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How Small Companies Can Use Big Data To Grow And Improve

Jennifer Horowitz

Small businesses can cost-effectively analyze large data sets to improve their marketing and product quality and accelerate customer relationships. Leaders from every business sector must learn how to grasp its changes for the future as Big Data becomes the key basis of competition.

Big Data is for organizations of any size, with data management having developed into an important skill to competitively differentiate today’s market leaders from those that are no longer influential. Signals and Systems’ mid-2014 report found that the Big Data market is expected to total $76 billion by 2020, an increase of 17%.

Technically, Big Data refers to technologies and initiatives that are too massive for traditional skills, technologies, and infrastructure efficiently address.

More than 70 years ago, in 1941, the first attempt to quantify the volume of data growth known as the “information explosion” was used, according to the Oxford English Dictionary.

Big Data was initially a unique resource only for large corporations and statisticians. With the growth in the Internet, smartphones, wireless networks, sensors, social media, and other digital technologies, small businesses and companies of all sizes are now able to leverage this trend.

As Big Data grows, MSPs can even connect to SMBs in offering their services as they look for new opportunities. Markets and Markets predicts that third-party MSPs cut recurring in-house costs by 30-40% and can add as much as a 60% improvement in efficiency. Small businesses face a big problem today with finding data storage, due to the increased growth and data volume of devices.

MSPs can expand their cloud services as SMBs look for bigger and better data storage alternatives. This means new growth and partnerships for MSPs that choose to expand their suite of services.

In addition to expanding storage options, MSPs can look to analytics performance and database management. By helping small businesses better evaluate their data, SMBs can provide a streamlined recovery and backup system to ensure data is not cluttered on a user’s mobile device.

Big Data leaders and laggards

A.T. Kearney, a global management consultancy firm, and Carnegie Mellon University investigated the corporate use of Big Data in its first-ever Leadership Excellence in Analytic Practices (LEAP) July/August 2014 study. They divided companies into four categories: leaders, explorers, followers, and laggards. Here’s what the leaders were doing with Big Data.

An inclusive atmosphere: This begins with a hands-on, dynamic policy of executive sponsorship and mindshare about Big Data. This fosters team-building, cross-functional collaboration, and company-wide confidence in data-driven methodologies.

The need for speed: Leaders used approaches that focused on rapid experimentation, mobilization, and deployment. This was primarily through pilot programs and proof-of-concept modeling.

Forward-thinking: These policies bred innovation, growth, and better operational efficiency. While Big Data was used for reporting on past efforts, leaders focused on future endeavors. They evaluated risks. They studied costs and benefits and balanced the tradeoffs between them. Then they charted a course.

Building on Big Data

According to the IBM Institute for Business, 26% of companies see returns from Big Data after 6 months. 63% see returns after one year. 40% reported that they use Big Data to solve their operational challenges.

The world will become more and more reliant on data-driven metrics in the years to come, and businesses need to recognize that fact. Using the power of analytics can shift a company into high gear, while failing to do so could leave them stuck in neutral.

Want more strategies to help your business tap the power of analytics? See Top Five Big Data Challenges For CIOs.

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Smart Machines Create Markets For Cyber-Physical Advances

Marion Heindenreich

Today, industrial machines are more intelligent than ever before. These intelligent machines are changing companies in many ways.

Why smart machines?

Mobile networked computers were a key breakthrough for making smart machines. Big Data allows machines and computers to store information and analyze complex patterns. Cloud computing offers broad access to information and more storage.

These computerized machines are both physical and virtual. Some call them “cyber-physical” machines. Technology lets them be self-aware and connected to each other and larger systems.

Businesses change their approaches

Intelligent machines allow companies to innovate in many areas. For one, the value proposition for customers is evolving. Businesses now model and plan in different ways in many industries.

Makers of industrial machines and parts work in new ways within the organization. Engineering now partners with mechanical, electronic, and software staff to develop new products. Manufacturing now seamlessly ties what happens on the shop floor to the customer.

Service models are changing too. Scheduled and reactionary servicing of machines is fading. Now intelligent machines track themselves. Machines detect problems and report them automatically. Major problems or failures are predicted and reported.

A data mining example

One good industrial example is mining, which can be dangerous and difficult. As ores become scarce, the costs of mining have increased.

“Smart machines” started in mining in the late 1990s. Software and hardware let remote users change settings. Operators moved hydraulic levers from a safe distance. Sensors observed performance and diagnosed issues.

Data cables connected machines to computers on the surface. Continuous and remote monitoring of the machines grew. Over time, embedded sensors helped improve monitoring, diagnostics, and data storage.

The technology means workers only go underground to fix specific issues. As a result, accident and injury risk is lower.

New wireless technology now lets mining companies connect data from many mine sites. Service centers access large amounts of data and can improve performance. Maintenance is prioritized and equipment downtime is reduced.

Opportunity abounds

For companies the time is now. Today, mobile “connected things” generate 17% of the digital universe. By 2020 that share grows to 27%.

You might not be investing in this so-called “Internet of Things” (devices that connect to each other). But it’s a good bet your competitors are. A December 2015 study reported 33% of industrial companies are investing in the Internet of Things. Another 25% are considering it.

There are risks

This new dawning era of manufacturing is exciting. But there are concerns. Cyber attacks on the Internet of Things are not new. But as the use of intelligent machines grows, the threat of cyber attacks in industry grows.

Data confidentiality and privacy are concerns. So too are software and hardware vulnerabilities. Exposure to attack lies not just in the virtual space but the physical too. Tampering with unattended machines and theft pose serious risk.

To address these threats, industries must invest in cybersecurity along with smart machines.

Conclusion

The potential advantages of smart machines are staggering. They can reshape industries and change how companies produce new products and create new markets.

For more information, please download the white paper Digital Manufacturing: Powering the Fourth Industrial Revolution.

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Marion Heindenreich

About Marion Heindenreich

Marion Heidenreich is a solution manager for the SAP Industrial Machinery and Components Business Unit who focuses on solution innovations like Product Costing on SAP HANA and cloud solutions, as well as providing financial and business analysis for industry business strategy definition and business planning.

Mining Firms Turn To Tech

Ruediger Schroedter

Gone are the days in mining when assessments of potential dig sites meant lots of waiting for results. Gone, too, is the uncertainty on a mine job about where to go next.

For mining executives, recent advances in digital technology allow companies to make decisions at a rapid pace. Decisions that used to take days and weeks now can be done in minutes and hours.

With more information available faster, mining leaders reduce both short- and long-term financial risk. Data from across the enterprise inform decisions about buying and selling assets. Profitability should increase, driven by key technology advances.

Digging in to the data

There are two key drivers to this digital revolution. The first is the rise of the Internet of Things (IoT). The IoT consists of devices that are equipped with sensors, software, and wireless capabilities. These devices are connected to each other and can detect, store, and send data.

Bonus: Click here to learn more about Digital Transformation in Mining.

The second is the rise of Big Data, mobile, and cloud computing. Today’s mobile devices can track, send, and receive data from remote sites worldwide. Cloud computing stores billions of bytes of data at low cost. Big Data analytics programs take data coming from many different locations and systems and synthesize it. Those programs then better inform decisions by offering dashboards, metrics, and predictive modeling.

Robots are able to venture into hazardous areas and move material with remote human oversight. On-site mining data is sent via mobile phone to a cloud-based platform. For mining, the convergence of these technologies provides extraordinary possibilities.

Technology at play

The potential impact is significant. A recent report by McKinsey & Co. showed the use of advanced analytics in mining and related industries had a major impact. Firms using these programs to assess production areas increased their profit margins by 2-3 percentage points.

One mining company used so-called Monte Carlo simulations to reduce certain capital expenses. Monte Carlo simulations use complex algorithms and repeated random sampling to model possible outcomes. They’re frequently used in finance, biology, and insurance. The Mining Journal reported how the company challenged assumptions about a project’s capital needs. It took historical data on certain disruptions such as rainfall patterns. Then models of its mines were made showing the impact of flooding and rainwater. The data led to a new strategy that maximized storage capacity and handling across all its mines. Capital costs dropped by 20 percent.

18 Aug 2012, South Dakota, USA --- USA, South Dakota, Lead, View of open pit --- Image by © Bryan Mullennix/Tetra Images/Corbis

Buy or sell?

With so many variables at play, mining valuation is not for the faint of heart. Integrated data streams available at the discovery stage make for better informed purchase decisions.

Software programs today can take data to build and validate exploration models. These programs use 3D visualization and validated geophysical, analytical, and drill hole data. In turn, detailed 3D topographical models are possible.

Other programs assess historical, assay, and drilling data. This information creates viable scenarios for determining whether to buy or sell a site.

These tools use data consistently from one potential site to the next, allowing for forecasting of economic risk that is consistent across the organization. The firm today can use “real options valuation” to develop models of outcomes given changing economic conditions. With clearer information about potential risks, firms can decide whether to stage, sell, abandon, expand, or buy.

Anticipating, not reacting

Mining companies realize today that these analytic platforms and dashboards offer many advantages. Users have a clearer interpretation of the aggregated and analyzed data points from multiple areas. Using predictive analytics, mining decisions are made based on smart assumptions, not past historical information.

Robust software programs can generate reports almost instantaneously. Supervisors have on-site access to the analysis through a web browser or app. This data has many uses. Drilling managers save time and can make quicker decisions on next moves. Supplies can be ordered faster. Needed data for accreditation and compliance is immediately accessible.

Selecting the right sites

One example is assay analysis. Today, geologists do not wait weeks or months for assay results. Instead of off-site analysis, web-based applications deliver information much faster to inform decisions.

Robots are sending information about field operations, safety, needed maintenance, and drilling performance.  Some devices send the information themselves. In other cases, staff use mobile phones, tablets, or laptops.  This information and analytics in turn help with site selection. Integrating data from mine planning, ventilation, safety, rock engineering, and mineral resources improves overall forecasting.

Discovery, particularly of Tier 1 sites, is an increasingly costly venture for mining companies. Demand for many products is increasing while discovery rates are dropping. Mined product is of a lesser quality, particularly in mature mining locations. Many possible sites are in areas that are underexplored areas with difficult and deep cover.

The advanced technologies available today are contributing to rapid improvement in these discovery issues.

Prospective drilling

Consider the drill hole. To reduce costs in exploration, there needs to be enough rich information from the opening drill hole. It needs to be delivered in as close to real time as possible. Doing so lessens the risk of the second drill hole. Better information from the start helps improve vectoring. It provides better information about what mineral systems are being drilled.

This approach, called prospective drilling, is becoming increasingly used in mining. It employs drilling activity to map covered mineral systems. In turn, geochemical and geophysical vectoring can lead firms toward deposits.

Australia has invested heavily in this area. The Deep Exploration Technologies Cooperative Research Centre (DET CRC) has a singular vision: uncovering the future. Its core purpose is “develop transformational technologies for successful mineral exploration through deep, barren cover rocks.”

To get to that point, the DET CRC is borrowing a drilling technique from the oil business. Coiled tubing is paired with downhole and top-of-the-hole sensors. The informaton provides petrophysical, structural, rock fabric, geochemical, and mineralogical data all at once.

Conclusion

To meet increasing demands for new viable sites, and to improve efficient on sites, mining is changing. Using smart, connected products and robust data modeling, mining is being done faster, safer, and more efficiently than ever.

Join a LiveTwitterChat on digitalization in mining on May 4th from 10-11 a.m. EST: #digitalmining

The global mining and metals industry will come together to discuss how digital innovation is impacting the mining industry July 12-14 at the International SAP Conference for Mining and Metals in Frankfurt, Germany.  Don’t miss this opportunity to meet with world leaders and learn how your organization can become a connected digital enterprise.

Follow speakers and pre-event activities by following sapmmconf and @sapmillmining on Twitter

AA Mining and Metals Forum

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Ruediger Schroedter

About Ruediger Schroedter

Ruediger Schroedter is responsible for solution management of SAP solutions for the mining industry worldwide. He has spent more than 15 years in the mill products and mining industries and has extensive experience implementing SAP solutions for customers in these industries before coming to SAP.

Live Businesses Deliver a Personal Customer Experience Without Losing Trust

Lori Mitchell-Keller, Brian Walker, Johann Wrede, Polly Traylor, and Stephanie Overby

Trust is the foundation of customer relationships. People who don’t trust your business are not likely to become or remain customers.

The trust relationship has taken some big hits lately. Beloved brands like Chipotle and Toyota have seen customer trust ebb due to public perception of their roles in safety issues. Consumers continue to experience occasional data breaches from large brands.

Yet these traditional threats have short half-lives. The latest threat could last forever.

Most customers claim they want personalization across all the channels in which they interact with companies. Such personalization should create long-term loyalty by creating a new level of intimacy in the relationship.

sap_Q216_digital_double_feature3_images2But that intimacy comes at a high price. For personalization to work, brands need to gather unprecedented amounts of personal information about customers and continue to do so over the course of the relationship. Customers are already wary: 80% of consumers have updated their privacy settings recently, according to an article in VentureBeat.

Companies must get personalization right. If they do, customers are more likely to purchase again and less likely to switch to a competitor. Personalization is also an important step toward the holy grail of digital transformation: becoming a Live Business, capable of meeting customers with relevant and customized offers, products, and services in real time or in the moments of customers’ choosing.

When done wrong, personalization can cause customers to feel that they’ve been deceived and that their privacy has been violated. It can also turn into an uncomfortable headline. When Target used its database of customer purchases to send coupons for diapers to the home of an expectant teen before her father knew about the pregnancy, its action backfired. The incident became the centerpiece of a New York Times story on Target’s consumer intelligence gathering practices and privacy.

Straddling the Line of Trust

Customers can’t define the line between helpful and creepy, but they know it when they see it.

Research conducted by RichRelevance in 2015 made something abundantly clear: what marketers think is cool may be seen as creepy by consumers. For example, facial-recognition technology that identifies age and gender to target advertisements on digital screens is considered creepy by 73% of people surveyed. Yet consumers were happy about scanning a product on their mobile device to see product reviews and recommendations for other items they might like, the survey revealed. Here’s what else resonates as creepy or cool when it comes to digital engagement with consumers, courtesy of RichRelevance and Edelman Berland (now called Edelman).

Creepy

  • Shoppers are put off when salespeople greet them by name because of mobile phone signals or know their spending habits because of facial-recognition software.
  • Dynamic pricing, such as a digital display showing a lower price “just for you,” also puts shoppers off.
  • When brands collect data on consumers without their knowledge, 83% of people consider it an invasion of privacy, according to RichRelevance’s research, and 65% feel the same way about ads that follow them from Web site to Web site (retargeting).

Cool

  • Shoppers like mobile apps with interactive maps that efficiently guide them to products in the store.
  • They also like when their in-store location triggers a coupon or other promotion for a product nearby.
  • When a Web site reminds the consumer of past purchases, a majority of shoppers like it.

There are no hard-and-fast rules about which personalization tactics are creepy and which are cool, but trust is particularly threatened in face-to-face interactions. Nobody minds much if Amazon sends product recommendations through a computer, but when salespeople approach customers like a long-lost friend based on information collected without the customer’s knowledge or permission, the violation of trust feels much more personal and emotional. The stage is set for an angry, embarrassed customer to walk out  the door, forever.

sap_Q216_digital_double_feature3_images3It doesn’t help that the limits of trust shift constantly as social media tempts us to reveal more and more about ourselves and as companies’ data collection techniques continue to improve. It’s easy to cross the line from helpful to creepy or annoying (see Straddling the Line of Trust).

Online, customers are similarly choosy about personalization. For example, when online shoppers are simply looking at a product category, ads that matched their prior Web-browsing interests are ineffective, an MIT study reports. Yet after consumers have visited a review site to seek out information and are closer to a purchase, personalized content is more effective than generic ads.

Personalization Requires a Live Business

Yet the limits of trust are definitely shifting toward more personalization, not less. Customers already enjoy frictionless personalized experiences with digital-native companies like Uber, and they are applying those heightened expectations to all companies. For example, 91% of customers want to pick up where they left off when they switch between channels, according to Aspect research. And personalization is helpful when you receive recommendations for products that you would like based on previous in-store or online purchases.

sap_Q216_digital_double_feature3_images-0004Customers also want their interactions to be live—or in the moment they choose. Fulfilling that need means that companies must become Live Businesses, capable of creating a technological infrastructure that allows real-time interactions and that allows the entire organization—its structure, people, and processes—to respond to customers in all the moments that matter.

Coordinating across channels and meeting customers in the right moments with personalized interactions will become critical as the digital economy matures and customer expectations rise. For instance, when customers air complaints about a brand on social media, 72% expect a response within an hour, according to consulting firm Bain & Company. Meanwhile, an Accenture survey found that nearly 60% of consumers want real-time promotions; 48% like online reminders to order items that they might have run out of; and 51% like the idea of a one-click checkout, where they can skip payment method or shipping forms because the retailer has saved their preferences. Those types of services build trust, showing that companies care enough to understand their customers and send offers or information that save them time, money, or both.

So while trust is difficult to earn, once you’ve earned it and figured out how to maintain it, you can have customers for life—as long as you respect the shifting boundaries.

“Do customers think the company is truly acting with their best interests at heart, or is it just trying to feed the quarterly earnings beast?” asks Donna Peeples, a customer experience expert and the former chief customer experience officer at AIG. “Customer data should be accurate and timely, the company should be transparent about how the data is being used, and it should give customers control over data collection.”

sap_Q216_digital_double_feature3_images-0005How to Earn Trust for a Live Business

Despite spending US$600 billion on online purchases, U.S. consumers are concerned with transaction privacy, the 2015 Consumer Trust Survey from CA Security Council reveals. These concerns will become acute as Live Businesses make personalization across channels a reality.

Here are some ways to improve trust while moving forward with omnichannel personalization.

  • Determine the value of trust. Customers want to know what value they are getting in exchange for their data. An Accenture study found that the majority of consumers in the United States and the United Kingdom are willing to have trusted retailers use some of their personal data in order to present personalized and targeted products, services, recommendations, and offers.
    “If customers get substantial discounts or offers that are appealing to them, they are often more than willing to make that trade-off,” says Tom Davenport, author of Big Data at Work: Dispelling the Myths, Uncovering the Opportunities. “But a lot of companies are cheap. They use the information but don’t give anything back. They make offers that aren’t particularly relevant or useful. They don’t give discounts for loyalty. They’re just trying to sell more.”
  • Let customers make the first move. Customers who voluntarily give up data are more likely to trust personalization across the channels where they do business. Mobile apps are a great way to invite customers to share more data in a more intimate relationship that they control. By entering the data they choose into the app, customers won’t be annoyed by personalization that’s built around it.
    For example, a leading luxury retailer’s sales associates may offer customers their favorite beverages based on information they entered into the app about their interests and preferences.
  • Simplify data collection and usage policies. Slapping a dense data- use policy written in legalese on the corporate website does little to earn customers’ trust. Instead, companies should think about the customer data transaction, such as what information the customer is giving them, how they’re using it, and what the result will be, and describe it as simply as possible.
    “Try to describe it in words so simple that your grandmother can understand it. And then ask your grandmother if it’s reasonable,” suggests Elea McDonnell Feit, assistant professor of marketing at Drexel University’s LeBow College of Business. “If your grandmother can’t understand what’s happening, you’ve got a problem.”
    The use of data should be totally transparent in the interaction itself, adds Feit. “When a company uses data to customize a service or offering to a customer, the customer should be able to figure out where the company got the data and immediately see how the company is providing added value to the customers by using the data,” Feit says.
  • Create trust through education. Yes, bombarding customers with generic offers and pushing those offers across the different Web sites they visit may boost profits over the short term, but customers will eventually become weary and mistrustful. To create trust that lasts and that supports personalization, educate the customers.

Procter & Gamble’s (P&G’s) Mean Stinks campaign for Secret deodorant encourages girl-to-girl anti-bullying posts on Twitter, Facebook, and Instagram. The pages let participants send apologies to those they have bullied; view videos; and share tips, tools, and challenges with their peers.

P&G has said that participation in Mean Stinks has helped drive market share increases for the core Secret brand as well as the specific line of deodorant promoted by the effort. Offering education without pushing products or services creates a sense that companies are putting customers’ interests before their own, which is one of the bedrock elements of trust. Opting in to personalization seems less risky to customers if they perceive that companies have built up a reserve of value and trust.

“Companies that do personalization well demonstrate that they care, respect customers’ time, know and understand their customers and their needs and interests,” says Peeples. “It also reinforces that interactions are not merely transactions but opportunities to build a long-term relationship with that customer.”

Laying the Foundation for Live, Personalized Omnichannel Processes

sap_Q216_digital_double_feature3_images-0006Creating a personalized omnichannel strategy that balances trust and business goals starts with knowing the customer. This can happen only when multiple aspects of your business are coordinated in a live fashion. But marketers today struggle to collect the kind of data that could drive more meaningful connections with customers. In an Infogroup survey of more than 500 marketers, only 21% said they are “very confident in the accuracy and completeness of their customer profiles.” A little over half of respondents said they aren’t collecting enough data overall.

Collecting enough of the right types of data requires more holistic data-collection techniques:

  • Take advantage of the lower costs for processing and storing terabytes of data, and develop a data strategy that combines and crunches all the customer data points needed to drive relevant interactions. This includes transactional, mobile, sensor, and  Web data.
  • Social media analytics is also a central tactic. Social profiles and activity are rich sources of data about behavior and character, merging what people buy or look for with their interests, for instance. Such data can feed predictive analytics and personalization campaigns.
  • Experiment with commercial tools that can filter and mine the data of customers and prospects in real time. This is a significant step beyond basic demographic data collections of the past.

sap_Q216_digital_double_feature3_images-0007Once the necessary data is available, companies need the technology, processes, and people to make sensible use of it in an omnichannel personalization strategy. Only when a company is organized as a Live Business can that happen. Here’s how your company can move toward being a Live Business:
Be live across channels. Having a consistent customer journey map across channels is core to omnichannel personalization. It requires integration across multiple systems and organizational silos to enable core capabilities, such as inventory visibility and purchase/pickup/return across channels. This integration also constitutes a major chunk of the transition to becoming a company that can act in the moments that matter most to customers. If all channels can sync in real time, customers can get what they want in the moment they want it.

Free the data scientists. Marketing rarely has full control over the omnichannel experience, but it is the undisputed leader in understanding customer behavior. While data science is part of that understanding, it has traditionally played a background role. Marketers need to bring the data scientists into efforts to sort through the different options for digitizing the omnichannel experience. The right data scientists understand not only how to use the tools but also how to apply the data to make accurate decisions and follow customers from channel to channel with personalized offers.

Walgreens’ Technology Approach to Personalization

Walgreens is a leader in building the kind of technology base that can enable real-time, omnichannel personalization. Its digital transformation is 16 years in the making, according to Jason Fei, senior director of architecture for digital engineering at Walgreens. At the heart of its infrastructure is a Big Data engine that feeds many customer interaction and omnichannel processes, including customer segmentation. The company adds third-party systems in areas such as predictive analytics and marketing software. Walgreens has a cloud-first strategy for all new applications, such as its image-processing and print-ordering applications. Other elements of the drugstore chain’s technology platform include:

  • Application programming interface (API)-driven architecture. Walgreens’ APIs enable more than 50 partners to connect with its apps and systems to drive customer-facing processes, including integrations with consumer wearables to drive reward points for healthy habits, as well as content partnerships with companies such as WebMD. “With APIs we can be an extensible business, allowing other companies to connect to us easily and help in the digital enablement of our physical stores,” Fei says.
  • Responsive Web sites. The company’s Web site is built using responsive and adaptive design practices so that the site automatically adapts to the consumer’s device, whether that is a mobile phone, tablet, or desktop computer. “We have a single code base that runs anywhere and delivers a consistent, optimized experience to all of our customers,” Fei says.

Making the Most of the Technology Base

This technology foundation has allowed Walgreens to push forward in personalization. For example, according to Fei the company uses sophisticated segmentation and personalization engines to drive outbound e-mail and text campaigns to customers based on their purchase history and profile. “We don’t blast out messages to customers; we use our personalization recommendations to be relevant,” says Fei.

The next phase of this strategy is to develop live inbound personalization tactics, such as recognizing customers when they come back to the Web site and tailoring their experience accordingly. These highly automated, self-learning systems improve over time, becoming more relevant at the moment a customer logs back in.

“When you search for a product, the Web site will take a good guess of what you might actually want. If you always print greeting cards at the same time of year, for example, the system would automatically deliver content around that,” Fei explains. “Everyone comes to Walgreens with a mission, so we can be very targeted with our communications.”

Walgreens’ mobile app combines real-time personalization with convenience. You can scan a pill bottle to refill a prescription, access coupons, send photos from your phone to print in the store, track rewards, and find the exact location of a product on the shelf.

Walgreens also recently deployed a new integrated interactive voice-response system that includes a personalization engine that recognizes the individual, says Troy Mills, vice president of customer care at Walgreens. The system can then predict the most probable reason for the customer’s call and quickly get them to the right individual for further help.

How to Get Started with Live Customer Experiences

sap_Q216_digital_double_feature3_images-0008As Fei can attest, getting Walgreens’ omnichannel and personalization infrastructure to this point has involved a lot of work, with much more to come. For companies just now embarking on this journey, especially midsize and large companies, getting started will mean overhauling an outdated and ineffective technology infrastructure where duplicate systems and processes for managing customer data, marketing programs, and transactions are common.

A bad internal user experience often transcends into a bad customer-facing experience, says Peeples. “We can’t afford the distractions of the latest app or social ‘shiny penny’ without addressing the root causes of our systems’ issues.”

Live Business Requires Striking the Right Balance

The boundaries of trust are a moving target. Sales tactics that used to be acceptable decades ago, such as the door-to-door salesperson, are unwelcome today to most homeowners. And consumers’ expectations are unpredictable. At the dawn of social media, many people were anxious about their photos unexpectedly showing up online. Now our identities are tagged and our posts and photos distributed and commented on regularly.

But while consumers are getting more comfortable with online technology and its trade-offs, they won’t put up with personalization efforts that make use of their data without their knowledge or permission. That data has value, and customers want to decide for themselves when it’s worth giving it away. Marketers need to strike the right balance between personalization and a healthy respect for the unique needs and concerns of individuals. D!

 

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Lori Mitchell-Keller

About Lori Mitchell-Keller

Lori Mitchell-Keller is the Executive Vice President and Global General Manager Consumer Industries at SAP. She leads the Retail, Wholesale Distribution, Consumer Products, and Life Sciences Industries with a strong focus on helping our customers transform their business and derive value while getting closer to their customers.

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Technology Trends Shaping The Way Utilities Work

Lloyd Adams

No one needs to tell you that the digital revolution is here to stay. Digital business is big business. This is especially true now that one-time novelties like the Internet and mass-scale analytics have exited their infancies. It’s safe to assume that while the digital transformation is not yet complete, it has extended to almost every corner of industry.

Chief among the ways in which technology is changing the world is in the realm of utilities. How we get our power matters, as does how it’s metered and distributed. Too little of it and the system isn’t working; too much and it becomes wasteful and inefficient. A happy middle ground both satisfies consumers and conserves resources. So how might the utility organizations use digital resources to make utilities more efficient?

How can we use technology to create a better world?

The wide array of digital resources makes it easier than ever before for utility companies to track their operations. This in turn enables them to respond to energy supply and demand more immediately. It also allows them to deliver electricity in a more responsive and less wasteful way.

The smart grid is a great example of how hyperconnectivity and supercomputing combine. Together they enable a much smarter means of energy distribution. The smart grid uses smart-meter technology in homes, renewable energy, and new data-driven systems to maintain efficient operations and save non-renewable resources.

The digital energy network is another cutting-edge idea. It enables a two-way flow of both power and information. Large stakeholders still help control the flow of electricity and energy. But now consumers can help as well. The result? Better efficiency, less waste.

Who showcases digital business models at their best?

Luckily, some utility front-runners are offering valuable insight into the future for all. Consider CenterPoint Energy. It integrates information technology and operational technology to emphasize streamlined results and conservation.

Or the Tokyo Electric Power Company. It aims to install 27 million residential smart meter devices by 2020. These meters track energy usage and relay it back to the central utility company. Such metering will help the company make better, greener decisions about energy usage.

Then there’s Tesla, which already has a reputation for cutting-edge environmentalism. Its Powerwall lithium ion battery enables homes to store solar energy. This historically tricky feat will allow residents to “go net zero.” That means keeping their home off the grid entirely.

Each digital business models showcases a different strategy. Together, these strategies can transform our current energy economy. The result? Environmental resource management on a scale never before seen.

Will you benefit from going digital?

Yes. Like any powerful technology, these digital business models reward utility companies who adopt them early. Using digital tools to monitor operations and output has many benefits. You can grow your company for stakeholders and shareholders. You can differentiate from competitors. And you can clean up power for the sake of your city and the world.

Of course, most likely you’ve already incorporated a fair bit of digital technology into your operations. If you’re like many utility companies, though, there’s much more you can do. Engage with customers more meaningfully by making them partners in the metering process. Digitize your business to streamline information gathering. Pair up with same or similar businesses to achieve economies of scale

These approaches enable energy portfolio management on scales never before seen. The result for your business goes well past innovation. You’ll also see differentiation, growth, and competitiveness on a whole new level.

For more information on Digital Transformation for Utilities, click here.

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Lloyd Adams

About Lloyd Adams

Lloyd Adams is national vice president of Utilities for SAP North America. In this role, he is responsible for sales and customer relations in the SAP Utilities North America practice.