A survival plan for bricks-and-mortar Starbucks

Posted on by .

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

Posted on by .

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

Posted on by .

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.

Group explores potential for paper-like electronics to revitalize U.S. manufacturing

Posted on by .

Wall Street Journal
By DON CLARK
Sept. 3, 2016

SAN JOSE, Calif.—Silicon Valley is rethinking one of its least glamorous and most ubiquitous building blocks, the circuit board, in a bet that flexible, form-fitting alternatives could reshape electronics and spur more manufacturing in the U.S.

Backers envision ultrathin boards like skin patches that could analyze the sweat of soldiers and pilots, wrap around gas pipelines and act as leak detectors, or provide grids of flexible sensors able to detect stress on airplane wings.

Such possibilities—the focus of a new manufacturing consortium here backed by the U.S. Department of Defense and others—require materials and production techniques that differ from conventional circuit boards, made of stiff plastic.
In some cases, circuitry is imprinted on paper, plastic or other organic materials using processes akin to inkjet printing. The results, which can be as thin as temporary tattoos, can be tailored for extended contact with the skin or in large formats applied to walls or roofs.

The concept of applying printing techniques to electronics has been around for more than a decade. Raghu Das, chief executive of the research firm IDTechEx, said past efforts in the field largely have failed because they attempted to replace silicon chips, which remain less expensive and more powerful for many purposes.
Instead, researchers now are looking to replace the circuit boards on which chips are placed, using techniques they call flexible hybrid manufacturing. Techniques include wiring semiconductors together on flexible surfaces, creating products that are more versatile than existing circuit boards.

“All the other electronics out there is in a box,” said Malcolm Thompson, executive director of the San Jose institute known as NextFlex. “We are out of the box.”

Progress may not come quickly. Though suppliers of materials and manufacturing technology are pushing the concept aggressively, Mr. Das said flexible electronics has suffered from a dearth of inventors applying the techniques in new products.
Besides expanding where technology can go, companies and government officials hope the new approach can influence where it is built.

Most high-volume production of chips and other electronic devices long ago moved from Silicon Valley to China and other lower-cost locations. But manufacturing specialists like Jabil Circuit Inc. and Flextronics International Ltd. keep facilities in the U.S. to help customers design products and build prototypes.

Flexible hybrid manufacturing could offer more opportunities for such work, some industry executives say, as companies devise new gadgets that must be introduced quickly and evolve rapidly.

“We are very interested and curious about democratizing manufacturing,” said Janos Veres, program manager for novel and printed electronics at Palo Alto Research Center Inc., a unit of Xerox Corp. Flexible electronics, he said, “will open up a whole new raft of business models.”

Some executives hope U.S. companies develop proprietary know-how to make flexible circuitry, which could make it harder for foreign factories to produce the same products. “You don’t want to share it,” said Daniel Gamota, vice president of Jabil’s hardware innovation group.
Techniques derived from printing are widely used to embed tiny wires in places like car windshields, antennas, solar cells and radio-frequency identification tags. Computer displays also are being fabricated using plastic, including organic light-emitting diodes. IDTechEx predicts flexible electronics revenue including those displays will triple to $26.2 billion by 2020 from $8.6 billion this year.

One key development, Mr. Thompson said, has been techniques devised by companies such as American Semiconductor Inc. and Uniqarta Inc. to make thinner silicon wafers for chip production. In some cases, the resulting chips can be rolled up like a piece of paper.

The U.S. military’s interest stems partly from a desire to reduce the weight of gear soldiers must carry and to track their condition. Mr. Thompson said flexible sweat-monitoring sensors can detect chemicals that indicate fatigue on wearers on the battlefield or in cockpits.

Boeing Co. on Wednesday showed off flexible antennas that could be deployed on aircraft for radar and other applications. Other efforts are aimed at intelligence agents who need to keep information out of enemy hands. PARC engineers have worked under a Pentagon research program to print information on glass that could disintegrate into unreadable bits in response to a remote command.

NextFlex, whose funding includes $75 million from the Defense Department, is among several institutes the Obama administration has set up to encourage collaboration in manufacturing technology. The institute is installing tools to help experiment with production techniques and funding development projects at corporate and university labs.

How the Best Business Leaders Disrupt Themselves

Posted on by .

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.” 

AI Is Learning to See the World—But Not the Way Humans Do

Posted on by .
MIT Technology Review
by Jamie Condliffe
June 30, 2016

AI systems are modeled after human biology, but their vision systems still work quite differently.

Computer vision has been having a moment. No more does an image recognition algorithm make dumb mistakes when looking at the world: these days, it can accurately tell you that an image contains a cat. But the way it pulls off the party trick may not be as familiar to humans as we thought.

Most computer vision systems identify features in images using neural networks, which are inspired by our own biology and are very similar in their architecture—only here, the biological sensing and neurons are swapped out for mathematical functions. Now a study by researchers at Facebook and Virginia Tech says that despite those similarities, we should be careful in assuming that both work in the same way.

To see exactly what was happening as both humans and AI analyzed an image, the researchers studied where the two focused their attention. Both were provided with blurred images and asked questions about what was happening in the picture—“Where is the cat?” for instance. Parts of the image could be selectively sharpened, one at a time, and both human and AI did so until they could answer the question. The team repeated the tests using several different algorithms.

Obviously they could both provide answers—but the interesting result is how they did so. On a scale of 1 to -1, where 1 is total agreement and -1 total disagreement, two humans scored on average 0.63 in terms of where they focused their attention across the image. With a human and an AI, the average dropped to 0.26.

In other words: the AI and human were both looking at the same image, both being asked the same question, both getting it right—but using different visual features to arrive at those same conclusions.

This is an explicit result about a phenomenon that researchers had already hinted at. In 2014, a team from Cornell University and the University of Wyoming showed that it was possible to create images that fool AI into seeing something, simply by creating a picture made up of the strong visual features that the software had come to associate with an object. Humans have a large pool of common-sense knowledge to draw on, which means they don’t get caught out by such tricks. That’s something researchers are trying to incorporate into a new breed of intelligent software that understands the semantic visual world.

But just because computers don’t use the same approach doesn’t necessarily mean they’re inferior. In fact, they may be better off ignoring the human approach altogether.

The kinds of neural networks used in computer vision usually employ a technique known as supervised learning to work out what’s happening in an image. Ultimately, their ability to associate a complex combination of patterns, textures, and shapes with the name of an object is made possible by providing the AI with a training set of images whose contents have already been labeled by a human.

But teams at Facebook and Google’s DeepMind have been experimenting with unsupervised learning systems that ingest content from video and images to learn what human faces and everyday objects look like, without any human intervention. Magic Pony, recently bought by Twitter, also shuns supervised learning, instead learning to recognize statistical patterns in images to teach itself what edges, textures, and other features should look like.

In these cases, it’s perhaps even less likely that the knowledge of the AI will be generated through a process aping that of a human. Once inspired by human brains, AI may beat us by simply being itself.

Shoppers Flock to Apps, Shaking Up Retail

Posted on by .

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.”

BT-AI061_SHOPAP_16U_20160412221805

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

Posted on by .

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.”

Ways Predictive Analytics Improves Innovation

Posted on by .

InformationWeek

3/12/2016

From drug discovery to price optimization, across virtually every industry, more companies are using predictive analytics to increase revenue, reduce costs, and modernize the way they do business. Here are some examples.

Disrupt An Industry Drug discovery has been done the same way for decades, if not centuries. Researchers have a hypothesis-driven target, screen that target against chemical compounds, and then iteratively take them through clinical trials. As history has shown, a lot of trial and error is involved, perhaps more than is necessary, particularly in this day and age. According to industry association PhRMA, it takes an average of more than 10 years and $2.6 billion to develop a drug. Pharmaceutical company BERG Health aims to change that. It is using predictive analytics and artificial intelligence (AI) to discover and develop lifesaving treatments. 'There's no way a human can process the amount of data necessary to dissect the complexity of biology and disease into form-based discovery,' said Niven Narain, founder and CEO of BERG. 'We use human tissue samples to learn about as many biological components as we can and we include that patient's clinical and demographic data.' Its platform builds a model of healthy individuals and then compares that to individuals with a disease. The AI then builds a model of the genes and proteins that pinpoints the core differences between health and disease. The model helps BERG target its drug discovery process. The company also uses the same process to identify which patients are the best candidates for a certain drug. Using a single tissue sample, its platform can create more than 14 trillion data points that collectively become a 'patient signature.' The patient signature indicates whether or not the individual will likely respond well to a treatment that, for example, is far more precise than first-line pancreatic cancer treatment. First-line pancreatic treatments fail 90% of the time, Narain said. (Image: bykst via Pixabay)

Disrupt An Industry

Drug discovery has been done the same way for decades, if not centuries. Researchers have a hypothesis-driven target, screen that target against chemical compounds, and then iteratively take them through clinical trials. As history has shown, a lot of trial and error is involved, perhaps more than is necessary, particularly in this day and age. According to industry association PhRMA, it takes an average of more than 10 years and $2.6 billion to develop a drug. Pharmaceutical company BERG Health aims to change that. It is using predictive analytics and artificial intelligence (AI) to discover and develop lifesaving treatments.

“There’s no way a human can process the amount of data necessary to dissect the complexity of biology and disease into form-based discovery,” said Niven Narain, founder and CEO of BERG. “We use human tissue samples to learn about as many biological components as we can and we include that patient’s clinical and demographic data.”

Its platform builds a model of healthy individuals and then compares that to individuals with a disease. The AI then builds a model of the genes and proteins that pinpoints the core differences between health and disease. The model helps BERG target its drug discovery process. The company also uses the same process to identify which patients are the best candidates for a certain drug.

Using a single tissue sample, its platform can create more than 14 trillion data points that collectively become a “patient signature.” The patient signature indicates whether or not the individual will likely respond well to a treatment that, for example, is far more precise than first-line pancreatic cancer treatment. First-line pancreatic treatments fail 90% of the time, Narain said.

(Image: bykst via Pixabay)

Meet Customer Demand

Handmade photo product company PhotoBarn has increased its throughput 500% by creating warehouse software and lean manufacturing processes that are built around predictive analytics. Before its transformation in 2015, the company struggled to balance supply and demand.

About halfway through 2015, the company started using predictive analytics to forecast sales, inventory, and raw materials to anticipate what it would need before and during the holiday season. That and its new lean manufacturing process enabled the company to move five times more product using the same number of people.

“The spikes and volumes in the holiday period are hard to handle. In 2015, we reimagined our supply chain from suppliers to customers,” said PhotoBarn’s business analytics and marketing chief Ryan McClurkin. “We were able to handle the order volumes without hiccups [because] we’re anticipating versus reacting, and it pays huge dividends.”

Right-Size Resources

Predictive analytics has helped Alabama’s Birmingham Zoo more accurately forecast attendance. As a result of that, the company can make more informed staffing and marketing decisions.

“The number of people who attend the zoo affects staffing, marketing and events planning. You could look at historical averages, but we pulled historical data and correlated that with weather data, school calendars, national holidays, [and other] variables to predict how many people would show on a given day,” said Joshua Jones, managing partner at data analytics and data science consulting firm StrategyWise.

The information is displayed on a digital dashboard that provides a much more accurate forecast. Instead of guessing that 10,000 people will come to the park based on historical information alone, Birmingham Zoo can now see it is likely that 7,131 visitors (or whatever the number happens to be) will attend on a particular day.

Create The Perfect Game

Success in the lottery industry is all about finding the right payout levels. Two of the most important factors are the sizes of the prizes and the frequency of payouts, which is why prize values and odds vary significantly in a single game. However, some games are more popular than others.

“Lotteries want to maximize their revenues so they can [contribute to] education and whatever social programs the state wants to support,” said Mather Economics director Arvid Tchivzhel. “We’ve measured responses in tickets purchased due to changes in the payout structure. You can almost build the perfect game based on where you set the payout levels and the frequency.”

Sell More Effectively

Jewelry TV (JTV), like many luxury goods retailers, was hit hard by the recession. The company tried a number of tactics to improve sales that didn’t work as well as hoped, so it eventually embraced predictive analytics.

“A regression model helps you understand what’s impacting your revenue. When you start building a predictive analytics model, it can tell you why what you’ve been doing isn’t working — customers don’t care,” said Ryan McClurkin, former director of strategic analytics at Jewelry TV and currently chief of business analytics and marketing at PhotoBarn. “Predictive analytics can tell you your customers care about this [instead].” That’s the power of predictive analytics. It allows you to see the variables you can innovate around.