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Top 50 #BigData Twitter Influencers

Jen Cohen Crompton

#BigData – Twitter Influencers

We are aimed at becoming an authority on business innovation and want to help you identify the top influencers so you can follow the latest trends, news and opinions of these influencers in the field of Big Data. We’ll be publishing more lists on cloud computing, analytics and enterprise mobility in the coming weeks. In the meantime, here is the list of Top 50 Big Data Influencers on Twitter.

Note: Big Data Twitter influencers were determined based on tweeted topics, influence as measured by Klout, number of followers, and number of tweets. Below are the “top” influencers at this time based on the combination of factors.

@bigdata – Ben Lorica
Big Data, Analytics, Cloud Computing resources from Ben Lorica, Chief Data Scientist @OReillyMedia – San Francisco, CA · http://www.bglorica.com
Klout – 49

@BigDataAnalysis – John Akred
Big Data R&D Lead at Accenture Technology Labs, Musician, Engineer, Technologist, Analog Audio and Vacuum Tube lover, Provocateur. These thoughts are my own. – Chicago / San Jose · http://www.linkedin.com/pub/john-akred/1/b0a/320
Klout – 44

@imbigdata – Manish Bhatt
News and Updates about BigData, NoSQL, Hadoop, BI and other Big Data related technologies from BigData enthusiast Manish Bhatt
Klout – 24

@ibmbigdata – IBM Big Data
Talking about the challenges and approaches to handling Big Data. Primarily managed by @TheSocialPitt – http://www.ibm.com/bigdata · http://www.smartercomputingblog.com/category/big-data/
Klout – 37

@bobgourley – Bob Gourley
A CTO. Also find me @CTOvision and @CTOlist. National Security, Cyber Security, Enterprise IT and tech fun are key topics of interest.
Washington, DC · http://ctovision.com
Klout – 50

@klintron – Klint Finley
I write for SiliconAngle. I also run Technoccult and build strange soundscapes.
Portland, OR · http://klintfinley.com
Klout – 45

@KristenNicole2 – Kristen Nicole
News editor at SiliconANGLE, writer at Appolicious, recovering social media addict –  http://kristennicole.com
Klout – 40

@dhinchcliffe – Dion Hinchcliffe
Business strategist, enterprise architect, keynote speaker, book author, blogger, & consultant on social business and next-gen enterprises. – Washington, D.C. · http://dachisgroup.com
Klout – 52

@dmkimball05 – Dan Kimball
CMO at Kontagent, helping companies interpret patterns in social & mobile data to optimize their customer economics – San Francisco, CA · http://www.linkedin.com/in/danielkimball
Klout – 18

@HadoopNews – John Ching
Latest news about Hadoop, NoSQL & BigData from John Ching, Big Data Guru, Consultant, and Evangelist for BI, Machine Learning, and Predictive Analytics
Klout – 44

@medriscoll – Michael E. Driscoll
CEO @Metamarkets. I ♥ Big Data, analytics, and visualization. – San Francisco, CA · http://medriscoll.com/
Klout – 48

@peteskomoroch – Pete Skomoroch
My mission is to create intelligent systems that help people make better decisions. Principal Data Scientist @LinkedIn. Machine Learning, Hadoop, Big Data.
Silicon Valley · http://datawrangling.com
Klout – 56

@hmason – Hilary Mason
chief scientist @bitly. Machine learning; I ♥ data and cheeseburgers.
NYC · http://www.hilarymason.com
Klout – 56

@TimGasper – Tim Gasper
@Infochimps product manager, @Keepstream co-founder, techie, app addict, music writer/lover, #BigData, #Cloud – Austin, TX · http://timgasper.com
Klout – 43

@flowingdata – Nathan Yau
Data, visualization, and statistics. Author of ‘Visualize This.’ Background in eating. – California · http://flowingdata.com
Klout – 58

@bradfordcross – Bradford Cross
design and data @prismatic – San Francisco · http://getprismatic.com/
Klout – 44

@CityAge – City Age
We amplify good ideas through unique dialogues and their associated campaigns. We’re now organizing The Data Effect, amid other projects. – Vancouver, BC · http://www.thedataeffect.org
Klout – 32

@BigDataExpo – Big Data Expo
Join 30,000+ Delegates in 2012 at World’s Largest #Cloud Events! New York [June 11-14] Silicon Valley [Nov 5-8] Register & Save! ▸ http://bit.ly/tucY2B – New York/Silicon Valley · http://BigDataExpo.net
Klout – 34

@acmurthy – Arun C Murthy
Founder & Architect, Hortonworks. VP, Apache Hadoop, Apache Software Foundation i.e. Chair, Hadoop PMC. Moving Hadoop forward since day one, since 2006. – online · http://people.apache.org/
Klout – 43

@infoarbitrage – Roger Ehrenberg
Big Data VC at IA Ventures. Data junkie. Quant dude. Baseball coach. – ÜT: 40.76136,-73.980129 · http://www.iaventures.com
Klout – 56

@jeffreyfkelly – Jeff Kelly
I am an Industry Analyst covering Big Data and Business Analytics at The Wikibon Project and SiliconANGLE – Boston · http://wikibon.org
Klout – 45

@timoreilly – Tim O’Reilly
Founder and CEO, O’Reilly Media. Watching the alpha geeks, sharing their stories, helping the future unfold. – Sebastopol, CA · http://radar.oreilly.com
Klout – 69

@digiphile – Alex Howard
Gov 2.0 @Radar Correspondent, @OReillyMedia: alex@oreilly.com. Intrigued by technological change, taken with ideas, cooking, the outdoors, books, dogs and media – Washington, DC · http://radar.oreilly.com/alexh
Klout – 74

@band – William L. Anderson
Sociotechnical systems developer, open access advocate, and editor at CODATA Data Science Journal – austin texas
Klout – N/A

@HenryR – Henry Robinson
Engineer @ Cloudera, Zookeeper committer / PMC member, professional dilettante – San Francisco, CA · http://the-paper-trail.org/
Klout – 43

@furrier – John Furrier
Silicon Valley entrepreneur Founder SiliconANGLE Network. Inventing New Things, Blogging, Tweeting Social Media – Palo Alto, California · http://SiliconAngle.com
Klout – 49

@mikeolson – Mike Olson
Cloudera CEO – Berkeley, California · http://www.cloudera.com/
Klout – 48

@davenielsen – Dave Nielsen
Co-founder of CloudCamp & Silicon Valley Cloud Center – Mountain View, Ca · http://www.platformd.com
Klout – 44

@znmeb – M. Edward Borasky
Media Inactivist, Thought Follower, Sit-Down Comic, Former Boy Genius, Real-Time Data Journalism Researcher, Open Source Appliance Maker And Mathematician – Portland, OR · http://j.mp/compjournoserver
Klout – N/A

@rizzn – Mark ‘Rizzn’ Hopkins
I’m the editor in chief for SiliconANGLE and the purveyor of fine content at rizzn.com. · http://rizzn.com
Klout – N/A

@edd – Edd Dumbill
Telling the story of our future, where technology is headed, and what we need to know now. O’Reilly Strata and OSCON program chair. Incurably curious – California · http://eddology.com/
Klout – 57

@kellan – Kellan E
Technological solutions for social problems. CTO, Etsy. (if you follow me, consider introducing yourself with @kellan message) #47 – Brooklyn, NY · http://laughingmeme.org
Klout – 58

@mikeloukides – Mike Loukides
VP Content Strategy, O’Reilly Media, pianist, ham radio op usually in Connecticut
Klout – 54

@laurelatoreilly – Laurel Ruma
Director of Talent (speaker and author relations) at O’Reilly Media. Homebrewer, foodie, farmer in the city – Cambridge, MA · http://www.oreilly.com
Klout – 45

@neilraden – Neil Raden
VP/Principal Analyst,Constellation Research;Analytics, BigData, DecisionManagement. Author/Writer,Blogger,Speaker.Husband/(Grand)Father
Santa Fe, NM ·http://www.constellationrg.com/search/node/Neil%20Raden
Klout – 53

@greenplum – Greenplum
Greenplum, a division of EMC is driving the future of big data analytics.
San Mateo, California · http://www.greenplum.com/
Klout – 48

@squarecog – Dmitriy Ryaboy
Analytics Tech Lead at Twitter. Apache Pig committer.
San Francisco
Klout – 48

@BigData_paulz – Paul Zikopoulos
Director of Technical Professionals for IBM’s Information Management, BigData, and Competitive Database divisions. Published 15 books and over 350 articles.
Klout – 38

@moorejh – Jason H. Moore
Third century professor, Director of the Institute for Quantitative Biomedical Sciences at Dartmouth College, Editor-in-Chief of BioData Mining – Lebanon, NH, USA · http://www.epistasis.org
Klout – 47

@GilPress – Gil Press
I launched the #BigData conversation; Writing, research, marketing services; http://whatsthebigdata.com/ & http://infostory.wordpress.com/
Boston
Klout – 41

@ToddeNet – Todd E. Johnson PhD
Educational Access and Academic Sustainability • STEM •Data Informed Decisions (DID) • Always Dreaming and Learning(ADL)….Tweets here are my own!!
Olympia, WA · http://www.linkedin.com/in/toddenet
Klout – 27

@digimindci – Orlaith Finnegan
Provider of Competitive Intelligence & Market Intelligence Software. Online Reputation, Real-time Web Monitoring and Analysis, Social Media Monitoring.
Boston, Paris, Singapore · http://www.digimind.com
Klout – 36

@SmartDataCo – Smart Data Collective
Expert writers on analytics, BI and big data brought to you by the folks at Social Media Today.com · http://smartdatacollective.com
Klout – 42

@al3xandru – Alex Popescu
NOSQL Dreamer http://mynosql.tv, Software architect, Founder/CTO InfoQ.com, Web aficionado, Speaker, iPhone: 44.441881,26.139629 · http://mynosql.tv
Klout – 49

@marksmithvr – Mark Smith
CEO & Chief Research Officer at Ventana Research – http://www.ventanaresearch.com & follow

@ventanaresearch – San Ramon, CA · http://marksmith.ventanaresearch.com/
Klout – 51

@BernardMarr – Bernard Marr
Leading global authority and best-selling author on delivering, managing and measuring enterprise performance – London
Klout – 50

@johnlmyers44 – John L Myers
Senior Analyst for EMA Business Intelligence and Data Warehousing practice specializing in telecom analytics and business process management – Boulder, Colorado · http://www.enterprisemanagement.com/about/team/John_Myers.php
Klout – 54

@leenarao – Leena Rao
Tech Writer (tech Crunch), dog-lover, foodie, quirky – Chicago
Klout – 68

@HKotadia – Harish Kotadia Ph.D
Big Data, Predictive Analytics, Social CRM and CRM. Work for Infosys (NASDAQ: INFY). Views and opinion expressed are my own. – Dallas, Texas, USA · http://HKotadia.com/
Klout – 42

@chuckhollis – Chuck Hollis
technologist, marketeer, blogger and musician working for EMC.
Holliston, MA · http://chucksblog.emc.com
Klout – 49

Disclosure: I am being compensated by SAP to produce a series of posts on the innovation topics covered on this site. The opinions reflected here are my own.

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About Jen Cohen Crompton

Jen Cohen Crompton is a SAP Blogging Correspondent reporting on big data, cloud computing, enterprise mobility, analytics, sports and tech, and anything else innovation-related. When she's not blogging, she can be caught marketing, using social media and/or presenting at conferences around the world. Disclosure: Jen is being compensated by SAP to produce a series of articles on the innovation topics covered on this site. The opinions reflected here are her own.

How To Use The Right Data At The Right Time For Better Customer Relationships

Bernard Chung

According to an eMarketer study, most marketers have only rudimentary data about their customers. In fact, 80% don’t have much customer data beyond basic contact information and product purchases. This greatly limits the ability of organizations to know customers well; to predict purchase patterns, preferences, and potential lifetime value.

It’s not surprising that deeper customer insights bolster customer satisfaction and loyalty. The challenge is how to capture insightful customer data and integrate it into actionable strategies across an organization. Oftentimes, even the data that is captured is often siloed and inaccessible, leaving marketers without a consolidated view of customers.

Think of all the ways customers are “touched” within the typical marketing/sales/service processes. With all of these systems, it’s difficult to gain the complete and consistent insight that would enable organizations to deliver a unified message that reflects an understanding of where customers are on their journey. Innovative marketing technology enables real-time understanding of the latest transactions, messages, and communications and brings everything into one unified view. This allows all customer-facing aspects of an organization to consistently and seamlessly engage with customers across channels, devices, and departments.

Here are three ways to leverage the right data at the right time to enrich customer relationships.

1. Get creative about capturing deeper customer insights

In most cases, customers are very protective about sharing their personal information, unless they perceive a proportionate value in doing so. Finding ways to make the exchange of information worth it for the consumer takes creativity as well as insight into the customer.

The first step to capture deeper insights is to identify your customers at the point of interaction. If you don’t know who are interacting with, you can’t deliver individualized experiences and you won’t be able to properly attribute that interaction to the correct customer. Identifying ways to identify and capture insightful customer information in exchange for value is critical.

Asics running shoe company successfully solved the problem of collecting the right customer data by getting around the natural customer tendency to keep personal information and preferences private. They did this by offering a truly value-added Foot ID program. The program measured the customers’ feet and put them on a video treadmill to analyze their running pattern, enabling the company to make personalized shoe recommendations. Asics capitalized on the customer data, gaining insight through point-of-sale and website interactions to learn how, where, when, and how frequently customers made purchases. This enabled proactive communication about new products rather than generic outreach. In other words, Asics gathered invaluable customer information by offering a service.

2. Real-time analysis and response to opportunities

Once you ask the right questions and collect the right data, it’s only useful if it’s accessible and actionable across the organization. In today’s digital world, where people are generating mounds of data with every interaction, organizations need the ability to process large volumes of data very quickly. They also need the advanced analytics to interrogate the series of data to look for opportunities. The window of opportunity to identify and respond to customers is getting shorter; customers are just another click or finger tap away from your competitors.

In the case of the National Hockey League (NHL), bringing together various siloed systems (e-commerce, data, ticketing, Yahoo’s fantasy NHL site, and others) helped streamline data collecting and enabled deeper analytics across the organization. The NHL already had rich customer data (such as favorite team and player and preferred way of interacting with the fantasy league), but initially lacked the technology to turn this information into more sales. Until it brought customer information with the analytics under one system, it wasn’t utilizing the full potential of its customer data.

A new breed of marketing technology changed all that. Once data collection and analysis was coordinated and streamlined, the NHL could mine customer information to identify fan opportunities and react in real-time to create unique and deeply personal customer experiences. For example, a customer might receive an email about an upcoming game with related analysis and ticket offers. If they attended a game where their preferred team won, they could then receive real-time merchandise offers relating to their favorite team or player. Capitalizing on this post-win euphoria allowed the NHL to drive them toward merchandise opportunities (e.g., their favorite player on the winning team) while the fan was still in the sporting venue. The NHL’s ability to consolidate data in real-time gave them the tools they needed to act quickly to take advantage of sales opportunities.

3. Leverage advanced analytics to gain deeper customer insights

By leveraging new marketing technologies that incorporate data-management capabilities, customer behavior insights and preferences management can be utilized to develop customer strategies. But it’s not enough to capture and then consolidate data. You also have to leverage the right analytics tools that enable machine learning, predictive analytics, and visualization capabilities to be more accurate in targeting and delivering enriching customer experiences. Otherwise, all the data in the world will lend zero intelligence to the marketing endeavor.

SAP Hybris offers the leading marketing technology solutions that integrate, streamline, deliver, and analyze data to give your marketing organization a deeper understanding of the customer so that you can finally say goodbye to silos, data reports that go nowhere, and high customer turnover. Are you providing the individually tailored experiences customers are asking for? Try our free Personalization Benchmarking Tool here!

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Bernard Chung

About Bernard Chung

Bernard Chung is Head of Audience Marketing for Marketing Line of Business at SAP Hybris.

The Journey To Modern Services Marketing Starts Here

Fred Isbell

The hallmark of great solutions marketing is the ability to unite products, software solutions, and services. As a high-tech marketing professional, my personal journey and constantly evolving messaging has now spanned four decades. My roles may have changed and evolved over the years, but I still enjoy speaking on behalf of SAP throughout the year at business conferences and universities.

I was very pleased to attend and speak at this year’s Technology Services World (TSW) event, hosted by Technology Services Industry Association (TSIA) in San Diego. TSIA is an amazing collection of services and systems integrator companies, and SAP is a longtime active member. TSIA is like an ecosystem of member organizations coming together to examine the world of technology services and support.

What’s new in the ever-evolving world of services marketing

TSW 2017 was centered on reoccurring revenues amid the massive transformation toward cloud-based businesses, subscription-based revenues, and entirely new business models. From the start of the conference, there were plenty of great lessons and ideas shared. From the opening-day keynotes, including two SAP Digital Business Services executives, to my session the last day it was another great experience.

I always say that a good conference provides several aha moments – and this year’s event certainly didn’t disappoint! Here are three of them:

1. Cloud subscriptions and reoccurring revenue are here

Cloud-driven business models are revolutionizing how the services business engages and collaborates with customers. Now, the customer is in the middle of a genuinely outside-in transformation.

Organizations of all sizes are undergoing this transformation, although there are risks. The hype cycle –popularized by Gartner – shows that the services market is experiencing productivity growth and proving real solution benefit. We have exited the exciting, albeit risky, cycle of pure hype to enter the reality of outcomes-based solutions and much more.

2. Services selling has expanded and changed

New ways of selling and engaging customers are coming into play, and the importance of services has never been greater. I was thrilled to see sessions on “expanded selling” – something that I’ve advocated for years.

The services organization has multiple and deeper exchanges with customers and offers a lead-generation opportunity with far lower costs. The challenge resides in unlocking intelligence, assistance, and expertise to unearth the inherent opportunity that customers have. It is critical to go beyond customer engagement to make good on promises of follow-up. Although this method may impact customer perception measurements such as net promoter scores if the follow-up and execution are not properly done, the advantages are significant.

3. Marketing needs to enable new revenue growth

Reoccurring, profitable revenues is one of the benefits of offering cloud services and enabling digital transformation. Marketing is part of this journey, tasked to deliver a total solutions approach. Through the organizational convergence of sales, marketing, and services, the business can articulate the value of its outcomes-based solutions and services while further transforming itself and the industry. Once merely the provider of events and/or advertising and related services, marketing has a strategic seat at the table.

10 best practices for the modern services marketer

After two days of learning new perspectives and networking, I presented my breakout session, “Marketing Outcome-Based Services in the Age of Digital Transformation,” which included my spin on David Letterman’s famous “Top 10” lists to offer some proven practices for modern services marketing. Here’s a short run-through of my advice.

  1. Understand how innovation technologies drive digital transformation: It is critical to comprehend and embrace technologies that are reshaping industries, such as cloud solutions, Big Data and analytics, social media and marketing platforms, and the Internet of Things (IoT). Although some of these technologies have yet to ascend to the peak of the hype cycle, they still offer incredible promise fueled by the unprecedented proliferation of data.
  1. Tell your story through thought leadership: This form of simplified storytelling continues to help modern marketers do everything from content management to creation and delivery of the right message at the right stage of the buyer’s journey. The key is to provide a point of view that adds value to the customer conversation.
  1. Plan and execute the modern webinar: As discussed earlier this year in my blog, “This Year’s Technology Services World (TSW) Confessions Of A Webcast King: Modern Marketing, Webinars, And The Future Of Digital Marketing,” webinars create the perfect storm of a story told by credible speakers and subject-matter experts and kept fresh with an engaging, conversational approach.
  1. Define and use buyer personas: The modern marketing conversation is outside-in and tied to the specific messages and value of buyer personas. SiriusDecisions and research and advisory firms have refined this approach to an art form, providing the right way to target buyer-centric messaging, value propositions, and conversations.
  1. Guide customers on the new buyer’s journey while uniting and aligning sales and marketing: It’s time to take “new” out of “buyer’s journey” and focus on executing successful customer experiences with the right content, multi-touch interactions, and unified sales and marketing operations and insights.
  1. Implement structured methodologies, processes, and governance: Even though the backdrop hasn’t changed, businesses still need an underlying framework of systems and processes, dashboards, and analytics/reporting, and most importantly, continuous governance and review. In my blog, “The Marketing Funnel May Not Be As Dead As We Think,” I discussed some issues of the traditional marketing funnel and the latest modern marketing models, SiriusDecisons’ Waterfall Model, addressing key process improvements.
  1. Leverage account-based marketing (ABM): This account-centric approach has traveled the hype cycle and proved to offer great marketing ROI. ITSMA and SiriusDecisions have also turned ABM into highly refined best practice, and SAP and others have seen tangible and quantifiable results.
  1. Embrace data science: The geeks won; it’s time to finally embrace advanced analytics. Business intelligence is seeing unprecedented opportunity as technology catches up to the challenges of the digital economy. Not everyone needs to be their own data scientist, but we all should leverage this skill as much as possible.
  1. Navigate the marketing technology landscape: A couple of years ago, we thought that the market for marketing technology providers and solutions had peaked and that a period of significant consolidation was coming soon. As it turns out, the predictions were wrong. The number of competitors continues to grow as customer relationship management, marketing automation, Big Data, analytics, dashboards, and predictive analytics solutions are used to expand and improve capabilities and skills. We must all understand and embrace this world of “MarTec.”
  1. Invest in change management: Whenever a new era emerges, organizations need to embrace change management collectively. But this is easier said than done. Any change is not easy; in fact, our human nature resists it. Nevertheless, everyone needs to be on board to manage a consistent, connected process to transition.

Please check out my virtual trip report from TSIA TSW 2017. In the meantime, I am getting ready to attend a few more conferences: First the SiriusDecisions 2017 Summit, and my return to the “Woodstock of B2B Marketing” – with a few marketing events coming to evangelize the modern marketing journey with a healthy dose of innovation and digital transformation. Stay tuned for my next series of blogs covering my experiences and newfound knowledge!

Fred is the senior marketing director for SAP HEC and Digital Business Services Marketing at SAP. Join Fred online: TwitterFacebookLinkedInsap.com

 

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About Fred Isbell

Fred Isbell is the Senior Director of SAP Digital Business Services Marketing at SAP. He is an experienced, results- and goal-oriented senior marketing executive with broad and extensive experience & expertise in high technology and marketing. He has a BA from Yale and an MBA from the Duke Fuqua School of Business.

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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Pulling Cities Into The Future With Blockchain

Dan Wellers , Raimund Gross and Ulrich Scholl

The next wave of the digital economy is just over the horizon, and it could be built on the blockchain.

Blockchain technology has been rapidly growing in influence since 2015, when it became apparent that the technology underlying the relatively arcane concept of cryptocurrency could transform the financial system. By the end of 2016, major players like Bank of America and Goldman Sachs were laying claim to promising blockchain technologies, filing patents at roughly twice the pace they had at the start of the year.

Enthusiasm for blockchain is not just accelerating, but spreading beyond financial services, as SAP and other global organizations consider all the ways it could remove friction and risk in business transactions. From traditional vendors like IBM and Microsoft to leading consultancies including Accenture and Deloitte, some of the world’s biggest companies are acknowledging themany possibilities inherent in the ability to maintain distributed, tamper-proof ledgers that permanently and transparently record transactions. Yet as promising as blockchain already is, the business world may still be underestimating how profoundly it could change transactions, organizations, and industries. It could ultimately change the entire economy.

Trustworthy data and interactions are the cornerstone of the digital economy. As the physical world becomes ever more quantified, being able to guarantee the integrity and provenance of digital and physical assets and the transactions in which they’re involved will become a core competitive advantage — and blockchain is deliberately designed to embed that guarantee in every transaction. Distributed ledgers, smart contracts, and other blockchain technologies could form the foundation on which other exponential technologies combine and scale.

The basic idea is simple: IoT sensors in drones, autonomous vehicles, 3D printers, and augmented/virtual reality gear would collect and record data in blockchain-based decentralized ledgers. This data would be immediately verified and could be made instantly available for use by any application. Smart contracts programmed into the blockchain would then execute business processes by drawing on these vast repositories of live data. Everything could be further automated by adding artificial intelligence into blockchain smart contracts to make decisions without human involvement.

Here are just a few of the possibilities that could be someday realized on a blockchain framework:

  • Democratized design and manufacture: A blockchain-enabled design and manufacturing platform would allow individuals and small businesses to play a larger role in the digital economy. Products designed from scratch in virtual reality, as well as copies of existing objects scanned with machine vision, could be easily bought, sold, shared, or even digitally remixed, at an affordable cost while protecting intellectual property rights. This would be true whether the work was complex multi-material physical products made with distributed 3D printers — or text, music, and images.
  • Autonomous logistics: Intelligent, self-driving delivery vehicles could shuttle products and materials to their destinations, or even use onboard 3D printers to create them in the location where they’re needed, while using blockchain technology to execute and verify every transaction. Machine learning apps programmed into smart contracts, which are also embedded in the blockchain, could optimize routing. This could make the current centralized model of warehousing and logistics obsolete.
  • Distributed commerce: Combining blockchain with virtual reality, 3D scanning and printing, artificial intelligence, and autonomous vehicles could create immersive, personalized shopping experiences anywhere consumers want to have them. Shoppers could grant permission for vendors to access their purchase history, preferences, and other data stored on a blockchain ledger. Vendor AIs could then generate more accurate recommendations and interact with ecommerce bots that complete purchases automatically. Customers would receive promotions for new styles, medication refills, or replacement parts without even having to think about it. Critically, blockchain would allow buyers to limit access to their personal or proprietary data to specific organizations over a defined period of time, for example, until the end of their shopping experience or the close of their fiscal year.

This may seem like far-future speculation, but a provocative white paper from consulting firm Outlier Ventures Research claims this shift is both inevitable and already underway.

Envisioning the future city

The more technologies we connect using the blockchain as a framework, the more value we can derive. Imagine that a city has a digital ledger in which every house or apartment has a presence containing all relevant information about the home, from property ownership and mortgage balance to transactional data like utility use, property tax assessment, and past and current contractor relationships. The city could access this “digital twin” to coordinate services and perform administrative tasks related to the property more efficiently and with greater accuracy. The property owner would have a verified, trustworthy way to perform transactions like renting a room, hiring contractors to do lawn work, or selling power generated by solar panels back to the grid. The city utility company could feed power consumption data into an AI to generate energy-saving recommendations, and leverage smart contracts that automatically manage power consumption between smart appliances and the grid to lower costs and improve energy efficiency.

By linking together multiple technologies, this “smart city” could then begin to automate basic city services. For example, IoT sensors could instantly sense a problem (say, a downed electrical cable) and alert the appropriate city agency’s AI to dispatch a technician. The AI might help the technician assess the necessary repair through AR glasses, send templates for parts to the 3D printer in the technician’s truck, reimburse the parts designer through a smart contract, and guide the repair via the AR glasses before finally informing the city agency and property owner when the repair is complete.

Now imagine extending that to the city’s broader infrastructure. A business traveler hops into an autonomous electric taxi at the airport and tells it to take her to a meeting in the city center. Knowing from traffic sensor data that there’s been an accident on the highway, the car automatically chooses an alternate route that ends at the parking lot nearest its destination with an available outlet for charging. As the car parks itself, it connects to an outlet that bills the taxi company in real time for the amount of electricity needed to top up the car battery. As the traveler leaves the parking lot and connects to the city’s public wifi via a social media account, she immediately receives a push notification with a discount at the nearby coffee shop. She stops for coffee and heads for her destination, where the elevator recognizes her phone and automatically takes her to the correct floor for her meeting, right on time.

Meanwhile, city staff can monitor the taxi’s safe operation and ensure the taxi company bills accurately for the ride, check traffic status and push out notifications to all affected drivers, make sure parking is available, confirm the traveler’s opt-in agreement for city wifi, provide the coffee shop’s owner with information on the effectiveness of the day’s coupon, and confirm that the building’s elevators are functioning according to the latest safety codes. Every interaction is transparent, verifiable, and nearly impossible to fake or alter — and just as importantly, it adds to a vast store of data the city can then use machine learning to analyze for future improvements and efficiencies.

A multitude of possibilities

The disruptive potential of already exponential technologies multiplies by orders of magnitude when they can intersect and combine. With blockchain creating the framework for that to happen, it’s not entirely hyperbole to put the potential economic transformation on par with the Industrial Revolution. But companies can’t simply wait until digital transformation is upon us.  Organizations need to start right now to think through the likely impacts in a disciplined and proactive way. Developing scenarios for the multitude of possibilities prepares us to maximize positive outcomes.

Read the executive brief Running Future Cities on Blockchain.


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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.

Raimund Gross

About Raimund Gross

Raimund Gross is a solution architect and futurist at SAP Innovation Center Network, where he evaluates emerging technologies and trends to address the challenges of businesses arising from digitization. He is currently evaluating the impact of blockchain for SAP and our enterprise customers.

Ulrich Scholl

About Ulrich Scholl

Ulrich Scholl is Vice President of Industry Cloud and Custom Development at SAP. In this role, Ulrich discovers and implements best practices to help further the understanding and adoption of the SAP portfolio of industry cloud innovations.