could check in to a store through the app, providing salespeople with their purchase history, even encrypted payment information, for a quicker and more personalized shopping experience.

But the main catch for apps may be the impulsive shoppers. “On bar nights, we see drunk shopping, which is very interesting,” said Alan Tisch, CEO of fashion app Spring Inc. “Maybe there’s an opportunity there.”

 

Coke learns big lessons from small startups

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FoodBusinessNews

by Monica Watrous

Coca-Cola’s V.E.B. unit has invested in and partnered with such brands as Honest Tea, fairlife ultra-filtered milk and Suja Juice.

ANAHEIM, CALIF. — Nearly a decade ago, the Coca-Cola Co. found itself at a crossroads. The Atlanta-based beverage company, along with the rest of the carbonated soft drink category, had come under fire for the negative health consequences of sugar consumption. That is when the company launched its Venturing and Emerging Brands (V.E.B.) unit to identify and partner with disruptive startups in the beverage category.

“When we started V.E.B. in 2007, we certainly were challenged on innovation and finding new growth in areas that were unfamiliar to us,” said  Matthew Mitchell, vice-president of portfolio strategy and ventures for V.E.B., during a March 11 panel discussion at Natural Products Expo West in Anaheim. “We talk about acquisitions and deals, but for us this was about behaving in a relationship. We knew that this was important for us to go out and not only buy brands but to invest in people because they were helping us change that dialogue.”

Coca-Cola has since invested in and partnered with such brands as Honest Tea, Suja Juice, Zico coconut water and fairlife ultra-filtered milk. Mr. Mitchell said the missions and values of the brands have influenced Coca-Cola’s business model.

Coca-Cola acquired Honest Tea in 2011 after an initial 40% investment in 2008.

Seth Goldman, co-founder of Honest Tea, said: “When we did the deal, Muhtar Kent, the c.e.o. of Coke, said, ‘At the end of the deal, if Honest Tea becomes more like Coca-Cola and we don’t become more like Honest Tea, then we failed.’”

Coca-Cola acquired Honest Tea in 2011 after an initial 40% investment in 2008. Mr. Goldman said the partnership has opened new doors for his organic beverage brand, including recent distribution in Wendy’s and Chick-fil-A restaurants.

“There isn’t anything we have done with the brand that we wouldn’t have done independently, and quite frankly wouldn’t be able to do without Coke, whether it’s getting into Wendy’s or Chick-fil-A with fair trade organic product… or upgrading to fair trade sugar in our (plastic-bottle teas),” Mr. Goldman said. “That decision came out of Atlanta, and for me, that means our DNA has penetrated.”

For Jeff Church, co-founder and chief executive officer of Suja Juice, which last year received a minority investment from Coca-Cola, the partnership is a step towards making his company’s organic cold-pressed juice beverages more affordable and accessible to mainstream consumers.

Suja Juice, Coca-Cola
Suja Juice received a minority investment from Coca-Cola last year.

“For us, when we felt we had proof of concept for conventional channels, we really wanted to go, and in order to do that, we had to make sure our cost structure was right, and we had to make sure we had proper funding to do that because we’re not going to win the day with an $8 bottle of juice,” Mr. Church said. “It’s got to be a product where the quality and integrity is there, but the price delta between our products and traditional products is not that high, and the way to do that is to partner with someone who can help us with that.”

Suja Juice leverages Coca-Cola’s scale, distribution and access to cost structure while retaining its entrepreneurial spirit and speed to market, Mr. Church said. Honest Tea also has maintained control over all major decisions, Mr. Goldman said.

“(We had) a three-year runway to demonstrate that we could scale the business the right way and do it with integrity,” he said. “By the time we got to the three-year point where Coke had the option to buy the rest of the company, they said this is working and bought the company.”

V.E.B. also invested in Zico coconut water.

Coca-Cola’s primary objective through V.E.B. is to seek and develop the next billion-dollar brand. The business unit tracks startups through four phases of growth: experimentation, proof of concept, pain of growth, and scale to win.  V.E.B. also analyzes consumer trends to predict how the beverage marketplace will evolve over the next five to 10 years.

“For me, the exciting part is not only have we shifted the way we’re looking at the brands, but we’re looking at how we operate,” Mr. Mitchell said. “Much more at the street level … (We have become) cognizant of segmentation and cognizant that what people buy in the store on one street may be very different than what we’re buying five blocks away. How we market that, how we divide our products in each one of those stores is very important.

“That’s not easy for us. That’s a long road for us, quite frankly.”