Friday, July 29, 2016

Amazon, once a big spender, is now a profit machine

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Until last year, Amazon was known as much for its ubiquity in online retail as it was for the bold and expensive visions of CEO Jeff Bezos. The company would spend mightily, only to assuage investor fears with promises of a bright future. Many of those investments, like cloud computing, have become huge and profitable businesses. Some — like the failed Fire Phone —have fallen flat on their face. Now, however, Amazon appears to have hit its stride as Bezos' long-term visions for the future begin paying off big time. For the fifth straight quarter, Amazon has earned a profit. This time around, that profit is bigger than it's ever been before, and by a large margin.
For the second fiscal quarter of 2016, Amazon posted a profit of $857 million, or $1.78 a share, on revenue of $30.1 billion, making these past three months its most profitable quarter in its history. Those earnings handily beat Wall Street expectations, with investors putting Amazon at EPS of $1.11 a share on revenue of $29.56 billion. Year over year, Amazon's profit is up 832 percent while sales have jumped 31 percent from the second quarter of 2015, when Amazon made a profit of only $92 million. After a brief and perplexing dive, Amazon's stock is now up 3 percent, and its share price has jumped more than 40 percent in the last 12 months.
AMAZON POSTED ITS BIGGEST QUARTERLY PROFIT IN THE COMPANY'S HISTORY
So where is this money coming from? Multiple places, in fact. Amazon has begun turning the corner on its more costly sectors like international retail. It's also ramped up investment in cloud computing with the highly profitable Amazon Web Services. Lastly, the company is figuring out ways to cut its logistics costs as it adds more delivery flexibility for its Prime subscription service, which continues to grow and now sits at 60 million members in the US alone. This past week, Prime launched in India. Despite its obvious free shipping perk, Amazon continues to use the service as a way to bring new products, like video streaming and food delivery, to new markets.
"It’s been a busy few months for Amazon around the world, and particularly in India — where we launched a new AWS Region, introduced Prime with unlimited free shipping, and announced that Prime Video is coming soon, offering Prime members in India exclusive access to Amazon Original Series and Movies — including original content featuring top Indian creators and talent," Bezos said in a statement. "The team in India is inventing at a torrid pace, and we’re very grateful to our Indian customers for their welcoming response."
AWS, Amazon's cloud computing platform used by companies as big as Netflix and Spotify, continues to grow. The division more recently added Salesforce as a primary customer, Amazon says, and it posted profit of $718 million on $2.9 billion in sales. That's a 135 percent jump in profit and 58 percent jump in sales compared with the year-ago quarter. For the second straight quarter, AWS earns Amazon more profit than its entire North American retail division.
Amazon's international retail division still loses it money, but it's closing the gap. This past quarter, the division posted an operating loss of $135 million, down 29 percent from $189 million in the second quarter of 2015.
It's an open question whether Amazon can continue to sustain this profit growth, but the answer may not arrive for some time. Due to the huge success of the company's second annual Prime Day, which occurred on July 12th and broke sales records, the fiscal third quarter ending September 30th may result in yet another big beat. Worldwide Amazon orders rose 60 percent from last year's Prime Day, which amounts to a potential bump of $600 million in additional sales. For the current quarter, Amazon forecasts sales of $31 billion to $33.5 billion.

9 Ways Retailers Are Using Big Data and Hadoop 

Datanami retail
The big data revolution is changing how business gets done in all industries. That includes the massive retail market, which drives $2.6 trillion in business in the U.S. and employs 42 million Americans. The use of advanced analytics and predictive modeling is changing the face of retail, and helping us all get what we want, when we want it.
Here are 9 ways retailers are using big data technology to create an advantage in the retail sector.

1. Recommendation Engines

This is one of the classic use cases of big data tech in retail (albiet mostly in ecommerce settings). Based on a customer’s purchase history, and the histories of others like him, what is that customer likely to purchase next? By training machine learning models on historical data, the savvy retailer can generate accurate recommendations before the customer leaves the Web page.
“When you think about recommendations, everybody wants to beat Amazon,” says Eric Thorston,Hortonworks‘ GM for consumer products. “Love them or hate them–most retailers hate them–Amazon makes from 35 percent to 60 percent revenue uplift on recommendations, and everybody is saying, 'How can we get a piece of that?' ”
This is one of the “low hanging fruit” opportunities because it is implemented relatively easily and can have an immediate impact on revenue and profit. According to Thorston, retailers are finding they can improve revenue uplift by generating recommendations on Hadoop, instead of using standard recommendation engines.
“One customer was using a third-party recommendation engine,” he says. “They brought that in house, let the merchants and developers build up some logic, and deployed it. They raised revenue uplift to 24 percent. So you’re still not getting an Amazon-like response, but you’re getting a huge boost.”

2. Customer 360

Forrester analyst Mike Gualtieri often talks about how consumers today want the “celebrity” treatment when they engage with companies. We all want to be treated like Taylor Swift (or perhaps Kanye West) when we enter a store. We expect companies to anticipate our needs, to have the products we want on-hand, to communicate with us in real time (via social media), and to adapt to their needs as they change.customer 360
This is a tall order for any retailer to achieve, but it would be practically impossible to do without some sort of Customer 360 initiative. And considering how many customers a retailer must interact with, and how many data sets are involved with getting there, big data technology and real-time processing is critical to making that happen.
Companies in lots of industries would like to have a 360 degree view of their customers. However, few actually obtain it. In the cutthroat world of retail, where profit margins are razor thin, developing a customer 360 system may be a matter of survival.

3. Market Basket Analysis

Market basket analysis is a standard technique used by merchandisers to figure out which groups, or baskets, or products customers are more likely to purchase together. It’s a well-understood business processes, but now it’s being automated with big data.
“We’ve been doing basket analysis the old fashioned way for decades, if not hundreds of years, but now have an opportunity to add mechanization to that,” Hortonworks’ Thorston says. “We have a fast food chain that’s doing that with us. They’re able to analyze what they’re selling when something is on sale or when something is given away in a promotion.”
Platforms like Hadoop help with basket analysis by enabling a retailer to analyze more data. The capability to store many year’s worth of receipts, instead of just one or two year’s worth, for many different items can help retailers get better confidence in their analyses.
“I’m seeing basket analysis a lot,” Thorston says. “Basket analysis is big.”

4. Path to Purchase

Analyzing how a customer came to make a purchase, or the path to purchase, is another way big data tech is making a mark in retail.
(6kor3dos/Shutterstock)
(6kor3dos/Shutterstock)
While marketing executives have studied path-to-purchase techniques for many years, the advent of big data and big data tech is enabling them to get much more out of this type of analysis. The rise of multi-channel marketing and retailing and omni-channel selling is creating a large number of different paths that customers can take to buying a product.
Tools like Hadoop, Spark, and machine learning libraries can be instrumental in eliminating a “shotgun” approach to understanding customer buying patterns and instead zeroing in on exactly what works in the real world. The rise of social media (people love to talk about what they just bought) also helps because it generates much more data to work with on path-to-purchase projects.

5. Social Listening for Trend Forecasting

As a retailer, if you’re not at least listening to social media at this point—let alone actively engaging with them on Instagram or Twitter–then you’re missing out on a slew of free and potentially invaluable information that can help you spot trends.
Platforms like Hadoop were designed to facilitate the handling and analysis of large amounts of unstructured data, such as Facebook posts or Pintrest pins. The use of natural language processing (NLP) to extract information from social media, and machine learning to make sense of them, can give the social enterprise an edge over the competition.
But care must be taken in using social listening, Hortonworks’ Thorston says. “Social listening is critical for understanding the target, and it’s very important in confirming what you have,” he says. “But the minute you make a wrong move, you lose. The obligation is to use it judiciously. That prevents the misuse and that also preserves and supports and aligns to the ultimate goal, which is customer intimacy, customer loyalty, increased revenue, and increased margin.”

6. Price Optimization

Having the right price on a product can mean the difference between making a sale and losing a customer. But what is the right price? That’s the million-dollar question merchants have struggled with for millennia. But retailers who approach this problem with big data tools may have an advantage over those that don’t.
shutterstock_retail_analytics_Sergey Nivens
(Sergey Nivens/Shutterstock)
In many cases, setting the right price requires knowing what your competitors are changing. In the past, retailers would employ “secret shoppers” to gather this intel. That data can be collected electronically using daemons that crawl competitors’ website to get detailed info about product pricing.  “That’s not new news,” Thorston says. “But the machine learning helps you by saying ‘Here’s what we think will resonate in this market at this time for this buyer.'”
Hortonworks helped one customer by building an automated price-optimization routine that features to a Web-based dashboard interface. “When the person who manages the category shows up, she can say, ‘Yes I accept the recommendation,'” Thorston says. “It’ll go out and reach into the structural system, change the price, which will flood out to stores. Then she can launch a promotion along with that.”

7. Workforce and Energy Optimization

What’s the single largest cost for retailers? If you said “labor,” then give yourself a big red star. While it’s true that big data tech can deliver benefits on the marketing and merchandising side, it can also help big retailers optimize their spending on human capital, which can have a sizable impact on the bottom line.
Figuring out where and when to staff people can have a big impact on the bottom line, according to Alexander Gray, Ph.D., the CTO and co-founder of machine learning automation company Skytree.
“One of the engagements we’re doing is around workforce optimization,” Gray tells Datanami. “An organization discovered that if they put different amount of manpower on different parts of the store, they can actually…have a large impact on revenue.  That’s another lever that’s opened up by having more powerful data discovery capabilities.”
Retailers can also save big bucks by using big data tech to analyze their energy usage, which is another factor on the cost side of the ledger. “We’re working with a supermarket where energy costs are very high,” Gray says. “Optimization the running of those machines, as well as preventative-maintenance type problems, turns out to be a big concern for them.”

8. Inventory Optimization

Inventory optimization is a complicated thing that touches many aspects of the consumer goods supply chain, and often requires close coordination among manufacturers and distributors. But with the rise of omni-channel fulfillment, retailers are increasingly looking for ways to improve the availability of in-demand products.Omni_channel
“I have one customer who is fulfilling to the store saying ‘Hey I’m out in the back room. Where am I going to fulfill from? Am I going to go to the store next door, to the fulfilment center, to the distribution center, or to the vendor,” Thorston says. “That logic is driven by a Hadoop cluster.”
Time and distance matter in this equation, and so does another factor that’s not so obvious: seasonality. For example, if a retailer is trying to locate a particular dress for a customer in Florida, the retailer may have to choose between shipping the dress from their Boston or Los Angeles location.
“Are they going to ship from Boston, where maybe you’ll have two months to sell that dress? Or are you going to ship from LA, where you might have a window of 8 months,” Thorston says. “You need to make sure you plan your seasonality in the shipping algorithms.”

9. Fraud Detection

Retail fraud is a huge problem, accounting for hundreds of billions of lost dollars every year. Retailers have tried every trick in the book to stop fraud, and now they’re turning to big data tech to give them an edge.
Big data analytics can help retailers fight fraud in a number of ways. For starters, they can use predictive capabilities to create a baseline sales forecast at the SKU level. If a product deviates noticeably outside of that range, it could indicate some fishy business.
Fraud committed by employees can be tough to stop. But with the power of big data tech, internal controllers may be able to create more transparency into internal activities.
Retailers have some work to do to beef up their anti-fraud activities, according to Robert Fowlie, a partner in the Forensic practice of Deloitte Canada. “Many retail companies have significant amounts of data at their disposal, captured daily through operations,” Fowlie says in a Wall Street Journal story. But turning that data into insight through continuous monitoring and real-time feedback remains a challenge.”

Supply Chains to Admire: 2016 Results

sc-to-admire-2016-general_miniFive months of analysis. Lots of heated debates. It is now over. This morning we announced the Supply Chains to Admire Winners and Finalists for 2016. The research starts in April and stretches over many weeks as we analyze the different elements and understand the patterns of each industry.
Why do we do it? Selfishly, we need a standard for our research, but we also want to help supply chain leaders gain new insights from a deep data-driven analysis. There is no agreed-upon standard for supply chain excellence. Leaders agree that it is easier to say than define. A clear goal is needed to drive progress. The answer? We think deep research to help companies determine benchmarks and set goals.
Background.
The Supply Chains to Admire analysis is now in its third year. It is data driven research: a deep analysis of performance, improvement and Price to Tangible Book Value of 320 companies across 31 industries for the period of 2009-2015. The source data for the analysis is public reporting of balance sheets and income statements.
To determine the winners, we compare the effectiveness of each company against relative performance within an industry-specific peer group. We determine which companies have driven higher levels of improvement (based on Supply Chain Index calculations) and shareholder value (as defined by Price to Tangible Book Value) while outperforming their peer group on growth, operating margin, inventory turns and Return on Invested Capital (ROIC).
Figure 1. Supply Chains to Admire Methodology
flows
The Results
In the analysis, we divide companies into three groups: winners, finalists and under-performers.
  • Winners. The winners of this analysis meet all of the criteria of improvement, value and performance when compared to a like industry peer group. Sixteen companies qualify against this criterion. This is a high-performing group representing 5% of public companies studied.
  • Finalists. A finalist drove higher levels of improvement, and value, and are within 10% of the industry average on three out of four of the performance factors, and no more than 25% below the mean on any of the four factors of growth, operating margins, inventory turns and ROIC. Twenty-one companies meet this criterion. In this analysis, 7% of companies studied are finalists. The combination of finalists and winners equals 12% of companies studied.
  • Underperformers. The winners and finalists are an elite group. 88% of companies do not meet the three criteria of improvement, value or performance. Unfortunately, we find most companies are moving backwards on the Supply Chain Metrics That Matter™ or making progress on singular metrics versus driving performance improvement on a balanced portfolio of supply chain metrics that correlate to market capitalization. (If you see one of my presentations, I am sure that you remember the gnarly patterns of the orbit charts.) This includes industry icons that are often referenced as best-in-class supply chains. (When you hear an industry consultant speak of a top performing supply chain, trust but verify. Check out their performance by plotting year-over-year metrics at the intersection of two ratios and look at the patterns. We find the patters and the intersection of inventory turns and operating margin and growth and Return on Invested Capital (ROIC) to be insightful.)
Figure 2. Results of the Supply Chains to Admire Analysis
2016 Supply Chains to Admire Winners and Finalists 2009-15
There are no winners or finalists in the industries of aerospace & defense (A&D), automotive, automotive suppliers, conglomerates, consumer durables, ecommerce retail, hospitals, over-the-counter drugs (OTC), packaging, pharmaceutical, third-party logistics or toy industries. Similarly, industries like beverages, contract manufacturing, food, oil & gas, restaurants and fast food, and retail apparel have finalists, but no winners. We find it ironic that the industries with the greatest challenges—high-tech & electronics—post the greatest progress, while industries with slow market shifts—household products, food, and beverage—are regressing. 
What Drives Value?
In the report, we wanted to determine which factors correlate to Price to Tangible Book Value (PTBV). We wanted to answer the question of, “What Should Supply Chain Leaders Do to Drive Value?”
As a part of this analysis, we wanted to answer the question for supply chain leaders on what drives value. To answer this question, in parallel with the Supply Chains to Admire research, we mined our quantitative data to answer the question of, “What steps should companies take to improve PTBV?”
In the analysis of the Supply Chains to Admire, we use PTBV as a proxy metric of value. We believe that improving the value of shares outstanding in relationship to assets and tangible book value is within the control of the supply chain leader. The definition of PTBV is:
Price to Tangible Book Value = Market Share Price / Tangible Book Value/Shares Outstanding
To help the supply chain leader reading this report, we wanted to use our survey database to understand the relationship between strategies/process options and improving PTBV. (Through this analysis, we find that companies that have a successful Supply Chain Center of Excellence, an effective S&OP process, and have less business pain with supplier reliability drive greater PTBV performance. In the spirit of transparency, in Figure 3, we include the correlations of these factors to PTBV.  In addition, factors considered, but had a correlation less than r=0.30 and greater than -0.30 are included in the full report. Check it out! As many commonly held factors, like a single instance of ERP, do not show a pattern of correlation to PTBV.
Figure 3. Steps to Take to Improve Price to Tangible Book
price-to-tangible-book-2012-15-infographic2
What Can We Learn from the Research?
When we interview companies making the Supply Chains to Admire list, we find commonalities and similar patterns. These are shown in Table 1.
Table 1. Characteristics of Supply Chains to Admire Leaders
characteristics of the Supply Chain to Admire winners
These companies have longer tenure of their leadership teams with a focus on long-term outcomes. In addition, there is consistency in direction. The teams sidestep fads with a dogged focus on supply chain excellence.
Complexity hampers results. In our analysis, we also find that these companies are more focused on the management of complexity through the adoption of customer segmentation, cost-to-serve analysis and item rationalization. They are better  at horizontal processes, supply chain planning and network design (form/function of inventory).
Over the course of the next six months, we will be sharing case studies of these companies in our conferences, podcasts, webinars, and writing. We want to raise the level of discussion on supply chain excellence and get out of the trap of blindly defining legacy processes as “best practices.”
For details check out the full report on Supply Chain Insights. Winners will be featured on the podcast Straight Talk with Supply Chain Insights and at the Supply Chain Insights Global Summit on September 7-9, 2016 in Scottsdale, AZ. We  hope to see you there! Please join me in congratulating the top 12% of supply chains that are driving improvement, value and higher levels of performance on the Supply Chain Metrics That Matter.

The future of vegetables is ugly

  
The future of produce is ugly. Twisted, blemished, mutated and deformed, to be specific.
That’s because an increasing number of grocery chains and crop-sharing services have begun stocking and distributing fruits and vegetables that were once deemed unfit for sale based solely on appearance. To be clear, these goods aren’t damaged or rotten or distasteful. If a chef chopped them up and served them in a souffle, most would never know the difference. Their banishment from shelves was purely produce prejudice.
In recent years, a small number of eco-conscious consumers have begun buying imperfect produce, often at discounted prices, in an effort to chip away at the planet’s staggering level of food waste. Now, the buy-ugly movement has been thrust into the mainstream. Walmart, the nation’s largest grocer with more than 4,000 produce-selling stores, announced last week it would sell less-than-pretty apples in 300 stores across Florida. This builds on an ugly potato program the retail behemoth launched in Britain earlier this year.
By virtue of its size, when Walmart makes a change, the broader retail sector notices. The company commands a massive and global supply chain and possesses tremendous purchasing power, a combination that allows it to disrupt traditional distribution channels and create demand for products where there once was none. And food is big business for Walmart. Groceries accounted for 56 percent of all sales in the company’s U.S. stores each of the past three years, according to Securities and Exchange Commission filings.
Walmart’s program “is a result of working with our suppliers to build the infrastructure and processes that create a new home for perfectly imperfect produce,” Shawn Baldwin, Walmart’s senior vice president for global food sourcing, produce and floral, wrote on the company’s blog. “Because ugly produce can occur unexpectedly in any growing season or crop, we want to have the systems in place to offer this type of produce whenever it may occur.”
Walmart is hardly alone in its embrace of ugly fruits and vegetables. Whole Foods has sold similar goods in the past, but it does not have the mass market reach of Walmart. And getting U.S. consumers to buy flawed produce has not been easy. Shoppers, particularly Americans, have long fixated on food with visual appeal in addition to (or instead of) nutritional value. There’s a reason the food that appears in television and magazine advertisements look like modern still-life paintings, meticulously crafted by professional stagers to the point of defying reality.
European shoppers, by contrast, have been pioneers in food conservation and buying ugly. Britain has conducted a “Love Food Hate Waste” campaign since at least 2007, and the European Union has pledged to cut its food waste in half over the next decade. Winning acceptance for ugly produce is just one tactic being pursued in Europe to meet that goal. Others see a moneymaking opportunity; a grocery store in Denmark called WeFood sells only discounted food that is either misshapen or past its stated expiration date.
Our vanity contributes to America’s waste epidemic. No matter how you slice it, the United States throws away tons of edible food every year. The Natural Resources Defense Councilestimates that 40 percent of food grown and produced in the United States each year goes uneaten. More fruit and vegetables are wasted than any other food category, with 52 percent being lost rather than consumed.
There are many factors that lead to this waste. Some food may simply spoil before it’s sold or fail to meet standards set by the Agriculture Department for consumable goods sold in the United States. Retailers also have guidelines for produce they are willing to shelve, and those guidelines are often more stringent than the government regulations.
For its part, the food industry has made efforts to eliminate some of this waste by selling misshapen produce as ingredients in other foods, said Kathy Means, vice president of industry relations at the Produce Marketing Association. Strawberries may be mashed into jam, for example. Baby-cut carrots were invented in 1986 as a way to repurpose full-size carrots that weren’t considered grocery-store caliber.
“It still is a huge challenge to get people to understand that ugly fruits and vegetables are perfectly fine to eat, and that it’s good for our planet,” said Evan Lutz, the chief executive of Hungry Harvest. Founded in 2014, the company makes weekly shipments of store-rejected produce to homes around the Mid-Atlantic.
And when the package arrives, the contents don’t always look like a middle school science project. Sometimes perfectly normal produce can be rejected by stores for logistical reasons, and could wind up in a landfill if not for alternative sellers such as Hungry Harvest. “People are really surprised often times when they get their box [of ugly produce]. It’s really the same stuff you would find in the grocery store,” Lutz said.