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THE TOP 20 MOST POPULAR SURVEY SOFTWARE SOLUTIONS [INFOGRAPHIC]

SurveyMonkey is the most popular software program for conducting online surveys and analyzing the resulting poll data, according to a recent report from Capterra.

The researchers ranked the popularity of survey software solutions based on three criteria: the number of customers (unique companies/organizations that are actively using the software), the number of users (employees who create and manage surveys), and social media presence (Facebook likes, Twitter followers, Google +1s, LinkedIn followers, Klout scores, and number of online reviews).

SurveyMonkey has the most customers (2.35 million) of any survey program examined, as well as the largest social media presence; it is tied with QuestionPro for the most number of users (2.5 million).

QuestionPro received the second-highest composite popularity score, followed by Qualtrics, SurveyGizmo, and FluidSurveys.

Check out the infographic for the full rankings from the report.

Click Image To Enlarge

Courtesy of an article dated October 22, 2015 appearing in MarketingProfs

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PREDICTIVE ANALYTICS: WHAT IT IS, APPLICATIONS, INDUSTRY USES, HOW TO GET STARTED

 

“The dilemma is, as it is often said, correlation does not imply causation. The discovery of a predictive relationship between A and B does not mean one causes the other, not even indirectly. No way, nohow.”  - Eric Siegel, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die

What is predictive analytics?

Predictive analytics is the use of data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data.

The goal is to go beyond descriptive statistics and reporting on what has happened to providing a best assessment on what will happen in the future. The end result is to streamline decision making and produce new insights that lead to better actions.

Predictive models use known results to develop (or train) a model that can be used to predict values for different or new data. The modeling results in predictions that represent a probability of the target variable (for example, revenue) based on estimated significance from a set of input variables. This is different from descriptive models that help you understand what happened or diagnostic models that help you understand key relationships and determine why something happened.

More and more organizations are turning to predictive analytics to increase their bottom line and competitive advantage using predictive analytics. Why now?

• Growing volumes and types of data and more interest in using data to produce valuable information.
• Faster, cheaper computers and easier-to-use software.
• Tougher economic conditions and a need for competitive differentiation.

With interactive and easy-to-use software becoming more prevalent, predictive analytics is no longer just the domain of mathematicians and statisticians. Business analysts and line-of-business experts are using these technologies as well.

What is predictive analytics?

A 2014 TDWI report found that the top five things predictive analytics are used for is to:

• Identify trends.
• Understand customers.
• Improve business performance.
• Drive strategic decision making.
• Predict behavior.

Some of the most common applications of predictive analytics include:

• Fraud detection and security – Predictive analytics can help stop losses due to fraudulent activity before they occur. By combining multiple detection methods – business rules, anomaly detection, link analytics, etc. – you get greater accuracy and better predictive performance. And in today’s world, cybersecurity is a growing concern. High-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate occupational fraud, zero-day vulnerabilities and advanced persistent threats.
• Marketing –  Most modern organizations use predictive analytics to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow the most profitable customers and maximize their marketing spending.
• Operations – Many companies use predictive models to forecast inventory and manage factory resources. Airlines use predictive analytics to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on any given night to adjust prices to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.
• Risk – One of the most well-known examples of predictive analytics is credit scoring. Credit scores are used ubiquitously to assess a buyer’s likelihood of default for purchases ranging from homes to cars to insurance. A credit score is a number generated by a predictive model that incorporates all of the data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.

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Predictive analytics use across industries -- real life examples

Any industry can use predictive analytics to optimize their operations and increase revenue. Here are a few examples:

• Credit card, banking and finan­cial services. Detect and reduce fraud, measure credit risk, maximize cross-sell/up-sell opportu­nities, retain customers and optimize marketing campaigns. Commonwealth Bank can reliably predict the likelihood of fraud activity for any given transaction before it is authorized – within 40 milliseconds of the transaction being initiated.
• Governments and the public sec­tor. Improve service and performance; detect and prevent fraud, improper payments and the misuse of funds and taxpayer dollars; and detect criminal activities and patterns. The Hong Kong government visualizes and analyzes big, unstructured data to anticipate and address public complaints.
• Health care providers. Predict the effectiveness of new procedures, medical tests and medications, and improve ser­vices or outcomes by providing safe and effective patient care. Taipei Medical University executives analyze and monitor performance across all hospitals in its system.
• Health insurers. Detect and handle insurance claims fraud, identify which patients are most at risk of chronic diseases and know which interventions make the most medical and financial sense. One of the largest pharmacy benefits companies in the US, Express Scripts, uses analytics to identify patients not adhering to their prescribed treatments, resulting in a savings of $1500 to $9000 per patient.
• Insurance companies. Determine insurance premium rates, detect claims fraud, optimize claims processes, retain customers, improve profitability and optimize marketing campaigns. Within two hours of an earthquake striking rural New Zealand, Farmers Mutual Group assessors were headed to affected areas. With SAS Analytics, they knew who their most at-risk policy holders were and chartered a helicopter to get to them quickly.
• Manufacturers. Identify factors leading to reduced quality and production failures, and optimize parts, service resources and distribution. Lenovo used predictive analytics to better understand warranty claims, leading to a 10 to 15 percent reduction in warranty costs.
• Media and entertainment. Deepen insight into audiences by identifying influencing attributes, trends, drivers and desires across properties, and score visitors to determine appropriate audience seg­ments and behavior value. How is the slot floor doing every day? How is the gaming floor performing? How are the nonsmoking tables compared to the smoking tables? The answers – which previously could take numerous weeks and many dollars to find out – are now coming in minutes and at a far lower cost for Foxwoods Resort Casino. 
• Oil, gas and utility companies. Predict equipment failures and future resource needs, mitigate safety and reliability risks, and improve performance. Salt River Project is the second-largest public power utility in the US and one of Arizona's largest water suppliers. A sophisticated forecasting model helps them know the best time to sell excess electricity for the best price.
• Retailers. Assess the effectiveness of promotional events and cam­paigns, predict which offers are most appropriate for consumers, deter­mine which products to stock where and how to build brand loyalty. Staples analyzes online and offlilne consumer behavior to provide a complete picture of their customers, and realized a 137% ROI.
• Sports franchises. Sports analytics is a hot area, thanks in part to Nate Silver and tournament predictions. The NBA’s Orlando Magic uses SAS predictive analytics to improve revenue and determine starting lineups.

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What do you need to get started?

• Problem to solve. The first thing you need to get started using predictive analytics is a problem to solve. What do you want to know about the future based on the past? What do you want to understand and predict? You’ll also want to consider what will be done with the predictions. What decisions will be driven by the insights? What actions will be taken?
• Data. Second, you’ll need data. In today’s world, that means data from a lot of places. Transactional systems, data collected by sensors, third-party information, call center notes, web logs, etc. You’ll need a data wrangler, or someone with data management experience, to help you cleanse and prep the data for analysis. To prepare the data for a predictive modeling exercise also requires someone who understands both the data and the business problem. How you define your target is essential to how you can interpret the outcome. (Data preparation is considered one of the most time-consuming aspects of the analysis process. So be prepared for that.)
• Modeling building tools.  After that, the predictive model building begins. With increasingly easy-to-use software becoming more available, a wider array of people can build analytical models. But you’ll still likely need some sort of data analyst who can help you refine your models and come up with the best performer. And then you might need someone in IT who can help deploy your models. That means putting the models to work on your chosen data – and that’s where you get your results.
• Predictive modeling requires a team approach. You need people who understand the business problem to be solved. Someone who knows how to prepare data for analysis. Someone who can build and refine the models. Someone in IT to ensure that you have the right analytics infrastructure for model building and deployment. And an executive sponsor can help make your analytic hopes a reality.

COMMENTARY:   According to Malene Haxholdt, Global Marketing Manager, Business Analytics, SAS, there are several good reasons why businesses should start predictive analytics initiatives.  

• Growing revenue.
• Lowering costs.
• Establishing governance and compliance.

Companies are looking to get value from predictive analytics in many business areas. The value comes when you can take data, apply analytics, and act on the results. The value ultimately means growing revenue, lowering costs, or establishing governance and compliance. Growing revenue is often associated with customer analytics and being better at retention modeling and cross-sell and up-sell activities. Lowering costs often comes from analytics used to improve processes as well as from using internal capacity in any form. Especially in the financial industry, the driver for predictive analytics can be part of a compliance and governance strategy.

Predictive analytics is relevant and useful across all industries. Some industries are more mature in their use and implementation of predictive analytics. The most common applications of predictive analytics come from a need to better manage fraud, risk, equipment failure, or customer interactions. What are common across industries are the accelerating growth of data and the desire to turn it into valuable information. Predictive analytics is a component of that journey.

It is key to remember that predictive analytics is only valuable if you can turn the results of analytical models into actions. The business process, the people, and the technology need to be aligned to successfully deploy predictive analytics. Think of predictive analytics as part of a life cycle that consists of (1) managing all of your data, (2) exploring all of your data, (3) building your models with the best techniques, and (4) deploying and monitoring your models.

It is important that the organization believes and understands that predictive analytics is driving better decisions. The software tools that allow you to start building predictive models are very approachable and do not require you to be a technical expert. We see a tremendous uptick in interest in interactive predictive modeling using visual analytics and visual statistics. Without any coding required, you can start exploring your data and get value. More mature companies are hiring more data scientists to further grow and explore the possibilities predictive analytics brings. Having human skills that can align the technology and business understanding is key to success.

With the reality of big data, new techniques are being explored by companies to leverage the value hidden in new types of data. Being able to explore all of your data quickly and in an interactive manner is driving the need for data visualization techniques and interactive predictive modeling on very big amounts on data—fast. Also, we see a growing interest in machine-learning techniques such as Random Forest and techniques to handle unstructured data. Take, for example, SAS Contextual Analysis. This next-generation text modeling software combines the ease of machine learning with subjectmatter expertise, enabling powerful text models to easily be defined from unstructured data.

To get sustainable value from predictive analytics, IT and business users are both key in the process. A modern CIO and IT department work closely with the business to enable predictive analytics throughout the organization by providing data access and approachable analytics tools to the right users. Think of it as self-service analytics. The IT department is, in some cases, the true driver of innovation because it can enable the use of all data and help get even more predictive power in the models used by the business. IT’s role is critical in selecting an architecture that will meet future demands around diverse analytical data preparation tasks, reducing model building latency and quickly deploying models into operational systems.

All companies, no matter the size, will benefit from making better decisions in a world of uncertainty. We see small companies that apply predictive analytics as a key strategy to growth. In fact, companies that embed predictive analytics as a cornerstone in their business tend to be more successful in the long run.

Getting A Predictive Analytics Initiative Started

Please read my previous blog post dated October 11, 2015 titled "The Ultimate How-To Guide For Planning A Predictive Analytics Project and Results From An Actual Case Study." if you would like to begin a predictive analytics project at your company. 

Courtesy of an article titled, "Predictive Analytics: What It Is and Why It Matters," by SAS Analytics and the TDWI eBook by SAS titled, "Predictive Analytics: Revolutionizing Business Decision Making"

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McDONALD'S CEO FACES FRANCHISEE REBELLION, BRAND IS IN A 'DEEP DEPRESSION' AND FACING 'ITS FINAL DAYS'

Can McDonald's new CEO Steve Easterbrook lead the company through a franchisee rebellion? (Click Image To Enlarge)

According to a new survey, McDonald's franchisees believe the brand is in a "deep depression" and could be facing its "final days." 

One franchisee wrote in response to the survey by Nomura analyst Mark Kalinowski.

"We are in the throes of a deep depression, and nothing is changing.  Probably 30% of operators are insolvent."

Another wrote,

"The CEO is sowing the seeds of our demise. We are a quick-serve fast-food restaurant, not a fast casual like Five Guys or Chipotle. The system may be facing its final days."

More than a dozen franchisees expressed frustration with McDonald's management, saying that CEO Steve Easterbrook's turnaround plan — which includes initiatives like all-day breakfast and a shift to digital ordering kiosks — is a distraction from the core issues of McDonald's, like food quality and customer service.

One franchisee wrote.

"The lack of consistent leadership from Oak Brook is frightening, we continue to jump from one failed initiative to another." 

A second wrote.

"I have been in this business since the early 1970s but have not seen us this leaderless in all my time."

The company's reaction to their frustration, one franchisee claimed, is for operators to "get out of the system" and quit the business.

Several franchisees complained about all-day breakfast, saying that it has complicated kitchen operations and goes against Easterbrook's repeated promises to simplify the menu.

One franchisee wrote.

"The system is very lost at the moment.  Our menu boards are still bloated, and we are still trying to be too many things to too many people. ... Things are broken from the franchisee perspective."

Franchisees also criticized the "Create Your Taste" program, which allows people to customize their burgers with premium ingredients.

One franchisee wrote.  

"They are throwing everything they can against the wall to see what will stick." 

Kalinowski interviewed 29 US franchisees covering about 226 restaurants for the survey. McDonald's has more than 14,000 restaurants in the US. 

In response to the survey, McDonald's said it's hearing a different story from franchisees — specifically pertaining to all-day breakfast. 

A company spokeswoman told Business Insider.

"We’re hearing from customers and the overwhelming majority of our 3,100 franchisees that all-day breakfast is a hit!  In fact, since the launch, McDonald’s has reached its highest brand score in two years according to YouGov BrandIndex."

McDonald's is trying to revive business following seven straight quarters of same-store sales declines in the US.

In addition to adding all-day breakfast and "Create Your Taste," McDonald's has also made some changes to its core menu items.

The company started toasting its hamburger buns longer, making its beef patties slightly larger, and changing how the patties are seared.

McDonald's has also announced plans to remove antibiotics from its chicken.

There are at least a few franchisees who are on board with the changes.

Among the myriad negative responses to Kalinowski's survey, several franchisees expressed hopeful attitudes.

One wrote.

"I think our leadership is headed in the right direction.  It will take time."

Another said.

"The CEO seems to be doing OK so far!"

COMMENTARY:    Although McDonald's new CEO Steve Easterbrook does not have the confidence of a lot of franchisees, there seems to be a disconnect between how McDonald's shareholders perceive Easterbrook's leadership and how the franchisees feel about him.  Proof this lies in the performance of McDonald's share price.    

McDonald's Stock Up 10% For Year

McDonald’s has been the target of relentless analyst fire for all of 2015; yet here we are in mid-October and McDonald’s shares are up 10% for the year. This week they hit a new all-time high of $103.84.

McDonald's Corporation Share Prices Last Five Years - Jan 2011 Through Oct 2015 - Google Finance (Click Image To Enlarge)

McDonald's Brand Stronger Than Ever

The naysayers mistakenly aim at the small target, but they miss the big picture: McDonald’s formidable brand recognition and performance-oriented culture virtually guarantee long-term success.

On the former, McDonald’s is a top 10 global brand, according to Interbrand, a brand management consultant. McDonald’s ranks ninth on Interbrand’s list, with a $42.3 billion market value. Strong brands have the same effect on consumers that magnets have on metal shavings – they draw them in.

McDonald’s magnetism is especially strong on kids, and not just kids in the United States. Here, I speak from experience. On a weekday road trip from Antibes, France to Geneva, Switzerland, my wife and I stopped at a McDonald’s near Digne, France. We stopped not because of hunger, but because of curiosity. The parking lot was packed.  When we walked in, we found a restaurant overflowing with middle-school-aged kids. So much for indomitable French cuisine.

McDonald's Culture Remains The Same 

But it’s really McDonald’s culture that magnetizes the brand. As Steve Wynn, chairman and CEO of Wynn Resorts (NASDAQ: WYNN), said in a 2014 Wall Street Journal interview,

“You got the culture, you got the problem solved.”

McDonald’s has the culture, and has had the culture for decades, so the problem always gets solved.

Franchisee Rebellion Is A Distinct Possibility

After much anticipation, the newly installed chief executive officer of McDonald's, Steve Easterbrook, has announced a sweeping turnaround plan aimed at cutting annual costs by around $300 million at the struggling fast-food behemoth. Wall Street was unimpressed: the stock fell after Easterbrook made his announcement.

But a far important constituency is also grumbling: the franchisees who own most of the outlets. Many have already expressed skepticism about Easterbrook's leadership, complaining that McDonald's was imposing new burdens, products and obligations without any understanding of the strain the changes will place on franchise owners.

There's little evidence that Easterbrook's plans to turn McDonald's into a "modern, progressive burger company" -- which include selling 3,500 of the restaurants it owns -- allayed the concerns of the franchise owners, who have experienced falling sales in many parts of the world.

Easterbrook's plan seemed largely aimed at creating value for shareholders, not helping franchisees. The vexed history of franchising suggests it may be a mistake to disregard this constituency.

After World War II, McDonald's and other fast-food restaurants introduced "business format" franchises. Unlike conventional franchises, this wasn't a way to delegate distribution; it was a means to sell an entire way of life to aspiring small-business owners. McDonald's founder Ray Kroc described this as an "updated version of the American Dream," and his company, along with Howard Johnson's and other restaurants, made it a mainstream phenomenon.

In business-format franchising, the parent company maintains a rigorous level of control over franchisees, holding them to standards of appearance, cooking times, the dress of employees, accounting methods, and just about anything else that can be standardized and controlled. McDonald's went so far as to create its famed "Hamburger University" in 1961.

In principle, this insured success for both the corporation and the franchise owners. But in practice it relegated franchisees to a genuinely subordinate position. Harold Brown, a lawyer and critic of business format franchising claimed in 1970 that "numerous franchisors have stated that their franchisees are like children, demanding constant discipline and control."

This attitude didn't always sit so well with franchisees. The control imposed from above was fine so long as the decisions made at corporate headquarters worked well on the ground, but that wasn't always the case. In the late 1960s, a number of high-profile franchising ventures ended in revolts. 

• Domino's Pizza, for example, expanded its franchise operations throughout the 1960s, but failed to deliver sufficient support, financing and training. Profits plummeted and the company almost went bankrupt in 1969.  Two years later, enraged franchise holders sued Domino's, alleging that the corporation had imposed obligations on outlets without providing the necessary support. There was much anger as well at Domino's insistence that outlets only purchase supplies from the parent company or an approved supplier. Eventually, Domino's bought out the embittered franchisees, increasing the number of outlets under direct ownership.
• Chicken Delight, founded in 1952, expanded over the course of the succeeding decade, but then failed to deliver the necessary support to franchise owners, despite forcing them to pay the corporation higher prices for everything from paper plates to batter for the chicken. A number of franchisees eventually sued the company, setting in motion a chain of events that led to what the Wall Street Journal described in 1972 as "the virtual dismantling of the Chicken Delight organization."
• Shakey's Pizza, Mister Donut and other companies faced similar revolts around the same time. Some, notably Mr. Donut, mended fences with their franchisees, granting them increased control over local decision-making, financing, accounting, and even the expenditure of ad dollars. Others moved away from top-down decision-making, setting up venues where franchisees could express their concerns. Still others sought to curtail the number of franchises, bringing stores back under corporate control. All of this worked: many companies turned around, and the reputation of business-format franchising eventually improved.

McDonald's, which sailed through this period without problems, might want to take a page from the past as it tries to right the ship. It's suffering from many of the same problems. In a recent survey, franchisees struck much the same tone as their disgruntled predecessors of the 1960s, citing an out-touch management that seems more interested in off-loading costs onto franchisees than fixing the problems that beset the chain.

Unfortunately, Easterbrook's turnaround plan made almost no mention of the franchisees that are the public face of the company. Worse still, is his vow to increase the number of stores in the hands of franchisees from 81 percent to 90 percent — which some franchisees believe will make the company even more remote from the concerns of the owners on the front lines.

Easterbrooks Turnaround Plan

In May 6, 2015, McDonald's CEO Steve Easterbrook announced a restructuring plan to help it compete with fast-casual restaurants like Chipotle Mexican Grill. To foster a quicker reaction to changing trends, McDonald’s is restructuring its units into four groups based on the maturity of its presence in the particular market, where previously the business was segmented by geography.

• The flagship U.S. market.
• Established international markets such as Australia and the United Kingdom.
• High-growth markets such as China and Russia.
• The rest of the world.

McDonalds also said that 90 percent of its more than 36,200 restaurants around the world will be franchised over the next four years. This is up from 81 percent currently, and will mean the company will rely more heavily on franchising fees and move away from the daily running of restaurants. The organizational changes will contribute to 300 million dollars in cost-cutting targeted by McDonald's, most of which will be realized by 2017.

The company is working on streamlining its menu recently cutting seven items including the Deluxe Quarter Pounder burger and six chicken sandwiches, while at the same time working on improving perceptions about the quality of its food with a trio of new sirloin burgers being added.

Standard and Poors were left underwhelmed by the plan and subsequently downgraded their rating on McDonalds to A-, while Moody’s put their rating on review for downgrade. Both rating agencies showed concern over McDonald's plans to return between 8 and 9 billion to shareholders in 2015 and to hit the high end of its 18 to 20 billion 3-year goal by the end of 2016, worrying that any stock repurchases will be at the expense of higher debt levels than initially anticipated.

The fast food chain in April reported first-quarter comparable sales down 2.3% and revenue of 5.96 billion, falling short of the 6.02 billion analysts had expected. Unfortunately for McDonalds shareholders, the quarterly results were a familiar tale, which for nearly two years has seen its share price fall and same-store sales crumble in the U.S. market and abroad.

Conclusion

Having served as the CFO for a tanning salon franchise, I was at the forefront of a franchisee rebellion. This taught me some valuable lessons:  1) Do not deviate from your core franchise principles and vision, 2) Always deliver on your franchise promises, 3) Select the most qualified and experienced franchisee owner/manages, 4) Location, location, location, select the best franchisee locations, 5) Invest in adequately training and providing marketing support to your your franchisee network 6) Don't let unresolved problems and differences vent themselves into an allout franchisee rebellion.

It would not surprise me if McDonads top management has ignored or strayed away from some of the above franchise must-do's.  In many businesses the customer comes first, but in a franchising business environment, the franchisees come first, because they are the face of McDonalds.  It is doubtful that the vast majority of McDonalds customers know who McDonald's CEO is.  They don't care either. They want their Big Mac sandwich made exactly the same each time they visit and served as quickly as possible.  

By paying more attention to the bottom line and improving shareholder value, McDonalds top management may have violated one of the most important covenants of franchising.  Take care of the franchisees, and they will take care of you.

Courtesy of an artricle dated August 9, 2015 appearing in Bloomberg, an article dated October 19, 2015 appearing in ETF Daily News, and an article dated October 16, 2015 appearing in Business Insider

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WALMART'S U.S. SALES ARE DOWN, ITS STOCK FELL 10% AND LOST $20 BIL IN VALUE, IS WALMART'S BUSINESS MODEL BROKEN?

This week, Walmart's shares crashed on October 14 after the company reported a disappointing profit outlook. Profits will fall 6% to 12% next year, the company said.

Walmart founder Sam Walton wrote in his 1992 memoir "Sam Walton: Made in America".

"Now, when it comes to Wal-Mart, there's no two ways about it: I'm cheap," 

If the notoriously cheap Walton were alive today, he would probably be surprised that his pioneering profit formula -- keep costs low so that prices are as low as possible and customers buy more -- is now virtually impossible to maintain. Full blame for that can be pinned on employees demanding higher wages and the rise of e-commerce giants such as Amazon (AMZN), which are open 24 hours a day, seven days a week. 

Walmart founder Sam Walton (Click Image To Enlarge)

Perhaps the retail legend saw some of this on display from up above on what was a dark day in Walmart's history on Wednesday. Walmart's usually stable stock price tumbled 11% as the company said at its annual investor day that higher wages for associates will dent profits by about $1.5 billion in the 2017 fiscal year, following a $1.2 billion hit this fiscal year.

For years, Walmart has had the upper hand with its employees, paying them state minimum wages, offering basic healthcare plans, and using computerized systems to control their hours. But with the rise of social media and the internet giving employees a free platform to express views, and the rising cost of living causing state governments to push through higher minimum wages, Walmart has had to change with the times.

As a result, it has lost a key element of its business model put in place by Walton that has supported the company's profits since its founding. Further, rising employee costs are arriving as online shopping is also limiting the ability of Walmart to raise prices at all to help deliver sustainable profit growth.

Walmart added on Wednesday that it expects earnings per share to decline between 6% and 12% next year. Wrote Sterne Agee analyst Charles Grom in a note to clients on Wednesday :

"What's surprising to us is that the new outlook is incorporating roughly $20 billion in share buybacks in the next two years, which implies significant margin contraction along with modest sales growth." 

Employee costs aren't the only line item on the income statement Wal-Mart is battling to control.

Walmart's investments in e-commerce and digital initiatives are expected to total about $1.1 billion in the 2017 fiscal year. This year, Walmart is projected to shell out $1.2 billion to $1.5 billion on e-commerce and digital.

These investments are a necessary evil to stay competitive with Amazon, as well as with Best Buy (BBY) and Target (TGT) , which are continuing down their own paths to reduce prices, improve checkout speeds and offer a range of delivery options.  

When Walton opened the first Walmart store in Rogers, Arkansas in 1962, though, it's unlikely he foresaw a day when a sweater could be purchased off a handheld device. Today it can be, however, and to make it happen seamlessly and at the lowest cost is proving to be a major profit headwind for the once mighty Walmart.

Even with such enormous investments in digital, however, Walmart still sees sales as being under pressure. Walmart said it now expects net sales growth for the current fiscal year to be relatively flat, down from a forecast of between 1% and 2% in February. Excluding the impact of currency exchange fluctuations -- mostly the stronger dollar -- net sales growth is estimated to be about 3%.

Goldman Sachs analyst Matthew Fassler in a note on Wednesday pointed out.  

"New guidance reflects that Walmart's competitive edge -- historically largely assortment and price -- has faded relative to purveyors of extreme value (warehouse clubs, hard discounters) or extreme convenience (dollar stores, hard discounters), as e-commerce has neutralized the impact of selection." 

The deterioration of Walton's pioneering business model has been playing out for some time in Walmart's numbers. 

According to Bloomberg data, Walmart's operating profit margins, which takes into account its costs to run stores and pay for wages and healthcare, has fallen from 7.1% in 1987 to 5.6% in 2015.  From 1988, Walmart's return on assets -- an indicator of how profitable a company is relative to its total assets -- has gone from 13.7% to 8.0% in 2015.

The alarming downtrends in these measures, partly fueled by investments to compete online and compensate workers better, have been overlooked for years by Walmart's investors who have focused on the retailer's healthy dividend and relative stability.

Walmart CEO Doug McMillon (Click Image To Enlarge)

But the stock's nosedive on Wednesday, and an 11% drop for the year prior to that point, suggests Walmart's investor base is growing concerned that the retail giant's business is broken. Wrote Stifel analyst David Schick in a note,

"The market is reacting to meaningful evidence that Walmart has substantially over-earned."

In other words, Walmart is now being forced to play catch up on investments it neglected to make in the past to keep the profits flowing. 

And if Walmart's business model is broken, profits are likely to stay under pressure for some time, and future dividend hikes may not be as robust as in years past. 

Now, Walmart has to find a way to channel Walton and become cheap once more. Indications have emerged that it may be willing to take drastic actions to return to some sense of frugality. 

 

Walmart CEO Doug McMillon said at the investor event.

"We are more than open to re-shaping our portfolio."

McMillon added the company continues to "evaluate its portfolio" of assets, and pointed to Walmart's history of exiting non-strategic assets and closing underperforming stores.

For example, the retailer exited Germany in 2006 and took a $1 billion hit to profits as a result. By shedding assets such as Sam's Club or lagging international operations, Walmart would also be shedding costs related to employees and maintaining physical stores. In turn, those savings could be used to offset the investments being made in core assets such as the online business and Walmart U.S., or simply be brought to the bottom line to appease investors.

Walton wrote.

"You can make a lot of different mistakes and still recover if you run an efficient operation -- or you can be brilliant and still go out of business if you're too inefficient."

Words of wisdom from beyond the grave for McMillon and the execs tasked with turning around the largest ship in retail.

Walmart did not respond to request for comment for this story.

COMMENTARY:   When I heard that Walmart's stock had plummeted 10% in mid-October over its shitty earnings report, decling profit margins and the CEO's statements that earnings will continue to be stagnant due to higher employee wages and investments in ecommerce, I shed crocodile tears and managed a smile.

Walmart has been the "king of exploiters." For years it has exploited its employees, vendors and foreign suppliers.

Walmart has been the subject of numerous lawsuits from employees seeking payment for overtime and unfair labor practices, including paying female managers less than their male counterparts, among other sins.

For years Walmart has squeezed its vendors for lower prices, often forcing some into bankruptcy, while others have fled the retail giant, and swore never to return again.  

The pricing deals that it has extracted from Chinese suppliers has cost hundreds of thousands of US jobs, and many U.S. small businesses have gone out of business.

So, if you were to ask, I don't feel the slightest remorse for this disgusting company?  Nope.  

The Walton family, prior to the recent stock market decline, were worth $140 billion, the wealthiest American family, and representing the very top of that 1% which owns 90% of America's total net worth. After the stock price crash, they are still worth at least $125 bllion.  Now I hear they are blaming the far left's campaigns to increase the minimum wage for cause of their problems.

From my viewpoint, the cause of Walmart's problems is mostly about Walmart's values, and they are all about profits, and more profits, at any cost.  

Do you think the Walton family cares about all the small retailers they have put out of business, or the Chinese workers being exploited by suppliers so that they can pay bottom dollar for their goods, the US vendors they squeezed for lower prices then put out of business, or even their employees who are finally getting paid minimum wage (a paltry $9.00 per hour)?  

This is more of a condemnation of Walmart's values coming back to bite them in the ass.  It is not about their business model going to pieces.  You can bet that Walmart's CEO Doug McMillon and his team will find ways to restore the viability of their dirty business model by screwing somebody.  Stay tuned for more bad news about this indecent company.   

The Wharton Business Radio ran an excellent broadcast titled Marketing Matters Walmart, that details where Walmart fucked up, their failure to react to the dollar discount stores, their botched entry into Canada, their badly executed store remodelling, and failure to gain traction in online sales to combat Amazon, and what is really going on, to get them into this disasterous hole.  Definitely worth listening to.

Courtesy of an article dated October 16, 2015 appearing in The Street, an article dated February 19, 2015 appearing in Newsmax, an article dated October 16, 2015 appearing in Business Insider, and an article dated October 14, 2015 appearing in Fortune Magazine

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THE STATE OF DATA-DRIVEN MARKETING, MARKETING BENEFITS, KEY GROWTH DRIVERS AND THE 15 ESSENTIAL MARKETING METRICS

Data-driven marketing, in theory, is about better using the flood of customer-related data to integrate and optimize marketing efforts in the age of an empowered consumer and Big Data.

What matters most is the optimization of the customer experience, relevance and (perceived) customer value as a driver of business value. Data-driven marketing certainly is not (just) about advertising and programmatic ad buying as some believe. Nor is it just about campaigns. On the contrary: if done well, data-driven marketing is part of digital marketing transformations whereby connecting around the customer across the customer life cycle is key.

The Role and Evolution of Data-Driven Marketing

One of the pioneers and leading voices is data analytics and marketing software vendor Teradata. A while ago, the company recently released the results of its Teradata 2015 Global Data-Driven Marketing Survey. According to Teradata, marketers who adopt an integrated and data-driven marketing approach become more empowered across all areas including the various stages of “engagement” as described in the illustration below from the page of the 2013 survey the company conducted.

The Various Stages of Customer Engagement - Teradata (Click Image To Enlarge)

At the occasion of the launch of the 2015 edition of the survey, Teradata said it saw some “dramatic shifts in how companies and marketers are deriving business value from data, integrated marketing platforms, and customer-centric data-driven marketing strategies” as compared with its 2013 report.

Among the key findings of the Teradata 2015 Global Data-Driven Marketing Survey:

• A large majority of marketers (90%) wants to move beyond segmentation towards one-to-one personalization in a real-time interaction context.
• Faster decisions and more accurate decisions are reported as key benefits of using data in this real-time economy. Two-thirds of respondents cite speed and accuracy in decisions as key benefit.
• For 38% of respondents the major challenge is improving both customer acquisition and customer retention.
• 78% of marketers in the survey claim to use data systematically. In 2013 this was only 36%.

Omni-channel consistency and silos remain hurdles

Respondents see data-driven marketing as a means to an end and for several an integrated marketing cloud approach seems like the way forward from a platform perspective. Of course, Teradata is one of several major providers of such a marketing cloud (like Adobe, Oracle, Salesforce.com, IBM, Microsoft etc.).

Obviously there is a gap between what marketers would like to achieve with data-driven marketing and what they actually achieve. Many hurdles remain, among others regarding consistency in an omni-channel marketing context whereby silos are the eternal challenge, also from a campaign marketing perspective.

More about the evolutions, challenges, customer data issues (privacy, regulation, ownership) etc. in the press release, the report and the infographic below.

Given the huge attention for data-driven marketing (and, admittedly, the big marketing cloud wars we see happening), there is of course more research looking at the state of data-driven marketing and the drivers of it. In a second infographic below the one from Teradata, we’ll take a closer look at more research – with a second infographic.

Data-driven marketing: customer-centricity and customer experience first?

It’s not just the vendors and marketing cloud providers such as Teradata who come up with research.

Recently, the GlobalDMA, “an organisation that represents, supports and unites marketing associations from around the globe that focus on data-driven marketing”, looked at the state of data-driven marketing and data-driven marketing practices in 17 global markets.

It did so in collaborations with strategic consultancy for the advertising and marketing industry, Winterberry Group.

You can access the report for free on the website of the GlobalDMA (registration required). Just like Teradata, the report found that marketers – obviously – increasingly see the importance of a smart use of data in marketing, customer experience and advertising (the whole programmatic advertising concept).

In an infographic on AdWeek, infographist Carlos Monteiro summarized some of the key takeaways from that research (and from Teradata’s 2015 Global Data-Driven Marketing Survey).

How Data-Driven Marketers Outperform Their Competition - Adweek by Carlos Monteiro (Click Image To Enlarge)

Data-driven marketers outperform their competitors according to The State of Marketing 2013 by IBM – another contender in the marketing cloud wars – source: presentation by Jason Heller of McKinsey at Integrated Marketing Week 2013

Among the key takeaways of the data-driven marketing report by the GlobalDMA:

• 77% of marketers are confident in the data-driven approach and 74% expect to increase data marketing budgets this year.
• Data efforts by far focus on offers, messages and content (marketing) first (69% of respondents). Second ranks a data-driven strategy or data-driven product development. Customer experience optimization unfortunately only ranks third with 49% of respondents.
• Among the key drivers of increased data marketing: first of all a need to be more customer-centric (reported by 53% of respondents). Maximizing efficiency and return ranks second followed by gaining more knowledge of customers and prospects.

The question is what will be done with this knowledge. And here we see that 20% reports a need to align with digital consumer preferences.

So, it seems the digital customer experience does matter as it’s essential for a more customer-centric approach and the – for many in practice – extremely urgent need to align with (digital) consumer preferences or, maybe better, catch up with the changed customer reality, in the end the essence of what digital marketing transformation is all about in the first place.

Infographic – via AdWeek and made by Carlos Monteiro – below. You can download the Winterberry Group/ GlobalDMA report here.

 Click Image To Enlarge

COMMENTARY:  Customers today expect—and demand—a seamless and relevant experience. They have grown accustomed to marketers’ knowledge of their preferences and anticipation of their needs. Fractured or conflicting messages from a brand make marketers seem unorganized and annoy customers, sometimes even driving them away.

For marketers, the only solution is data-driven marketing for individualized insights. Teradata defines data-driven marketing as

"the process of collecting and connecting large amounts of online data with traditional offline data, rapidly analyzing and gaining cross-channel insights about customers, and then bringing that insight to market via a highly personalized marketing campaign tailored to the customer at his/her point of need."

Data-driven marketing is the means that leads to the end of individualized insights: moving beyond segmentation to true one-to-one personalization in a real-time context— achieving the capability to distill insights at the individual level, and the ability to target known customers in a digital marketing ecosystem that often settles for “close enough.” 

A well-integrated data-driven marketing program that provides a single view of the customer is the path to such individualization, and can result in business benefits that are just waiting to be tapped. The data in this report is based on a survey of 1,506 marketing and communications executives worldwide, representing all major industries. The survey was conducted in the fall of 2014, and follows a similar survey conducted in 2013.

According to Mark Jeffrey, Kellogg School of Management, Northwestern University, and author of "Data Driven Marketing," there are 15 essential marketing metrics that all marketers should know.

Click Image To Enlarge

People-Centric Targeting Across All Screens

What is it: Media consumption habits are more fragmented and diverse than ever, as consumers engage with multiple screens and devices. And in this multi-channel world, isolated, one-off marketing strategies and closed technologies no longer make sense. Advertisers don’t need a social plan or a mobile plan; they need to take full advantage of all available data to develop a people-centric plan that zeroes in on customers wherever they are and when they are most likely to engage. This requires platforms that can integrate data across channels and provide transparent, objective analysis of performance across every marketing touch point.

In the following schematic, Mark Jeffrey illustrates the process of gathering customer data from multiple sources, the flow of that data through the processes of validation, cleaning, transformation, aggregation and loading that data into a centralized data warehouse for business intelligence purposes.   

Click Image To Enlarge

Why it’s important: People-centric marketing is even reaching the world of linear TV, where new streams of data from set-top boxes and connected televisions are making it possible for advertisers to programmatically buy specific audience segments rather than focusing only on shows and time slots, which is how TV has traditionally been bought and sold.

Data will continue to blur the lines between TV and digital, between mobile and display, and even between direct response and branding. It all comes down to the person — whether the screen they are looking at is on their wall, at their desk or in the palm of their hand.

Why it’s hard: Cross-channel engagement is the cornerstone of people-centric marketing. The challenge, though, lies in evaluating attribution — an understanding of how well channels performed in engaging targets. Several years ago, this degree of accountability was impossible to achieve because data analysis tools hadn’t yet innovated to the point of granular attribution. Today, however, multi-touch attribution platforms, driven by more recent advances in machine learning and predictive analytics, have made this a reality.

Courtesy of an article appearing in i-scoop, an exerpt from the Teradata's 2015 Data-Driven Marketing Survey and an article dated August 28, 2015 appearing in VentureBeat

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47 SOLAR-POWERED CAR TEAMS GEAR UP FOR THE GRUELING 1,864 MILE WORLD SOLAR CHALLENGE IN AUSTRALIA

The University of Michigan's Aurum solar car features an asymmetrical catamaran body (Click Image To Enlarge)

If you're looking to bring together the world's brightest budding engineers to push solar technology to its very limits, then there may be no better backdrop than the dusty, sun-drenched expanses of central Australia. The biennial World Solar Challenge will kick off this Sunday, with competitors set to cover a monster 3,000 km (1,864 mi) journey from Darwin, Northern Territory to Adelaide, South Australia in cars powered purely by the sun. As hopefuls from all over the globe ready their rides for the ultimate in solar-powered endurance racing, here's a quick look at some of the interesting vehicle designs, who's new to the party and a few that have been around the block before.

This year's event marks the 13th World Solar Challenge and features 47 teams from 25 different countries, which is more than ever before. The vehicles taking part have traditionally resembled spaceships more than cars you might see on the street, but at the last World Solar Challenge in 2013 organizers introduced the Cruiser Challenge, a second class catering to solar cars that can carry passengers and are designed more with practicality in mind.

The Challenger Class is the mainstay of the competition and sees a fleet of sleek, aerodynamic cars covered in solar panels battle it out to be the first across the finish line. This year's event features 29 teams who have had to tailor their designs to result in vehicles no longer than 4.5 m (14.7 ft) and no wider than 1.8 m (5.9 ft), which is a downsizing from the previous event. The field includes a number of teams who have been regular place-getters, if not winners, of the World Solar Challenge, along with a few making their debut appearances.

Nuon Solar Team: Nuna

The Nuon Solar Team is the reigning champ and has been one of the first three across the finish line in every World Solar Challenge since 2001, claiming the first prize a total of five times. Hailing from Holland, this year the team rides in the Nuna8, the eighth rendition of its Nuna solar car and, perhaps in a warning to its competitors, claims to have learnt some design lessons from its successful run in 2013.

The Nuon Solar Team is the reigning champ and has been one of the first three across the finish line in every World Solar Challenge since 2001 (Click Image To Enlarge)

Tokai University: Tokai Challenger

Japan's Tokai University has taken the fight right up to the Nuon Solar Team in recent years, winning both the 2009 and 2011 events, then coming in second place in 2013. It says the new and improved Tokai Challenger is lighter, features improved power generation and also better aerodynamics.

Tokai University is back and looking for another title at the World Solar Challenge with its Tokai Challenger solar car (Click Image To Enlarge)

University of Michigan: Aurum

The University of Michigan has been in the game since 1990, when it placed third in the second ever World Solar Challenge, and it takes solar car design seriously. Consecutive third place finishes in 2009 and 2011 were followed by a crash in 2013, which crippled the team's chances of success despite the team's best efforts to repair the vehicle. Aurum is its 2015 ride, featuring an asymmetrical catamaran body that is said to be more aerodynamic that any vehicle the team has ever produced.

The University of Michigan team says its focus in building Aurum was speed, reliability, and safety (Click Image To Enlarge)

Stanford University: Arctan

A product of Stanford University's Solar Car Project program, Arctan is claimed to feature some of the most advanced photovoltaic and encapsulation technologies. Will it be enough to yield Stanford's first ever triumph at the World Solar Challenge?

Stanford University's Arctan solar car during a test run (Click Image To Enlarge)

Eindhoven University of Technology: Stella Lux

The (comparatively) spacious Stella Lux is a second take on solar car design from the team at Eindhoven University of Technology. Running in the Cruiser Challenge, the vehicle seats up to four people, features a range of more than 1,100 km (684 mi) and has a top speed of 125 km/h (78 mph). It has a tunnel running through its center to maximize aerodynamics.

Stella Lux will take part in the Cruiser Class of the Bridgestone World Solar Challenge (Click Image To Enlarge)

New entries

Three new teams are looking to make their mark on the World Solar Challenge this year. Thailand's Siam Technology College claims it has designed its STC-1 solar vehicle to the same standards as a typical racing car, while the GAMF Hungary team says its goal with its Megalux car was to build a vehicle where all the components are in harmony with one another. Meanwhile, South Africa's University of KwaZulu-Natal will debut a solar car called Hulamin, which features an asymmetrical design with a small frontal area to reduce drag

Thailand's Siam Technology College is entering the World Solar Challenge for the first time with its STC-1 solar car (Click Image To Enlarge)

While these vehicles are the result of years of research, refinement and careful consideration, there's no way to completely prepare for the harsh Australian outback where daytime temperatures are expected to reach 100° F (37.8° Celsius) in the coming days. There's certain to be a few twists and turns down the road, so stay tuned to Gizmag to see which team comes out on top. The awards ceremony is due to start in Adelaide on Wednesday as the teams start rolling in, but in the meantime you can click through to our gallery to see these vehicles from all angles.

The GAMF Hungary team says its goal with its Megalux car was to build a vehicle where all the components are in harmony with one another (Click Image To Enlarge)

COMMENTARY:   I've been covering green technology and solar energy for quite some time now.  I especially enjoy these long distance, solar-powered car races.  The World Solar Challenge is without any doubt the most grueling, covering a distance of 1,864 across Australia north-to-south.  

The Nuon Solar Team from the Netherlands cross the finish line in southern Australia to win the 2013 Bridgestone World Solar Challenge, in which vehicles use only solar power. The car race from Darwin to Adelaide is 1,877 miles long and attracts teams from around the world. Nuon finished in a time of 33.05 hours and jumped into inflatable paddling pools to celebrate their win.

 

Courtesy of an article dated October 15, 2015 appearing in Gizmag

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CREDIT CARD PAYMENTS STARTUP SQUARE FILES FOR AN IPO, BUT THE IPO MARKET IS SHAKY, AND HUGE CHALLENGES REMAIN AHEAD

Square's IPO filing comes as market for IPOs remains shaky

Payments startup Square Inc. filed to go public Wednesday, even as it grapples with mounting financial losses and an unusual corporate structure that requires its founder to spend part of his day leading a larger technology company.

Square, known for its credit-card readers that attach to mobile devices, is run by Twitter Inc. co-founder and Chief Executive Jack Dorsey.

Square card swiper attaches to any mobile device allowing anyone to accept credit card payments (Click Image To Enlarge)

Square said in Wednesday’s regulatory filing that it would offer up to $275 million in stock, a figure that could change leading up to the IPO.

The offering is seen as a harbinger for tech companies valued at $1 billion or more—Square was valued by investors last fall at about $6 billion.

Most of these highly valued companies have stayed away from the IPO market with ample private capital available—in fact, there have been just 22 tech IPOs through the third quarter this year, compared with 53 through the third quarter of 2014, according to Dealogic.

Recent sharp stock-market swings have deterred some firms from pursuing public offerings. The Square filing came as two of the year’s biggest IPOs ran into trouble. Supermarket operator Albertsons Companies Inc. delayed its IPO plan, according to people familiar with the offering, while payment-processing company First Data Corp. priced its IPO below expectations.

San Francisco-based Square, meanwhile, is hewing closely to a script followed by other Silicon Valley technology firms: swelling losses despite growing revenue.

Square said sales rose 54% last year to $850.2 million and are on a pace to exceed $1 billion this year.

But Square’s net loss in 2014 grew at nearly the same rate, widening to $154.1 million from $104.5 million the year before.

Square also will have to assuage investor concerns about Mr. Dorsey. Earlier this month, the 38-year-old was appointed permanent chief of social-media company Twitter, of which he was also a founder and helped guide to an IPO in 2013.

The company said in its securities filing on Wednesday. 

“This may at times adversely affect his ability to devote time, attention and effort to Square.”

It isn’t clear how much of any given day Mr. Dorsey spends at Square or Twitter offices, nor how his time on the IPO roadshow may affect Twitter’s operations.

Twitter and Square CEO Jack Dorsey (Click Image To Enlarge)

More than being the face of the company, Mr. Dorsey is the largest shareholder, holding nearly one-quarter of Square’s stock.

Mr. Dorsey also profited by selling $1.2 million in marketing consulting services to Square through West Studios LLC, a private firm in which he holds a stake. West Studios also purchased 375,000 shares.

Including Mr. Dorsey’s stake, venture-capital firm Khosla Ventures’s 17.3% stake and smaller shares held by board members and executives, about two-thirds of Square is held by insiders.

A few company insiders—including co-founder James McKelvey,General Counsel Dana Wanger and director Larry Summers—cashed out of some of their shares in January 2014 at $13.53, according to the filing.

According to Square's S-1 filing, a total of 185,361,631 class B shares are held by outside investors, insiders and directors.   The percentage of total class B shares held by major shareholders include Jack Dorsey (24.4%), Khosla Ventures (17.3%) and Director James McKelvey (9.4%).    

Square Inc Class-B Shares Beneficially Owned Before the IPO Filing (Click Image To Enlarge)

Perhaps underscoring the risks Square’s investors see in the company, the company raised about $150 million last September and October in a round that came with potentially onerous restrictions.

Investors including J.P. Morgan Chase & Co. and Rizvi Traverse paid $15.46 a share and required that the IPO must price at $18.56 a share or higher, otherwise they are entitled to receive additional common shares, the filing shows.

Those firms will also get their money back before any other investor in the case of a company sale.

An $18.56 IPO price would suggest a pre-IPO valuation of $7.4 billion including stock awards, or $5.6 billion excluding them.

Square’s IPO, if successful, would cap a tumultuous six years for the payments company. As recently as last year, Square discussed with Alphabet Inc., then Google, a possible acquisition as its losses rose to around $100 million, people familiar with the matter told The Wall Street Journal at the time.

The company also backed off its e-commerce marketplace and mobile wallet, products it had touted as meaningful advances from its core card-swiping business.

The company typically charges merchants 2.75% to swipe credit cards through its reader, according to the company’s website.

The bulk of that money is spent on fees to payment networks, other financial intermediaries and fraud costs. And it faces competition from PayPal Holdings Inc., newly separated from eBay Inc., among other payment companies.

Square reported it was bilked by a single seller, costing it $5.7 million in this year’s first quarter, raising questions about its fraud-prevention systems.

Investors, too, may wonder how Square will replace lost revenue when its deal With Starbucks  Corp. to process credit- and debit-card transactions in most of its stores concludes in next year’s third quarter.

That deal, while unprofitable, brought in $123 million in sales last year, or 14.5% of revenue.

Square said in its filing that it expected Starbucks to seek another payment processor and had given the Seattle company leeway to do so starting this month.

More recently the company has been focusing on peer-to-peer payments and small business lending to help boost its results. Square said it had originated $225 million in loans since May 2014.

COMMENTARY:    Square has never generated a profit since it was founded.  For the year ending December 31, 2014, Square had a net loss of $154 million.  For the six months ending June 30, 2015, Square had a net loss of $77.6 million, so it appears to be on course to lose about as much money in the year 2015 as it did in 2014.    

Gross Payment Volume (GPV)

Click Image To Enlarge

• Processed $23.8 billion in payments in 2014, and on course to do $35-36 billion in 2015. 
• Square processed 446 million individual payments from 144 million cards.
• Retail businesses make up 21% of Square’s merchants, followed by services at 17%, and food businesses at 15%.
• The tiniest sellers (processing less than $125,000 in payments per year) represented 92% of its merchants in the second quarter of 2011, compared with 63% in the same quarter this year— meaning that its stable of large customers has grown, relatively speaking
• Square’s deal to handle payments for Starbucks is a money loser. Square lost $28 million from the partnership in 2014, and $14.3 million in the first six months of 2015 (luckily for Square, the relationship will end by October 2016, if not earlier)

Square is a relative small fry when you compare the firms Gross Payment Volume (GPV) with the $4.9 trillion in total purchases paid by credit card as of 2013.     

By comparison, Paypal, the gross payment volume (GPV) leader, processed $65.94 billion in credit card payments in the Q2 2015 alone and $248.23 billion in GPV during the 12 months up to and including Q2 2015.  Paypal also generated net income of $419 million for the year ending December 31, 2014.  When you compare Square's numbers, it's no contest.  

Consolidated Statement of Operations 

Click Image To Enlarge

Quarterly Results of Operations 

Click Image To Enlarge

Consolidated Balance Sheets

Click Image To Enlarge

Pending Litigation

Square has a pending patent infringement litigation with Robert E, Morley and a related entity and has yet to be settled.  This should be of concern to potential IPO investors because it has serious consequences if the court rules against Square.  Facebook was faced with three major lawsuits prior to its IPO, but these were all settled before the IPO date.  The most famous lawsuit was filed by Cameron and Tyler Winklevoss, classmates of Mark Zuckerberg at Harvard.  The Winklevoss twins claimed that Mark Zuckerberg had stolen the idea for Facebook from a social network that Zuckerberg had contracted to write the software for.  This lawsuit was settled for a reported $100 million.  

Here's what the IPO filing says about the Morley lawsuit on page 39.

"We are currently in litigation with Robert E. Morley and a related entity regarding the inventorship of certain patents related to our intellectual property (Morley Litigation). If one or more claims in the Morley Litigation were determined adversely to us, or if the Morley Litigation were settled on unfavorable terms, this could affect our ability to use certain intellectual property and could also result in substantial monetary liabilities. In addition, Mr. Morley filed a subsequent lawsuit containing allegations that the formation of Square and the development of our card reader and decoding technologies constituted, among other things, breach of an alleged oral joint venture, fraud, negligent misrepresentation, civil conspiracy, unjust enrichment, and misappropriation of trade secrets, as well as other related claims. Mr. Morley contends that he was an equal partner with Jack Dorsey and Jim McKelvey in the business enterprise that ultimately evolved into Square, and that Mr. Dorsey and Mr. McKelvey breached their alleged oral joint venture agreement with Mr. Morley by excluding him from ownership in Square. Mr. Morley is seeking a judgment and order that Square, Mr. Dorsey, and Mr. McKelvey hold ownership of Square in constructive trust for Mr. Morley, as well as a variety of damages, injunctive relief, royalties, and correction of inventorship of certain of our patents."

Square SEC Form S-1 (IPO Filing)

Below is the complete Square Inc SEC Form S-1 (IPO Filing). 

Square, Inc SEC Form S-1 - IPO Filing from Tommy Toy

I am always concerned when a startup that has never generated a profit since it began operations, that startup competes in a mature and highly competitive industry (credit card payments) and that industry is dominated by larger and more established players, and that startup is led by a CEO (Jack Dorsey) who splits time between two troubled companies (Twitter and Square), and then that CEO thinks its time for an intial public offering when stock market indicators say otherwise, then it makes me wonder about the wisdom of that CEO.  This is the case with Square, Inc. and CEO Jack Dorsey.

Jack Dorsey has his work cut out for him, not only with Square, but especially with Twitter, a social network which has never generated a profit, has lost numerous executives since the start of this year, whose monthly active users has stalled, and advertisers are questioning Twitter's viability as an advertising platform.  

Dorsey recently announced that Twitter would layoff about 10% of its workforce or about 336 employees, mostly from its engineering staff.  This will cost the company about $20 million in severance payments and other separation entitlements.  Even after you factor the savings from in wages, payroll taxes and employee benefits due to these layoffs, this will not make a substantial dent in their operating losses to date.  This makes you wonder if Square will require some staff pruning after the IPO.   

Even if you adjust Square's earnings for the money that it is losing from the Starbucks deal, the company is still losing a substantial amount of money.  Unfortunately, Square will continue to lose money off the Starbucks deal until October 2016.   

Lise Buyer, an IPO consultant with Class V Group in Silicon Valley who also helped guide Google Inc's IPO, said so eloquently, and dead on.  

"Management and management focus are the single most determining factor of the success or lack thereof of a company pursuing an IPO.  Were I a (Square) investor, I would want to be compensated for the cost of a part-time CEO who already had a full plate. And by compensated I mean I would expect a lower valuation."

David Erickson, a longtime banker and venture capitalist who teaches at the University of Pennsylvania Wharton School of Business, has real concerns about Jack Dorsey's ability to run two troubled companies simultaneously.

"Investors would have to get comfortable with how he would do both jobs well.  What would also, I think, make this particular situation more challenging is the turnaround currently going on at Twitter, and the time I assume it would require."

Lisa Buyer, of Class V Group, echos similar concerns about turning round two companies simultaneously.

"There is so much uncertainty and so much attention needed at Twitter that it certainly should not inspire confidence in potential investors about Square.”

Let's come to some agreement here.  Nothing significant is going to change at either Twitter or Square by the end of this year, or even within the next year.  Twitter may never grow much beyond 300 million active users or increase engagement significantly without a major increase in new users. I am already juggling too much time between Facebook, Twitter and LinkedIn.  I think most users are not going to abandon Facebook so they can tweet all day long.  They simply have to much invested in Facebook and other social networks.  

In conclusion, after reviewing Square's achievements and numbers, I am of the opinion that the Square IPO is just too high risk.  There is none of the hype that existed when Facebook had their IPO. Facebook shares were selling in the secondary markets for over a year before their IPO, setting the market price, and raising expectations to intolerable levels. Square would need to add a dozen major accounts with the volume of a Starbucks, and those accounts would have to be profitable, between now and the actual IPO date to create investor excitement and hopes of a successful IPO.   I don't see any evidence that Square is raising expectations and creating excitement among potential investors.  In fact, the reverse is true -- there are a lot of doubts about the Square IPO.  

In short, the Square IPO is DOA.  It was a bad idea from the beginning, and only going to benefit early investors and a few of the employees.  In fact, most of the early VC's have clauses that in the event of an IPO, they get their money first.  Isn't capitalism great?!!  Yippee Kiyeaa!!    

Courtesy of an article dated October 14, 2015 appearing in The Wall Street Journal, an article dated October 15, 2015 appearing in MediaPost Mobile Marketing Daily, an article dated October 14, 2015 appearing in Fortune Magazine and the SEC Form S-1 Registration Statement filed by Square, Inc. 0n October 14, 2015

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APPLE RETAIL STORES AVERAGED $4,798 IN SALES PER SQ FOOT IN 2014, AND HOW THEY GET CUSTOMERS TO BUY MORE

In 2010, the company opened a store in what used to be a bank, just across the street from Paris' famous opera house (Click Image To Enlarge)

The Apple Store is an incredible retail success.

On a quarterly basis over 100 million people shop at the company's 437 worldwide stores during the fiscal year ending September 30, 2014.  According to the San Jose Mercury News, Apple stores generate more revenue per square foot than any other U.S. retailer.  

Number of Apple Retail Stores Worldwide by Year - Fiscal Year September 30, 2005 Through September 30, 2014 - Statista (Click Image To Enlarge)

According to Fortune Magazine, Apple extended its lead over the top 10 U.S. retailers with its stores generating a staggering $4,798.82 in sales per square foot on an annualized basis at the end of 2014, according to brand new data provided to Fortune by eMarketer.

The store on New York's Fifth Avenue consists of a 32-foot glass cube that's entirely self-supporting. A glass staircase leads into the store below (Click Image To Enlarge)

That compares to $4,551 earlier in 2014 and well ahead of Apple’s closest rival on this front, luxury jeweler Tiffany & Co, which saw sales of $3,132.20 per square foot. (As a point of comparison, department stores such as J.C. Penney and Macy’s generate $160 to $200 per square foot — although to be fair, aisles and aisles of low-priced clothes and towels are less of a draw than than highly coveted electronics.)

Apple opened its first store in the southern hemisphere in 2008, a three-story shop located in the middle of Sydney's shopping district (Click Image To Enlarge)

Part of what has made Apple stores so successful over the years is the stunning design details put into each one. 

Inside, all of the stores look pretty much the same — Apple trademarked its store layout in 2013 — but each one offers its own unique look from the outside. 

 

In 2009, the late Apple CEO and founder Steve Jobs hired famed British architect Norman Foster to take over the design for its upcoming campus 2, dubbed the "Apple Spaceship" in Silicon Valley.  Foster has also designed Apple's future retail stores.  That development, along with the hiring of former Burberry CEO Angela Ahrendts as retail chief, is a sign that Apple's retail dominance shouldn't be ending anytime soon.

Apple's retail store in Istanbul is the first location designed by Norman Foster. It's recessed into the ground and looks a lot like a MacBook from above (Click Image To Enlarge)

If you own a piece of Apple tech there's a good chance you've visited one of their retail locations. Every detail of each Apple store has been designed and planned out to influence the customer into spending more money as they shop and browse. Here's how it works.

In Beijing's Sanlitun district, a large stainless-steel structure makes an imposing impression (Click Image To Enlarge)

COMMENTARY:  I've written about the Apple retail stores before, and came away amazed at the number of customers that visit the stores each day, the simplicity of the store design and layout of each store, high level of customer service they offer, and the way Apple products are displayed to appeal to evangelists who lust for their products.  It is all about offering a great customer experience from beginning to end.

The Apple store in Shanghai's Pudong district is one of its most stunning. A glass cylinder leads to the main part of the store, situated underneath the courtyard (Click Image To Enlarge)

Every detail of each Apple store has been designed and planned to influence the customer into spending more money as they shop and browse.  The following video explains how each store is deliberately designed to make their customers spend more money.

   

Apple store employees are known for their "insanely great service,"  They are not known as store clerks or associates, but as "Specialists," for their knowledge of all Apple products.  Apple hires Specialists for their "magnetic personality as much as, if not more than proficiency."     You can teach proficiency, but personality is a natural trait that you either have, or you just don't.  Here's a video that illustrates this point.

 

I had a look at Apple's Annual Report for the fiscal year ending September 30, 2014, and the company had 437 stores generating $21.5 billion in net revenues and employing 46,200.   The following table presents Retail information for the years ended and as of September 27, 2014, September 28, 2013 and September 29, 2012 (dollars in millions):

  Apple Stores, Net Revenues and Head Counts (Click Image To Enlarge)

The growth in Retail segment net sales in 2014 was primarily driven by increases in net sales and unit sales of iPhone and Mac, partially offset by declines in net sales and unit sales of iPad and iPod. With an average of 424 and 403 open stores during 2014 and 2013, respectively, average revenue per store increased to $50.6 million in 2014 from $50.2 million in 2013.

The growth in Retail segment net sales during 2013 was primarily driven by increased unit sales of iPhone and iPad following the new product introductions in the first half of 2013 and increased sales of services. With an average of 403 and 365 open stores during 2013 and 2012, respectively, average revenue per store decreased to $50.2 million in 2013, compared to $51.5 million in 2012.

The Retail segment’s operating income was $4.6 billion, $4.0 billion and $4.6 billion during 2014, 2013 and 2012, respectively. The year-over-year increase in Retail segment operating income in 2014 was attributable to higher net sales and higher gross margin primarily due to a higher mix of iPhone. The year-over-year decrease in Retail segment operating income in 2013 was primarily attributable to lower gross margin similar to that experienced by the Company overall, partially offset by higher net sales.

Courtesy of an article dated October 16, 2015 appearing in Business Insider, an article dated May 27, 2014 appearing in Business Insider, an article dated March 13, 2015 appearing in Fortune Magazine, the fiscal year ending September 30, 2014 Apple Annual Report (Form 10-K), and an article dated September 4, 2015 appearing in De Zeen Magazine

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MVRDV COMPLETES 'THE COUCH', A CLUBHOUSE WITH A ROOFTOP GRANDSTAND FOR AMSTERDAM’S IJBURG TENNIS CLUB

THE ROTTERDAM-BASED ARCHITECTURE FIRM HAS TURNED THE ROOF OF A TENNIS CLUB INTO SPECTATOR SEATING

MVRDV, the Rotterdam-based architecture firm known for its bold, futuristic designs including everything from small town community centers to South Korean Skygardens to dazzling skyscrapers that look like Twizzlers, is now jumping into the sporting arena. Its latest design is just as much furniture as it architecture though: a comfy red tennis clubhouse that doubles as a seating area for over 200 spectators.

Click Image To Enlarge

Click Image To Enlarge

The new building is simply called the Couch, because of course it is. Located in IJburg, near Amsterdam, and built for the appropriately named Tennisclub IJburg, first founded in 2008. The tennis club already has a number of facilities, including 10 clay courts and a tennis school. But with the addition of the Couch, Tennisclub IJburg finally has not just a place to relax between games, but a place to watch them.

Click Image To Enlarge

Click Image To Enlarge

MVRDV co-founder and architect Winy Maas says.

"With the Couch, we have integrated sport into society By turning the roof of the club-house into a tribune, a Centre Court is created. Here we celebrate the talent of the amateur players who are challenged to excel in front of the public. By covering the entire building in red polyurethane the club gains visibility: an advert for sports and movement. The essence of the building is the stimulation of fitness and the fight against obesity and inactivity."

Click Image To Enlarge

MVRDV developed the clubhouse together with a co-architect, Studio Bouwkunde. In execution, the building is reminiscent of Bjarke Ingels's work on Copenhagen's Gammel Hellerup School. For that project, Ingels used the roof of an undulating rec center and the school's new arts building as common spaces for lounging around, eating meals, and watching sports. MVRDV's take, though, looks warm and accommodating, like a pillow a giant sat on after deciding to sit and watch a tennis match for a spell.

Click Image To Enlarge

Click Image To Enlarge

COMMENTARY:   I am a huge architectural design buff, and just love architecture that is on the leading edge of architectural design and is both multi-functional and energy efficient.  MVRDV has really blown me away with The Couch.  

The Tennis Club IJburg club house is a 36-metre long open volume with services on either end such as dressing rooms, a kitchen, storage and toilets. The main space is multifunctional, so it can be used for the club’s many events. The roof dips down towards the south side and is raised towards the north up to a height of seven metres, creating an informal tribune for the club with terraced seating. This ‘Couch’ can seat up to 200 spectators comfortably. The wide glass front to the north side allows extensive natural lighting and provides a view out over the waters of the IJ-lake.

Inside the club house, the in-situ concrete structure is clad with FSC-certified wood, while the outside is fully sealed with an EDSM polymer hotspray in the same colour and texture as the clay tennis courts. The reduced glass surface to the south helps to cool the building. The thermal mass of the building, characteristic of its materialisation in concrete and wood, are used to reach a high degree of energy efficiency. It clubhouse will be heated by a district heating system made efficient by a heat exchanger. In summer there will be natural ventilation, adding to the ambitious sustainability profile of the structure.     

Tennis club IJburg is located on a manmade island in the East of Amsterdam and opened in 2008. The tennis club, currently with 2000 members, has 10 clay courts and a tennis school. The new club house is not the centre of the club's activities.

MVRDV developed the clubhouse together with co-architect Studio Bouwkunde and structural engineer ABT. Contractor Romijn Bouw realised the building, which is MVRDV's eighth project to be built in Amsterdam.

Courtesy of an article dated October 16, 2015 appearing in Fast Company Design and an article dated October 14, 2015 appearing in MVRDV

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THE ULTIMATE HOW-TO GUIDE FOR PLANNING A PREDICTIVE ANALYTICS PROJECT AND RESULTS FROM AN ACTUAL CASE STUDY

The explosion of interest in predictive analytics and in particular sophisticated  predictive analytics has led to renewed interest in the topic of how to plan for an predictive analytics project.

The topic is hardly new but anyone involved in planning for, or conducting a serious analysis project needs to cognizant of the fundamentals. Here we abstract from insights and ideas from two classic discussions, the 1996 paper by Fayyad, Piatetsky-Shapiro, and Smyth "From Data Mining to Knowledge Discovery in Databases,” and the 1999 “Cross-Industry Standard Process (CRISP) for Data Mining” developed by analytical practitioners at Daimler-Chrysler, ISL (later acquired by SPSS and now part of IBM) and NCR (Teradata). While these first-generation discussions clearly date from an earlier technological era the principle ideas are still relevant and are being rediscovered and translated into modern terminology daily. Here we offer a summary of the essentials viewed from our perspective of 20 years of experience in the world of data mining and predictive analytics.

Stage #1: Business Understanding (Specification of an Objective)

To successfully conduct an analytics project it is first necessary to have a notion of what is hoped to be accomplished, described in the practical language of business or decision-making. Often the objective can be clearly stated. Examples include:

• In an election campaign, identify the swing voters most likely to ultimately vote for a specific candidate (precisely what the Obama campaign undertook in 2012).
• Rank the offers you might provide to a website visitor by the probability of acceptance or expected value of the response (something Salford Systems undertook on behalf of a client).

Not all projects have such a clear and focused objective and may be more generic:

How can I make my overall grocery store operation more efficient logistically? 

In this discussion we have in mind the more clearly defined, and thus more narrowly scoped, projects. The CRISP approach calls this first prerequisite “Business Understanding” which as Fayyad et. al. point out, includes the identification of the objective from the point of view of the client.

Stage #2: Data Inventory and Understanding

Getting a good grasp of exactly what data is available for analysis is critical and involves canvassing both internal (proprietary) and external (publicly available or purchasable) data sources. Identifying, locating, and gaining access to potentially useful data within an organization can be challenging, and a project may be forced to proceed using less data than actually exists due to institutional constraints isolating data silos. An effort to identify all sources of potentially useful data is generally worthwhile as many large organizations have a myriad of data stores, some of which are generally unknown outside the department that collected it.  External data is increasingly available via the Internet and can come from government sources and/or private organizations.  Such external data can be extremely helpful in improving the predictive performance of a model and refining of the analytical insights it can yield. Examples of such data include:

• Popularity of certain Internet search terms
• Stock market index movements
• Snowfall in Fairbanks, Alaska

Supplemental data can be of enormous value in providing the relevant context for what is tracked from the perspective of a single enterprise. For example, supplemental data may include prices charged by competitors for the same or similar products offered by a retailer.

In our consulting experience for financial services enterprises, we have generally found that the data such enterprises amass on their own customers is far more valuable than anything that could be collected or purchased about those same customers externally, but the added value of such data could still justify the cost of the acquisition of this data.

 

Stage #3: Assessment of Data for Suitability

Having identified objectives and located potential data the next step involves an assessment of the data with the objective of determining whether the available data can in fact support the original objective or perhaps some modified and even scaled-back objectives. One may discover that the available data is missing essential details (descriptors for the ad actually shown to a web site visitor) or that data is available only for a subpopulation (e.g. only data for the clicks was retained and all non-click information was discarded). Such data omissions make the job of predictive analytics vastly more difficult if not impossible. The topic of mishandled or mismanaged data is a large one and all experienced analysts have their own catalog of horror stories. Our point here is simply that we have to allow for the possibility that data problems may complicate the project if not stop it in its tracks. 

The data may also offer too small a sample size for meaningful analysis. This may appear to be a thing of the past, something that will never occur again in the brave new world of big data.  But even with huge data stores the fraction that is relevant to a specific analysis may be small. 

If you need to build a click model for an ad that has only been clicked on a 15 times you are short of data even if you have one billion impressions for that ad.  In sum, we need both a sufficient quality and a sufficient quantity of data.

Stage #3: Assessment of Data for Suitability

Having identified objectives and located potential data the next step involves an assessment of the data with the objective of determining whether the available data can in fact support the original objective or perhaps some modified and even scaled-back objectives. One may discover that the available data is missing essential details (descriptors for the ad actually shown to a web site visitor) or that data is available only for a subpopulation (e.g. only data for the clicks was retained and all non-click information was discarded). Such data omissions make the job of predictive analytics vastly more difficult if not impossible. The topic of mishandled or mismanaged data is a large one and all experienced analysts have their own catalog of horror stories. Our point here is simply that we have to allow for the possibility that data problems may complicate the project if not stop it in its tracks. 

The data may also offer too small a sample size for meaningful analysis. This may appear to be a thing of the past, something that will never occur again in the brave new world of big data.  But even with huge data stores the fraction that is relevant to a specific analysis may be small. 

If you need to build a click model for an ad that has only been clicked on a 15 times you are short of data even if you have one billion impressions for that ad.  In sum, we need both a sufficient quality and a sufficient quantity of data.

Stage #4: Pilot Project 

During the last five years or so, all of our consulting projects have begun with a significantly scaled-down dry run or pilot project; one major year-long project we conducted in 1996 began with a 3-month scaled down trial. This is something of an innovation in the practice of advanced analytics even though the notions are anything but new. Engineers and architects build scale models, jet engines are tested in stationary mounts on the ground long before they are placed on an airplane, and new web pages and marketing campaigns are typically first exposed to just tiny fractions of a major web site’s traffic. Obvious or not, the notion that data analysts should consciously opt for a trial run may be new to some practitioners and is well worth considering.  At the very least, analytics could be conducted on a radically slimmed down version of the data set to accelerate modeling run times.

Real-World Example: In a project to predict the sales of individual promoted products in a chain of large grocery stores we started with data for a few dozen products for one department, and then expanded with further selected products from other departments. When done with the pilot project we moved on to making the system work for 122,000 products.

Stage #5: Prepare (and Explore) Data 

From a manager’s point of view the important point here is that the preparation of the data (“beating it into shape”) so that it can be successfully modeled may require considerable time and resources.  David Vogel et. al. were the winner’s of the first (and second) round progress prizes in the $3 million Heritage Provider Network predictive modeling competition. In their paper explaining their approach and methods, about one third of the paper is devoted to documenting their preparation of the data. While some data mining and predictive analytics tools can work remarkably effectively with unprepared data (TreeNet is clearly one such tool), careful data preparation invariably leads to better, and often substantially better, performance.

Much of the data preparation is guided by rather straightforward examination of the main components of the data expected to be relevant to modeling.

Routine first Steps:

1. Ensuring that the data display appropriate values
2. Scanning for extreme values and/or invalid values
3. Locating oddities in the data such as unexpected gaps

Often special care needs to be devoted to handling of missing information, which might be encoded in different ways in different parts of the data. Data that has been collected over a fairly lengthy period of time may also suffer from changes in data coding conventions over time rendering the overall inconsistent. A simple example would be post codes initially stored as 9 digit zip codes and then later stored always in the ZIP+4 format. Without paying attention to such details certain aspects of the analysis will suffer.

There is nothing especially glamorous about data preparation and perhaps for this reason there are very few books or courses devoted specifically to this topic. The data cleaning process is crucial for achieving the best possible results, however, and we expect to offer a series of blog entries on this topic in the future.

Stage #6: Modeling

For the professional data analyst, modeling is the most enjoyable and stimulating part of the project.  It s here that we get to show off our skills and the magic of modern technology, delivering results with modern analytical tools that were simply unthinkable using only standard statistical methods. In SPM 7.0 we offer CART, MARS, TreeNet, RandomForests, GPS Generalized Pathseeker (Lasso style regression, logistic regression, and the extended elastic net), ISLE model compression, and conventional regression which gives the modeler a fair of options to choose from.

Although modelers enjoy building and assessing models, the process of moving from first drafts to final versions can involve a fair bit of experimentation and trial and error. To accelerate this search process we recommend making use of Modeling Automation such as embedded in the SPM BATTERY feature.

By modeling automation we do not mean letting the machine do all the work, but in allowing the machine the assist the modeler by automatically running and summarizing a series of experiments in which variations on a theme, tailored by the modeler, are run. This allows the modeler to keep analytical servers running 24 hours a day, building model variations that can be quickly reviewed in graphical summaries.

Stage #7: Evaluation, Interpretation, Understanding

Although any model can be treated as a black box prediction machine there are usually valuable insights that can be extracted from the model and it is worthwhile spending the time to secure them. We might learn for example that certain subpopulations of customers are unusually favorable or unfavorable for our objectives or we might learn that we can shift selling effort from one segment to another. Evaluation, interpretation, and insight are by definition the domain of the thoughtful and informed reviewer and we do not expect this stage of the project to be automated any time soon (with or without Watson).

The traditional large consulting companies have always understood this very well and have typically devoted the lion’s share of their efforts to telling the story unveiled by the technical analysis in any project. When analytics are undertaken in house the responsible department should take pains to perfect this portion of the project as well.

Stage #8: Full Project

The pilot project may yield results good enough to justify moving forward with the resulting models and insights.  In this case, we would move immediately to “Deployment” below and then consider expansion of the pilot.

If the pilot is successful but the business requires the full project before deployment can be considered, then the steps above must be repeated but in the context of a much larger scope. While much should have been learned from the pilot the full project may reveal a host of new problems and challenges and several conclusions derived form the pilot may need to be revised.

Pilot projects often benefit from the luxury of allowing detailed study of a narrowly defined set of data, supporting very high quality results. A risk for the expansion of the project is that insufficient resources are allocated under the mistaken understanding that the simple scaling up of the project is routine. Salford Systems has been part of several projects in which we thought the pilot was of far higher quality than the full project in all aspects of its execution due to resource allocation.

Stage #9: Deployment

Unless a model is intended to guide only strategic thinking its real payoff comes only when it is deployed in some real-world process. Our retail sales prediction models are used to guide the logistics of a grocery store chain, dictating the number of cans of tuna fish to ship to each store in the network next week. Our ad optimization system recommends which of possibly many thousands should be displayed right now for a given visitor of a given web page. Our lifetime customer value model scores credit card applicants on the basis of their likelihood to have nonzero balances on their card 12 months from now. These models all needed to be embedded in business processes to speed and improve the quality of decisions. Without deployment, models become little more than academic exercises or illustrations of what might be accomplished.

Deployment is a complex topic deserving of an extended discussion all of its own. The only point here is that a modeling system should support easy deployment, by, for example, allowing export of models into reusable code such as Java, C, or PMML. (SPM offers all three and more).

Conclusion

Here are the stages we described once again:

1. Business Understanding  (Specification of an Objective)
2. Data Inventory and Understanding
3. Assessment of Data for Suitability
4. Pilot Project
5. Prepare (and Explore) Data
6. Modeling (Build Predictive Models)
7. Evaluation, Interpretation, Understanding
8. Full Project
9. Deployment

We might also add a final stage: Extract lessons learned and refine “best practices” documents to guide future projects. 

COMMENTARY:     As consultant I often discover that my business clients have only a very general and superficial understanding of who their customers are.  Many claimed they know their customers, but they are only seeing the first layer or veneer of their core customers.  They lack a deeper understanding of their customers.  They are surprised when they discover that their are many different layers of customers that makeup their core customers.

It is no longer sufficient to target customers based solely on demographics. Targeting has become a lot more complicated and difficult than in the past because customers are found both online and offline.   They read less newspapers for their news, listen to less radio broadcasts for their music, watch less television programs than in the past and don't go to movie theaters like your parents did. We are living in the "digital age" and the internet has changed everything.  Customers are spending more time online now, consuming more online content and spending more time sharing and engaging with friends and brands on social networks.   Customers are shifting from the desktop to mobile devices like smartphones, tablets and wearable devices adding an additional layer of complexity in targeting them.  Finally, your customers must not only be targeted demographically but by customer segments and lifestyles -- in short, how they spend their time and money.       

I cannot stress the importance of gaining a deeper understanding and knowledge of your customers in order to effectively target them.  Customer data is no longer homogenous, but originates from many different sources and comes in different formats, and this data must be captured, stored, validated, cleaned, transformed and aggregated, then it must be procesed through a business intelligence system for data mining purposes, online analytical processing (OLAP), data visualization, reports and management dashboards.

Data Sources and Data Flows Essential For an Efficient Business Intelligence System (Click Image To Enlarge)

As a former CFO for Solar Planet, a very successful indoor tanning salon franchise headquartered in San Francisco, I speared the effort to gain a deeper understanding and knowledge of the tanning customers of our tanning salon franchisees located across the U.S.  This required esablishing a data warehouse, then going through numerous steps (see above schematic) to get our customer data in a format that could be used for business intelligence analysis and reporting purposes.  

When we began this effort, we had a pre-conceived notion of what we thought our tanning salon customers were like, but when we analyzed the customer data through our business intelligence system, we discovered that our customers were not all the same.  True, they enjoyed tanning for different reasons, but they were different as night and day, and this deeper understanding, valuable insights and knowledge allowed us to more accurately target them while using the most efficient media channels to reach them.  Armed with this information we were able to provide our existing Solar Planet franchisees with better service and marketing advice and information, and identify the best locations for future Solar Plant franchise locations.  Solar Planet was able to customize the advertising and promotions campaigns for each franchisee based on the knowledge we gained about their customers.  Our franchisees were able to gain measureable improvements in new customers, increases in revenues and lower marketing expenses, and the results of each marketing campaign became much more predicable.  Below is a presentation that I prepared for our management team and potential investors.   I hope you find it valuable.

Solar Planet Company Owned Salon Presentation 3 from Tommy Toy

Courtesy of Salford Systems how-to guide titled, "The Ultimate How-To Guide For Planning A Predictive Analytics Project." 

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