Monthly Archives: March 2016

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.”

Ways Predictive Analytics Improves Innovation

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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.