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5 Ways The Internet Of Things Is Changing The Game For Education And Learning

Geetika Shukla

There’s been so much buzz about the Internet of Things (IoT) lately – maybe not as much as for the U.S. presidential campaigns, but it’s pretty close. For today’s youngsters, the day will come when a computer is no longer seen as a separate object or device. With technology very much entwined in the basic fabric of everyday living, our children might feel offended if their obedient room lamp doesn’t immediately acknowledge their presence by switching itself on.

Over time, the IoT will be a mindset, rather than a steady stream of technology. Even though every other device in our home, workplace, or surrounding environment will be intelligent enough to connect and talk to each other, people will inevitably focus on the transformational possibilities for our world.

The realm of education is no exception to the IoT’s influence. Until now, educational technology has pivoted more or less around virtual conferencing and classrooms, online tutorials, and similar offerings. However, this is only the beginning. Here are five ways the IoT can transform education.

1. Connect academies all over the map

Some of the latest IoT artillery in this field includes digital highlighters, smart boards, and even smarter boards. This means your printed text could be digitally transferred to your smartphone or any other app at an incredible speed through tools like C-Pen and Scanmarker. Interactive boards can receive, acknowledge, and reciprocate information, simplifying and accelerating the overall learning experience.

Just imagine a scenario where students sitting in a classroom or at their desk at home can interact with their classmates, mentors, and educators scattered across the world. Now, let’s suppose the lesson of the day is focused on sea life. To give students a really exciting – and highly educational – experience, the teacher decides to access live information generated through sensors and live feeds monitoring a particular body of water.

2. Conserve and sustain to survive and flourish

With the aid of the IoT, a variety of options are possible in terms of environmental and energy conservation, ecosystem regulation, traffic, and transport, to name a few, that can help schools build up their budgets and offer better learning opportunities. For example, a school district in Pennsylvania saved a fortune on energy by using the IoT to support its energy monitoring and control program and reinvested the savings into its education programs. After all, living a green lifestyle is the way to go for all of us – we might as well put it to work so we can invest in more critical areas.

3. Win over students (and parents) with a safe and secure learning environment

The safety and security of students are paramount – whether you are a parent, educational authority, security official, or concerned citizen. With empowered sensors, RFIDs, cameras, and connected devices, monitoring and surveillance of entire buildings is possible. Instant notifications, alerts, and configured actions would be a significant addition to the security and safety of schools and other educational institutions.

4. Grant parity for all

The connected world of everything has a lot to offer students who need modified learning plans and exceptions. There are already a number of devices, tools, and apps that create appropriate learning experiences while bringing them on par with the rest of the class. One such example is the Lechal shoe project, which enables the visually challenged to better navigate the world through technology.

5. Turn learners into creators

The IoT indeed promotes and paves the way for creativity – and for children, there’s nothing better than learning the nuances and applications of hyperconnectivity firsthand. After all the predictions regarding the enormous number of connected communication and decision-making devices in the years to come, this is an excellent opportunity for schoolchildren to understand, build, and control such systems themselves.

The future trajectory of IoT-enabled education: Bumpy or smooth?

The IoT has the potential to strip away common barriers in education such as economic status, geography, language, and physical location. But once the initial glitz of being “super and hyperconnected” fizzles out, there are more important questions that need to be answered.

Converging education with technology is not just about bringing learning resources or making learning simpler and faster – it’s about quality, impact, and community acceptance too. Even with all the fancy resources and technology at our children’s fingertips, it is still a long and tough road ahead for the IoT to reform education in a path-breaking and everlasting way. Nevertheless, the seeds are sown well and the harvest appears to be promising.

Learning doesn’t stop when you graduate; if you want to be successful, it’s a lifelong endeavor. Learn How to Create a Culture of Continuous Learning.

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Geetika Shukla

About Geetika Shukla

My association with SAP is eight wonderful years. I have a disposition for the latest technological trends and a fascination for all the digital buzz apart from the world of process orchestration, cloud, and platforms.

What Do Digital Degrees Mean To Recruiters?

Tiffany Rowe

For centuries, earning a university degree required students to actually attend a university—complete with ivy-covered walls, musty library books, and professors droning on and on about one topic or another. Achieving higher education was difficult, so most employers rewarded those who put forth the effort to earn a university degree with better titles, better pay, and better benefits.

However, times have changed. Now those who want to boost their professional prowess can work and attend school through convenient (and often less expensive) online programs. In fact, digital academic opportunities have become so common that it is causing many employers to wonder: Is an online degree an asset or a liability in a new hire?

Do we need online degrees?

In the early days of the Internet, anyone could purchase a phony degree from one of the so-called “diploma mills” that operated online. Thus, recruiters were understandably wary of applications that cited unfamiliar universities or web-based programs, as it was entirely possible that the education was false and the applicant claiming it untrustworthy. Some states enacted strict legislation to punish organizations peddling fake degrees, as well as job applicants citing sham credentials on their resumes. As a result, the number of online diploma mills dramatically decreased in the early 2000s.

Meanwhile, legitimate online programs increased. In the past 15 years, the popularity of online education has soared. Today, at least 14 percent of students in higher education are enrolled in programs that are exclusively online, and more than one-third of all university students take at least one digital course.

Nearly all private and public universities offer some type of online education—even prestigious schools like Harvard and MIT. A few websites, like Coursera and Academic Earth, offer authentic classes from top-tier universities free of charge (though certification or a full degree still requires some tuition). As academia goes increasingly digital, recruiters should become accustomed to seeing more and more online programs listed on job applicants’ paperwork.

Some recruiters are going further by actively seeking candidates with a background in online education, as it demonstrates a sense of responsibility that traditional education may omit. Often, online students choose that path because they have other commitments, such as work or family, that make being a full-time on-campus student impossible.

For example, a recruiter reviewing the credentials of a job applicant with a management degree earned online might assume that that person is more than capable of juggling multiple duties successfully, which is indeed ideal for many management positions. Because classes are available all day every day, web-based management students must be disciplined enough to craft their own schedules, and they must adhere to them until their program is complete—no  easy task considering the many distractions we face in everyday life. Their graduation proves they are independent and responsible, which could put them ahead of similarly qualified candidates seeking the same management position.

Ultimately, the ubiquity of online education today makes verifying the authenticity of online degrees unnecessary. Instead, recruiters should devote their time to understanding why applicants chose the online route and what benefits their online programs provided that might serve their future careers.

What Do Digital Degrees Mean to Recruiters image 2Are there any downsides to online ed?

Some brands may be intent on culling employees only from Ivy League schools; others might prefer the familiarity of traditional campus-based universities and require employees to have a similar background. But studies prove that more than 75 percent of businesses fully embrace workers who have taken advantage of online degree programs. Unfortunately, the remaining 25 percent of businesses continue to resist the idea that online education is equal (or even superior) to traditional schooling.

Still, as online programs grow—and more people have access to higher education thanks to web-based schools—recruiters should expect the number of traditionalists to fall. As long as a school is accredited and a program meets the standard criteria for the field, there is usually little or no difference between online and offline university degrees.

Recruiters should be aware of their clients’ inclinations before interviewing applicants with online backgrounds, but most will quickly learn on their own that workers with online degrees are equal, if not superior, to their traditional peers.

For more on online education, see Higher Ed Offers New Opportunities For Digital Learners.

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Tiffany Rowe

About Tiffany Rowe

Tiffany Rowe is a marketing administrator who assists in contributing resourceful content. Tiffany prides herself in her ability to provide high-quality content that readers will find valuable.

MOOCs: Growing In Popularity Every Day

Nicole Dominique Le Maire

MOOCs (massive open online courses) are growing in popularity every day—and not surprisingly, because you can access these educational courses online; there is theoretically no limit to the number of people who can sign up for them; and they are almost always free to take and are often free to copy and redistribute, too.

MOOCs provide digital learning resources and use tests (or problem sets, to use the lingo) to help learners self-evaluate their progress. While most do not offer academic credits, successful students can often “buy” a certificate for use with their resume or curriculum vitae (CV).

MOOCs are sometimes split into two types: cMOOCs and xMOOCs.

  • cMOOCs follow a connectivist model and take a “pick and mix” approach to learning, combining the best resources and involving students in the course development process.
  • xMOOCs are more traditional in nature and usually deliver a set curriculum.

The benefits of MOOCs

MOOCs evolved from the Open Educational Resources (OER) movement, a collaboration between private and public organizations dedicated to providing free access to education by removing barriers such as price and location that prevent aspiring students from engaging in learning. Many MOOCs are associated with leading institutions such as Harvard, Stanford, and MIT.

MOOCs offer a wide variety of different courses. For example, HR professionals may benefit from taking a course on human resources, while offerings such as Chinese Language & Culture, or Negotiation & Conflict Resolution, might help other learners overcome specific challenges or take advantage of new career opportunities.

MOOCs often adhere to specific timeframes, which provides flexibility for students with limited time to access learning resources. Some MOOCs are designed to fully utilize the digital resources that most of today’s students can access. These resources might include HD videos, modeling software, and Powerpoint/Keynote presentations combined with community forums to provide a highly interactive and engaging platform.

Challenges with MOOCs

One challenge with MOOCs is that they can be inconsistent: For every course that uses a full array of digital presentation tools, there is another that is barely more than a dated distance learning video (you know, like the ones that air late at night to help cure insomnia). The fact that MOOCs tend to have low completion rates suggests that more effort needs to go into improving course content.

So far MOOCs haven’t exactly revolutionized education as the OER movement first envisioned. Part of this is probably due to the inconsistency problem, but some critics have also blamed poor marketing and a lack of communication. For example, some courses fail to identify their target audience or are too narrow in scope to appeal to most learners. In other cases, students have missed start dates because of poor communication.

Where to find MOOCs

There are so many MOOC providers, it can be daunting to make a decision. One way to evaluate them is to read student reviews on the listings of those that include them.

Alternatively, there are a number of MOOC providers that have already built up a good name for themselves. These include Coursera, the most popular MOOC provider, with over 10 million members; edX, a non-profit provider associated with Harvard and MIT; and Udacity, founded in association with Stanford University.

Try taking a MOOC – you will be amazed at what you will learn!

For more insight on online learning, see From MOOCs To YouTube: Free Digital Lessons Fuel Lifetime Learning.

The post MOOCs: Growing In Popularity Every Day appeared first on TalentCulture.

Photo Credit: skool.master via Compfight cc

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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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5 Things Pokémon Go Taught Me About The Future Of Marketing

Madelyn Bayer

In case you haven’t been outside lately, there is a game taking over the millennial world right now – it’s called Pokémon Go.

Pokémon Go is a mobile app that you can download for iOS or Android. It’s free to download and play, but you have the option to use real money to buy in-game currency called PokéCoins. PokéCoins are used to purchase Pokéballs, the in-game item you need to catch Pokémon. The game uses your phone’s GPS to obtain your real-world location and augmented reality to bring up Pokémon characters on your screen, placing them on top of what you see in front of you. You—the digital you—can be customised with clothing, a faction (a “team” of players you can join), and other options, and you level up as you play.

On the surface, it’s a fun mobile game whose popularity is as intriguing as it is entertaining, but the superficial fun of the app has led to some real results: Developer Nintendo’s valuation has increased by an estimated $7.5 billion thanks to the game.

With results like that, this app is more than just a game, but a possible whole new realm of digital marketing. I started to research some of the key learnings from Pokémon go from a marketing perspective.

  1. Keep it small and simple. Gone are the days of needing to invest in large ad campaigns and advertising budgets. How many ads did we see leading up to the Pokémon Go launch? Very few. Pokémon Go didn’t invest much into advertising because it didn’t need it – either the ad executives in charge knew that the success of the app would be dependent on the marketing and viral factors listed here, or they didn’t expect the app to be a breakout hit. Regardless, the bottom line is that you don’t need a massive advertising budget to be a great marketer; you just need to be able to connect with people. Simplicity is key: Well-designed websites, e-commerce platforms, apps, and products should welcome new users and make it extremely easy for all to get involved (a lesson learned from breakout social media apps like Instagram and Snapchat).
  1. Have an agile digital platform. If you don’t have an agile digital marketing platform, you will miss the boat. This lesson has been proven time and time again in today’s digital world. The marketing game changes faster than most brands can keep up with – but being able to react quickly to trends like this is essential. Failing fast, minimum viable product, and agile: These are fast becoming key phrases in marketing teams’ vocabulary. Whether you are launching a social campaign, a consumer app, or a large-scale marketing operation, you must be able to stand it up quickly, test it, iterate on it, and send it out quickly.
  1. Loyalty is everything. If you want to increase customer loyalty, you must reward your users for continuing to invest in your product. Pokémon Go players get bonuses and incentives for levelling up, taking on gyms, catching new Pokémon, and even walking. The thrill of finding a rare Pokémon or winning an intense battle is enough to keep users yearning for more, even through the less-active parts of the game. There are definite rewards for continued investment, and that’s what keeps users playing—sometimes at the expense of productivity. When I think of the apps I know and love, this feature is nothing new, but it is very important. Gamification and loyalty are what keep me checking in on the highly addictive Air New Zealand app, for example, tuning in each Tuesday to watch the reverse auctions grab flight seats. Creating an individualised offering to every consumer is a hot trend for retailers right now, and it may also be part of the lessons learned from Pokémon Go.
  1. Appeal to the new generation of augmented reality and virtual-reality natives. Just as Gen Y are considered digital natives because they grew up with Internet access, the emerging gen Z will be known as AR and VR natives – what feels new to us now will be the new normal for kids growing up today. That’s not to say every brand should jump on the AR or VR bandwagon. But learn from what this game has taught us: Why is this game taking over the world? What insights can be adapted to generate positive brand engagement? We have evolved past the age of disruptive placement and are now in an era of behavioral targeting. One of the biggest challenges retailers face is knowing where their customers are at any given point in time. How do they reward their customers at the point of sale? Could the next wave of retail disruption be the gamification of shopping in a virtual reality?
  1. Privacy vs. Personalisation. That old chestnut. According to the SAP New Zealand Digital Experience Report 2016, New Zealanders rated having relevant offers without infringing on privacy amongst the highest consumer experience attributes when considering importance to digital experience satisfaction. This is interesting considering the backlash concerning the data Niantic is actively collecting on Pokémon Go users. It seems this hasn’t deterred users too much; the explanation for this may lie further in the New Zealand Digital Experience report research.

Arguably, Pokémon Go ticks all the boxes when we look at the consumer-rated digital experience attributes listed below – though there may be one exception if we consider recent user safety horror stories that are starting to come out.

So what has all this taught us? It links back to the report: The better the digital experience – defined by the above attributes – the happier consumers are to give up their data. The graphs below show consumers’ willingness to give up certain personal information, depending on whether or not they have a satisfactory digital experience. As we all know, data, or information, is the currency of the future, and lessons like these raise important takeaways for all digital marketers looking to gain real consumer insights and preferences.

If you haven’t already given Pokémon Go a go, see what all the fuss is about. Whether the game is a passing fad or the newest trend of digital marketing is yet to be determined, but it offers some interesting thoughts to consider before you launch your next campaign to consumers.

For more insight on where marketing is headed, see MarTech: The Future Of Digital Marketing.

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Madelyn Bayer

About Madelyn Bayer

In my role as an Industry Value Associate at SAP Australia and New Zealand, I help organisations calculate and realise the value that new systems and technology will have on their operations. My role covers industries spanning utilities, public sector, consumer products and retail with a specific focus around customer engagement and commerce solutions and through this role I have developed a strong understanding of mega trends, cloud computing, enterprise software, the networked economy, Internet of Things, millennials and digital consumers. I am particularly passionate about creating sustainable solutions to solving world problems through technology.