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Competition Promises To Be Fierce For Brands This Holiday Season

Steve Olenski

Healthy competition is a good thing. I happen to believe that, for it is the spirit of competition that brings out the best in all of us. Or in Lance Armstrong’s case, the worst.

But I digress.

The holiday season is (a) upon us and (b) often the deciding factor in whether a given brand had a successful year or not. Not exactly the kind of information that makes one sit up and say “I did not know that.” Of course you know that.

In recent weeks I have told you how the Lack Of Mobile Integration Means Not So Happy Holidays For Brands and also How Retailers Can Make Their Email Marketing Glisten This Holiday Season.

The former article speak to the fact that more and more consumers are turning to their cell phones to shop this holiday season and more than half of America’s fastest growing retailers have yet to fully connect online and offline shopping experience, leaving shoppers unsatisfied with their shopping experience.

The latter piece tells you how you can make the most of your email marketing campaign this holiday season by downloading and reading the Retail Email Guide For the Holiday Season, put out by Responsys. Full disclosure: At the time I wrote the latter piece I was not employed by Responsys but have since taken the position of Sr. Content Strategist.

Ok, now that that’s out of the way, let’s get to why I think competition will be fierce this holiday season. The cold hard truth is of course it has to do with the economy as it was in years past. This year there is the added uncertainty of who will be in the White House as well. A good recipe for shopper weary which translates into major competition among brands.

Shoppers Remain Conservative With Holiday Gift Budgets

This was the headline of a press release on the website of the National Retail Federation. The headline spoke directly to the findings the NRF uncovered during a recent survey of consumers.

Logo of the National Retail Federation

From the release:

“According to the survey, the biggest portion of shoppers’ budget this year will go towards gifts for family members with the average person planning to spend $421.82 on children, parents, aunt, uncles and more. Additionally, people will spend $75.13 on friends, $23.48 on co-workers and $28.13 on others, such as pets and community members.

Consumers will also spend on food and candy ($100.76), greeting cards ($28.66) and flowers ($19.55.) When it comes to decorations, the average person will spend $51.99, up from $49.15 last year and the most in the survey’s history. Total spending on holiday décor will reach $6.9 billion.”

Wow, who knew holiday décor was so popular?

But I digress, again.

NRF President and CEO Matthew Shay says the competition will heat up and in fact, already has. “As the most promotional time of the year, retailers will continue to look for ways to  stand out, specifically with attractive deals on toys, electronics and apparel, even well before the ‘official’ start of the holiday shopping season – Black Friday and Cyber Monday.”

Judging by the survey results, I would say Shay is right as four in 10 (41.4%) of consumers say they will begin holiday shopping before Halloween.

So what this basically tells me and should tell all brand managers and brand marketers and ALL marketers that if they have not already started their holiday marketing and advertising push, they may in fact already be too late to the dance.

If The Price Is Right

Not surprisingly over one-third of respondents (36.6%) said the most important factor in deciding where to shop are offers for sales and discounts.

Source: National Retail Federation

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How Warby Parker Became A Billion-Dollar Disruptor

Tom Redd

Good listening is still a crucial skill for business success. Where you listen is also important. The founders of online eyewear retailer Warby Parker grew up with social media, and that is where Neil Blumenthal and David Gilboa put their ears to the ground: They listened for the rumble of their generation’s needs, wants, and social conscience.

In April 2015, Warby Parker was only five years old but was already valued at $1.2 billion. This company has disrupted the eyewear industry with its understanding of millennial generation shopper trends.

Blumenthal and Gilboa are millennials, a generation that ranges from late teenagers to people just about to enter middle age who were born between the early 1980s and the early 2000s. One important millennial trait is a feeling of instability due to family struggles during the Great Recession.

Wharton School graduates Blumenthal and Gilboa followed millennial worries on social media, such as difficulties in finding jobs and paying off student loans. Blumenthal and Gilboa also focused on what their generation posted about fashion sense. They listened to the millennial desire to do business with socially responsible companies, which led to the company’s customer-centric merchandising model.

Charming a financially insecure millennial audience

Kristin Wong is one example of a consumer Warby Parker reached. She needed new glasses and did not want to spend a lot for them.

Wong had worked hard to save money so she could quit her day job and become a full-time freelance journalist. She specializes in budget topics at her Brokepedia website and writes about the subject in a casual but appealing way.

Wong tried Warby Parker because the eyeglasses on its website fit her taste for retro fashion. She wanted a pair with large circular lenses like the ones that movie star Harold Lloyd wore in silent films.

The company’s pricing also attracted Wong. Warby Parker sells eyeglasses for $95, which includes frames plus lenses—about one-fourth the price of eyewear at other well-known retailers and brands owned by Luxottica, the world’s largest retail eyewear company. The steep price difference is one way that Warby Parker has disrupted Luxottica’s business.

Moving from virtual to brick and mortar

Warby Parker first interrupted Luxottica and the traditional eyewear industry by reaching out to millennials digitally. Millennials have grown up with online shopping, social media, and the hyperconnectivity of mobile devices. The digital business environment is native ground for the youngest members of the generation, who are comfortable posting their pictures online and asking for viewers’ opinions.

Warby Parker’s digital strategy includes asking prospective customers to try on its eyeglasses online. Customers do this through a virtual reality feature on the company’s website: You simply upload your picture, select your frames, and ask people to vote for your best look on social media sites. Alternatively, the company will mail you five pairs of frames for a 5-day trial period.

Until recently, the company had few brick and mortar stores and popup centers.In April, however, Warby Parker received $100 million in new funding and surprised the eyewear industry again by using the money to open more stores. While millennials have grown up with mobile devices, consider wi-fi to be a primary utility, and are comfortable with mobile tools and e-commerce, they also like to have the option of visiting physical retail stores.

Doing social good

Many studies show that corporate social responsibility (CSR) programs, such as those that focus on employee benefits, environmental protection, or helping those in need, also motivate millennials to buy goods.

For each pair of glasses Warby Parker sells, it contributes to its nonprofit program for supporting vision centers in developing nations. The program trains partners to give basic eye exams and sell eyewear at prices local citizens can afford.

Warby Parker reports that it tallies the number of glasses sold each month and donates an amount equaling what it would cost to supply the same number of eyeglasses to the partners in those countries. In that way, the company donates a pair for every pair sold.

This well-defined CSR program is another way that the company has disrupted retail eyewear. It matches the company’s business purpose with its charitable goal — a slam dunk.  Blumenthal and Gilboa say they want to make good vision available at a reasonable price all around the world.

Maintaining a sense of humor

Warby Parker went into business with the intention of disrupting the eyewear industry. In an interview with Forbes magazine, Gilboa said the industry has “been ripping consumers off for decades” with products that are overpriced. Gilboa noted that he and Blumenthal also started with the goal of showing that business can have a tremendous positive impact on social good.

Another way Warby Parker is disrupting the retail world is in its lighthearted attitude, which includes production of less-than-serious annual reports. The company’s 2014 Make-Your-Own Annual Report, for example, includes useful Big Data questions, such as “What was your favorite color in 2014?” But it also asks participants, “Why did the chicken cross the road?” Among the multiple-choice answers: “To break in his new jeans” and “Because he’s a strong, independent chicken.”

Come to think of it, Warby Parker is like the chicken. It tried on an old, stiff industry and is softening it to fit the company’s own comfort. It isn’t clucking nonsense to say that Warby Parker is strong, independent, and knows how to strut its stuff.

For more information on digital transformation in the retail industry, visit here.

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Tom Redd

About Tom Redd

Tom Redd drives strategic communications for the Retail Global Industry Business Unit for SAP. With over 32 years of experience in retail technology, Tom is focused on the latest action within the retail industry. He is also shared his knowledge with SAP communication teams, including analyst relations and public relations.

Americans Are Too Afraid To Shop Online

Danielle Beurteaux

According to a new report released by the National Telecommunications and Information Administration (part of the U.S. Department of Commerce), Americans are very concerned about online security and privacy. And that’s a problem for e-commerce.

Based on Census Data collected in July 2015 from 41,000 households with at least one Internet user, the study reports that 25% of households with four devices have experienced a security problem, and that figure goes up to 31% for those who have five or more devices. Using a mobile data plan out of the home? Then you might be part of the 22% who had a security problem.

When those surveyed were asked about their biggest online concerns, 63% came up with identity theft, the top response, which increased to 70% among those who had already had a security problem. Next came 45% who were concerned about bank or credit card fraud. The third biggest concern – data collection by online services – was over 20 percentage points lower. What’s really interesting about these responses is that they weren’t pre-written or multiple choice – respondents were free to come up with whatever concerned them most.

“Interviewers did not suggest possible answers when asking this question, and respondents were free to give multiple answers or to say that they had no concerns. Despite the lack of prompting, 84% of online households named at least one concern they had about online privacy and security risks, and 40% cited at least two different concerns,” wrote policy analyst Rafi Goldberg.

And those concerns are translating to online behaviors. For instance, 29% of households surveyed were wary of conducting financial transactions online, and that figure increased to 40% for those who’ve already had a security breach. Similarly, 26% of all households don’t buy online because of security concerns, and 35% of households with security breach experiences don’t partake in e-commerce.

This isn’t great news for two reasons: The world is moving online and this could create a group of people with limited online engagement.

The second is that a number of that size is probably giving nightmares to anyone involved in online commerce, from banking to selling.

One solution to increasing consumers’ comfort level with online activity is a bill introduced last spring, the Consumer Privacy Bill of Rights. The goal of the bill is “[t]o establish baseline protections for individual privacy in the commercial arena,” i.e. give consumers more information and control over their personal information. But when the first draft of the bill was released, some privacy advocates claimed it didn’t go far enough to offer real protections. On the other hand, some said it goes too far and would be detrimental to business. (Also, it probably won’t be passed, but at least it’s a start).

Meanwhile, according to the 2016 BDO Retail RiskFactor Report, an annual report on risk in the retail landscape, found that online security is at the forefront of retailers’ minds. “Privacy Concerns Related to Security Breach” actually tied for first place this year, with “General Economic Conditions.” Consider that only two years ago, in 2014, security breaches came in eighth place.

The next challenge for e-commerce will be to create an environment of trust that maintains a level of personalization. Because everyone knows that a security breach is bad for business.

See how businesses are balancing customers’ desire for a more personalized experience with their concerns about privacy and security in Live Businesses Deliver a Personal Customer Experience Without Losing Trust.

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Unlock Your Digital Super Powers: How Digitization Helps Companies Be Live Businesses

Erik Marcade and Fawn Fitter

The Port of Hamburg handles 9 million cargo containers a year, making it one of the world’s busiest container ports. According to the Hamburg Port Authority (HPA), that volume doubled in the last decade, and it’s expected to at least double again in the next decade—but there’s no room to build new roads in the center of Hamburg, one of Germany’s historic cities. The port needed a way to move more freight more efficiently with the physical infrastructure it already has.

sap_Q216_digital_double_feature1_images1The answer, according to an article on ZDNet, was to digitize the processes of managing traffic into, within, and back out of the port. By deploying a combination of sensors, telematics systems, smart algorithms, and cloud data processing, the Port of Hamburg now collects and analyzes a vast amount of data about ship arrivals and delays, parking availability, ground traffic, active roadwork, and more. It generates a continuously updated model of current port conditions, then pushes the results through mobile apps to truck drivers, letting them know exactly when ships are ready to drop off or receive containers and optimizing their routes. According to the HPA, they are now on track to handle 25 million cargo containers a year by 2025 without further congestion or construction, helping shipping companies bring more goods and raw materials in less time to businesses and consumers all across Europe.

In the past, the port could only have solved its problem with backhoes and building permits—which, given the physical constraints, means the problem would have been unsolvable. Today, though, software and sensors are allowing it to improve processes and operations to a previously impossible extent. Big Data analysis, data mining, machine learning, artificial intelligence (AI), and other technologies have finally become sophisticated enough to identify patterns not just in terabytes but in petabytes of data, make decisions accordingly, and learn from the results, all in seconds. These technologies make it possible to digitize all kinds of business processes, helping organizations become more responsive to changing market conditions and more able to customize interactions to individual customer needs. Digitization also streamlines and automates these processes, freeing employees to focus on tasks that require a human touch, like developing innovative strategies or navigating office politics.

In short, digitizing business processes is key to ensuring that the business can deliver relevant, personalized responses to the market in real time. And that, in turn, is the foundation of the Live Business—a business able to coordinate multiple functions in order to respond to and even anticipate customer demand at any moment.

Some industries and organizations are on the verge of discovering how business process digitization can help them go live. Others have already started putting it into action: fine-tuning operations to an unprecedented level across departments and at every point in the supply chain, cutting costs while turbocharging productivity, and spotting trends and making decisions at speeds that can only be called superhuman.

Balancing Insight and Action

sap_Q216_digital_double_feature1_images2Two kinds of algorithms drive process digitization, says Chandran Saravana, senior director of advanced analytics at SAP. Edge algorithms operate at the point where customers or other end users interact directly with a sensor, application, or Internet-enabled device. These algorithms, such as speech or image recognition, focus on simplicity and accuracy. They make decisions based primarily on their ability to interpret input with precision and then deliver a result in real time.

Edge algorithms work in tandem with, and sometimes mature into, server-level algorithms, which report on both the results of data analysis and the analytical process itself. For example, the complex systems that generate credit scores assess how creditworthy an individual is, but they also explain to both the lender and the credit applicant why a score is low or high, what factors went into calculating it, and what an applicant can do to raise the score in the future. These server-based algorithms gather data from edge algorithms, learn from their own results, and become more accurate through continuous feedback. The business can then track the results over time to understand how well the digitized process is performing and how to improve it.

sap_Q216_digital_double_feature1_images5From Data Scarcity to a Glut

To operate in real time, businesses need an accurate data model that compares what’s already known about a situation to what’s happened in similar situations in the past to reach a lightning-fast conclusion about what’s most likely to happen next. The greatest barrier to this level of responsiveness used to be a lack of data, but the exponential growth of data volumes in the last decade has flipped this problem on its head. Today, the big challenge for companies is having too much data and not enough time or power to process it, says Saravana.

Even the smartest human is incapable of gathering all the data about a given situation, never mind considering all the possible outcomes. Nor can a human mind reach conclusions at the speed necessary to drive Live Business. On the other hand, carefully crafted algorithms can process terabytes or even petabytes of data, analyze patterns and detect outliers, arrive at a decision in seconds or less—and even learn from their mistakes (see How to Train Your Algorithm).

How to Train Your Algorithm 

The data that feeds process digitization can’t just simmer.
It needs constant stirring.

Successfully digitizing a business process requires you to build a model of the business process based on existing data. For example, a bank creates a customer record that includes not just the customer’s name, address, and date of birth but also the amount and date of the first deposit, the type of account, and so forth. Over time, as the customer develops a history with the bank and the bank introduces new products and services, customer records expand to include more data. Predictive analytics can then extrapolate from these records to reach conclusions about new customers, such as calculating the likelihood that someone who just opened a money market account with a large balance will apply for a mortgage in the next year.

Germany --- Germany, Lower Bavaria, Man training English Springer Spaniel in grass field --- Image by © Roman M‰rzinger/Westend61/CorbisTo keep data models accurate, you have to have enough data to ensure that your models are complete—that is, that they account for every possible predictable outcome. The model also has to push outlying data and exceptions, which create unpredictable outcomes, to human beings who can address their special circumstances. For example, an algorithm may be able to determine that a delivery will fail to show up as scheduled and can point to the most likely reasons why, but it can only do that based on the data it can access. It may take a human to start the process of locating the misdirected shipment, expediting a replacement, and establishing what went wrong by using business knowledge not yet included in the data model.

Indeed, data models need to be monitored for relevance. Whenever the results of a predictive model start to drift significantly from expectations, it’s time to examine the model to determine whether you need to dump old data that no longer reflects your customer base, add a new product or subtract a defunct one, or include a new variable, such as marital status or length of customer relationship that further refines your results.

It’s also important to remember that data doesn’t need to be perfect—and, in fact, probably shouldn’t be, no matter what you might have heard about the difficulty of starting predictive analytics with lower-quality data. To train an optical character recognition system to recognize and read handwriting in real time, for example, your samples of block printing and cursive writing data stores also have to include a few sloppy scrawls so the system can learn to decode them.

On the other hand, in a fast-changing marketplace, all the products and services in your database need consistent and unchanging references, even though outside the database, names, SKUs, and other identifiers for a single item may vary from one month or one order to the next. Without consistency, your business process model won’t be accurate, nor will the results.

Finally, when you’re using algorithms to generate recommendations to drive your business process, the process needs to include opportunities to test new messages and products against existing successful ones as well as against random offerings, Saravana says. Otherwise, instead of responding to your customers’ needs, your automated system will actually control their choices by presenting them with only a limited group of options drawn from those that have already received the most
positive results.

Any process is only as good as it’s been designed to be. Digitizing business processes doesn’t eliminate the possibility of mistakes and problems; but it does ensure that the mistakes and problems that arise are easy to spot and fix.

From Waste to Gold

Organizations moving to digitize and streamline core processes are even discovering new business opportunities and building new digitized models around them. That’s what happened at Hopper, an airfare prediction app firm in Cambridge, Massachusetts, which discovered in 2013 that it could mine its archives of billions of itineraries to spot historical trends in airfare pricing—data that was previously considered “waste product,” according to Hopper’s chief data scientist, Patrick Surry.

Hopper developed AI algorithms to correlate those past trends with current fares and to predict whether and when the price of any given flight was likely to rise or fall. The results were so accurate that Hopper jettisoned its previous business model. “We check up to 3 billion itineraries live, in real time, each day, then compare them to the last three to four years of historical airfare data,” Surry says. “When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now,’ or ‘no, we think that fare is too expensive, we predict it will drop, and we’ll alert you when it does.’ And we can give them that answer in less than one second.”

When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now’.

— Patrick Surry, chief data scientist, Hopper

While trying to predict airfare trends is nothing new, Hopper has told TechCrunch that it can not only save users up to 40% on airfares but it can also find them the lowest possible price 95% of the time. Surry says that’s all due to Hopper’s algorithms and data models.

The Hopper app launched on iOS in January 2015 and on Android eight months later. The company also switched in September 2015 from directing customers to external travel agencies to taking bookings directly through the app for a small fee. The Hopper app has already been downloaded to more than 2 million phones worldwide.

Surry predicts that we’ll soon see sophisticated chatbots that can start with vague requests from customers like “I want to go somewhere warm in February for less than $500,” proceed to ask questions that help users narrow their options, and finally book a trip that meets all their desired parameters. Eventually, he says, these chatbots will be able to handle millions of interactions simultaneously, allowing a wide variety of companies to reassign human call center agents to the handling of high-value transactions and exceptions to the rules built into the digitized booking process.

Port of Hamburg Lets the Machines Untangle Complexity

In early 2015, AI experts told Wired magazine that at least another 10 years would pass before a computer could best the top human players at Go, an ancient game that’s exponentially harder than chess. Yet before the end of that same year, Wired also reported that machine learning techniques drove Google’s AlphaGo AI to win four games out of five against one of the world’s top Go players. This feat proves just how good algorithms have become at managing extremely complex situations with multiple interdependent choices, Saravana points out.

The Port of Hamburg, which has digitized traffic management for an estimated 40,000 trucks a day, is a good example. In the past, truck drivers had to show up at the port to check traffic and parking message boards. If they arrived before their ships docked, they had to drive around or park in the neighboring residential area, contributing to congestion and air pollution while they waited to load or unload. Today, the HPA’s smartPORT mobile app tracks individual trucks using telematics. It customizes the information that drivers receive based on location and optimizes truck routes and parking in real time so drivers can make more stops a day with less wasted time and fuel.

The platform that drives the smartPORT app also uses sensor data in other ways: it tracks wind speed and direction and transmits the data to ship pilots so they can navigate in and out of the port more safely. It monitors emissions and their impact on air quality in various locations in order to adjust operations in real time for better control over environmental impact. It automatically activates streetlights for vehicle and pedestrian traffic, then switches them off again to save energy when the road is empty. This ability to coordinate and optimize multiple business functions on the fly makes the Port of Hamburg a textbook example of a Live Business.

Digitization Is Not Bounded by Industry

Other retail and B2B businesses of all types will inevitably join the Port of Hamburg in further digitizing processes, both in predictable ways and in those we can only begin to imagine.

sap_Q216_digital_double_feature1_images4Customer service, for example, is likely to be in the vanguard. Automated systems already feed information about customers to online and phone-based service representatives in real time, generate cross-selling and upselling opportunities based on past transactions, and answer customers’ frequently asked questions. Saravana foresees these systems becoming even more sophisticated, powered by AI algorithms that are virtually indistinguishable from human customer service agents in their ability to handle complex live interactions in real time.

In manufacturing and IT, Sven Bauszus, global vice president and general manager for predictive analytics at SAP, forecasts that sensors and predictive analysis will further automate the process of scheduling and performing maintenance, such as monitoring equipment for signs of failure in real time, predicting when parts or entire machines will need replacement, and even ordering replacements preemptively. Similarly, combining AI, sensors, data mining, and other technologies will enable factories to optimize workforce assignments in real time based on past trends, current orders, and changing market conditions.

Public health will be able to go live with technology that spots outbreaks of infectious disease, determines where medical professionals and support personnel are needed most and how many to send, and helps ensure that they arrive quickly with the right medication and equipment to treat patients and eradicate the root cause. It will also make it easier to track communicable illnesses, find people who are symptomatic, and recommend approaches to controlling the spread of the illness, Bauszus says.

He also predicts that the insurance industry, which has already begun to digitize its claims-handling processes, will refine its ability to sort through more claims in less time with greater accuracy and higher customer satisfaction. Algorithms will be better and faster at flagging claims that have a high probability of being fraudulent and then pushing them to claims inspectors for investigation. Simultaneously, the same technology will be able to identify and resolve valid claims in real time, possibly even cutting a check or depositing money directly into the insured person’s bank account within minutes.

Financial services firms will be able to apply machine learning, data mining, and AI to accelerate the process of rating borrowers’ credit and detecting fraud. Instead of filling out a detailed application, consumers might be able to get on-the-spot approval for a credit card or loan after inputting only enough information to be identified. Similarly, banks will be able to alert customers to suspicious transactions by text message or phone call—not within a day or an hour, as is common now, but in a minute or less.

Pitfalls and Possibilities

As intelligent as business processes can be programmed to be, there will always be a point beyond which they have to be supervised. Indeed, Saravana forecasts increasing regulation around when business processes can and can’t be digitized. Especially in areas involving data security, physical security, and health and safety, it’s one thing to allow machines to parse data and arrive at decisions to drive a critical business process, but it’s another thing entirely to allow them to act on those decisions without human oversight.

Automated, impersonal decision making is fine for supply chain automation, demand forecasting, inventory management, and other processes that need faster-than-human response times. In human-facing interactions, though, Saravana insists that it’s still best to digitize the part of the process that generates decisions, but leave it to a human to finalize the decision and decide how to put it into action.

“Any time the interaction is machine-to-machine, you don’t need a human to slow the process down,” he says. “But when the interaction involves a person, it’s much more tricky, because people have preferences, tastes, the ability to try something different, the ability to get fatigued—people are only statistically predictable.”

For example, technology has made it entirely possible to build a corporate security system that can gather information from cameras, sensors, voice recognition technology, and other IP-enabled devices. The system can then feed that information in a steady stream to an algorithm designed to identify potentially suspicious activity and act in real time to prevent or stop it while alerting the authorities. But what happens when an executive stays in the office unusually late to work on a presentation and the security system misidentifies her as an unauthorized intruder? What if the algorithm decides to lock the emergency exits, shut down the executive’s network access, or disable her with a Taser instead of simply sending an alert to the head of security asking what to do while waiting for the police to come?

sap_Q216_digital_double_feature1_images6The Risk Is Doing Nothing

The greater, if less dramatic, risk associated with digitizing business processes is simply failing to pursue it. It’s true that taking advantage of new digital technologies can be costly in the short term. There’s no question that companies have to invest in hardware, software, and qualified staff in order to prepare enormous data volumes for storage and analysis. They also have to implement new data sources such as sensors or Internet-connected devices, develop data models, and create and test algorithms to drive business processes that are currently analog. But as with any new technology, Saravana advises, it’s better to start small with a key use case, rack up a quick win with high ROI, and expand gradually than to drag your heels out of a failure to grasp the long-term potential.

The economy is digitizing rapidly, but not evenly. According to the McKinsey Global Institute’s December 2015 Digital America report, “The race to keep up with technology and put it to the most effective business use is producing digital ‘haves’ and ‘have-mores’—and the large, persistent gap between them is becoming a decisive factor in competition across the economy.” Companies that want to be among the have-mores need to commit to Live Business today. Failing to explore it now will put them on the wrong side of the gap and, in the long run, rack up a high price tag in unrealized efficiencies and missed opportunities. D!

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Erik Marcade

About Erik Marcade

Erik Marcade is vice president of Advanced Analytics Products at SAP.

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How The World Of Work Is Changing [INFOGRAPHIC]

Lauren Kirkpatrick

The workplace is dramatically different than it was a few years ago. Technology advancements and social media have changed the landscape and are still changing it. Social change has also made an impact on the workforce with more women taking roles in what used to be a “man’s world.”

As we continue to move through advancements, legislative changes, social changes, and different initiatives, what will the world of work look like for future generations? Thanks to our friends at NeoNam Studios, check out this awesome infographic that breaks down what we might be looking at in the future.

world of work changing

How the World of Work is Changing [Infographic] by Next Generation

For more insight on the changing workplace, see Why Social Media Is Shaping The Future Of Work.

 

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