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10 Things Businesses Should Consider When Choosing A Delivery Service

besttransportation

Delivery man in van with handheld computerDo you ship items or send documents regularly for your business? If so, how thoroughly have you researched your delivery service options? Many businesses simply use the major, well-known parcel delivery services and call it done. However, doing so may be costing them a bundle. Depending on your delivery needs, you may want to consider other options for at least some of your packages.

How do you determine which delivery service will best meet your needs? Here are 10 things to consider when evaluating a courier or delivery service for your business:

  1. Delivery service type. You will want to choose the delivery service that is best equipped to economically meet your shipping needs. This will depend largely on the size and number of your packages, and how far you are sending them. For example, if you are shipping large volumes of material over long distances, a regional or national freight carrier is your best bet. For same-day delivery of small packages and documents within your local area, a local courier or messenger service is almost always the most efficient and economical choice. Do you send multiple types of shipments, and/or also require warehousing? You may be lucky enough to find an integrated transportation company in your area that can handle all of your delivery needs. This will simplify your billing and make it easier to train employees in your shipping procedures.
  1. Security. If you are considering a local company, make sure they are licensed to do business in your area. Also, find out if they are bonded and insured.
  1. Speed of delivery. For many businesses, fast delivery is critical. Even if timing is not usually a big concern for you, you never know when you might need to rush an order. What rush options does the company you are considering offer? Overnight? Same day? If it is a local service, do they offer a two- to three-hour emergency delivery option? Choosing a company that offers multiple delivery options could save your reputation at some point.
  1. Reliability. Each time you promise delivery, you are putting your company’s reputation on the line. You need a courier service that won’t let you down. Be sure to ask your package delivery service if they offer proof of delivery, and look for a company that offers package tracking via the Internet. Also, check out their Better Business Bureau rating and online review sites such as Yelp.
  1. Professional appearance. Like it or not, the appearance of your courier company’s trucks and drivers reflects on your business — especially if you never meet your customers in person yourself. Choose a company whose trucks are clean and whose drivers maintain a neat appearance.
  1. Security. Ask your carrier about their security policy. Are packages ever left unattended? Are their vehicles and drivers well-identified?
  1. Special needs. Do they handle oversize packages? Are they equipped to handle special needs such as delivering perishable or hazardous goods? Do they offer help loading or unloading? Find out in advance before you are in a time crunch.
  1. Price. The advertised price isn’t always the final price. There may be hidden costs that can raise the price of delivery considerably. Always ask for the final price before you commit!
  1. Ease of doing business. Can you order easily online? What is their invoicing procedure? You want to choose a company that makes your life easier, not more difficult.
  1. The customer experience. Last but certainly not least, is the company you are considering friendly and pleasant to do business with? You may be interacting with these people on a daily basis, so why not choose people who make you glad to work with them?

It’s important to realize that there may not be a single delivery service that will suit all your needs ideally, all the time. You may discover that you’re best off using multiple companies for your different shipping needs. Even so, evaluating each of them with the above criteria can help you make the best shipping decisions possible for your business. Over time, this is sure to save you time and money, and deliver the best package possible: peace of mind!

Brad Poulter is the Executive Vice President of Operations at Best Transportation Services, Inc., a Chicago-based delivery service providing reliable and customized courier solutions for businesses throughout a variety of industries.

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4 Steps To Revolutionize Your Industrial Manufacturing [VIDEO]

Conor Brophy

For generations, we have lived in a mass production society. Imagine a world where a 50,000-square foot warehouse, once filled with products and inventory as far as the eye can see, now sits empty. Inventory has shifted to a new location: the cloud. Products are now manufactured, or “printed,” on-demand and delivered to the location where the demand exists.

UPS and Fast Radius are two innovative companies with a shared vision for the future of manufacturing. Fast Radius is UPS’s partner in 3D printing and a pioneer in on-demand manufacturing. UPS is a global leader in logistical services. Together, these two companies are teaming up and leveraging IoT technology in order to rewrite the rules of manufacturing.

The digital supply chain

“[There] are problems that [manufacturing] companies have been trying to solve for decades. How do I make my inventory management more efficient? How do I match the supply of product that I’m making with the actual demand?” said Rick Smith, co-founder of Fast Radius, at the Fast Radius facility in Chamblee, Georgia.

To answer these questions, we must take a step back and look at the big picture. Over the last 100-plus years, conventional manufacturing has been based on a simple equation: “The more things you make, the lower the cost of each of those things,” according to Rick Smith. This equation presents problems in cost and infrastructure when considering factory build and maintenance, tooling and molds, and piles of excess inventory. Instead of asking how to simply make this process more efficient, UPS and Fast Radius are taking a step further by enabling a digital supply chain.

Simplifying industrial manufacturing in four easy steps

1. Design

The first step towards on-demand manufacturing requires a seamlessly integrated solution for collaboration. Fast Radius is leveraging a distributed manufacturing solution to virtually collaborate with customers and ensure design specifications and the correct level of certifications are met. This platform is the digital workshop where designers and engineers, on both sides, can make sure a printed product will be the same as one produced on an assembly line.

2. Internet of Things (IoT)

UPS is using an IoT platform which specializes in making machines smarter and drives end-to-end digital transformation. A distributed manufacturing solution, sensors, automation, robotics, and 3D printers feed massive amounts of data processed within an IoT portfolio, enabling more dynamic data leading to seamless customer experiences.

“It’s really an ecosystem of technologies that work together. It’s not just one thing, it’s many things that are working together,” said Alan Amling, VP of strategy at UPS.

3. 3-D printing

3D printing is the third step in the process to set forth on-demand manufacturing. After taking the world by storm, the technology continues to evolve every day. “Traditionally with printing, you would be stuck with a very limited amount of materials. You could replicate some of the harder types with traditional methods, but with the new technologies and new materials coming to market, you can almost replicate any kind of production,” said Rick Smith.

4. Logistics

Finally, a designed, printed product must do one last thing: Get to the customer. How? By leveraging UPS and its areas of logistical expertise. Simply box it up and slap on a UPS Next-Day Air sticker. The customer will receive the part within 24 hours of initial purchase.

By harnessing the power of an IoT platform, UPS and Fast Radius are quickly revolutionizing product manufacturing. They have streamlined the process of design, IoT, 3-D printing, and logistics to create a seamless customer experience. The impact to manufacturers, to businesses, and to individuals will undoubtedly be disruptive. Watch the video below to learn more.

Effective IoT connectedness requires a unifying foundation. SAP has addressed this need by introducing the SAP Leonardo portfolio, an innovative IoT solution portfolio designed to help organizations digitally transform existing processes and evolve to new digital models. Learn more by downloading an SAP Leonardo brochure, reading about real-world use cases, attending our flagship event Leonardo Live this summer, visiting sap.com/iot, and following us on Twitter at @SAPLeonardo.

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Conor Brophy

About Conor Brophy

Conor Brophy joined SAP as an intern with the SAP HANA Enterprise Cloud go-to-market team. He currently works with the SAP Global Marketing Customer Storytelling team as a content specialist. When he is not writing about stories of "Run Simple," he enjoys golf, hiking and spending time with his fiance, friends and family.

IoT And Connected Markets

Pushkar Ranjan

The Internet of Things (IoT) involves connected products, assets, fleets, infrastructures, markets, and people. In this series of blogs, we’ll address each of these connected aspects in turn.

We’ve grown accustomed to the idea of IoT in retail environments – for example, beacons that can recognize participants in frequent-shopper programs and automatically deliver personalized offers to their cellphones. But IoT can transform other kinds of markets, as well.

In rural settings, IoT can augment physical infrastructure to enable new connectivity and capabilities for agribusiness and related supply networks. In urban landscapes, IoT can connect traffic, buildings, and public spaces for greater efficiency and more effective services.

In both cases, IoT can empower new insights, innovations, and business models. It can also optimize the use of assets and natural resources; reduce energy usage, emissions, and congestion; and improve efficiency and quality of life.

But it’s still early days for connected markets. To see return on their IoT investments, organizations operating in these contexts will need to identify opportunities for quick wins – while understanding that the most significant payoffs will be achieved over time.

Connecting for transformation

Connected markets can enable transformations in three key contexts:

Market insights: Consumers and citizens increasingly demand consistent and seamless experiences across time, space, and geography. IoT enables retail and other public spaces to respond. Connected markets leverage beacons, mobile connectivity, and identity solutions to understand behaviors and preferences and then deliver hyper-personalized experiences.

For instance, a leading provider of sports apparel and equipment created a customer-activity repository to achieve a 360-degree view of customers. In one initiative, the company is leveraging a customer fitness app to track running, cycling, and other sports activities to serve up personalized offers.

Rural areas: Agriculture is big business, and modern farms and ranches can be large enough that they need to be managed by aircraft and other extensive physical infrastructure. And in both developed and emerging economies, agribusiness increasingly must do more with less to feed growing populations.

Connected markets augment and transform physical infrastructure to deliver new insights and capabilities. You can capture data from agricultural equipment to improve efficiency and operation. You can connect partners up and down the supply network to create transparent and sustainable food supply chains and better manage price volatility. Satellite, GPS, cloud, and related technologies are connecting even the most remote operations, from food producers through to wholesalers, retailers, and consumers.

Urban areas: As the global population grows, it is becoming more urbanized. Today, 50% of people live in cities, and by 2050, 75% will. To manage this growth and deliver services effectively, cities will need to become more connected.

IoT enables cities to respond as the work and personal lives of citizens become more mobile and automated. It can optimize traffic, energy usage, public spaces, ports, and other physical infrastructure.

The trend has already started with simple applications like smart parking. Smart parking combines sensors, cameras, and apps to help citizens quickly find parking spaces, help cities predict parking needs, and measurably ease traffic congestion. It will continue with autonomous living choices. One example is laundry. When designing high-density housing, rather than include laundry space in every apartment, urban developers might centralize laundry, using connected technologies to optimize the experience.

In both rural and urban markets, governments have a vested interest in driving IoT deployments. But governments often lack budgets for new IT initiatives. As a result, public-private partnerships will be key to the development of connected markets.

The potential upsides of connected markets are compelling. Once a market is virtualized, the opportunities for reaching new customers, partners, and even industries grow exponentially. The organizations that figure out connected markets will gain significant first-mover advantages – and position themselves for longer-term industry leadership.

Effective IoT connectedness requires a unifying foundation. SAP has addressed this need by introducing SAP Leonardo, an innovative IoT solution portfolio designed to help organizations digitally transform existing processes and evolve to new digital models. Learn more by downloading an SAP Leonardo brochure, reading about real-world use cases, attending our flagship event Leonardo Live this summer, visiting sap.com/iot, and following us on Twitter at @SAPLeonardo.

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Pushkar Ranjan

About Pushkar Ranjan

Pushkar Ranjan is part of the global Internet of Things (IoT) Go-To-Market team at SAP, where he is responsible for business development, sales enablement, and community engagement with prospects, customers, partners, and developer ecosystems in and around the topic of the IoT. Pushkar has worked with SAP for more than 15 years across disciplines of product management, strategy consulting, and operations management in the areas of customer engagement and enterprise performance management. He has academic degrees of a Master’s in Business Administration from the Indian Institute of Management with a focus in the areas of Information Systems, Strategy, and Marketing.

Primed: Prompting Customers to Buy

Volker Hildebrand, Sam Yen, and Fawn Fitter

When it comes to buying things—even big-ticket items—the way we make decisions makes no sense. One person makes an impulsive offer on a house because of the way the light comes in through the kitchen windows. Another gleefully drives a high-end sports car off the lot even though it will probably never approach the limits it was designed to push.

We can (and usually do) rationalize these decisions after the fact by talking about needing more closet space or wanting to out-accelerate an 18-wheeler as we merge onto the highway, but years of study have arrived at a clear conclusion:

When it comes to the customer experience, human beings are fundamentally irrational.

In the brick-and-mortar past, companies could leverage that irrationality in time-tested ways. They relied heavily on physical context, such as an inviting retail space, to make products and services as psychologically appealing as possible. They used well-trained salespeople and employees to maximize positive interactions and rescue negative ones. They carefully sequenced customer experiences, such as having a captain’s dinner on the final night of a cruise, to play on our hard-wired craving to end experiences on a high note.

Today, though, customer interactions are increasingly moving online. Fortune reports that on 2016’s Black Friday, the day after Thanksgiving that is so crucial to holiday retail results, 108.5 million Americans shopped online, while only 99.1 million visited brick-and-mortar stores. The 9.4% gap between the two was a dramatic change from just one year prior, when on- and offline Black Friday shopping were more or less equal.

When people browse in a store for a few minutes, an astute salesperson can read the telltale signs that they’re losing interest and heading for the exit. The salesperson can then intervene, answering questions and closing the sale.

Replicating that in a digital environment isn’t as easy, however. Despite all the investments companies have made to counteract e-shopping cart abandonment, they lack the data that would let them anticipate when a shopper is on the verge of opting out of a transaction, and the actions they take to lure someone back afterwards can easily come across as less helpful than intrusive.

In a digital environment, companies need to figure out how to use Big Data analysis and digital design to compensate for the absence of persuasive human communication and physical sights, sounds, and sensations. What’s more, a 2014 Gartner survey found that 89% of marketers expected customer experience to be their primary differentiator by 2016, and we’re already well into 2017.

As transactions continue to shift toward the digital and omnichannel, companies need to figure out new ways to gently push customers along the customer journey—and to do so without frustrating, offending, or otherwise alienating them.

The quest to understand online customers better in order to influence them more effectively is built on a decades-old foundation: behavioral psychology, the study of the connections between what people believe and what they actually do. All of marketing and advertising is based on changing people’s thoughts in order to influence their actions. However, it wasn’t until 2001 that a now-famous article in the Harvard Business Review formally introduced the idea of applying behavioral psychology to customer service in particular.

The article’s authors, Richard B. Chase and Sriram Dasu, respectively a professor and assistant professor at the University of Southern California’s Marshall School of Business, describe how companies could apply fundamental tenets of behavioral psychology research to “optimize those extraordinarily important moments when the company touches its customers—for better and for worse.” Their five main points were simple but have proven effective across multiple industries:

  1. Finish strong. People evaluate experiences after the fact based on their high points and their endings, so the way a transaction ends is more important than how it begins.
  2. Front-load the negatives. To ensure a strong positive finish, get bad experiences out of the way early.
  3. Spread out the positives. Break up the pleasurable experiences into segments so they seem to last longer.
  4. Provide choices. People don’t like to be shoved toward an outcome; they prefer to feel in control. Giving them options within the boundaries of your ability to deliver builds their commitment.
  5. Be consistent. People like routine and predictability.

For example, McKinsey cites a major health insurance company that experimented with this framework in 2009 as part of its health management program. A test group of patients received regular coaching phone calls from nurses to help them meet health goals.

The front-loaded negative was inherent: the patients knew they had health problems that needed ongoing intervention, such as weight control or consistent use of medication. Nurses called each patient on a frequent, regular schedule to check their progress (consistency and spread-out positives), suggested next steps to keep them on track (choices), and cheered on their improvements (a strong finish).

McKinsey reports the patients in the test group were more satisfied with the health management program by seven percentage points, more satisfied with the insurance company by eight percentage points, and more likely to say the program motivated them to change their behavior by five percentage points.

The nurses who worked with the test group also reported increased job satisfaction. And these improvements all appeared in the first two weeks of the pilot program, without significantly affecting the company’s costs or tweaking key metrics, like the number and length of the calls.

Indeed, an ongoing body of research shows that positive reinforcements and indirect suggestions influence our decisions better and more subtly than blatant demands. This concept hit popular culture in 2008 with the bestselling book Nudge.

Written by University of Chicago economics professor Richard H. Thaler and Harvard Law School professor Cass R. Sunstein, Nudge first explains this principle, then explores it as a way to help people make decisions in their best interests, such as encouraging people to eat healthier by displaying fruits and vegetables at eye level or combatting credit card debt by placing a prominent notice on every credit card statement informing cardholders how much more they’ll spend over a year if they make only the minimum payment.

Whether they’re altruistic or commercial, nudges work because our decision-making is irrational in a predictable way. The question is how to apply that awareness to the digital economy.

In its early days, digital marketing assumed that online shopping would be purely rational, a tool that customers would use to help them zero in on the best product at the best price. The assumption was logical, but customer behavior remained irrational.

Our society is overloaded with information and short on time, says Brad Berens, Senior Fellow at the Center for the Digital Future at the University of Southern California, Annenberg, so it’s no surprise that the speed of the digital economy exacerbates our desire to make a fast decision rather than a perfect one, as well as increasing our tendency to make choices based on impulse rather than logic.

Buyers want what they want, but they don’t necessarily understand or care why they want it. They just want to get it and move on, with minimal friction, to the next thing. “Most of our decisions aren’t very important, and we only have so much time to interrogate and analyze them,” Berens points out.

But limited time and mental capacity for decision-making is only half the issue. The other half is that while our brains are both logical and emotional, the emotional side—also known as the limbic system or, more casually, the primitive lizard brain—is far older and more developed. It’s strong enough to override logic and drive our decisions, leaving rational thought to, well, rationalize our choices after the fact.

This is as true in the B2B realm as it is for consumers. The business purchasing process, governed as it is by requests for proposals, structured procurement processes, and permission gating, is designed to ensure that the people with spending authority make the most sensible deals possible. However, research shows that even in this supposedly rational process, the relationship with the seller is still more influential than product quality in driving customer commitment and loyalty.

Baba Shiv, a professor of marketing at Stanford University’s Graduate School of Business, studies how the emotional brain shapes decisions and experiences. In a popular TED Talk, he says that people in the process of making decisions fall into one of two mindsets: Type 1, which is stressed and wants to feel comforted and safe, and Type 2, which is bored or eager and wants to explore and take action.

People can move between these two mindsets, he says, but in both cases, the emotional brain is in control. Influencing it means first delivering a message that soothes or motivates, depending on the mindset the person happens to be in at the moment and only then presenting the logical argument to help rationalize the action.

In the digital economy, working with those tendencies means designing digital experiences with the full awareness that people will not evaluate them objectively, says Ravi Dhar, director of the Center for Customer Insights at the Yale School of Management. Since any experience’s greatest subjective impact in retrospect depends on what happens at the beginning, the end, and the peaks in between, companies need to design digital experiences to optimize those moments—to rationally design experiences for limited rationality.

This often involves making multiple small changes in the way options are presented well before the final nudge into making a purchase. A paper that Dhar co-authored for McKinsey offers the example of a media company that puts most of its content behind a paywall but offers free access to a limited number of articles a month as an incentive to drive subscriptions.

Many nonsubscribers reached their limit of free articles in the morning, but they were least likely to respond to a subscription offer generated by the paywall at that hour, because they were reading just before rushing out the door for the day. When the company delayed offers until later in the day, when readers were less distracted, successful subscription conversions increased.

Pre-selecting default options for necessary choices is another way companies can design digital experiences to follow customers’ preference for the path of least resistance. “We know from a decade of research that…defaults are a de facto nudge,” Dhar says.

For example, many online retailers set a default shipping option because customers have to choose a way to receive their packages and are more likely to passively allow the default option than actively choose another one. Similarly, he says, customers are more likely to enroll in a program when the default choice is set to accept it rather than to opt out.

Another intriguing possibility lies in the way customers react differently to on-screen information based on how that information is presented. Even minor tweaks can have a disproportionate impact on the choices people make, as explained in depth by University of California, Los Angeles, behavioral economist Shlomo Benartzi in his 2015 book, The Smarter Screen.

A few of the conclusions Benartzi reached: items at the center of a laptop screen draw more attention than those at the edges. Those on the upper left of a screen split into quadrants attract more attention than those on the lower left. And intriguingly, demographics are important variables.

Benartzi cites research showing that people over 40 prefer more visually complicated, text-heavy screens than younger people, who are drawn to saturated colors and large images. Women like screens that use a lot of different colors, including pastels, while men prefer primary colors on a grey or white background. People in Malaysia like lots of color; people in Germany don’t.

This suggests companies need to design their online experiences very differently for middle-aged women than they do for teenage boys. And, as Benartzi writes, “it’s easy to imagine a future in which each Internet user has his or her own ‘aesthetic algorithm,’ customizing the appearance of every site they see.”

Applying behavioral psychology to the digital experience in more sophisticated ways will require additional formal research into recommendation algorithms, predictions, and other applications of customer data science, says Jim Guszcza, PhD, chief U.S. data scientist for Deloitte Consulting.

In fact, given customers’ tendency to make the fastest decisions, Guszcza believes that in some cases, companies may want to consider making choice environments more difficult to navigate— a process he calls “disfluencing”—in high-stakes situations, like making an important medical decision or an irreversible big-ticket purchase. Choosing a harder-to-read font and a layout that requires more time to navigate forces customers to work harder to process the information, sending a subtle signal that it deserves their close attention.

That said, a company can’t apply behavioral psychology to deliver a digital experience if customers don’t engage with its site or mobile app in the first place. Addressing this often means making the process as convenient as possible, itself a behavioral nudge.

A digital solution that’s easy to use and search, offers a variety of choices pre-screened for relevance, and provides a friction-free transaction process is the equivalent of putting a product at eye level—and that applies far beyond retail. Consider the Global Entry program, which streamlines border crossings into the U.S. for pre-approved international travelers. Members can skip long passport control lines in favor of scanning their passports and answering a few questions at a touchscreen kiosk. To date, 1.8 million people have decided this convenience far outweighs the slow pace of approvals.

The basics of influencing irrational customers are essentially the same whether they’re taking place in a store or on a screen. A business still needs to know who its customers are, understand their needs and motivations, and give them a reason to buy.

And despite the accelerating shift to digital commerce, we still live in a physical world. “There’s no divide between old-style analog retail and new-style digital retail,” Berens says. “Increasingly, the two are overlapping. One of the things we’ve seen for years is that people go into a store with their phones, shop for a better price, and buy online. Or vice versa: they shop online and then go to a store to negotiate for a better deal.”

Still, digital increases the number of touchpoints from which the business can gather, cluster, and filter more types of data to make great suggestions that delight and surprise customers. That’s why the hottest word in marketing today is omnichannel. Bringing behavioral psychology to bear on the right person in the right place in the right way at the right time requires companies to design customer experiences that bridge multiple channels, on- and offline.

Amazon, for example, is known for its friction-free online purchasing. The company’s pilot store in Seattle has no lines or checkout counters, extending the brand experience into the physical world in a way that aligns with what customers already expect of it, Dhar says.

Omnichannel helps counter some people’s tendency to believe their purchasing decision isn’t truly well informed unless they can see, touch, hear, and in some cases taste and smell a product. Until we have ubiquitous access to virtual reality systems with full haptic feedback, the best way to address these concerns is by providing personalized, timely, relevant information and feedback in the moment through whatever channel is appropriate. That could be an automated call center that answers frequently asked questions, a video that shows a product from every angle, or a demonstration wizard built into the product. Any of these channels could also suggest the customer visit the nearest store to receive help from a human.

The omnichannel approach gives businesses plenty of opportunities to apply subtle nudges across physical and digital channels. For example, a supermarket chain could use store-club card data to push personalized offers to customers’ smartphones while they shop. “If the data tells them that your goal is to feed a family while balancing nutrition and cost, they could send you an e-coupon offering a discount on a brand of breakfast cereal that tastes like what you usually buy but contains half the sugar,” Guszcza says.

Similarly, a car insurance company could provide periodic feedback to policyholders through an app or even the digital screens in their cars, he suggests. “Getting a warning that you’re more aggressive than 90% of comparable drivers and three tips to avoid risk and lower your rates would not only incentivize the driver to be more careful for financial reasons but reduce claims and make the road safer for everyone.”

Digital channels can also show shoppers what similar people or organizations are buying, let them solicit feedback from colleagues or friends, and read reviews from other people who have made the same purchases. This leverages one of the most familiar forms of behavioral psychology—reinforcement from peers—and reassures buyers with Shiv’s Type 1 mindset that they’re making a choice that meets their needs or encourages those with the Type 2 mindset to move forward with the purchase. The rational mind only has to ask at the end of the process “Am I getting the best deal?” And as Guszcza points out, “If you can create solutions that use behavioral design and digital technology to turn my personal data into insight to reach my goals, you’ve increased the value of your engagement with me so much that I might even be willing to pay you more.”

Many transactions take place through corporate procurement systems that allow a company to leverage not just its own purchasing patterns but all the data in a marketplace specifically designed to facilitate enterprise purchasing. Machine learning can leverage this vast database of information to provide the necessary nudge to optimize purchasing patterns, when to buy, how best to negotiate, and more. To some extent, this is an attempt to eliminate psychology and make choices more rational.

B2B spending is tied into financial systems and processes, logistics systems, transportation systems, and other operational requirements in a way no consumer spending can be. A B2B decision is less about making a purchase that satisfies a desire than it is about making a purchase that keeps the company functioning.

That said, the decision still isn’t entirely rational, Berens says. When organizations have to choose among vendors offering relatively similar products and services, they generally opt for the vendor whose salespeople they like the best.

This means B2B companies have to make sure they meet or exceed parity with competitors on product quality, pricing, and time to delivery to satisfy all the rational requirements of the decision process. Only then can they bring behavioral psychology to bear by delivering consistently superior customer service, starting as soon as the customer hits their app or website and spreading out positive interactions all the way through post-purchase support. Finishing strong with a satisfied customer reinforces the relationship with a business customer just as much as it does with a consumer.

The best nudges make the customer relationship easy and enjoyable by providing experiences that are effortless and fun to choose, on- or offline, Dhar says. What sets the digital nudge apart in accommodating irrational customers is its ability to turn data about them and their journey into more effective, personalized persuasion even in the absence of the human touch.

Yet the subtle art of influencing customers isn’t just about making a sale, and it certainly shouldn’t be about persuading people to act against their own best interests, as Nudge co-author Thaler reminds audiences by exhorting them to “nudge for good.”

Guszcza, who talks about influencing people to make the choices they would make if only they had unlimited rationality, says companies that leverage behavioral psychology in their digital experiences should do so with an eye to creating positive impact for the customer, the company, and, where appropriate, the society.

In keeping with that ethos, any customer experience designed along behavioral lines has to include the option of letting the customer make a different choice, such as presenting a confirmation screen at the end of the purchase process with the cold, hard numbers and letting them opt out of the transaction altogether.

“A nudge is directing people in a certain direction,” Dhar says. “But for an ethical vendor, the only right direction to nudge is the right direction as judged by the customers themselves.” D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


About the Authors:

Volker Hildebrand is Global Vice President for SAP Hybris solutions.

Sam Yen is Chief Design Officer and Managing Director at SAP.

Fawn Fitter is a freelance writer specializing in business and technology.

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The Big (Data) Problem With Machine Learning

Dan Wellers

Historically, most of the data businesses have analyzed for decision-making has been of the structured variety—easily entered, stored, and queried. In the digital age, that universe of potentially valuable data keeps expanding exponentially. Most of it is unstructured data, coming from a wide variety of sources, from websites to wearable devices. As a recent McKinsey Global Institute report noted: “Much of this newly available data is in the form of clicks, images, text, or signals of various sorts, which is very different than the structured data that can be cleanly placed in rows and columns.”

At the same time, we have entered an era when machine learning can theoretically find patterns in vast amounts of data to enable enterprises to uncover insights that may not have been visible before. Machine learning trains itself on data, and for a time, that data was scarce. Today it is abundant. By 2025, the world will create 180 zettabytes of data per year (up from 4.4 zettabytes in 2013), according to IDC.

Big Data and machine learning would seem to be a perfect match, coming together at just the right time. But it’s not that simple.

The connected world is ever-widening, enabling the capture and storage of more—and more diverse—data sets than ever before. Nearly 5,000 devices are being connected to the Internet every minute today; within ten years, there will be 80 billion devices collecting and transmitting data in the world. Voice, facial recognition, chemical, biological, and 3D-imaging sensors are rapidly advancing. And the computing muscle that will be required to churn through all this data is more readily available today. There’s been a one trillion-fold increase in computing power over the past 60 years.

The importance of data prep

But having vast amounts of data and computing power isn’t enough. For machine learning tools to work, they need to be fed high-quality data, and they must also be guided by highly skilled humans.

It’s the age-old computing axiom writ large: garbage in, garbage out. Data must be clean, scrubbed of anomalies, and free of bias. In addition, it must be structured appropriately for the particular machine-learning tool being used as the required format varies by platform. Preparing data is likely the least sexy but most important part of a data scientist’s job—one that accounts for as much as 50 percent of his or her time, according to some estimates. It’s the unglamorous heavy lifting of advanced analytics, and it takes experience and skill to do it—qualities that are, and will continue to be, in short supply even as demand for data scientists is predicted to grow at double-digit rates for the foreseeable future.

It took one bank 150 people and two years of painstaking work to address all the data quality questions necessary to build an enterprise-wide data lake from which advanced analytics tools might drink. That’s the kind of data wrangling that has to be done before companies can even begin to test the value of machine-learning capabilities.

More data, more problems

There’s also the misperception that having access to all this new data will necessarily lead to greater insight. There’s great enthusiasm around data-driven decision-making and the promise of Big Data and machine learning in boardrooms and executive suites around the world. But in reality, says UC Berkeley professor and machine learning expert Michael I. Jordan, more data increases the likelihood of making spurious connections. “It’s like having billions of monkeys typing. One of them will write Shakespeare,” said Jordan, who noted that Big Data analysis can deliver inferences at certain levels of quality. But, he said, “we have to be clear about what levels of quality. We have to have error bars around all our predictions. That is something that’s missing in much of the current machine learning literature.”

Again, this is where the expertise of the data scientist is of critical value: deciding what questions machine learning might be able to answer, with what data and at what level of quality.

These problems are not insurmountable. Tools are being developed to help businesses deal with some of the data management blocking and tackling that stands in the way of advanced analytics. One company, for example, has developed a machine-learning tool for real estate and finance companies that it says can extract unstructured data in 20 different languages from contracts and other legal documents and transform it into a structured, query-ready format.

What is clear is that the business of combining Big Data and big computing power for new insight is harder than it looks. The benefits almost certainly will be huge. But companies are still at the early stages of experimenting with new data types and emerging machine-learning tools and discovering the drawbacks and complications we will need to work through over time.

This blog is the fifth in a six-part series on machine learning.

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About Dan Wellers

Dan Wellers is the Global Lead of Digital Futures at SAP, which explores how organizations can anticipate the future impact of exponential technologies. Dan has extensive experience in technology marketing and business strategy, plus management, consulting, and sales.