Cornell’s new certificate program boosts presentation skills to further career growth

The ability to present on any subject, in any place, to any audience is an invaluable asset for every professional. According a Forbes report on a new survey, seventy percent of Americans who give presentations agree that strong presentation skills are critical to their success at work. Yet twenty percent of survey respondents said they would do almost anything to avoid giving a presentation.

Today’s accomplished working professionals lead through their ability to communicate information and emotion effectively, even in the most intimidating circumstances. Cornell’s new Executive Presence certificate program will help learners conquer performance anxiety, refine public speaking skills, and build confidence, with the end goal of maximizing speaker-listener connection and furthering career growth.

“Executive Presence is not magic,” says faculty author David Feldshuh, Professor of Performing and Media Arts, Cornell University College of Arts and Sciences. “It’s a skillset that learners can master with practice and experience.”

The course teaches the learner how to use breathing, visual focus, voice, and gesture to deliver authentic and engaging presentations in a wide variety of settings, from formal speeches to personal interviews. Through the use of video feedback, self-coaching questions, rubric self-analysis, and expert real-time coaching, students learn to diminish restrictive presentation habits, including performance anxiety and mannerisms, and work to become more responsive and expressive. They gain increased insight by watching others present, and understand more fully the importance of effective body language and vocal variety.

“It’s not about achieving perfection or competing with someone else,” says Dr. Feldshuh. “In this class, you compete with yourself. The core skills, analytical tools, and transformative training exercises are intended to position the learner for a lifetime of development. A fundamental learning goal is for learners to know themselves as presenters, and become experts in self-coaching to ensure continued improvement after the course is completed.”

Aspiring leaders, managers, senior leaders and executives, CEOs, actors and performers, and anyone who wants to strengthen his or her ability to connect with others while speaking will find value in this certificate program, which is available online through eCornell.

Upon successful completion of the Executive Presence certificate program, which consists of a single, 15-week course, learners earn an Executive Presence Certificate from Cornell College of Arts and Sciences, and 60 professional development hours.

Human-Centered Approach to Decision-Making Strategy

C-level executives are recognizing creativity’s role in strategy. A recent global IBM survey of over 1,500 CEOs identified creativity as the most vital skillset needed to navigate an increasingly complex world. The ability to generate efficient and effective solutions to problems of significant complexity is a desired skill not only for engineers, but also for anyone whose job requires substantial decision-making.

As a response to the growing need for creative thinking in the business world, Cornell has announced the launch of the new Design Thinking certificate program. Available 100% online through eCornell, this certificate program will help learners adopt a robust, human-centered approach to designing and improving products, experiences, and systems at any scale.

“It’s often a challenge to blend design thinking with traditional systems engineering,” says Sirietta Simoncini, Lecturer at the Cornell School of Engineering and author of the Design Thinking certificate program. “Within this program, learners will leverage systems engineering tools that are integrated with the traditions of design thinking to make decisions that are intuitive, creative, and data-driven.”

Regional planners, engineers, user experience designers, program and product managers, consultants, systems analysts, marketers, and entrepreneurs involved in product conception will all find this certificate program valuable. The program offers a seamless and linear path from course to course, where learners have the ability to bring their group project and peer relationships with them as they move through the program.

Once learners have completed the Design Thinking certificate program, they can immediately begin applying their creativity in the workplace. Their new skills will enable them to define challenges, gather key user insights and emotions to help develop personas and user narratives based on empathy, and generate ideas and prototyping for potential solutions and improvements. In addition, learners will be able to iterate on and refine prototypes using design thinking methodology to ultimately generate a rigorous, viable solution to challenges.

Courses include:

  • Identifying and Framing a Challenge
  • Gathering User Emotions
  • Crafting User Narratives
  • Generating User-Centered Solutions
  • Design Prototyping
  • Testing and Iteration

Upon successful completion of all six courses, learners earn a Design Thinking Certificate from the Cornell School of Engineering.

Why the ability to read data is just as important as the ability to read

Working with spreadsheets and analyzing data is no longer reserved only for those who crunch numbers. Today, all fields are relying more heavily on making data-driven decisions and utilizing spreadsheet modeling as a tool for growth. Donna Haeger, a Cornell professor of economics and management, sat down with eCornell’s Chris Wofford to discuss the growing impact spreadsheet modeling is having on business.

What follows is an abridged version of that conversation.

Wofford: How does spreadsheet modeling relate to business analytics? How do we distinguish the two?

Haeger: The spreadsheet modeling piece is really taking the unstructured data. We’re structuring it into an organized fashion. The business analytics piece is really the data-driven decision-making that we’re doing, so making the decisions on the model are what we’re doing when we’re performing business analytics. If we’re using optimization, we want the result of the model to tell us what we should do – how many of a particular product we should produce based on our criteria and our goals. We could also do predictive which is a forecast, like a simulation.

Wofford: What are some typical obstacles? For some people, this is very fresh and if you’re really starting to take your analytics and your modeling seriously, what are the typical obstacles that people run up against when they’re first starting to think about this as a strategy for their company?

Haeger: I think the biggest obstacle today is how much data we have. We’re drowning in data. I always tell my students data is not the problem. We have so much data that we don’t know what to do with it. Most of the startup companies that are working in data analytics are basically becoming specialists in spreadsheet modeling and other types of data modeling so that they can answer questions. And I like to say that every company that has data, which is every company at this point, they’re swimming in answers to questions they haven’t even begun to ask – and that’s a pretty amazing place to be.

Wofford: What is your experience as far as when you work with students? Can you just speak to that broadly about this as a career path or if somebody’s actually already established in a position, how might it benefit them to learn about this?

Haeger: That’s an interesting question and I get this all the time – things like what job titles am I looking for. I have not found a position, an internship or a permanent job, that does not involve data in the business realm as of late. And so that’s interesting because I like to say that this whole thing about data is ubiquitous, like it’s everywhere. There are very few jobs right now that do not relate to having some data literacy, so understanding how to take data and turn it from data to information, which is structuring the data and then analyzing it and turning it to some knowledge is it’s really hard to find a position where you don’t need to know how to do that.

In fact, we’re starting to hear that people are being encouraged to learn a programming language on top of being able to be working with the data.

Wofford: What is that?

Haeger: When you’re working with spreadsheet modeling, you have a lot of control over the data that you receive, however, it depends. Everyone has a choice. I call it a Venn diagram. We’ve gone from where we used to send an email to IT and say, “I need some data, please send it to me” and the business people would get the data from IT.

Wofford: I mean if we talk about data literacy across an organization, for example, there’s certainly a case to be made that everybody should be to literate in some way so we know what we’re talking about. Are visuals where it’s at?

Haeger: We all love pictures, right? I think most of us are visual and even if we’re not visual having the spreadsheet model – and when we say spreadsheet model, it could be a pivot table, table, columns, rows, a chart – when we turn it into a visualization, we’re answering a lot more questions in one image. When we illustrate it this way and if we do a good job with it, it’s much easier for people to answer their own questions and interact with the visual. We’re starting to actually create dashboards now where we’ll create several different pivot tables, pull out some visualizations, put them all on one tab and create slicers where the individual isn’t just looking at three or four images but also being able to hit the slicers and interact with the data. So now you’re answering thousands of questions by interacting with what you see on the dashboard.

Want to hear more? Watch the recorded live eCornell WebSeries event, Why the Ability to Read Data is Just as Important as the Ability to Read, and subscribe to future events.

 

Cornell’s new certificate program teaches how to leverage blockchain technology

Interest in blockchain is rapidly growing, as companies in every industry begin to recognize the many benefits of this innovative new technology. From smart contracts to cloud and supply chain storage, applications of this technology extend well beyond cryptocurrency and financial transactions.

Cornell has launched the new Blockchain for Business certificate program online through eCornell. It will help learners explore blockchain’s cryptographic roots, demystify the technology and recognize how to use blockchain to solve myriad business problems.

“Many associate blockchains with cryptocurrency such as bitcoin, but they have many other applications,” says Ari Juels, professor of computer science at the Jacobs Technion-Cornell Institute at Cornell Tech and author of the program. “Our Blockchain for Business certificate program will explore the full power of blockchains with learners, enabling them to leverage blockchains’ powerful capabilities, recognize their often-overlooked technical limitations and apply them to real-world business goals.”

Business and technology leaders, entrepreneurs, developers and software engineers interested in learning blockchain fundamentals, and anyone seeking to develop a greater understanding of blockchain and cryptocurrency will find value in the program.

Upon completion of the Blockchain for Business certificate program, learners can apply their new skillset back at work by identifying key areas where blockchain technology can help create efficiency, save time and money, and increase security. Further, learners will be able to recognize whether a blockchain is backed by solid cryptographic building blocks to avoid bad business decisions. Courses include:

  • Cryptocurrencies and ledgers;
  • Cryptography essentials;
  • Blockchain fundamentals; and
  • Applications of blockchain technology.

Upon successful completion of all four courses, learners earn a Blockchain for Business Certificate from Cornell Tech.

Teaching Online Sharpens Instruction in the Classroom

Allan Filipowicz, clinical professor of management and organizations at the Samuel Curtis Johnson Graduate School of Management, recently faced a seemingly impossible task: Teach three days of material in just one day.

Cultural and language barriers made time even tighter. He usually taught the material, on the psychology of leadership, to MBA students in their late 20s who were native English speakers. This particular class was for Middle Eastern senior executives in their 50s for whom English was a second language.

Luckily, Filipowicz had an ace up his sleeve. He had previously worked with eCornell to re-engineer his campus course on the subject into an online certificate program. The design process has transformed the way he and other professors think about and organize how they teach their on-campus courses.

As Cornell’s online learning unit, eCornell delivers online professional certificate programs and executive master’s degree programs. eCornell has offered online learning courses and certificate programs for 17 years to more than 130,000 students around the world at more than 2,000 companies.

“The fact that I did this work with eCornell gave me the ability to take any topic and make it incredibly modular,” Filipowicz said. “I essentially took what was three days’ worth of material and compressed it into one-third of the time without losing either the large ideas or the interrelation between them. You can collapse the structure. It’s like you can fold the course any way you want once you examine it on that level.”

When creating an online course or certificate program, eCornell’s instructional designers work with faculty members to define specific learning outcomes. “Our design process is very much built on what students need to be able to do to be successful in this area, not necessarily what they need to know,” said Chad Oliveiri, eCornell’s vice president of curriculum development.

That could be, for example, how to introduce one’s self to a large group or draft a business plan. Students learn in an interactive, small cohort format to gain skills they can immediately apply in their jobs, Oliveiri added.

The instructional designer and the professor take apart the classroom curriculum and redesign it with an eye toward the student audience, the online format and, above all, the learning objectives for each module. “What are you doing, in that second, that is helping the student achieve the goal in this module?” Filipowicz said. They also decide which tools – video sessions, discussion groups, projects, an assignment done at the workplace – will help the learner achieve those outcomes.

In the process, professors often realize they’ve shifted how they approach on-campus classes they may have been teaching for years.

“It really impacts you,” said Deborah Streeter, the Bruce F. Failing Sr. Professor of Personal Enterprise in the Charles H. Dyson School of Applied Economics and Management. Streeter’s Women, Leadership and Entrepreneurship class on campus parallels her eCornell Certificate Program in Women in Leadership. “Suddenly you’re thinking, I should go back and look through my learning outcomes for campus and make sure I have thought deeply enough about them,” she said.

And the process forces the faculty member to cast off extraneous details, Streeter said: “It has sharpened the curation of the material.”

For example, she recently created a course on executive presence – that is, the ability to project confidence and leadership. The design process called for a checklist in this area to give students. As Streeter dug into the research, she found a “nauseating” report by a respected source giving contradictory and subjective advice, such as “dress conservatively, but not too conservatively; be attractive, but not sexy; don’t remind people of their daughter, but don’t remind people of their mother; wear jewelry, but not too much.”

She could have just given students the report. Instead, the design process prompted her to define the message she wanted convey. In the end, she told them, do not take any piece of advice unless the results make you feel powerful.

“The design process took me there, because you’re thinking so much about the learner, and when they’re done with that module of the course, what do they have to put into play in their life the next day?” Streeter said.

The design process is a luxury, said Filipowicz, a form of mentoring. “At no time in your professional career does anyone say, ‘Let’s go through your course second by second and think about what each element is doing and ask are there ways to do it better.’”

Streeter agrees.

“Having a second set of eyes on your curriculum is so much fun,” she said. “Somebody’s paying attention to what you’re teaching.”

Want Better Data? Build from the Bottom Up.

At Cornell University’s Center for Hospitality Research, one of the main aims is to make research available in a digestible format for those in the hospitality and service industries. A large part of that work involves helping the industry not only collect significant data but to make sense of it in order to make better business decisions.

As part of eCornell’s webcast series, the center’s director, Professor Chris Anderson, joined eCornell’s Chris Wofford for a discussion on data analytics and why industry professionals should adopt a bottom-up approach to data. What follows is an abridged transcript of their conversation.

Wofford: Welcome. Let’s talk about data-driven analytics and what the bottom-up approach means.

Anderson: The first thing to note is that good analytics is not necessarily new. I’ve been in this space for a little more than 25 years now. What’s really happened in the last five to ten years is that analytics have become much more accessible — and with that new accessibility comes lower costs. As a result, it’s become much more widely adopted.

But I think we’ve kind of lost a bit of what I refer to as the bottom-up approach, which is involving those who are critically close to the business itself in the data analytics. You need to have an understanding of where that data came from, what potential variables you’re missing, and how it was sampled. In order to get the most out of data analytics, you need a firm understanding of the business itself and how things should be working towards some sort of outcome. In the opposite scenario, the top-down approach, we let technology tell us what’s going on and we sort of let the data drive the solution.

Wofford: Can you give us a real-world example of what you mean?

Anderson: I come at this historically from the hospitality space, from the demand and pricing side of things. That space to me has always been fascinating because, in order to price and control a hotel or an airline, you really have to have a fundamental understanding of where demand comes from, how the business manages that demand, and what kind of decisions they can make. You really get this deep insight into how you make money.

So for a lot of data analytics, that becomes this core set of skills and once we’re good at it, then we really understand our business well and it brings a lot of opportunities for us.

Wofford: What kinds of data analytics are relevant to the hospitality and service industries?

Anderson: There are three basic forms of data analytics. The first is what we refer to as descriptive, where we’re just describing what has happened or just reporting. It’s kind of a backward view of the world.

Our second is the predictive world, it’s the forward-looking part of analytics where we’re trying to use our insight from reporting to help us look at relationships and make predictions about the future. And then predictive analytics goes one step further and tries to see what factors resulted in us achieving previous metrics, what we might do to impact those and what the future outcome might be.

The third part is prescriptive analytics. Once you understand where you’ve been and have a good sense of how to go forward, then you want to use some tools and techniques to make sure you’re going forward in the profit-maximizing or cost-minimizing sort of way.

It’s about using a set of tools to help us do the best going forward, given the insight that we’ve been able to extract from this both descriptive and predictive framework.

Wofford: What are those tools? What are you looking for within the data?

Anderson: We use things like optimization, where we are looking at making multiple decisions at a time. We use things like decision analysis and programming.

We work on incorporating uncertainty into our decisions. No decision is made out of certainty, so we don’t want to just ignore that. We want to make decisions knowing that there is some uncertainty and once I make one decision I can adjust to those uncertainties and make subsequent decisions.

We use different tools if there’s a lot of uncertainty that’s evolving over time and we might use another set of tools if there’s so much complexity that it’s hard for us to map out how things are all working together.

We think about the starting block as being reporting. Your goal is really to understand how well you’ve been doing, so you’re focused on key performance indicators. How was I pricing? How was my competitor pricing? We are just looking at some of these things together in concert with our backward-looking metrics.

This really lays the groundwork of the predictive part, in which we are trying to understand that these things may be impacting some of our key performance indicators, and we may look at those in different ways.

Even before we can start to do this we’ve got to collect the data, put it in a data warehouse, and have it organized in some sort of centralized way. One of the trickiest parts about this is we have to make sure that we have a lot of integrity around that data. We want to have a secure process from which we can extract, pull and analyze, but we don’t want to necessarily change that underlying structure.

There are a lot of pieces we have to make sure are lined up so that if we have lots of users, they are not going to distract from the quality of that data.

Wofford: In your experience, do you find that most companies have their data in order or when you go to work with them, or do you find you have a lot of work to do right out of the gate?

Anderson: For most organizations, it’s about getting their data house together. It’s often not well organized.

Wofford: So getting that data organized is almost always the biggest challenge?

Anderson: That’s right.

Wofford: Once things are put in order, are we then looking at the predictive component? You mentioned using this to reduce uncertainty – how do we do that?

Anderson: Well, let’s say you are looking at what your sales were last year. That would provide a naive estimate for the next year, right? But while you might be able to take last year’s average, there is a lot of variance around that average. So our goal is to generate a better estimate for the future that has less variance around it, so it’s a more refined guess. We try to make less naive guesses by using information from other attributes that may be impacting sales. If we know those factors going forward, that will help us refine the estimate for whatever that metric is, whether it’s sales or some other key performance indicator. The predictive part is all about reducing uncertainty and we do that through different kinds of relationships.

Wofford: Like competitive analysis, for instance?

Anderson: Right. How my competitor is pricing relative to how I’m pricing. But we have to be cautious because there’s no point in looking at the impacts of relationships unless you know those factors in the future. My sales are a function of how I price and how my competitors price but I don’t necessarily know how my competitors are going to price tomorrow or next week or next year.

Once we’ve got those two parts under our belts – the reporting and the predictive – then we can start to make better decisions going forward instead of just shooting from the hip. And that entails using a lot of these mathematical tools, along with our knowledge, intuition and expertise, to look at some of this complexity.

The prescriptive part is getting us beyond just making obvious logical decisions and trying to look at how things are interconnected. We don’t necessarily jump into this part unless we have our foundations in the information because the prescriptive modeling component is going to need inputs from reporting or inputs from our predictive components. They’re the critical first two steps before we get into part three.

Wofford: And the prescriptive element involves running a simulation in some way?

Anderson: Yes, you could think of it like that. You can think of a hotel trying to set optimal prices to maximize revenue. To do that, the hotel owners have to have some estimate of future demands and ideally some estimate of future price-dependent demand. That estimate of future price-dependent demand from our predictive analysis will then be input into our optimization models to help us formulate those decisions going forward.

Wofford: We hear a lot about things like “text analysis” and other new techniques that help us look beyond simple numeric data. Can you tell me about that?

Anderson: Think of Amazon reviews. We’re selling products on Amazon and we’re looking at what consumers are saying. We have to be cognizant that other consumers are reviewing that content. They’re paying attention to that average review score on Amazon but they’re also actually looking at what people said about the product. So we need to look for keywords and repetition of those keywords.

Yes, I could read all that information manually, but we can now use tools to help us pull up keywords and their frequencies to help us get a sense of what’s going on.

Wofford: I’m guessing this is probably common across all industries at this point.

Anderson: Yes, because now you can review anything. And there’s hardly any business that doesn’t have some sort of online chat service where consumers are typing information. So it’s about trying to look at what questions they’re asking, what problems they’re having with your product and then asking yourself how you can use that data to improve the product.

There’s just so much unstructured text today so we’re trying to look for ways to streamline how we extract insight because we don’t have infinite time to read it. Most of the tools for analyzing text are pretty standardized and most of the algorithms that we can use have been well developed. We’re ten-plus years into things like sentiment analysis so it’s not like we have to reinvent the wheel. There are a lot of off-the-shelf approaches.

Wofford: I’d like to turn to a question from the audience. Peter, who identifies himself as a “non-analytics person” posed this question: “In terms of decisions, I sometimes hear, ‘The numbers don’t support that.’ But it’s often on content that I know has not been marketed. So it seems the decision may be made on numbers that are correct, but that the decision comes from a faulty premise. Is this something you see often?”

Anderson: One of the classic things that I see is that organizations think price is going to impact demand, and they think they are changing prices but what they’re really doing is moving prices seasonally. And when things move together, you can’t really tell the impact of the season versus the price, because those are both adjusting together.
So one of the things we see in that data is that we may not have created the right kind of variance in order to see the outcomes.

Most of us don’t experiment with our business on a regular basis but in order to get insight from data, we have perturb those inputs. It’s just like the science experiments with two petri dishes, where you pour bleach on one and not on the other one to see what kind of bugs grow.

We have to have that experimental mindset when generating this data, because if we’re not making those little perturbations to our business practices, then it’s very hard for us to see how A leads to B because we’ve never manipulated A. Or we’ve only manipulated A at the same time we’ve manipulated B, C and D. If I always drop prices and spend more on marketing together, it’s hard for me to unravel which of those was the driving factor. Our data will not tell us that unless we’re cognizant from the business standpoint of having manipulated those things in such a fashion to generate that variance.

Wofford: So to glean real insight, you’ve got to be willing to take risks?

Anderson: Right. Be like a scientist and do some experimenting. You know, the online world has dramatically changed because of what we call A/B testing. Now it’s so easy to tweak something, so we can do all of these little A/B experiments. It’s very easy to create variances and see the outcome.

Wofford: So in some ways, you describe this as a linear process, but at the same time, it’s not. It’s iterative.

Anderson: It is. One minute to the next. The goal of predictive analysis is to look for robust insight into the future. And that is where, for me, the bottom up approach is critical. Yes, we’re trying to understand your business model but nothing is constant. There could be a new competitor, underlying changes in dynamics or some sort of disruption happening. In order to be robust to those changes, the models that we build from the predictive framework have to be grounded in our business practices.

And that comes from this bottom-up approach, versus just letting the data tell us what’s going on. For me, as a data analyst, it’s always about thinking about my two minute elevator pitch. How do I justify my models and can I clearly explain those models in layman’s terms? If I need to use statistical terminology to explain my insight and my models, that is going to tell me that I’m not necessarily grounded, that I’m relying on the data versus relying on my intuition.

It’s some give and take. You have to go back and forth, but the more bottom-up you are, the easier it is for you to justify models and to communicate those models to other people.

Wofford: I want to thank Chris Anderson for joining us today.

Anderson: Thank you, Chris, this was great.

 

Want to hear more? This interview is based on Chris Anderson’s live eCornell WebSeries event, A Bottom Up Approach to Data-Driven Analytics and Why We All Need to Be Involved. Subscribe now to gain access to a recording of this event and other Hospitality topics. 

Here’s How to Make Innovation Real

There’s a wealth of information on innovation out there, but how can you turn all the theories into action?

Professor Neil Tarallo, a senior lecturer at Cornell’s Hotel School, focuses on how to foster innovation within the workplace in his most recent discussion with eCornell’s Chris Wofford.

Wofford: Neil, it’s great to have you back here with us. What do your students and our webinar viewers need to know about innovation?

Tarallo: My favorite definition of innovation—and believe me, there are many of them out there—was articulated by Peter Drucker, a great researcher out of Harvard who really had a great take on how and where innovation and entrepreneurship come together.

Drucker said, “Innovation is change that creates a new dimension of performance.” I really like that concept of a new dimension. It provides us with opportunities to apply things in a very different way than they’ve ever been applied before.

Innovation really comes in two different levels. One is incremental, or what some people call “sustainable innovation.” And then there is disruptive innovation.

Wofford: Can you walk us through what you mean by those?

Tarallo: Well, an incremental innovation is really a small improvement or upgrade to an existing concept. It can be technology, a product or service. Think of software upgrades that fix minor things or add new features. In the physical world, think of Gillette. They started with a single razor. Over time, Gillette has innovated and created a lot of different things. They went to disposable razors, they have multi-angle blades, they have lubrication bars. So Gillette is an interesting example of how incremental innovation has allowed a company to sustain market leadership for a very long time.

When it comes to disruptive innovation, we’re talking about innovation that changes the way people think about things. Clay Christensen coined the term to describe a process by which a product or service takes root initially in a very small and unnoticed way but gathers momentum as it goes to the market and then people start to really grab onto it.

One of the interesting things about disruptive innovations is that we don’t know if disruption is happening before it actually starts to happen. An example of this, to stay within the shaving realm, is Dollar Shave Club, which has taken us away from the expensive replacement blades by saying they’ll just send you new blades every month for just a couple of dollars.

Wofford: Right, for them it is a subscription-based product and a recurring revenue model for the company. That’s kind of how they get you.

Tarallo: Exactly, and that disrupts the old business model that Gillette created, which was to give you the razor handle and then sell the blades at very high prices.

The important thing to remember is that both incremental and disruptive innovations are necessary for organizations. In my opinion, you need to have a plan for both the incremental improvements that you’ll be doing as well as constantly searching for that disruption that’s going to completely change things.

These two innovations generally focus around three areas: technical innovation, product innovation, and service innovation. A great example of service innovation is how Netflix changed the movie rental business.

But in order for innovation to be successful, you have to create an opportunity within the organization for entrepreneurial behavior to really manifest itself and to take hold. You need an organizational architecture that fosters and supports entrepreneurial behaviors so that you can be chasing after these innovations as you go along.

Wofford: How does a business go about establishing that?

Tarallo: There are two components that I think of when I try to action innovation within an organization. One is what I like to think of as “opportunity discovery” and the second is value proposition design.

When it comes to opportunity discovery, there is a clear methodology that applies. It starts with observation of the market and of potential customers, getting them to talk about their experiences—doing interviews with them to gather information, applying surveys and focus groups and the science behind those. It’s observing people and how they behave as they go throughout their days.

This process is actually called ethnography, which is sort of a fancy word, but it’s really a simple process of just watching people and taking note of what they’re doing and what they’re experiencing as they go through their daily lives. We’re looking for things that are missing for them as they try to accomplish tasks. We’re looking at problems that they encounter that need to be overcome and we’re also looking for pain that they’re experiencing along the way.

In those observations, I’m thinking about three specific behaviors: the functionality of what it is that they’re trying to apply, the social implications of what they’re doing, and also any emotional implications.

Wofford: We have some open-ended questions here that I think it would be good to get the audience to think about. When was the last time that you spoke with your customers regarding their experience with your product, service, or technology? If you would put your answers in the chat window please.

Tarallo: Look at that, someone wrote “just yesterday.” I like that, that’s great. Someone else replied “every single day.” That’s a lot of work so I would be curious how that actually works. Is that through a survey, and what do you do with that information when you get it? That’s really the big thing.

Wofford: That brings me to the natural follow-up question. What do you do with this feedback?

Tarallo: For me, every time a student comes into my office to talk to me about anything, I get information from them for some research that I’m doing about entrepreneurship and how we teach entrepreneurship. I’ll ask a very simple open-ended question, which is “What has it been like for you registering and taking entrepreneurship classes here at Cornell?” And when that student leaves, I have a little red book on my desk and I’ll open it up, put the date and their name and take notes on what they told me. So it’s good to pose these really open-ended questions to get your respondents to tell you stories.

Storytelling is really one of the most powerful tools in this process. When you can get people talking you through their experience, you really learn things you wouldn’t have anticipated.

Obviously an important part of that is to listen carefully, but you also need to look for those nonverbal cues as well as you go along. From there, you can start to generate a hypothesis about what is going on in the marketplace or what is happening within the context of your interest.

Wofford: And what’s the best way to get people to share their stories with you?

Tarallo: It can be through singular and/or multiple engagements, meaning I can sit down with you for an extended period of time and have a longer interview, or we can do multiple engagements where I’m talking to you for 10 to 15 minutes over a long period of time. Both of those are powerful applications of the interview process.

Surveys are another step. Here we start to move away from the qualitative aspects of our research and we start moving more into the quantitative. Surveys primarily help validate hypotheses by reaching a very large sample size. You can also use surveys at the very beginning of your process simply to identify the people that you want to be speaking with to make sure that you have a diverse population in your sample size. You’ll be looking for demographic information like race, geographic location, household income, age, and so on to ensure that you get the right population to ask. Otherwise, your validation is not going to be accurate and you’ll have problems going forward.

Focus groups are another popular thing but I personally stay away from them because there is a real science to focus groups. To run a focus group well, I think you need to be trained in it or you need to hire people to do it for you. And they’re expensive, so when I’m in startup mode, focus groups are generally not something I’ll incorporate.

Wofford: Whichever method you choose, you need to then do something with the information you gather. What do you do?

Tarallo: When we analyze the data, there are a handful of things we do: code the information that we have, look for patterns or anomalies in the information, and then start to generalize and create a narrative around it.

We want to track our assumptions and any biases that we think were brought in and then we want to validate it in the market through a series of controlled experiments and prototyping. We know that these experiments are often designed to fail, but they’ll teach us something that will help us move forward again and take that next step.

I’m a big fan of controlled experiments that are likely to fail, but when they fail, they’re not going to impact our market or people’s perception of our company. I’m not a proponent of the philosophy these days of going out and failing big and failing fast. Failure is just not a good thing, I’m sorry. Controlling it makes a lot more sense, as I can learn more when I do that.

This, in my opinion, is particularly true in the service industries. Service-based businesses are much harder to innovate in and much harder to build business models around. It’s relatively easy to hand somebody a tangible product or put a technology in front of them and have them tell you what’s wrong with it and then you can do an update or a new iteration. But if you’re trying something new out in your restaurant and it results in bad service, most of the affected customers won’t come back to your restaurant a second time. So we want to be very careful. It’s about testing. It’s about failing in a controlled way. It’s about repeating that process until we have what we need as we go forward.

Wofford: Can you tie all of this together for us?

Tarallo: As I’m going through the innovation process, I’m thinking about and utilizing all of the information that I got through the ethnographic research that I’ve done. Through that process of observation, storytelling, interviewing, surveying, and validation, I’m bringing those components in and placing them on the value proposition canvas, which is a powerful tool that presents a graphic illustration of what I’m experiencing and what I’m seeing in the marketplace. I use Post-it notes so that I can take them down and move them as things change, so it’s a very dynamic tool and really lets us get to where we want to go.

My advice to folks is always that if you find yourself sitting at a computer doing research, you’re doing it wrong. It’s all about really interfacing with the marketplace. All your hypotheses need to be validated in the market. We need to be doing those small controlled experiments that help us validate things without damaging our relationship with our customers.

Wofford: Neil, thank you so much for joining us once again.

Tarallo: Thank you, Chris. And just remember, it’s all about getting out there and trying it. You probably won’t succeed the first time you do it, but don’t be dissuaded. Get out and practice. It’s the application of it that you get better and better at.

 

Want to hear more? This interview is based on Neil Tarallo’s live eCornell WebSeries event, A Fresh Look at Innovation. Subscribe now to gain access to a recording of this event and other Entrepreneurship topics. 

Building Digital Brands with Analog Products

Here’s How Today’s Hottest New Companies Actually Disrupt

Micah Rosenbloom, managing partner at Founder Collective in San Francisco, knows a thing or two about bringing digital ideas to the analog world. The Cornell grad started the 3D scanning company Brontes, which introduced a handheld scanner to replace the conventional dental impressions used for centuries.

His company was purchased by 3M, which he says “put enough money in my pockets to start angel investing.” At Founder Collective, he and his partners built a fund to invest in the next generation of great businesses at the seed stage. While the company originally focused on technology companies like Uber, BuzzFeed and Hotel Tonight, Rosenbloom quickly became attracted to brands that were building digital success while selling old-school analog products like mattresses, jewelry and eyeglasses.

Rosenbloom joined eCornell’s Chris Wofford as part of our Entrepreneurship WebCast Series. What follows is an abridged version of his presentation.

Casper. Dollar Shave Club. Warby Parker. These days, some of the fastest-growing companies aren’t what you think of, traditionally, as tech businesses.

These companies are what Andy Dunn, the CEO of Bonobos, has dubbed ‘DNVBs”—Digitally Native Vertical Brands.” In other words, brands that have a direct relationship with their customers. For example, if you were to buy a mattress from Sealy in the past, Sealy didn’t know that you yourself were the buyer of their mattress. All the company knew was that it was selling mattresses to Sleepy’s Mattress Store, and Sleepy’s was the one that actually had the knowledge of the buyer. What most businesses come to realize is that whoever knows the end customer has the most value. In the end, the customer information and that relationship is the most valuable.

Many new companies have decided to go directly to the customer rather than through retailers. Some may not even go through Amazon. There are exceptions to this, of course, but the idea is that most of these brands are primarily online.

Most of these “DNVB” brands were born in the last ten years and that newness has allowed them to be built  on modern systems. They’re not saddled with legacy software. They’re not bogged down by crazy inventory systems that have to talk to Target and Walmart. They’re building on web-based technologies that give them a way of speeding up the process in the way they communicate with customers or offer promotions.

That’s just too hard for the incumbents—they simply couldn’t build a business this way.  So that’s why you are seeing a lot of the big players starting to buy these new guys up.

That’s the D—digital—in DNVB. Now let’s talk about the V: the vertical specific. One of the things you learn in the startup world is focus, focus, focus. While conventional wisdom has always been that it is better to have a bigger menu and the more options the better, what some of these new hot brands have realized is that people are overwhelmed with information. It’s actually very difficult to pick which glasses, which mattress, which this and which that. We want people to help us pick it. So what we’re seeing is a realization of the benefits of just having one or maybe a few products.

Price is also really important here. The best Digital Native Vertical Brands are a fifth to a tenth of the cost of the traditional ones. Dollar Shave Club is probably the perfect example of this. Just as the name suggests, they came in and said, “It’s a dollar.” There are those who say that the blades at Dollar Shave Club are not as good as Gillette blades, but for the lion’s share of the customer base, being able to save that much money on the product and still get a good enough product is a really strong value proposition.

So when you’re thinking about these businesses, both as an investor and as a builder, the goal is to be able to charge five to ten times less than a similar equivalent by pulling out of physical retail, by focusing on a few products and by not paying a margin to a distributor.

Some DNVB companies have also managed to disrupt the traditional buying practices. I think most consumers kind of hate the experience of buying a mattress and even to an extent, buying things like glasses or luggage. You just feel like it was a lot of B.S., a lot of marketing. One of the interesting things about Casper is they have a 100 night trial. After a hundred nights or fewer, if you don’t like it, they’ll take it back for a full refund. Changing the way people think about buying things—taking out the risk, the anxiety, the dealing with that sleazy salesperson—really changes the game and that’s why you’re seeing the growth of a lot of these brands.

I feel like I’m doing a commercial for Casper, but it is also a good example of how these types of businesses focus on design and packaging. If you search on YouTube, you can find all of these videos of people unboxing their mattresses and they spring out because they are so highly compressed into this box. If you think about it, it’s a real innovation. In the past, if you were buying a mattress, not only would you have to buy it from a sleazy dude but usually a couple other sleazy guys would come into your home, maybe take their shoes off if you’re lucky, go into your bedroom, take out the old mattress, and put in the new one. The reason they did this is that mattresses just couldn’t ship via UPS. Now all of a sudden, you’ve got a brand that uses traditional delivery services, and that changes the way one can get a mattress.

A lot of the innovation also centers around packaging. We’re involved with a company called PillPack, which is a pharmacy that fills all your prescriptions. Instead of those little brown bottles with the white tops that are hard to open, they put the pills in this beautifully designed sealed envelope that will have your name and the day of the week. The pack will say ‘Thursday’ and then you rip it open and it’s everything you should take on Thursday. It’s a great experience but it’s not a different product. These are the same pills you would take if you went to CVS or Walgreens, but it’s the packaging and the design.

The other thing these great brands share is having a voice. They have a unique edge, a unique perspective, a unique way of talking to their customers. Casper’s branding, for example, uses a lot of animation. The Warby Parker branding uses a lot of cool literary names to market the glasses. The Honest Company has the actress Jessica Alba as the spokesperson and face behind it and even that word, Honest, suggests something very specific about the company’s voice, which is, “You’re going to know the ingredients, you’re going to trust us, and this is going to be good for you.”

That voice has to be very inherent to the owners of the brand, it can’t be artificial. I think that’s what sets these modern companies apart from the traditional guys. When you see a Gillette  commercial you kind of think, “Yeah, it was entertaining, but there was probably an agency, and there was a committee, and there were edits.” Then you watch the Dollar Shave Club video with the the dude who says, “Our blades are f’ing great,” and you think, “ I like that guy.” There’s something really authentic about it and it makes you want to buy them.

We’re starting to see the fruits of these new approaches and I think it’s just the beginning. You see Unilever buying Dollar Shave Club for a billion dollars, you see Unilever also in talks to buy Honest Company, and I suspect it’s only a matter of time before we start seeing some of the other brands, like Warby and Casper and these others, potentially get acquired or maybe even go public.

That’s sort of the next chapter, but as they say, it’s the early days. Having said everything I’ve  said about these brands, I think you have to be careful. It’s not as easy as simply saying you’re going to come up with the best new earbuds or shirts or cell phone cases. Most of these categories have been historically extremely hard to break into, and there are many, many companies out there.

Product companies, unlike traditional technology companies, require building stuff, making stuff, slow turnarounds, and taking inventory. It is not just bits and bytes like software, where you can write some more code, see how it goes, release it into the wild, and fix it tomorrow. You can’t do that with glasses or mattresses or shampoo.

I think one needs to do a lot of homework before thinking about launching their own brand, or investing in one. So here’s some of the cold water. One, the multiples in these industries are low. Historically, consumer goods have traded at one to 3x sales, not five to 10x like software and technology. Second, you’ll quickly find that you have a lot of competitors. If you Google “buy a mattress online,” you’ll see Casper was first but is now hardly alone. So new companies will find that others will quickly copy them and undercut them on pricing, so that’s a challenge. Third, the brand message is hard to get right. You really have to have that voice and it’s got to be authentic. Some brands do that well and some don’t.

Finally, this idea of creating a hipster brand for everything that’s going to cost less is just not that simple. There are structural differences between all these industries. You’ve got to really look at the industry structure and see if you think there are some soft spots.

Let’s take glasses for example. If you Google Luxottica you’ll discover it owns almost every other brand in glasses you could ever imagine. But I think what what is nice about monopolies from an entrepreneur’s standpoint is that typically one company that owns a lot of the market will price a little bit too high because it’s had pricing power, so if you can get in there with a new product you can really disrupt practices that have been around for a long time.

 

Want to hear more? This article is based on Micah Rosenbloom’s live eCornell WebSeries event, Building Digital Brands in Analog Product Categories. Subscribe now to gain access to a recording of this event and other Entrepreneurship topics. 

eCornell’s New Data Analytics Certificate Equips Professionals to Translate Big Data into Actionable Business Insights

— Program is essential step in data science career, ranked best job in America for 2017 —

Data scientists and data analysts are hot commodities; they were ranked the #1 job in America for 2017 by Glassdoor and named the sexiest job of the 21st century by Harvard Business Review. Demand for these roles—and their intersecting skills in business, statistics, and programming—is driven by organizations swimming in data but hamstrung by a shortage of employees with the critical mindset needed to translate it into meaningful decisions. Yet educational institutions lag in preparing students for these jobs. To close the gap, Cornell University is now offering professionals the opportunity to earn an executive certificate in Data Analytics so they can build core fluency in data analysis and a foundation for further technical study.

“Data analysis requires professionals to be informed consumers of data. Technical knowledge is necessary, but it’s actually even more valuable to know which questions to ask, how to ask them, test them, and translate them into business intelligence. Done well, data analysis provides a valid narrative business leaders can follow to make more successful strategic decisions,” said Chris Anderson, Ph.D., the certificate’s faculty author from Cornell University.

The Data Analytics certificate consists of three intensive courses that provide professionals with an essential understanding of how and why data is used to create value in business: Understanding and Visualizing DataImplementing Scientific Decision-Making, and Using Predictive Data Analysis. Each three-week course builds the analytical mindset, starting with what data is, and moving into how to visualize data and build predictive models and reporting. Students strengthen their ability to connect data to decisions—learning how to make inferences about data samples and analyze relationships across data to predict future outcomes, with the option to use datasets from their own companies.

Courses offer step-by-step “How Tos” for all statistical processes and teach universal Excel-based analysis tools. From data visualization to predictive analytics, Professor Anderson combines accessible terminology with his wide-ranging experience in management science and statistics to teach skills that translate across software platforms.

The Data Analytics certificate is a critical credential for today’s professionals across many industries, complementing several eCornell certificate programs in marketing, leadership, revenue management, and human resources. For students new to statistics, courses expose them to the fundamentals and remove barriers to getting started. Professionals with deeper statistical knowledge will learn to ground data in the language of business decisions, and current data analysts will enhance their ability to communicate with key audiences and make meaning out of data. Senior executives will also become more critical consumers of data, and better able to guide and manage analysts productively.

Students who complete the program receive an Executive Certificate from Cornell University and will earn 0.6 Professional Continuing Education Units (CEUs) for each course completed.

 

About eCornell
As Cornell University’s online learning unit, eCornell delivers online professional certificate courses to individuals and organizations around the world. Courses are personally developed by Cornell faculty with expertise in a wide range of topics, including hospitality, management, marketing, human resources and leadership.  Students learn in an interactive, small cohort format to gain skills they can immediately apply in their organizations, ultimately earning a professional certificate from Cornell University. eCornell has offered online learning courses and certificate programs for 15 years to over 130,000 students at more than 2,000 companies.

Why Branding is Dead, and Why Mindset Is Your Only Hope In the Future

There was more content created online in the last two years than was created in all of the prior 2000 years. Every conversation, tweet, and piece of content is a part of your brand image, and impacts a prospect’s experience with your brand.

The future success of your brand relies on you being able to provide and manage a positive experience across over 60+ marketing channels, 24 hours a day. Simple branding no longer works; the only sustainable way to consistently provide a positive experience to your prospects is by understanding Mindset.

Salesforce.com’s Mathew Sweezey explains why the modern digital landscape has killed the traditional concept of branding, and why Mindset is your only hope for building a consistent brand in the future.

Mathew Sweezey is the Head of B2B Marketing Thought Leadership for Salesforce.com. A consummate writer, he authors a column for Clickz.com on marketing automation, has been featured in publications such as Marketing Automation Times, DemandGen Report, Marketing Sherpa, ZDNet, and is the author of Marketing Automation for Dummies. Mathew speaks more than 50 times per year around the world at events such as Conversion Conference, Dreamforce, SugarCon, and to companies including Microsoft, Investec, NetJets, and Restaurants.com, to name a few.