Superstar status remains contestable, it’s easy to fall from the top, and possible to rise even from bottom all the way to the top, write James Manyika, Sree Ramaswamy, and Michael Birshan in Harvard Business Review.
Interestingly, we can pair this with the forecast presented in the World Economic Forum's The Future of Jobs Report. According to this report, specialized sales jobs will be "critically important" by 2020. Sales reps will need to acquire "people skills" -- in particular, persuasiveness, teaching techniques, and the ability to explain offerings and their value to individual clients. In addition, more than a third of what will be core skills are currently not considered job-crucial.
In other words, while tech skills (like data science) have been getting all the attention, there’s going to be a strong need for people with other skills – especially in sales. So what skills should sales reps be adding to their repertoire? What will the rep’s role be in an AI-enabled world?
It will be being human, engaging with their customers on a human level and bringing their experience into play. Here are four important sales skills all reps will need in the future.
Creativity and its closely allied trait of flexibility are part of what set today’s top-performing salespeople apart. Creative reps are resourceful in problem-solving and imaginative in how they approach customers and pitch products. These qualities are still widely considered the exclusive domain of the human brain; whether machines will ever catch up is in doubt. Reps who can take data delivered by their AI system and apply their own imagination to come up with out-of-the-box solutions will always be welcome in the sales team.
AI can show us the stages of a buyer’s journey and how they get there; it can even tell us what might be motivating their interest. But it can’t deeply understand and empathize with the buyers themselves. This is where reps come in. They have to take the time to learn about their customers: their unique needs, their experiences and their viewpoint. They should approach collateral (such as brochures and websites) from a customer’s standpoint, and they should understand how it feels to go along that journey from interest to purchase to relationship.
AI might be improving its game when it comes to analyzing sentiment, but it’s nothing compared to how humans understand each other. Humans can get subtext and detect humor or sarcasm without conscious thought; AI isn’t there yet. It can only be programmed to analyze conversations. When you consider that experts say 80 to 90 percent of all communication is nonverbal, this really highlights the need for strong social skills in a sales situation.
Humans are hardwired to connect with other humans. This fact drives everything from massive social networks to small-scale interactions. It also influences how people pick their favorite brands — they tend to be loyal to those brands that reinforce their own self-image, values and aspirations. While most of us are OK with having Alexa or Siri recommend the next song to add to our playlist, we’re less likely to let them guide the purchase of, say, our next car. Once again, we have an opportunity for a powerful partnership here AI can give the sales rep a curated selection of solutions or information, but the rep is the one to establish trust and a personal connection with the buyer.
Where Does AI Fit into the Equation?
AI is here to stay — and so are humans. While companies rush to jump on the AI train, it’s imperative that they remember that humans will always be their first and foremost asset and their real audience. Organizations do need to keep up with the times, and those times do include AI. But they can’t exclude the human element.
This is where sales leaders and company leadership need to step up, providing the training (and the motivation) for teams to play well with their AI “teammate." In the human-AI partnership, each half has a unique skill set. AI can crunch numbers and find patterns with a tireless accuracy that leaves humans far behind. Humans have imagination, intuition, and people-reading abilities that machines may not ever reach. Together, that’s quite a team.
Products fueled by data and machine learning can be a powerful way to solve users’ needs. They can also create a “data moat” that can help stave off the competition. Classic examples include Google search and Amazon product recommendations, both of which improve as more users engage. But the opportunity extends far beyond the tech giants: companies of a range of sizes and across sectors are investing in their own data-powered products. At Coursera, we use machine learning to help learners find the best content to reach their learning goals, and to ensure they have the support — automated and human — that they need to succeed.
The lifecycle of a so-called “data product” mirrors standard product development: identifying the opportunity to solve a core user need, building an initial version, and then evaluating its impact and iterating. But the data component adds an extra layer of complexity. To tackle the challenge, companies should emphasize cross-functional collaboration, evaluate and prioritize data product opportunities with an eye to the long-term, and start simple.
Stage 1: Identify the opportunity
Data products are a team sport
Identifying the best data-product opportunities demands marrying the product-and-business perspective with the tech-and-data perspective. Product managers, user researchers, and business leaders traditionally have the strong intuition and domain expertise to identify key unsolved user and business needs. Meanwhile, data scientists and engineers have a keen eye for identifying feasible data-powered solutions and a strong intuition on what can be scaled and how.
To get the right data product opportunities identified and prioritized, bring these two sides of the table together. A few norms can help:
Educate data scientists about the user and business needs. Keeping data scientists in close alignment with product managers, user researchers, and business leads, and ensuring that part of their role is to dig in on the data directly to understand users and their needs will help.
Have data scientists serve as data evangelists, socializing data opportunities with the broader organization. This can range from providing the organization with easy access to raw data and model output samples in the early ideation stages, to building full prototypes in the later stages.
Develop the data-savvy of product and business groups. Individuals across a range of functions and industries are upskilling in data, and employers can accelerate the trend by investing in learning programs. The higher the data literacy of the product and business functions, the better able they’ll be to collaborate with the data science and tech teams.
Give data science a seat at the table. Data science can live different places in the organization (e.g., centralized or decentralized), but no matter the structure having data science leaders in the room for product and business strategy discussions will accelerate data product development.
Prioritize with an eye to the future
The best data products get better with age, like a fine wine. This is true for two reasons:
First, data product applications generally accelerate data collection which in turn improves the application. Consider a recommendations product powered by users’ self-reported profile data. With limited profile data today, the initial (or “cold start”) recommendations may be uninspiring. But if users are more willing to fill in a profile when it’s used to personalize their experience, launching recommendations will accelerate profile collection, improving the recommendations over time.
Second, many data products can be built out to power multiple applications. This isn’t just about spreading costly R&D across different use-cases; it’s about building network effects through shared data. If the data produced by each application feeds back to the underlying data foundations, this improves the applications, which in turn drives more utilization and thus data collection, and the virtuous cycle continues. Coursera’s Skills Graph is one example. A series of algorithms that map a robust library of skills to content, careers, and learners, the graph powers a range of discovery-related applications on the site, many of which generate training data that strengthen the graph and in turn improve its applications.
Too much focus on near-term performance can yield underinvestment in promising medium- or long-term opportunities. More generally, the criticality of high-quality data cannot be overstated; investments in collecting and storing data should be prioritized at every stage.
Stage 2: Build the product
De-risk by staging execution
Data products generally require validation both of whether the algorithm works, and of whether users like it. As a result, builders of data products face an inherent tension between how much to invest in the R&D upfront and how quickly to get the application out to validate that it solves a core need.
Teams that over-invest in technical validation before validating product-market fit risk wasted R&D efforts pointed at the wrong problem or solution. Conversely, teams that over-invest in validating user demand without sufficient R&D can end up presenting users with an underpowered prototype, and so risk a false negative. Teams on this end of the spectrum may release an MVP powered by a weak model; if users don’t respond well, it may be that with stronger R&D powering the application the result would have been different.
While there’s no silver bullet for simultaneously validating the tech and the product-market fit, staged execution can help. Starting simple will accelerate both testing and the collection of valuable data. In building out our Skills Graph, for example, we initially launched skills-based search — an application that required only a small subset of the graph, and that generated a wealth of additional training data. A series of MVP approaches can also reduce time to testing:
Lightweight models are generally faster to ship and have the added benefit of being easier to explain, debug, and build upon over time. While deep learning can be powerful (and certainly is trending) in most cases it’s not the place to start.
External data sources, whether open source or buy/partner solutions, can accelerate development. If and when there’s a strong signal from the data the product generates, the product can be adapted to rely on that competitive differentiator.
Narrowing the domain can reduce the scope of the algorithmic challenge to start. For example, some applications can initially be built and launched only for a subset of users or use-cases.
Hand-curation — where humans either do the work you eventually hope the model will do, or at least review and tweak the initial model’s output — can further accelerate development. This is ideally done with an eye to how the hand-curation steps could be automated over time to scale up the product.
Stage 3: Evaluate and iterate
Consider future potential when evaluating data product performance.
Evaluating results after a launch to make a go or no-go decision for a data product is not as straightforward as for a simple UI tweak. That’s because the data product may improve substantially as you collect more data, and because foundational data products may enable much more functionality over time. Before canning a data product that does not look like an obvious win, ask your data scientists to quantify answers to a few important questions. For example, at what rate is the product improving organically from data collection? How much low-hanging fruit is there for algorithmic improvements? What kinds of applications will this unlock in the future? Depending on the answers to these questions, a product with uninspiring metrics today might deserve to be preserved.
Speed of iteration matters.
Data products often need iteration on both the algorithms and the UI. The challenges is to determine where the highest-value iterations will come from, based on data and user feedback, so teams know which functions are on the hook for driving improvements. Where algorithmic iterations will be central — as they generally are in complex recommendation or communication systems like Coursera’s personalized learning interventions — consider designing the system so that data scientists can independently deploy and test new models in production.
By fostering collaboration between product and business leaders and data scientists, prioritizing investments with an eye to the future, and starting simple, companies of all shapes and sizes can accelerate their development of powerful data products that solve core user needs, fuel the business, and create lasting competitive advantage.
Most leaders are, deep down, afraid of failure. But our 10-year CEO Genome study of over 2,600 leaders showed almost half (45%) suffered at least one major career blow-up — like getting fired, messing up a major deal, or blowing an acquisition. Despite that, 78% of these executives eventually made it to the CEO role.
We conducted additional research on 360 executives, analyzing their careers in depth. While all of them experienced a variety of setbacks, 18% of executives in this dataset faced what many view as the very worst-case scenario: getting fired or laid off. Most of them lost their job at a relatively senior point in their career (only 17% were in their first decade in the workforce at the time they were let go).
What we found is that being fired or laid off doesn’t necessarily have catastrophic effects on leaders’ prospects. We also found that leaders can do some specific things to make sure that a major setback doesn’t become a career-killer.
The good news: 68% of executives who had been let go landed in a new job within six months. An additional 24% had a new job by the end of one year. Even better? 91% of executives who had been fired took a job of similar or even greater levels of seniority.
We even found some signs that the experience of losing a job — when handled the right way — might even make one a stronger candidate for future roles. In our study, when the interview process included expert third-party assessors engaged by employers to prevent hiring mistakes, 33% of executives who had been previously fired were recommended for hire — compared to 27% of candidates who had never been fired. Experienced hiring managers know that setbacks are inevitable and want to see how individuals have handled failure in the past. The riskiest hires are the ones who are untested by failure. Executives who have faced failure and learned from it can demonstrate resilience, adaptability, and self-awareness prized in leaders.
About the Research
This article is based on research conducted over 10 years in support of our 2018 book The CEO Next Door. ghSMART has assembled a data set of assessments of over 18,000 C-suite executives across all major industry sectors and company sizes. Each executive assessment includes detailed career and educational histories; performance appraisals; and information on patterns of behavior, decisions, and business results. This data was gathered through structured 4-5 hour interviews with every executive.
That said, executives who had been let go were also more likely to receive a strong “do not hire” recommendation than those who were never fired (46% vs. 36%), indicating that the reason why someone was removed from a role and the way in which they processed that experience did impact their future career potential.
Leaders whose careers soared — not sank — after this setback, did three things differently:
Looked facts in the face… without shame. Those who deflect ownership and instead point to external factors or blame others for failures on their watch don’t do as well. Our data shows that candidates who blamed others cut their chances of being recommended for hire by one-third. Strong performers own their mistakes, and describe what they learned and how they adjusted their behavior and decision making to minimize the chances of making the same mistakes in the future. Having several different types of career blow ups does not derail you. Repeating the same blowup over and over does.
While they own their mistakes, they do so without guilt or shame. Executives who saw their mistakes as failures were 50% less successful than those who took a more learning/growth-oriented approach.
Taking ownership without shame enabled these executives to show themselves as likeable and confident in the interview process for the next role — qualities proven to increase chances of getting the job. Analysis of ghSMART assessments by Kaplan and Sorensen showed that the more likable leaders had higher odds of getting hired for any leadership position. Our research with SAS found that highly confident candidates were 2.5 times more likely to be hired.
Leaned on their professional network to get the next job: Candidates were twice as likely to find a job through a professional network than via recruiters or personal network (59% vs. 28%). While friends may be eager to help and lend their sympathetic ear, ultimately the most powerful support comes from those who have seen the results you can deliver based on their direct working experience with you. Search firms have a wide exposure to available positions but typically play it safe and may be reluctant to put their credibility on the line with their client by presenting a candidate who had been fired before. Proactively reaching out to former bosses, colleagues, customers, or peers for whom you have delivered before proves more fruitful than golfing with friends from university or blasting your CV to the recruiting world — although those most eager do all three.
Relied on their experience: 94% of those who landed a new job within 6 months had prior experience in that industry. Hence, one would be well advised to get experience across 2-3 industries early in one’s career, so that if one gets fired, there are multiple industries to rebound into rather than being pigeonholed.
The most important advice both for those looking to rebound and to prevent getting fired in the first place: Pick jobs in the “bull’s eye” of your skills and motivations.
We hope this offers some hopeful news both to people who’ve been let go, and to managers who are in the position of needing to let someone go. One third of the leaders in our CEO Genome study took too long to make people changes — often with damaging consequences for themselves, their teams, and the executive who is poorly fit to the job. If you are agonizing over the need to move someone out of your team, worried about destroying their career, hopefully this research helps you make the right decision for the wellbeing of your whole team and gives you the tools to support the person you are moving out to help them land in the right next opportunity.
We also hope this is useful research for everyone suffering from the fear of failure. While mistakes and career setbacks are painful, a much bigger mistake, according to our data is not taking risks. When we analyzed careers of executives who got to the top faster than average, what set them apart was taking risks to take messy jobs or smaller jobs that nobody wanted or taking on big leaps that felt way over their head.
More than 20 years of advising and coaching leaders has shown us that when you try to achieve something meaningful, you’ll face blow-ups from time to time. What matters more, is that you address the failure as an opportunity for growth. It can be a real travesty when, by playing defense throughout their careers, so many of us miss a chance to grow to our full potential and to live more meaningful lives. In the words of Oliver Wendell Holmes “Many people die with their music still in them.”
As the wave of #MeToo stories have come to light over the past year, it’s become painfully clear that whatever organizations are doing to try to prevent sexual harassment isn’t working.
Ninety-eight percent of companies say they have sexual harassment policies. Many provide anti-sexual harassment training. Some perpetrators have been fired or fallen from grace. And yet more than four decades after the term “sexual harassment” was first coined, it remains a persistent and pervasive problem in virtually every sector and in every industry of the economy, our new Better Life Lab report finds. It wreaks financial, physical, and psychological damage, keeping women and other targets out of power or out of professions entirely. It also costs billions in lost productivity, wasted talent, public penalties, private settlements, and insurance costs.
So what does work? Or might?
Sadly, there’s very little evidence-based research on strategies to prevent or address sexual harassment. The best related research examines sexual assault on college campuses and in the military. That research shows that training bystanders how to recognize, intervene, and show empathy to targets of assault not only increases awareness and improves attitudes, but also encourages bystanders to disrupt assaults before they happen, and help survivors report and seek support after the fact.
Researchers and workplace experts are now exploring how to prevent sexual harassment in companies by translating that approach. The Equal Employment Opportunity Commission in its 2016 task force report encouraged employers to offer bystander training, for one. And New York City passed a law in May requiring all companies with more than 15 employees to begin providing bystander training by April 2019. It could prove a promising, long-term solution.
But culture change is hard — it can take anywhere from months to several years, experts say. It’s much easier to go for the annual, canned webinar training on sexual harassment that checks the legal-liability box. Yet culture change is exactly why bystander interventions could be powerful: the strategy recognizes that, when it comes to workplace culture, everyone is responsible for creating it, every day, in every interaction.
Jane Stapleton, co-director of the Prevention Innovations Research Center at the University of New Hampshire and an expert in bystander interventions, told me about an all-too-familiar scenario: Say there’s a lecherous guy in the office — someone who makes off-color jokes, watches porn at his cubicle, or hits on younger workers. Everyone knows who he is. But no one says anything. Co-workers may laugh uncomfortably at his jokes, or ignore them. Maybe they’ll warn a new employee to stay away from him. Maybe not. “Everybody’s watching, and nobody’s doing anything about it. So the message the perpetrator gets is, ‘My behavior is normal and natural,’” Stapleton said. “No one’s telling him, ‘I don’t think you should do that.’ Instead, they’re telling the new intern, ‘Don’t go into the copy room with him.’ It’s all about risk aversion — which we know through decades of research on rape prevention, does not stop perpetrators from perpetrating.”
When bystanders remain silent, and targets are the ones expected to shoulder responsibility for avoiding, fending off, or shrugging off offensive behavior, it normalizes sexual harassment and toxic or hostile work environments. So bystander intervention, which Stapleton and others are beginning to develop for workplaces, is designed to help everyone find their voice and give them tools to speak up.
It’s all about building a sense of community. “Bystander intervention is not about approaching women as victims or potential victims, or men as perpetrators, or potential perpetrators” she said. “Rather, it’s leveraging the people in the environment to set the tone for what’s acceptable and what’s not acceptable behavior.”
At the most fundamental level, bystander interventions could begin — long before an incident of harassment — with workers having non-threatening, informal conversations in unstressed moments about how to treat each other, how they can help each other do their jobs or make their days better, and practice giving positive feedback. Normalizing talking about behavior and defining respectful behaviors everyone agrees on may make it easier for coworkers to see and give negative feedback if a worker later crosses a line, Fran Sepler, who for 30 years has worked as a consultant, trainer, and investigator on workplace harassment prevention, told me in an interview. “So when a co-worker tells an offensive joke, it’s easier to say, ‘Remember how we talked, and we all agreed about what’s OK to say at work? That’s not it.’”
In testimony before the EEOC, Sepler suggested organizations create “feedback rich” environments, where middle managers are trained to respond to complaints and issues in an emotionally intelligent way, and where people feel comfortable speaking up and listening, no matter the issue.
In campus settings, bystanders are trained to recognize when a sexual assault may be imminent and intervene by, for instance, disrupting the environment — turning the lights on at a party, or turning the music off — defusing the situation, with humor perhaps, distracting or interrupting a potential perpetrator, drawing a potential target away, or drawing others in.
But disrupting sexual harassment in the workplace requires a very different set of tools. “Too often people let things slide, concerned that if they get involved, it might affect their own career aspirations,” Alberto Rodríguez. supervising attorney for the New York City Commission on Human Rights, told me.
Because careers and reputations can be on the line, Sepler suggests considering a matrix of questions before acting: “Can I have an impact? Is it safe? What is the best strategy given the culture of the organization and my level of influence?”
Bystanders in the workplace can defuse harassing or offensive language or situations with humor, she said, or verbal or nonverbal expressions of disapproval. They can interrupt a situation by changing the subject, or inserting themselves into the situation. “If it’s the first time you hear someone say something offensive, you might try humor as a way of getting their attention, making a caustic remark, or saying, ‘What year is this? 1970?’ as a way of getting their attention,” Sepler said. Even so, she cautioned that bystanders must weigh whether the colleague has the reputation for being a jerk. Another option bystanders could consider is having a conversation after the fact, when tensions have cooled, laying out why the behavior was offensive.
For a harassing boss or someone who holds power over your career or livelihood, where direct confrontation could be riskier, defusion, distraction, or interruption are still possible tools for bystanders in the moment. And after the fact, bystanders can also seek out a supervisor or influencer, make a report, or help a target make a report.
At a minimum, bystanders can always show support to targets, who often feel isolated, humiliated, diminished, and alone after a harassing incident. “Going to someone and saying, ‘I saw how they were treating you. I didn’t like it. Is there anything I can do to help?’ Or, ‘It’s not your fault, let’s go talk with human resources.’ That might be all you can do,” Sepler said. “That’s not nothing.”
Like many professionals, my job doesn’t require expertise in data or analytics. I’m a writer and editor, so I deal with words, not numbers. Still, nearly every knowledge worker today needs to be a regular consumer of data analysis. For example, I need to understand whether and why articles on having a mid-career crisis outperformed ones on receiving feedback or why pieces with particular headlines get more traffic than others.
I also need to be able to read research on the topics I cover and understand whether the findings in those studies are valid and generalizable, and be able to articulate the findings — and their limitations — to you, our readers.
To do all of this, I need a more-than-basic understanding of data analytics. And while the statistics course I took in graduate school was helpful, it didn’t fully equip me to grasp the important concepts and have the conversations I need to around data analysis.
Fortunately, I had the opportunity to talk with some of the best experts in the field — Tom Redman, author of Data Driven: Profiting from Your Most Important Business Asset, and Kaiser Fung, who founded the applied analytics program at Columbia University — about several critical topics when it comes to data analysis. Here are four refreshers from our archives on data analytics concepts that every manager should understand.
Randomized controlled experiments
One of the first steps in any analysis is data gathering. This often happens via a spectrum of experiments that companies do — from quick, informal surveys, to pilot studies, field experiments, and lab research. One of the more structured types is the randomized controlled experiment. Many people, when they hear this term, immediately think of costly clinical trials but randomized controlled experiments don’t have to be costly or time consuming and they can be used to gather data on things like whether a particular customer service intervention improved customer retention or whether a new, more expensive piece of equipment is more effective than a less costly one. In this refresher, Tom Redman helps me understand what it means for a test to be “controlled” and how you make sure it includes an element of “randomization.” The article also addresses questions like: What are dependent and independent variables? And what are the steps to designing and conducting one of these experiments?
One of the more common experiments companies use these days is the A/B test (which is a type of randomized controlled experiment). At their most basic, these tests are a way to compare two versions of something to figure out which performs better. Companies use it to answer questions like, “What is most likely to make people click? Or buy our product? Or register with our site?” A/B testing is used to evaluate everything from website design to online offers to headlines to product descriptions. It’s critical to understand how to interpret the results and to avoid common mistakes, like ending the experiment too soon before you have valid results or trying to look at a dashboard of metrics when you really should be focusing on a few. You can learn more about A/B tests here.
Once you have the data, regression analysis helps you make sense of it. Of course, there are many ways to analyze the data, but linear regression is one of the most important. It’s a way of mathematically sorting out whether there’s a relationship between two or more variables. For example, if you are in the business of selling umbrellas, you might want to know how many more items you sell on rainy days. Regression analysis can help you determine whether and how inches of rain impacts sales. It answers the questions: Which factors matter most? Which can we ignore? How do those factors interact with each other? And, perhaps most importantly, how certain are we about all of these factors?
Fortunately, regression is not something you typically do on your own. There are statistics programs for that! But it’s still important to understand the math behind it and the types of mistakes to avoid. In this refresher, I explain how regression works and share a common — but often misunderstood — warning against confusing correlation with causation.
Once you’ve done the analysis, you need to figure out what your results mean, if anything. This is where statistical significance comes in. This is a concept that is also often misunderstood and misused. And yet because more and more companies are relying on data to make critical business decisions, it’s an essential concept to understand. Statistical significance helps you quantify whether a result from an experiment is likely due to chance or from the factors you were measuring.
This is a concept I sometimes struggled to fully understand myself but, fortunately, the average professional doesn’t need to understand it too deeply. According to Tom Redman, who helped out with this refresher, it’s more important to understand how to not misuse it.
While you’re boning up on these four concepts, it would also be helpful to read this overview on quantitative analysis from my colleague, Walt Frick. It is a nice primer on why data matters, picking the right metrics, and asking the right questions from data. There’s also a great chart on correlation vs. causation to help you make decisions about when to act on analysis and when not to.
Lastly, if you’re interested in analytics because you need to consume social science research, I highly recommend this piece from Eva Vivalt, a research fellow and lecturer at the Australian National University. She gives several tips for determining whether the evidence from a study should be trusted.
Data analytics is ultimately about making good decisions. It doesn’t matter what business you are in or what your role is at your company, we all want to — need to, really — make smart, informed, evidence-based decisions.