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3 Ways Digital Transformation Can Protect Your Business

Shelly Dutton

Tightening of the U.S. monetary system. Europe’s struggle with the refugee crisis and rising debt. Financial instability in China. Global political instability. For anyone reading these headlines, 2016 is gearing up to be anything but boring and predictable. But is it really as bad as we think?

In a recent story in The Atlantic, James Pethokoukis, Dewitt Wallace Fellow of the American Enterprise Institute, expresses a different take on what’s happening. “Perhaps what’s happening is that we’re really bad at measuring the effects of technological progress, especially in the digital economy,” he theorized. Pethokoukis argues that innovations can take a considerable amount of time to impact productivity. Just like it took years, decades even, for factories to determine how to manufacture mass quantities efficiently, it may take years for recent advancements – such as Big Data, cryptocurrency, and the Internet of Things – to transform the world on a broad scale.

However, this realization is not convincing CEOs to set aside plans for such digital transformation. In fact, IDC predicts that two-thirds of Global 2000 enterprises will put digital transformation at the center of their corporate strategy by the end of 2017. Meanwhile, 80% of smaller companies will likely be dragged into digital transformation because of these larger businesses.

Why are so many enterprises relying on digital transformation to move forward? Simply put, it can be a protective layer that cushions the impact of a downturn – whether fueled by speculation, a misunderstanding of standard economic indicators, or real monetary constraints. Here are three of IDC’s 2016 predictions that provide some clues into how.

Prediction #1: 65% of large enterprises will shift their focus from resources, labor, and fixed capital to digital mastery of relationships, operations, and intangible capital.

This is not to say that cost containment of resources, labor, and fixed capital is no longer concern – it’s just not the only one. Senior executives are quickly realizing that relationships with customers, employees, and the entire business network; operations; and intangible capital (such as brand perception) are equally, if not more, important.

Considering that the average life expectancy of a Fortune 500 company has declined from around 75 years half a century ago to less than 15 years today, it’s easy to see how technology is accelerating a business’ lifespan. However, companies with staying power can build a brand reputation that makes customers feel invested, trust, and secure – even during lean, economic conditions.

Prediction #2: 80% of B2C and 60% of B2B companies will create immersive, authentic, omni-experiences for customers, partners, and employees by 2018.

Business-to-business (B2B) and business-to-consumer (B2C) companies with advanced omni-experience capabilities can study the entire ecosystem to learn from every customer interaction and take those lessons to the next deal over and over again in an iterative cycle. Although this capability is ingrained in the retail industry, every business – no matter the industry and size – needs to better understand customer preferences and needs, factors that trigger purchase patterns, and activities that have proven to improve sales revenue and profitability.

Prediction #3: 75% of the Global 2000 will deploy full, information-based, digital twins of their products, services, supply network, sales channels, and operations.

The combination of the digital economy with economic volatility can create an environment that is incredibly fast-paced, high-risk, and operationally lean. And for business, any error or design flaw can spell disaster. This is where digital transformation can become a catalyst for product innovation and lifecycles.

With a digital twin of a product or service, companies can avoid costly quality issues or rework because performance, processes, updates, and errors can be modeled before they occur in the physical offering. Thanks to the Internet of Things, digital twins can live well beyond the initial launch because embedded sensors and data flowing to and from a business’ system can enable performance tracking, usage monitoring, and predictive analytics of potential risks and opportunities for optimization.

Will digital transformation safeguard your company from an economic downturn? It all depends on you on how you take advantage of it. Check out Bill McDermott’s advice on the Timeless Truths in a Digital Age. Simple is key.  

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Reinventing Law Firms For A Digital Age

Ginger Shimp

It’s an industry based on precedent that sometimes stretches back hundreds of years, and its practitioners rely on terminology coined in a language long dead, but make no mistake – law firms are making an open-and-shut case for the benefits of 21st-century digital transformation.

In a recent episode of Digital Industries: Changing the Game radio broadcast entitled Law Firms: How to Thrive in Digital Disruption hosted by Bonnie D. Graham, a jury of experts convened to discuss technology’s impact on the legal industry and the ways innovative firms are embracing its potential. Here’s what was on the docket:

Even the law isn’t above the law (of digital transformation)

Today’s law firms aren’t immune to the way digital transformation has empowered consumers and expanded their choices. According Patrice Cappello, North America Industry Lead for the professional services industry at SAP, firms find themselves grappling with the same cutthroat market pressures as any business: “Consumers of legal services are smarter, more demanding and their expectations are high. If they don’t like the way you deliver your services, they will just go to the next firm.”

In some cases, the clients themselves are even emerging as the primary competition – especially larger businesses with their own roster of in-house legal experts. Ahmed Shaaban, founding member of Fulcrum Global Technologies, noted that some law firms “don’t talk about each other as competitors. They talk about the big four accounting firms, banks, and large engineering firms as their main competitors, because those firms are all now practicing law one way or another.”

If businesses are becoming more like law firms, law firms are becoming more like the businesses they serve. As Capello puts it, “I’ve been seeing a lot of firms bringing in leadership from the commercial side to help the firm run more like a business and less like a law firm, so it’s interesting to see all of these changes occurring and how the firms are responding.”

The good news for up-and-coming firms is that next-generation technology is helping streamline core business practices and leveling the competitive playing field. “The technology is available to everyone at all price points, at all sizes, in all practice areas, in every single region,” added Ari Kaplan, attorney and principal of Ari Kaplan Advisors. “Many of these organizations are applying it, and it’s really quite an extraordinary transformation that will take hold almost on a global basis in very short order.”

Why your lawyer wants to friend you on Facebook

The free-for-all world of social media may seem like the last venue where you’d encounter a buttoned-down, by-the-book law firm, but the panel members agreed that firms need a foot firmly planted in the world of likes, sharing, and friending.

Kaplan stated it this way: “What are their clients saying? What are people saying about their clients? What’s the media saying about particular issues? What is the commentary about trends or changes? Social media channels aren’t just ways to stay well informed. They also connect a firm more closely with the people they serve.”

Capello pointed to one of the surprising ways firms can use social media to serve clients, beyond relationship building. “It’s using things like predictive analytics to advise a client to go in one direction or another depending on potential outcome. There’s this world that has so much information and so much connectivity. Firms are asking: How do we harness that power, bring it into the firm, and leverage it to be better at what we do?”

The verdict is unanimous: people matter

When it’s time for closing arguments, the law is still an industry of living, breathing human experts – but advanced technology has a role here as well. It can make a firm a more attractive workplace and maximize the contribution of the people it hires. “The bottom line is, when you have great talent, it hedges the risk of the firm in many different regards,” said Shaaban. “As a result, if you can find systems and programs that allow you to manage your talent, you’re going to be in a healthier position. It’s a huge trend.”

Firms aren’t just paying lip service to the value of their human resources. They’re also investing in the tools to improve the employee experience. “Technology is enabling firms to automate a lot of processes and really make a much better experience for the firm, for potential new employees, and for the existing employees,” added Cappello. “We see a lot of investment by firms in that area for the first time.”

The evidence is incontrovertible: If your idea of what a law firm and lawyers do hasn’t changed since you’ve watched reruns of Perry Mason, it’s time for an update.

For more insight on how digital disruption is transforming traditional business models, see Delivering On Transformational Innovation

Join me on Twitter and LinkedIn.

This story originally appeared in the SAP Business Trends community.

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About Ginger Shimp

With more than 20 years’ experience in marketing, Ginger Shimp has been with SAP since 2004. She has won numerous awards and honors at SAP, including being designated “Top Talent” for two consecutive years. Not only is she a Professional Certified Marketer with the American Marketing Association, but she's also earned her Connoisseur's Certificate in California Reds from the Chicago Wine School. She holds a bachelor's degree in journalism from the University of San Francisco, and an MBA in marketing and managerial economics from the Kellogg Graduate School of Management at Northwestern University. Personally, Ginger is the proud mother of a precocious son and happy wife of one of YouTube's 10 EDU Gurus, Ed Shimp.

From Machine Learning To Company Learning: What Artificial Intelligence Can Tell Us About The Future Of Business

Dinesh Sharma

Adaptive systems are a special class of open systems that are continuously evolving. They feature dynamic networks of agents that interact with each other and their environments. Examples of complex, adaptive systems include ant and bee colonies, the stock market, and biological ecosystems, as well as human organizations such as political parties, companies, and cities. And because of its ability to shift and change, the behavior of the whole system cannot be predicted by just analyzing its parts in isolation.

So what happens when we reimagine a company more like an adaptive organism, rather than a stable machine? We can tap into an ever-changing array of stimuli to instantly decide on the best course of action.

Why companies can no longer operate like machines

In today’s knowledge economy, we tend to predict how a business is doing by measuring key components like sales, finance, and HR. However, this analysis would only be accurate if the corporate world is a closed system, which was the case years ago. Historically, we designed companies like machines. We constructed the organizational chart to divide large chunks of work and separate them from each other into finance, sales, operations, and so forth. We devised workflows that process inputs into outputs: raw materials into products; prospects into customers; and complaints into resolutions.

This kind of company – the divided company – needed separate functions. In turn, decision makers did not always have a sense of the larger environment they were working in. Over time, they became masters at handling tasks, but lost touch with the bigger picture. They were disconnected from customers and the overall purpose of the organization. Even worse, inelastic policies and procedures were needed so people could function efficiently without impeding on each other’s work.

In this case, the problem was scale. As the number of employees grew, the profit per employee shrank. Efficiencies of scale were offset by an increase in bureaucracy, which led to siloed and disconnected divisions and overhead costs that grew as the company expanded.

Eventually, the company reached a point where the cost of controlling such a business exceeded the benefits of new growth. The company became so focused on its internal structures and organizational chart that it lost touch with its customers.

Artificial intelligence taps into all of a business’ senses to predict and win 

To compete, companies should use advanced technologies, such as artificial intelligence (AI), to make sense of an exponentially growing volume of inputs. This approach allows two-way communication between humans and systems and the opportunity to take maximum advantage of low-friction capabilities.

Natural interactions that remove unnecessary aspects from our daily work lives allow us to seize the full promise of hyperconnectivity. These capabilities drive better ease of use, require less training, and accelerate time to meaningful insight. In essence, the rigid corporate machine of the past is transformed into a responsive organism – a connected company, so to speak.

A connected company is a complex, adaptive system that functions more like an organism than a machine. It contains a distributed network of brains, eyes, and ears everywhere – whether they are employees, partners, suppliers, customers, or assets.

When you design for a connection, you design for a company powered by people. But more important, you design for agility, robustness, resilience, productivity, and longevity – all characteristics of a Live Business. And when your company can respond dynamically to change, it can learn and adapt amid an uncertain, ambiguous, and constantly evolving environment, continuously learning as it moves forward.

For more on the digital economy and its impact, check out the research paper “Live Business: The Digitization of Everything” on the Digitalist Magazine online. 

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About Dinesh Sharma

Dinesh Sharma is the Vice President of Digital Economy at SAP. He is a GM-level technology executive with leadership, technical innovation, effective strategic planning, customer and partner engagement, turnaround management and focused operational execution experience at both large enterprise and startup companies.

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.