The Internet of Things and the era of “mass personalization”

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There was a time when cars were assessed largely by their horsepower or miles-per-gallon. These were the yardsticks of comparison in the analog age. But now, the question asked of car owners is more likely to become, “What OS version do you have?”

Increasingly, our vehicles are becoming traveling data centers, with Wi-Fi hotspots, operator-assisted navigation, sophisticated diagnostic computers, smartphone-based control apps and real-time tools like parking assist and lane change warnings. They are no longer dumb machines, nor are these innovations exclusive to the most expensive prestige automobiles.

In fact, such talents are not exclusive to cars at all, since refrigerators, washing machines, thermostats, and devices of all kinds have joined this intelligent party. Even the most intangible of objects, insurance, has started to move into this sphere.

These items play a role in the new era known collectively as the Internet of Things (IoT), in which devices large and small communicate with owners, operators, manufacturers and suppliers to pro-actively maintain their operational health, and more importantly, to create a direct channel for up-selling and ongoing revenue opportunities. No company is excluded from this transformation.

As General Electric CEO Jeff Immelt said at a 2014 Minds+Machines summit, “If you went to bed last night as an industrial company, you’re going to wake up this morning as a software and analytics company.”

IoT-based products are different from their analog predecessors. A permanent, high-speed Internet-based relationship guarantees that vendor and manufacturer need never be detached from the products they sold, or from the customers they sold them to. IoT has spawned new partner ecosystems, and these, in turn, demand new business models.

  • The Cactus, a compact car from European automaker Citroën, comes with a variation on the traditional lease, one based on “pay-per-use.” Citroën acknowledges “there is a portion of the population that is not willing to buy a car, but is willing to buy the use of a car.” This manufacturer joins the ranks of  car-as-a-service operations like ZipCar, but ups the ante – and improves the convenience factor – by allowing a customer to keep the vehicle permanently in the driveway as their own car (for a small base rate), and then pay the balance according to when and how it is used.
  • Inside the home, IoT washing machines can now schedule service calls and part replacements directly, but more importantly can offer the owner a subscription for detergent delivery, with a three-month free trial, as well as other up-sells and cross-sells.
  • New insurance companies are rolling out health plans whose premiums decrease with the amount of exercise performed by the member, as reported by a wearable fitness tracker.
  • Cars and trucks of all types are now busy sending their diagnostic data back to the dealership, to ensure prompt servicing as well as increased up-selling potential between dealer and owner.

These physical examples of IoT represent more than an innovation in product design. They also demand a change in the perception of the consumer. Whether in the B2B or B2C markets, there is now just an audience of one. This forces a shift from a mass production mindset to one of “mass personalization,” using the power of data to understand and serve each customer on their own terms.

Pricing, too, must change. Where once there was one pricing model, other options exist, for even the largest of big-ticket items. These can include:

  • “buy-once” products bundled with a subscription
  • free trial/freemiums, leading to recurring payments or subscriptions
  • pay-per-use pricing; and
  • entitlements (such as 2 free appointments with a personal trainer when purchasing exercise equipment)

The IoT revenue model ushers in an updated and more reliable style of loss-leader pricing, starting with a negative BOM (bill of materials), but then subsidizing the cost of the hardware with the revenues from bundled services. Loss-leader pricing in itself is not new; razor blade manufacturers, among others, have been using a negative BOM model for years. But now the analytics have become more accurate, and the customer relationship more individualized, consequently tipping the concept from a traditional loss leader to a more reliable positive cash flow formula.

Bringing these thoughts back to the notion of cars being assessed by their OS version, it is important to bear in mind that an operating system is, by its very nature, upgradeable, and reinforces the link between manufacturer and purchaser. In a way, the purchase is never complete, since upgrades bring along with them new features and additional support and purchase opportunities.

That is where the Internet of Things truly shines. It is a permanent pathway linking seller and buyer—a dynamic relationship of mutual benefit that grows and improves over time.

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How to Rewire the Organization for the Internet of Things

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Success in the IoT requires new levels of speed, agility, and flexibility, not just from the systems delivering IoT services but also from the people charged with making those services happen.

Hyperconnectivity, the concept synonymous with the Internet of Things (IoT), is the emerging face of IT in which applications, machine-based sensors, and high-speed networks merge to create constantly updated streams of data. Hyperconnectivity can enable new business processes and services and help companies make better day-to-day decisions. In a recent survey by the Economist Intelligence Unit, 6 of 10 CIOs said that not being able to adapt for hyperconnectivity is a “grave risk” to
their business.

IoT_Isbel_QA02IoT technologies are beginning to drive new competitive advantage by helping consumers manage their lives (Amazon Echo), save money (Ôasys water usage monitoring), and secure their homes (August Smart Lock). The IoT also has the potential to save lives. In healthcare, this means streaming data from patient monitoring devices to keep caregivers informed of critical indicators or preventing equipment failures in the ER. In manufacturing, the IoT helps drive down the cost of production through real-time alerts on the shop floor that indicate machine issues and automatically correct problems. That means lower costs for consumers.

Several experts from the IT world share their ideas on the challenges and opportunities in this rapidly expanding sector.

qa_qWhere are the most exciting and viable opportunities right now for companies looking into IoT strategies to drive their business?

Mike Kavis: The best use case is optimizing manufacturing by knowing immediately what machines or parts need maintenance, which can improve quality and achieve faster time to market. Agriculture is all over this as well. Farms are looking at how they can collect information about the environment to optimize yield. Even insurance companies are getting more information about their customers and delivering custom solutions. Pricing is related to risk, and in the past that has been linked to demographics. If you are a teenager, you are automatically deemed a higher risk, but now providers can tap into usage data on how the vehicle is being driven and give you a lower rate if you present a lower risk. That can be a competitive advantage.

Dinesh Sharma: Let me give you an example from mining. If you have sensored power tools and you have a full real-time view of your assets, you can position them in the appropriate places. Wearable technology lets you know where the people who might need these tools are, which then enables more efficient use of your assets. The mine is more efficient, which means reduced costs, and that ultimately results in a margin advantage over your competition. Over time, the competitive advantage will build and there will be more money to invest in further digital transformation capabilities. Meanwhile, other mining companies that aren’t investing in these technologies fall further behind.

qa_qWith the IoT, how should CIOs and other executives think and act differently?

Martha Heller: The points of connection between IT and the business should be as strategic and consultative as possible. For example, the folks from IT who work directly with R&D, marketing, and data scientists should be unencumbered with issues such as network reliability, help desk issues, and application support. Their job is to be a business leader and to focus on innovative ideas, not to worry for an instant about “Oh your e-mail isn’t working?” There’s also obviously the need for speed and agility. We’ve got to find a way to transform a business idea into something that the businessperson can touch and feel as quickly as possible.

Greg Kahn: Companies are realizing that they need to partner with others to move the IoT promise forward. It’s not feasible that one company can create an entire ecosystem on their own. After all, a consumer might own a Dell laptop, a Samsung TV, an Apple watch, a Nest device, an August Smart Lock, and a Whirlpool refrigerator.

It is highly unrealistic to think that consumers will exchange all of their electronic equipment and appliances for new “connected devices.” They are more likely to accept bridge solutions (such as what Amazon is offering with its Dash Replenishment Service and Echo) that supplement existing products. CIOs and other C-suite executives will need to embrace partnerships boldly and spend considerable time strategizing with like-minded individuals at other companies. They should also consider setting up internal venture arms or accelerators as a way to develop new solutions to challenges that the IoT will bring.

qa_qWhat is the emerging technology strategy for effectively enabling the IoT?

Kavis: IT organizations are still torn between DIY cloud and public cloud, yet with the IoT and the petabytes of data being produced, it changes the thinking. Is it really economical to build this on your own when you can get the storage for pennies in the cloud? The IoT also requires a different architecture that is highly distributed, can process high volumes of data, and has high availability to manage real-time data streaming.

On-premise systems aren’t really made for these challenges, whereas the public cloud is built for autoscaling. The hardest part is connecting all the sensors and securing them. Cloud providers, however, are bringing to market IoT platforms that connect the sensors to the cloud infrastructure, so developers can start creating business logic and applications on top of the data. Vendors are taking care of the IT plumbing of getting data into the systems and handling all that complexity so the CIO doesn’t need to be the expert.

Kahn: All organizations, regardless of whether they outsource data storage and analysis or keep it in house, need to be ready for the influx of information that’s going to be generated by IoT devices. It is an order of magnitude greater than what we see today. Those that can quickly leverage that data to improve operational efficiency, and consumer engagement will win.

Sharma: The future is going to be characterized by machine interactions with core business systems instead of by human interactions. Having a platform that understands what’s going on inside a store – the traffic near certain products together with point-of-sale data – means we can observe when there’s been a lot of traffic but the product’s just not selling. Or if we can see that certain products are selling well, we can feed that data directly into our supply chain. So without any human interaction, when we start to see changes in buying behavior we can update our predictive models. And if we see traffic increasing in another part of the store in a similar pattern we can refine the algorithm. We can automatically increase supply of the product that’s in the other part of the store. The concept of a core system that runs your process and workflow for your business but is hyperconnected will be essential in the future.

qa_qPrivacy and security are a few of the top concerns with hyperconnectivity. Are there any useful approaches yet?

IoT_Isbel_QA03Kavis: We have a lot less control over what is coming into companies from all these devices, which is creating many more openings for hackers to get inside an organization. There will be specialized security platforms and services to address this, and hardware companies are putting security on sensors in the field. The IoT offers great opportunities for security experts wanting to specialize in this area.

Kahn: The privacy and security issues are not going to be solved anytime soon. Firms will have to learn how to continually develop new defense mechanisms to thwart cyber threats. We’ve seen that play out in the United States. In the past two years, data breaches have occurred at both brick-and-mortar and online retailers. The brick-and-mortar retail industry responded with a new encryption device: the chip card payment reader. I believe it will become a cost of business going forward to continually create new encryption capabilities. I have two immediate suggestions for companies: (1) develop multifactor authentication to limit the threat of cyber attacks, and (2) put protocols in place whereby you can shut down portions of systems quickly if breaches do occur, thereby protecting as much data as possible.

A New Model for Corporate Learning

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A slow but steady revolution is occurring in the world of learning. If you have a child between the ages of 5 and 18 living at home, you’re probably seeing it unfold every day. Want to confirm you got your math problem correct? Just ask Siri. Need to understand how weather balloons work for a science project? Check out The Weather Channel Kids Web site. Forgot your homework assignment? Ask a friend to snap it and send it on Instagram.

SAP_Learning-Isbell_INQUIRY_image2400x1600_2The future of learning is here and it’s digital, social, continuous, and highly immersive. For companies, traditional training methods, such as classrooms, are still relevant, but they are no longer the prime delivery method for learning. They are slow to set up, are expensive, and consume too many productive hours. Many companies are beginning to view the classroom as a strategy for customized educational needs, such as corporate strategy or branding.

Static online-learning tools, such as asynchronous simulations and narrated slide decks, are not engaging enough to be effective as a replacement for live training, however. Meanwhile, many employees are unable to keep up with technological advances that affect their everyday work processes. Because knowledge becomes obsolete so quickly, people need continuous, always-on learning.

CGI, a global IT consulting company with 68,000 employees, was struggling with this very problem. Classroom training for consultants couldn’t keep up with the education required to service clients with sophisticated technology needs. CGI adopted a cloud-based learning platform to bridge the gap. The system, which can be personalized to the learner, includes video-based courses and online-learning rooms to foster social learning opportunities with other students and instructors. CGI is now training 50% more consultants, and learners are consuming 50% more training content than in the past.

The move to continuous, on-demand learning is also saving CGI money and enabling it to onboard new consultants faster. “It is a ‘moment of need’ reference tool that helps our employees in their day-to-day tasks,” says Bernd Knobel, a director at CGI.

Workforce and economic drivers for learning transformation

Learning needs are growing across all disciplines of content due to the speed of globalization, competition, and new disruptive business practices. During the fallout from the 2008 global recession, companies scaled back on organizational development, but that’s beginning to change as companies struggle to rebuild their businesses, says Josef Bastian, a senior learning performance consultant with Alteris Group.

The same forces that drove CGI to abandon the classroom are being felt across industries. The main drivers for change include:

1. Creating competitive advantage

SAP_Learning-Isbell_INQUIRY_image175px_1Uber, Netflix, Amazon, Airbnb, Bloom Energy, and health insurer Oscar are among the companies considered highly disruptive in their markets today. They achieved innovation and market share by looking ahead and taking advantage of new technologies faster than competitors or in novel ways. Digital learning enables companies to stay ahead of the curve. Companies need to understand the new technologies before they are even available, so that they can understand the impact on the business and even invent new business models.

2. Closing the skills gap

SAP_Learning-Isbell_INQUIRY_image175px_2We are now in an era that will rival the Industrial Age in terms of transformation. For example, a financial analyst today needs to know how to work with Big Data, including how to ask the right questions and how to use the related information systems. Jim Carroll, a speaker, consultant, and author on business transformation, uses the automotive industry as one rubric for change. “You’ve got folks who are struggling with all this new high-tech gear inside the car or the dashboard,” says Carroll. “And you look at a typical auto dealer or the person manufacturing a car, and the knowledge they need to do their job today is infinitely more complex than it was even 5 or 10 years ago.”

3. Retaining and motivating a new workforce

SAP_Learning-Isbell_INQUIRY_image175px_3By 2025, Millennials will make up 75% of the workforce, according to the Brookings Institution. Various studies have shown that Millennials crave learning and collaboration and will do whatever it takes to get the information they need expediently. “I’ve got two sons who are 20 and 22 and they seem to learn in an entirely new and different way,” Carroll says. “To borrow from Pink Floyd, it is short, sharp shocks of knowledge ingested. They won’t sit down and read 50 pages of a textbook.” Sophisticated learning programs are one way to keep this generation engaged. “Millennials will be an increasing challenge for companies to attract and retain because of their high expectations,” says Bastian. “They’re not interested just in money but also in a career path and the opportunity for diverse experiences.”

It’s risky to assume that your business isn’t in a prime spot for disruption (see “Corporate Learning Trends”). Companies will need to adapt or suffer the consequence of a disengaged and unprepared workforce. An Oxford Economics Workforce 2020 survey found that the top concern of employees is the risk of becoming obsolete; nearly 40% of North American respondents said that their current skills will not be adequate in three years, and only 41% of global respondents said that their companies are giving them opportunities to develop new skills.

Corporate Learning Trends

  • Nearly 40% of North American respondents said that their current job skills will not be adequate in three years, with the majority agreeing that the need for technology skills, especially in analytics and programming, will grow.
  • Less than half (47%) of executives say they have a culture of continuous learning. A similar percentage says that trouble finding employees with base-level skills is affecting their workforce strategy.
  • Spending on technology education in the Americas will have a compound annual growth rate (CAGR) of4.2% from 2014 to 2019, with the highest growth in the United States for collaborative applications (11.9% CAGR), followed by data management applications (7.8%).
  • The global e-learning market was worth US$24 billion in 2013, with predicted growth of $31.6 billion by 2018.
  • Of the $31.6 billion predicted worldwide spend on corporate e-learning by 2018, $22.5 billion will be on content.
  • A majority of chief learning officers (57%) say that learning technology is a significant priority for spending.
  • In 2014, 32.6% of training was delivered through e-learning (asynchronous and synchronous); 30.4% took place in the classroom, 18.9% was on the job, and 18.1% was “other,” which includes video and text.
  • E-learning is the preferred method for developing IT skills, said 34% of participants, compared with 29.2% for classroom training. For developing business skills, an overwhelming 57.3% chose classroom training.

Evolution of learning: personal, social, mobile, and continuous 

SAP_Learning-Isbell_INQUIRY_image2400x1600_3Online courses have become a standard way to gain knowledge, and that’s shifting to even more interactive learning through mobile, which is available anywhere and anytime. Like many large companies, SAP had created a vast library over time of more than 50,000 training assets, which was cumbersome to navigate and manage. The curriculum was organized across regions, lines of business, and disciplines. As a result, mapping learning to broader business goals was difficult.

To modernize its learning environment, SAP deployed a cloud-based learning management system and a social collaboration tool. Today, more than 74,000 employees can create personalized training through a combination of online self-study that incorporates video and documentation, social learning tools for exchanging ideas with other employees, and hands-on practice using SAP applications in a sandbox environment.

Now the company is engaging four times more employees in learning activities than it did with the older on-premise learning management system (LMS). The new approach is also creating between €35 million and €45 million in increased operating profit with just a 1% increase in engagement. Administrative costs have decreased by €600 per new content item added. Managers and employees alike can create and access learning paths much more easily and track progress from their personal pages. This integrated, simple-to-use online-learning approach is an example of how learning departments need to evolve to stay relevant.

There are several characteristics of digital learning transformation:

  • Micro-learning. The concept of breaking lessons into smaller bites minimizes productivity disruptions and mirrors consumer behavior of watching three-minute videos and reading social media to get information on anything under the sun. Micro-learning is perfect for learning how to write a business plan, develop code in Ruby on Rails, or learn about a manufacturer’s latest appliance before a service call, for example. It can mean segmenting a longer course into small lessons, which the employee could view over lunch or in the evening from home. Several Alteris clients are now looking to deploy mobile learning apps, ideal for micro-learning, as the main delivery platform, says Bastian. These apps work best when integrated with the LMS and HR systems and push relevant material to users based on their learning profile.
  • Self-serve learning. Just-in-time learning is critical when learning needs accelerate. Companies can help by providing continually updated tools and content that can be accessed from any device, at the moment of need. It’s the best way for learning departments to keep up with employees’ needs; you can schedule only so many Webinars and classroom training courses.
  • Learning as entertainment. Gamification has been hot in marketing for a few years and is also a viable tool for corporate learning. New employees at Canadian telecommunications company TELUS earn badges as they complete different orientation tasks, such as creating a profile on the corporate social network. Leaders can spend eight weeks coaching a virtual Olympic speed-skating team, competing against colleagues to earn gold medals. Winning requires demonstrating the leadership behaviors that TELUS values.Training is also starting to incorporate virtual reality. For example, the U.S. military is using a gaming platform that incorporates avatars to create simulations that train soldiers to deal with dangerous or problematic situations. “This is more immersive and has the potential to help with the human connection failings of online learning,” says Joe Carella, managing director of executive education at the University of Arizona. Regardless of the method, adding an element of fun and recognition for reaching milestones is important for capturing the attention of younger workers who have grown up on games and apps.
  • Social learning. Learning is an emotional experience and most people don’t want to be alone when they learn. In that regard, social media models can be profoundly valuable because they foster sharing and collaboration, which helps employees retain the knowledge they gain through formal training programs. That’s why social collaboration platforms have become as important to the overall learning strategy as the specific types of training delivery methods themselves.
  • User-generated content. A common theme spanning all of the previously mentioned areas has played out in mainstream media and social media over the past few years. “What learners value the most today is the raw, user-created content over the highly polished corporate-created content,” says Elliott Masie, founder of The MASIE Center, a think tank focused on learning and knowledge in the workforce. “What’s really fascinating is that this trend is creating a town-square model where learners are ripe to learn from others.”
  • Video. “Almost anyone can produce a training video, and it’s technically more convenient than ever before,” says Cushing Anderson, a VP and analyst focusing on HR and learning at IDC. “Digital learning is often about substituting convenience for perfect quality.”

Universities and MOOCs: What We’ve Learned So Far

Degrees and certifications have been going online through massive open online courses (MOOCs) for a few years, reflecting the changing needs of students as well as the escalating costs of traditional education.

Threatened with disruption from independent MOOC startups such as Coursera and Udacity, universities and colleges have scrambled to keep pace. More than 80% now offer several courses online and more than half offer a significant number of courses online, according to the EDUCAUSE Center for Analysis and Research. The survey found that more than two-thirds of academic leaders believe that online learning is critical to the long-term strategic mission of their institutions.

MOOCs have delivered a transformation of higher learning that wasn’t possible a decade ago, when access to a Harvard professor was available only to the elite few who had earned their place in those hallowed halls and who could afford the stratospheric tuition.

However, MOOCs have not been proven out yet as an effective replacement for traditional degrees, much less the acquisition of knowledge. Completion rates for courses are low, and MOOCs so far seem best suited for technical or tactical topics or as a supplement to the classroom, observes Joe Carella, managing director of executive education at the University of Arizona.

Yet MOOCs are playing a growing role in companies. Getting access to real business experts, such as a well-known speaker like Jim Collins, is especially valuable for a small or midsize business that couldn’t afford to hire that individual otherwise.

Making the shift

For decades, corporate learning departments have delivered education through a fairly narrow, top-down funnel: curriculum is designed months ahead of time and learning paths are structured for targeted roles in the organization. In moving toward accelerated, continuous learning, chief learning officers will need to help foster a culture of accountability and excitement around learning, as follows:

  • SAP_Learning-Isbell_INQUIRY_image1600x2400_1Develop a close alignment between learning departments and senior business leaders to understand skill gaps, customer needs, and employee shortfalls.
  • Become a content curator and take on a customer service role in the business.
  • Ensure that learning is specific to the individual and relates to specific business and career goals.
  • Have managers help by motivating and guiding employees through the tools, helping them develop personalized plans, and monitoring their progress.

In most cases, companies should be relatively hands-off when it comes to employee learning, says Eilif Trondsen, director of learning, innovation, and virtual technologies at Strategic Business Insights. “It is the responsibility of the workers to learn and acquire the needed skills and competencies for their jobs,” says Trondsen, “and it’s important to monitor the outcomes and not micromanage the process they use for getting there.”

However, it’s important that leaders motivate employees to learn by setting a good example. At TELUS, a company vice president started an internal online community and his own blog to share information about working in his division. The company views corporate learning not as curriculum but as a set of experiences, including classroom courses, online training, coaching, mentoring, and informal collaboration. TELUS measures the direct impact of learning through surveys of both employees and their managers. One metric reports on the learning tools that are most effective for acquiring different types of knowledge, while another measures return on performance from a specific learning program.

Measuring learning effectiveness is a difficult key performance indicator, just as customer engagement is, yet digital learning platforms often have built-in analytics to create a starting point. The analytics allows companies to run reports on usage to see what’s most effective and to retire those assets that aren’t being used. Ultimately, companies should work toward connecting the dots between learning outcomes and business outcomes, such as attrition, employee engagement, and sales growth.

The human equation of digital learning

Today and into the future, no matter the technology or method deployed, excellent learning depends on excellent instructors. They must have credibility with their audiences or the program will flop. For example, when Sun Microsystems (now owned by Oracle) first offered e-learning on its programming language, Java, customers balked because they wanted to know who the expert behind the course was, just like in a classroom. So Sun included a video introduction by the original developer of Java, James Gosling, and the program took off.

Another caution with digital learning is that it can never replace the five senses one gets in a physical setting and lacks spontaneity. “With e-learning, you can pause the course whenever you wish, but sometimes breakthroughs happen when you are out of your comfort zone and challenged,” Carella says. A discussion can merge into a novel direction in ways that don’t typically happen when people are chatting online. Ideally, online learning should be interspersed with in-person educational experiences, whether that’s attending a classroom training or meeting with a mentor.

Blending formal and informal training, as well as offline and online training, is a historical trend that will continue, says Masie, who also leads The Learning CONSORTIUM, a coalition of 230 global organizations, including CNN, Walmart, Starbucks, and American Express. Incorporating multiple modes of learning is critically important for gaining knowledge that sticks.

“A learner who isn’t motivated will sit in front of the screen and complete a course but may never actually develop the skill,” he says. To close the loop, managers and learning departments can develop a process that includes practice, feedback, and on-the-job experience.

SAP_Learning-Isbell_INQUIRY_image2400x1600_1The long-term goal of digital learning: grow the business

As executives consider how learning and training should evolve, a grounding consideration is the level of commitment. Few companies spend enough on it, says IDC’s Anderson. Those with world-class training programs can gain an edge in hiring and possibly even in the market. Introducing innovative learning tools and programs that allow employees to study independently and experiment with new ideas is also motivating, which can lead to higher engagement, productivity gains, and even bottom-line benefits. In fact, says Masie, research has shown that organizations that invest at least 3% of income on learning have better stock performance and employee retention.

Highest ROI in e-commerce? Email remarketing and retargeted ads

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Digital marketers know they must measure and optimize all of their efforts, with the goal of increasing sales. They must also be able to prove a positive return on their investments. That said, digital marketers are constantly on the hunt for the latest technologies to help with both.

Shopping Cart Abandonment Emails Report Highest ROI

The highest ROI reported is from shopping cart abandonment emails. This shouldn’t be a surprise — 72 percent of site visitors that place items into an online shopping cart don’t make the purchase. Since they did almost purchase, cart abandoners are now your best prospects. And, a sequence of carefully timed emails will recover between 10-30 percent of them.

It’s these types of recovery rates that propel shopping cart abandonment emails to the top. They generate millions in incremental revenue for only a small effort and cost.

Retargeted Ads Complement Shopping Cart Abandonment Emails

The second most successful technique is retargeted advertising, a fantastic complement to shopping cart abandonment emails. Retargeted advertising works in a similar way, by nudging visitors to return to a website after they have left. And while retargeted advertising works across the entire funnel — from landing to purchase — the biggest opportunities lie where there is some level of intent to purchase, such as browsing category and product pages.

While the two techniques deliver a high ROI, they are definitely not the same. For example, brands using SeeWhy’s Conversion Manager to engage their shopping cart recovery emails average a 46 percent open rate and 15 percent click-through rate. Retargeted ads, by comparison, average a 0.3 percent click-through rate.

See the difference?

The real power comes when you combine the two techniques together — using retargeted advertising when no email address has been captured and email remarketing when it has.

Don’t “Set ‘Em and Forget ‘Em”

To achieve the highest possible ROI combining cart abandonment emails with retargeted advertising, you should plan to test and tune your campaigns. It’s dangerous to go live with your new campaign and then ‘set it and forget it.’ Testing and tuning your campaign can double or triple your revenues. SeeWhy tracks more than $1B in Gross Market Value ecommerce revenues annually and analyzes this data to understand what factors have the biggest impact on conversion.

A SeeWhy study of more than 650,000 individual ecommerce transactions last year concluded that the optimal time for remarketing is immediately following abandonment. Of those visitors that don’t buy, 72 percent will return and purchase within the first 12 hours.

So timing is one of the critical factors; waiting 24 hours or more means that you’re missing at least 3 out of 4 of your opportunities to drive conversions. For example, a shopping cart recovery email campaign sent by Brand A 24 hours after abandonment may be its top performing campaign. But this campaign delivers half the return of Brand B’s equivalent campaign which is real time.

Scores of new technologies and techniques will clamor for your attention, making bold claims about their ROI and conversion. But if they aren’t capable of combining shopping cart abandonment emails and retargeted ads, the two biggest ROI drivers in the industry, then they aren’t worth your time.

@JovieSylvia @ITChamps_SAP

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What Engaged And Disengaged Companies Do Differently

When there’s something you want to improve about your organization and its workforce, it’s only natural to look to the companies that are doing it right. And when it comes to employee feedback, that means looking to today’s most highly engaged companies.

The info-graphic below — created by Quantum Workplace, a company dedicated to providing every organization with quality engagement tools that guide their next step in making work better every day — narrows in on what engaged and disengaged companies do differently when it comes to one of the most important aspects of employee engagement: feedback. Some highlights include:

  • Employee engagement is important to leadership at 90 percent of highly engaged companies, compared to only 20 percent of disengaged companies.
  • Employee engagement is a year-round initiative for 78 percent of highly engaged companies, compared to only 30 percent of disengaged companies.
  • Disengaged companies are 15 times more likely to never have administered an employee survey, compared to highly engaged companies.
  • Highly engaged companies report seeing a higher percent of employees participating in their employee surveys (60 percent vs. 20 percent).

Check out the full info-graphic below to find out the main communication differences between engaged and disengaged companies — and what it means for your organization.

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Our Digital Planet: Rise of The Digital Worker The New Breed of Worker

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British-Australian mining giant Rio Tinto has employed autonomous trucks, excavators and drills recently to create the first workerless iron ore mine in Western Australia. The drivers – if they can still be called that – work out of a remote operations centre hundreds of kilometres away, where data scientists mine data collected from the vehicle’s sensors. This dynamic, known as the ‘human and digital recombination’, is but a single step on the path to a changed workplace, as connectivity and automation drive the transition to digital on an unprecedented scale.

digital_planet_02_image1Real-time analysis, together with emerging digital technologies and intelligent digital processes, have upended the workplace as we know it; and businesses are today subject to a deep cultural shift in work organisation, culture and management mind set. The impact is a shift towards workers looking at available information as opposed to ‘explorative surgery’ measures when the damage is already done.

Human and digital recombination, cutting-edge decision making, realtime adaptation and experiment-driven design are pushing this transformation, not just in manufacturing but in every conceivable area of the workplace. And while the technology has done much to facilitate the transition to digital, the challenges are many.

Fat tags

Aside from Rio Tinto’s automated vehicles, other software-enabled, manufacturing- friendly marvels are around the corner, such as kilobyte-rich radio frequency identification (RFID) tags. Basically position finders at present, tomorrow’s tags will have so much storage capacity that they will act like transponders and actually tell people what to do.

As Siemens’ Markus Weinlander, Head of Product Management, predicted: “[RFID tags] can make a major contribution to the realisation of Industry 4.0 by acting as the eyes and ears of IT. For the first time, transponders will be able to carry additional information such as the production requirements together with their assembly plan. All of this will be readable at relatively large distances.”

These ‘fat tags’ will do more than boost automation. They will also make companies more nimble-footed and, say experts, allow small businesses to compete with the giants. According to Weinlander, the new wave of RFID rags will greatly facilitate customised products because they will contain all the essential information for small runs. “To remain competitive in today’s global market environment, many companies have to be able to produce in tiny batches without higher costs”, he said.

Other practical benefits are likely. For instance, maintenance and repair work will be made simpler, faster and more timely. As BCG Consulting points out, technicians will identify any problems with a machine from a stream of realtime data and then make repairs with the help of augmented-reality technology supplemented, if necessary, by remote guidance from off-site experts. In this way, downtime per machine will be reduced from one day to an hour or two.

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Digital people

In this brave new world of hyperconnectivity, the ‘digital worker’ – a data-driven individual skilled in converting information into revenue – will stand in the middle and direct traffic, as it were. As SAP put it in its D!gitalistmagazine, the digital worker will “create instant value from the vast array of real-time data.”

Instead of the traditional approach of gathering, processing, and moving data around while spending valuable time creating reports, digital workers will be forced to move towards predictive, scenario, and prognosis-based decision- making. SAP’s article goes on to explain: “The speed of information and data is driving such significant change in how and where we work that the digital worker is becoming a critical resource in decision-making, learning, productivity, and overall management of companies.”

HYPERCONNECTIVITY HAS LED US TO A NEW ERA, WHERE PETER DRUCKER’S “KNOWLEDGE WORKER” HAS COME TO AN END AND THE “DIGITAL WORKER” NOW NEEDS TO STEP UP AND CREATE INSTANT VALUE FROM THE VAST ARRAY OF REAL-TIME DATA

In organisations where data-savvy individuals may know more about what’s happening than the boss, the top-down hierarchy will be overturned. In short, everybody will be a leader in their own particular area of expertise. “The traditional management and organisational model is quickly getting outdated in the digital economy, and true leaders are changing their management approach to reflect this”, said SAP. Senior executives will have to be more visible and approachable for employees and customers alike – in short, both colleague and captain.

“[Managers] must juggle a distributed contingent workforce with digital workers who require real-time analysis, prognosis, and decision making. At the same time, they must develop the next generation of leaders who will actively take responsibility for innovation and engagement”, said SAP.

If done properly, this new collaborative workplace could reduce the complexity that bedevils most large organisations in an era of globalisation. According to the Economist Intelligence Unit, 55 percent of executives believe their organisational structure is ‘extremely’ or ‘very’ complex and 22 percent say they spend more than a quarter of their day managing complexity. More than three-quarters say they could boost productivity by at least 11 percent if they could cut complexity by half.

More jobs

But will the superconnected workplace destroy jobs? BCG Consulting thinks not. In a study of German manufacturing released in October, the think tank concluded that higher productivity actually equals higher employment at home. “As production becomes more capital intensive, the labour cost advantages of traditional low-cost locations will shrink, making it attractive for manufacturers to bring previously off-shored jobs back home”, the study predicted. “The adoption of Industry 4.0 will also allow manufacturers to create new jobs to meet the higher demand resulting from the growth of existing markets and the introduction of new products and services.”

Experts such as Ingo Ruhmann, Special Adviser on IT systems at Germany’s Federal Ministry of Education and Research, agree with this finding. “Complete automation is not realistic”, he told BCG Perspectives. “Technology will mainly increase productivity through physical and digital assistance systems, not the replacement of human labour.”

However, it will be a new kind of human labour. “The number of physically demanding or routine jobs will decrease while the number of jobs requiring flexible responses, problem solving, and customisation will increase”, Ruhmann predicts. For most employees, tomorrow’s workplace should be a lot more fun.

Identifying Performance Problems in ABAP Applications

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Analyze Statistics Records Using the Performance Monitor (Transaction STATS)

IT landscapes tend to become more complex over time as business needs evolve and new software solutions for meeting these needs emerge. This complexity can become a challenge for maintaining smooth-running business operations and for ensuring the performance and scalability of applications.

Performance means different things to different people. End users demand a reasonable response time when completing a task within a business process. IT administrators focus on achieving the required throughput while staying within their budget, and on ensuring scalability, so that a software’s resource consumption changes predictably with its load (the number of concurrent users or parallel jobs, or the number or size of processed business objects, for example). For management, optimal performance is about more productive employees and lower costs.

The overall objective is to reach the best compromise between these perspectives. This requires reliable application monitoring data, so that developers or operators can analyze an application’s response time and resource consumption, compare them with expectations, and identify potential problems. SAP customers running SAP NetWeaver Application Server (SAP NetWeaver AS) ABAP 7.4 or higher can access this data with a new, simple, user-friendly tool: the Performance Monitor (transaction STATS).

This article provides an introduction to the statistics records that contain this monitoring data for applications that run on the ABAP stack, and explains how you can use transaction STATS to analyze these records and gain the insights you need to identify applications’ performance issues.

What Are Statistics Records?

Statistics records are logs of activities performed in SAP NetWeaver AS ABAP. During the execution of any task (such as a dialog step, a background job, or an update task) by a work process in an ABAP instance, the SAP kernel automatically collects header information to identify the task, and captures various measurements, such as the task’s response time and total memory consumption. When the task ends, the gathered data is combined into a corresponding statistics record. These records are stored chronologically — initially in a memory buffer shared by all work processes of SAP NetWeaver AS ABAP. When the buffer is full, its content is flushed to a file in the application server’s file system. The collection of these statistics records is a technical feature of the ABAP runtime environment and requires no manual effort during the development or operation of an application.

The measurements in these records provide useful insights into the performance and resource consumption of the application whose execution triggered the records’ capture, including how the response times of the associated tasks are distributed over the involved components, such as the database, ABAP processing (CPU), remote function calls (RFCs), or GUI communication. A detailed analysis of this information helps developers or operators determine the next steps in the application’s performance assessment (such as the use of additional analysis tools for more targeted investigation), and identify potential optimization approaches (tuning SQL statements or optimizing ABAP coding, for example). In addition to performance monitoring, the statistics records can be used to assess the system’s health, to evaluate load tests, and to provide the basis for benchmarks and sizing calculations.

Productive SAP systems process thousands of tasks each second and create a corresponding number of statistics records, which contain valuable measurements for identifying performance issues. While existing tools provide access to the data in these records, transaction STATS offers an enhanced user interface that makes it much easier for you to select, display, and evaluate the data in statistics records, and devise a targeted plan for optimization.

Ensuring the Validity of Your Measurements

The value of a performance analysis depends on the quality of the underlying measurements. While the collection of data into the statistics records is performed autonomously by the SAP kernel, some preparatory actions are needed to ensure that the captured information accurately reflects the performance of the application.

The test scenario to be executed by the application must be set up carefully; otherwise, the scenario will not adequately represent the application’s behavior in production and will not yield the insights you need to identify the application’s performance problems. A set of test data that is representative of your productive data must be available to execute the scenario in a way that resembles everyday use. The test system you will use for the measurements must be configured and customized correctly — for example, the hardware sizing must be sufficient and the software parameterization must be appropriate, so that the system can handle the load. To obtain reliable data, you must also ensure that the test system is not under high load from concurrently running processes — for example, users should coordinate their test activities to make sure there are no negative interferences during the test run.

You must then execute the scenario a few times in the test system to fill the buffers and caches of all the involved components, such as the database cache, the application server’s table buffer, and the web browser cache. Otherwise, the measurements in the statistics records will not be reproducible, and will be impaired by one-off effects that load data into these buffers and caches. This will make it much more difficult to draw reliable conclusions — for example, buffer loads trigger requests to the database that are significantly slower than getting the data out of the buffer, and that increase the amount of transferred data. After these initial runs, you can execute the measurement run, during which the SAP kernel writes the statistics records that you will use for the analysis.

Displaying the Statistics Records

To display the statistics records that belong to the measurement run, call transaction STATS. Its start screen (see Figure 1) consists of four areas, where you specify criteria for the subset of statistics records you want to view and analyze.

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Figure 1 — On the STATS start screen, define filter conditions for the subset of statistics records you want to analyze, specify from where the records are retrieved, and select the layout of the data display

In the topmost area, you determine the Monitoring Interval. By default, it extends 10 minutes into the past and 1 minute into the future. Records written during this period of time are displayed if they fulfill the conditions specified in the other areas of the start screen. Adjust this interval based on the start and end times of the measurement run so that STATS shows as few unrelated records as possible.

In the Record Filter area, you define additional criteria that the records to be analyzed must meet — for example, client, user, or lower thresholds for measurement data, such as response time or memory consumption. Be as specific and restrictive as possible, so that only records relevant for your investigation will be displayed.

By default, statistics records are read from all application instances of the system. In the Configurationsection, you can change this to the local instance, or to any subset of instances within the current system. Restricting the statistics records retrieval to the instance (or instances) where the application was executed shortens the runtime of STATS. The Include Statistics Records from Memory option is selected by default, so that STATS will also process records that have not yet been flushed from the memory buffer into the file system.

Under Display Layout, select the resource you want to focus on and how the associated subset of key performance indicators (KPIs) — that is, the captured data — will be arranged in the tabular display of statistics records. The Main KPIs layouts provide an initial overview that contains the most important data and is a good starting point.

Analyzing Selected Statistics Records

Figure 2 shows the statistics record display based on the settings specified in the STATS start screen. The table lists the selected statistics records in chronological order and contains their main KPIs.

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The header columns — shown with a blue background — uniquely link each record to the corresponding task that was executed by the work process. The data columns contain the KPIs that indicate the performance and resource consumption of the tasks. Measurements for times are given in milliseconds (ms) and memory consumption and data transfer are measured in kilobytes (KB).

The table of statistics records is displayed within an ALV grid control and inherits all functions of this well-known SAP GUI tool: You can sort or filter records; rearrange, include, or exclude columns; calculate totals and subtotals; or export the entire list. You can also switch to another display layout or modify the one you have chosen on the start screen. To access these and other standard functions, expand the toolbar by clicking on the Show Standard ALV Functions (Show Standard ALV Functions button) button.

The measurements most relevant for assessing performance and resource consumption are the task’sResponse Time and Total Memory Consumption. The Response Time measurement starts on the application instance when the request enters the dispatcher queue and ends when the response is returned. It does not include navigation or rendering times on the front end, or network times for data transfers between the front end and the back end. It is strictly server Response Time; the end-to-end response time experienced by the application’s user may be significantly longer. The most important contributors to serverResponse Time are Processing Time (the time it takes for the task’s ABAP statements to be handled in the work process) and DB Request Time (the time that elapses while database requests triggered by the application are processed). In most cases, Total Memory Consumption is identical to the Extended Memory Consumption, but Roll Memory, Paging Memory, or Heap Memory may also contribute to the total.

Since even the most basic statistics record contains too much data to include in a tabular display, STATS enables you to access all measurements — most notably the breakdowns of the total server Response Timeand the DB Request Time, and the individual contributions to Total Memory Consumption — of a certain record by double-clicking on any of its columns. This leads to an itemized view of the record’s measurements in a pop-up window, as shown in Figure 3. At the top, it identifies the particular statistics record via its header data. The up and down triangles (up navigation button and down navigation button) to the left of the header data support record-to-record navigation within this pop-up. The available technical data is grouped into categories, such as Time, DB, andMemory and Data. Use the tabs to navigate between categories containing data. Tabs for categories without data for the current statistics record are inactive and grayed out.

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To assess the data captured in a statistics record, consider the purpose that the corresponding task serves. OLTP applications usually spend about one fourth of their server Response Time as DB Request Time and the remainder as Processing Time on the application server. For tasks that invoke synchronous RFCs or communication with SAP GUI controls on the front end, associated Roll Wait Time may also contribute significantly to server Response Time. For OLTP applications, the typical order of magnitude for Total Memory Consumption is 10,000 KB. Records that show significant upward deviations may indicate a performance problem in the application, and should be analyzed carefully using dedicated analysis tools such as transaction ST05 (Performance Trace). In comparison, OLAP applications usually create more load on the database (absolute as well as relative) and may consume more memory on the application server.

Saving Statistics

As mentioned earlier, productive systems create statistics records with a very high frequency, leading to a large volume of data that has to be stored in the application server’s file system. To limit the required storage space, the SAP kernel reorganizes statistics records that are older than the number of hours set by the profile parameter stat/max_files and aggregates them into a database table. After the reorganization, STATS can no longer display these records.

If you need to keep statistics — that is, a set of statistics records that match conditions specified on the STATS start screen — for a longer period of time for documentation reasons, reporting purposes, or before-after comparisons, you have two options:

  • Export the statistics to a binary file on your front-end PC
  • Save them into the statistics directory on the database

Both options are available via the corresponding Export Statistics to Local Front End button and Save Statistics to Database button buttons, respectively, on the STATS start screen (Figure 1) and in the tabular display of the statistics records (Figure 2).

To access and manage the statistics that were saved on the database, click on the Show Statistics Directory button button on the STATS start screen (Figure 1), which takes you to the statistics directory shown in Figure 4. In the two areas at the top of the screen, you specify conditions that the statistics must fulfill to be included in the list displayed in the lower part of the screen. Statistics are deleted automatically from the database four weeks after they have been saved. You can adjust this default Deleted On date so that the data is still available when you need it. Similarly, you can change the description, which you specified when the statistics were saved. Double-clicking on a row in the directory displays the corresponding set of statistics records, as shown in Figure 2. All capabilities described previously are available.

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Statistics that were exported to the front-end PC can be imported either into the STATS start screen, which presents the content in the tabular display shown in Figure 2, or into the statistics directory, which persists it into the database. In both cases, the import function is accessed by clicking on the Import Statistics from Local Front End button button. You can also import statistics into an SAP system that is different from the system where the statistics were exported. This enables the analysis of statistics originating from a system that is no longer accessible, or cross-system comparisons of two statistics.

Conclusion

To optimize the performance of applications and ensure their linear scalability, you need reliable data that indicates the software’s response times and resource consumption. Within the ABAP stack of SAP solutions, this data is contained in the statistics records that the SAP kernel captures automatically for every task handled by a work process. Using this information, you can identify critical steps in an application, understand which component is the bottleneck, and determine the best approach for optimization.

The Performance Monitor (transaction STATS) is a new tool available with SAP NetWeaver 7.4 for selecting, displaying, and analyzing statistics records. It helps developers and operators find the best balance between fast response times, large data throughput, high concurrency, and low hardware cost. The tool is easy and convenient to use, and employs a feature-rich UI framework so that you can focus on the data and its interpretation, and set a course for high-performing applications.