Marina Gorbis's Blog, page 1358

How Philosophy Makes You a Better Leader

The goal of most executive coaching and leadership development is behavior change—help the individual identify and change the behaviors that are getting in the way of, and reinforce the behaviors associated with, effective leadership.  But what about the beliefs and values that drive behavior?

The benefits of introspection and reflection on one’s own character and beliefs receive less attention in a typical coaching session than the benefits of behavior change. Perhaps this is not surprising in our fast-paced and technology-driven business world, where there is little time to stop and think, and where people want (and are paying for) immediate outcomes.  Despite growing recognition of the benefits of “mindfulness” activities (such as yoga and meditation) and an introverted style, self-reflection on philosophical issues—such as values, character virtues, and wisdom—is relatively neglected.  Executive coaching and leadership development programs rarely include much, if anything, about the power of clarifying one’s philosophical world-view. But there is mounting evidence that they should.

Neuroscience research on self-reflection supports this notion. A recent study reported in BMC Neuroscience revealed that a critical brain region—the anterior cingulate cortex (ACC) —was activated during self-reflection tasks. The ACC is essential because, as the researchers noted, it can “detect discrepancies between the actual and the desired state,” “mediate integration and evaluation of emotional, motivational, and cognitive information,” and “modulate attention.” Activating the ACC via self-reflection, in other words, can promote business success by helping leaders to identify their values and strategic goals, synthesize information to attain those goals, and implement strong action plans.

Clearly, most self-reflection doesn’t occur in laboratory settings—it must be adapted to the C-suite and other work situations. An exciting way to do this in a focused and intensive manner is via “philosophical counseling.” A growing international movement, philosophical counseling has been called “therapy for the sane” because it helps rational, mentally healthy individuals to clarify their world-views and goals in the face of challenges and transitions. Philosophical counselors and their clients engage in structured conversations that incorporate self-reflection on values and goals. Drawing on ancient philosophers of Eastern and Western traditions (from Socrates to Confucius), as well as contemporary philosophers, it supports people’s development of their own personal philosophies and empowers them to reach their highest human aspirations and ideals.

Consider a CEO who demeans his colleagues by rolling his eyes at them, interrupting them, and otherwise devaluing their roles. He now faces a thorny ethical challenge for the company, one that could damage its financial position and reputation. The CEO has nowhere to turn to discuss the dilemma, because he has alienated his executive team. Philosophical counseling could help him to curtail his obnoxious behaviors and improve his “positivity ratio” by facilitating self-reflection on his character and values. A CEO client in this situation found that contemplating the writings of an ancient philosopher (Socrates) and a 20th century philosopher (Habermas) empowered him to implement an enhanced process of dialogue, consensus building, and “communicative rationality” with his leadership team. Philosophical reasoning, coupled with positive behavior changes, positioned him to lead the firm through a treacherous time.

Philosophical self-reflection is essential at inflection points in one’s career, when a leader faces a serious challenge, dilemma, or crisis. How can leaders benefit from this kind of self-reflection without necessarily entering into a formal engagement with a philosophical counselor? They first need to pause and contemplate their core values. The works of a range of philosophers, (female and male, from many cultural traditions) can help. As an example, I often suggest my “SANE” mnemonic, drawing on key questions posed by preeminent Western philosophers: Socrates, Aristotle, Nietzsche, and the Existentialists.

Socrates: What is the most challenging question someone could ask me about my current approach?

Aristotle: What character virtues are most important to me and how will I express them?

Nietzsche: How will I direct my “will to power,” manage my self-interest, and act in accordance with my chosen values?

Existentialists (e.g., Sartre): How will I take full responsibility for my choices and the outcomes to which they lead?

This is no academic exercise, but should have “cash value” in the real world. By reflecting seriously on these questions, the CEO discovered a structured format to handle the financial and ethical dilemma facing the firm. He realized that he viewed “respect for others” and “modesty” as among his core values and desired virtues, prompting him to curtail his demeaning behaviors and hold productive discussions with his team about next steps. This ultimately yielded a consensus and reasoned decision-making. By taking responsibility for reflecting on his values and choices for how to collaborate, the CEO completely transformed the situation and solidified his leadership role.

Like “mindfulness” activities, self-reflection requires time and effort. But it doesn’t call for an intentional shutting down of thought. Instead, it requires the leader to think rigorously about profound philosophical issues like value and purpose. The reward of self-reflection is what Aristotle called phronēsis (“practical wisdom”). Contemplating timeless philosophical values can fuel timely behavior changes in the service of growth and lasting success.

Finding Entrepreneurs Before They’ve Founded Anything

Venture capital is slowly but surely becoming a more data-driven business. Although data on private companies can sometimes be scarce, an increasing number of firms are relying on quantitative analysis to help determine which start-ups to back. But Bloomberg’s venture capital arm, Bloomberg Beta, is going one step further: it’s using an algorithm to try to select would-be entrepreneurs before they’ve even decided to start a company.

I asked Roy Bahat, head of the fund, to tell me a little more about it, and just how good an algorithm can be at picking out entrepreneurs.

HBR: Tell me a little bit about the fund.

Bahat: Our fund is backed by Bloomberg LP, the financial data and news company. We were created a little bit more than a year ago because Bloomberg recognized that there was something special happening in the world with start-ups. And really the only way to have a productive relationship with what I call a “day zero start-up” is to invest in them, because many of them are too early to take on big corporate partnerships, or they’re still figuring out what they’re doing. And what’s unique about start-ups now is that in past decades, you could wait a while and watch a start-up develop before you decided how important it was. Today, in a blink of an eye something can go from two people nobody ever heard of to a significant force affecting business; hence, you have to get involved earlier. The fund invests for financial return not for quote-unquote “strategic value.”

Tell me about the program with Mattermark.

We started to think, was there a way to get to know people even earlier? And we’d seen what companies were doing with predictive analytics to predict and select their customers using data. And so we just wondered: before a founder explicitly became a founder, could we predict that and develop a relationship with them? And so together with Mattermark, we built this model based on data from past and present venture-backed founders and we used it to try and predict, from a pool of 1.5 million people, the top 350 people in Silicon Valley and New York, which is where we’re focused, who had not yet started a venture-backed company but we believed would do so. And so that’s what we did and we reached out to them.

What factors are you drawing on that you believe are predictive?

It’s drawn from a variety of public sources. It’s mostly people’s professional background. So the factors are things like: Did you work for venture-backed company? What role were you in that company? Educational background definitely plays a role.

But what’s interesting about what it predicted is the predictions absolutely were not the caricature of a typical start-up founder. For one, the groups skewed older than the caricature of the typical start-up founder. For example, we found that being in the same job for a long time — even a decade or more in the same company — was not a disqualifier.

Second, it was an incredibly diverse group. Even though we collected zero demographic data, the output of the model was an incredibly diverse group and when we held the first event in San Francisco, it was one of the more diverse rooms that I had ever been in at an event in the technology industry. And that was just really gratifying.

And then the last thing I’d say is it was actually less engineering concentrated, less technical than we expected. We expected it to be virtually everybody having CS degrees and that kind of thing. And while many people worked at technology companies, the proportion of people who were business people was actually quite high. Having a business background actually turned out to be highly correlated with starting a venture-backed start-up.

Once you had this model, what did you do next?

We held a kick-off event in San Francisco and another in New York. The funny thing was a bunch of people who received our email saying, “You’ve been selected as a future founder” thought it was a scam. And so a bunch of people just simply didn’t believe it, but then eventually they started to realize that actually we were completely serious.

We realized in those first few conversations that the most valuable thing in the program is the relationship they can form with each other and with actual start-up founders. And so we started hosting lunch once every other week with a small group of these future founders and some of our portfolio companies and friends in the industry and it’s been great. The response has been terrific.

Our goal with them is to simply support them in achieving what they want to achieve in their careers because whether or not they end up starting a company, these people all have enormously high potential and some of them might end up being executives who we partner with at other companies. Some of them end up being recruits for our portfolio companies. Or some of them might end up inspiring us with ideas and being friends.

Is there a tension between looking for existing patterns of founder success using data and looking beyond the traditional paths? You don’t want to just reflect back whatever biases might already exist in the data.

Yeah. That was one of our huge worries. Of course, you can’t be exclusively data-driven. This is a business of creativity and invention. One of our worries about this future founder group was that if you use the data from past founders to predict future founders, they’re all going to look exactly the same. They’re going to have the same background. They’re going to be identical. And it just turned out not to be true. It’s interesting. When you look at the backgrounds of those founders and applied the model to new people, you ended up with a surprisingly diverse group because the data doesn’t discriminate.

How will you gauge whether this works?

It’s already worked. We’re getting to know wonderful, unusual people with a wide range of backgrounds. They’ll go places.

Is UPS Making Big Money on All Those Amazon Packages? Not Quite

Amazon’s sales volume and its dependence on UPS have grown so great that packages from the e-commerce giant account for as much as one-third of the delivery company’s residential loads, says the Wall Street Journal. But don’t assume that UPS is getting rich on all this business: To provide free shipping to Prime members, Amazon has driven hard bargains with deliverers, and UPS’s average revenue on each internet-related package is dropping. One former industry executive doubts UPS’s margins are as high as 5%, the Journal says.

Mobile Money Is Driving Africa’s Cashless Future

The evolution of African markets faces significant barriers: cost, distance, and a lack of infrastructure. Less than 30% of the population have bank accounts, and even fewer have credit cards. Informal retail and cash purchases are the norm, and that has its risks and costs. The amount of cash in one’s pocket at any given moment drives purchasing decisions. With no means to track sales, little data is available, and channels are too fragmented for companies to forecast production and distribution with any degree of accuracy.

Many companies are taking advantage of this opportunity to steer emerging African economies toward a mobile-driven, cashless (or cash lite) future by introducing a plethora of new products, services, and business models. Financial services in Africa are experiencing a moment of exciting change. While U.S. consumers are just being introduced to Apple Pay, mobile money services like MPesa and MTN Money have been flourishing in African markets. More people have mobile money accounts than bank accounts in at least nine African countries, up from four in 2012. And the continent as a whole leads the world in the adoption of financial services on the mobile platform.

In Rwanda, Uganda, and Ghana, mobile service provider MTN has taken the lead by launching ATMs where customers can withdraw cash from their MTN Money accounts without a bank card (they send a message, then receive a one-time-use PIN on their phone). Other mobile service operators are also vying to release innovations to help customers pay for things without cash, receive money from abroad, and obtain micro loans and insurance products. With barely any legacy infrastructure or status quo to be overcome, the existing commercial landscape feels ready for disruption.

Three distinct factors are driving this transformation. First and foremost is the desire for financial inclusion. Africa’s unbanked majority needs access to affordable tools for savings, loans, and credit. The second is the need to lower the costs and risk of retail and trade based primarily on cash transactions. The third is the introduction of cashless policies from regulators in countries like Nigeria, Kenya, and Ghana. Low consumer confidence in traditional financial institutions also makes this an opportune moment for new players to enter the solution space. And cellular technology is leading the way.

Bankelele, an award-winning Kenyan blogger on all things banking, shared some pragmatic insights from Nairobi, the heart of Africa’s mobile money revolution: “People seem to trust the mobile operators more than they do banks,” he said. “Transparency and consistency in transaction costs have a lot to do with this. If they have 400 shillings in their mPesa account, they know that it will still be there six months or even a year later, but bank accounts seem to eat away their comparatively meagre savings with all manner of fees and charges.”

This has been driving ever closer relationships between banks and mobile operators. One of the best known is Equity Bank of Kenya launching an MVNO (mobile virtual network operator) through Airtel Africa’s mobile infrastructure to provide an entirely new distribution channel for all their banking products. They rolled out their own SIM card to give their extensive customer base (8.7 million at last count) secure mobile banking, and they also distributed 300,000 smartphones to the retail trade. So now, Equity Bank account holders can pay for purchases with a swipe of their phone and access a wide range of financial products much faster and cheaper.

Steep transaction costs for receiving money from relatives abroad has also led to more experimental startups like BitPesa, which is testing a service to transfer money from the UK to Kenya. They convert bitcoins, purchased through their website, into Kenyan shillings, and then send those to any Kenyan mobile money wallet. Econet Wireless is another mobile operator laying the foundation for an entire suite of such services and paving the way for a cashless future for their customers in Zimbabwe.

These trends are not limited to customers. Numerous solutions are popping up to help small traders and merchants convert to this emerging cashless future. KopoKopo, a Kenyan startup, provides software solutions that facilitate and incentivize merchants to go cashless. They set up a digital payment network and mechanism for a merchant to accept digital payment, and then they offer Business OS tools to manage everything from analytics to credit to marketing to supplier payments. And South African start-up Nomanini has developed a small portable PoS terminal specifically designed for the micropayments – such as to buy a bus ticket – prevalent in the informal economy.

Adoption of these services is still unevenly distributed, as people resist switching from the familiar and flexible interpersonal transactions to electronic ones. But the possibilities for ecommerce and consumer marketing are enormous. Nigeria’s ecommerce market alone generates $2 million worth of transactions per week, and online transactions are expected to cross $6 billion by the end of 2014. Interestingly, the fears regarding Ebola and Boko Haram are driving more people to shop online (and stay at home). E-tail could help consumer goods companies leapfrog the need for extensive distribution infrastructure, something consumer product companies are already exploring.

Still, as Bankelele cautions, consumer confidence and trust must be built through transparency and honesty, and interoperability and seamless transactions between banking institutions are still a ways off. There is time for the African consumer markets to come into their own, offering consumer-facing companies breathing room to consider the impact of these changes on their market entry strategies.

Better Teachers Receive Worse Student Evaluations

A 1-standard-deviation increase in university teachers’ effectiveness in boosting student performance reduces the students’ evaluations of their professors’ teaching quality by about half of a standard deviation, on average — enough to significantly reduce the teachers’ percentile ranking at the university, says a team led by Michela Braga of Bocconi University in Italy. Students, especially the least able, appear to respond negatively in their evaluations to the extra effort that good teachers require of them, a finding that casts doubt on universities’ reliance on student evaluations to inform faculty-promotion decisions. The researchers also found that student evaluations improve when there is fog and as the weather gets warmer, and they deteriorate on rainy days.

Ken Burns on “The Roosevelts” and American Leadership

More than nine million viewers tuned in to watch the first episode of Ken Burns’s new film “The Roosevelts” on PBS earlier this week—a sign that even in an era of reality TV and critically-acclaimed cable dramas, people want to understand more about real-life leaders. For Burns, the seven-part, 14-hour series (which is available via streaming video on the PBS website), is the latest in a career in which he’s trained his lens on leaders from Jefferson and Lincoln to Susan B. Anthony and David Sarnoff. Burns spoke with HBR about how his work as a filmmaker has influenced how he thinks about leadership. What follows are edited excerpts from our conversation: 

HBR: Why did you decide to pair the two President Roosevelts in a joint documentary?

Burns: It’s sort of strange that after all these years they haven’t been paired together in some major book or film. They have incredibly related and intertwined narratives that taken by themselves are strong, but are even more powerful when put together.  I assume it’s just the laziness of traditional media culture that it hasn’t been done until now—because Theodore Roosevelt was a Republican and Franklin Roosevelt was a Democrat, people feel that you should put them in different silos.

Businesses typically avoid nepotism, but American voters seem to accept family dynasties. Why is that?

I think it’s less a dynastic thing than the younger generation of politicians taking advantage of the last name of the older one. While I think Americans are resistant, in some way, shape and form to the notion or idea of dynasties, there is a convenience to having a well-known name. George W. Bush would not have been president without his father preceding him, and Hillary Clinton would not stand out as much as a presidential aspirant had she not been First Lady and married to Bill Clinton. They are familiar faces in a media culture that self-selects to boldfaced names.

As a historian, do you think about how today’s leaders will be viewed in 100 years?

It’s an unfair task—you just don’t know. I’m in the story business, and because I’ve chosen to work in American history, the stories are usually concluded 25 years out from the present moment. That’s the nature of history.

Has your view of leadership changed since you began this work 30 years ago?

I think it’s remained fairly constant. What’s so delightful is what we call “leadership” comes in so many different varieties—so many different human beings, and so many varieties of human experience. Look at Abraham Lincoln and Franklin Roosevelt—Lincoln was born into poverty on the frontier, and FDR was born to such great privilege that he could have spent his life in idleness, as many of his relatives did.

How is online streaming video changing your business?

One of the ways we abolish the cacophony of noise and the information deluge is to binge watch, which is how streaming video is allowing us to control our content. When I met television critics this year, none of them complained that “The Roosevelts” is 14 hours. At every juncture of my professional life critics have said no one will watch long historical documentaries, but now they realize that people are starved for 14 hours of content, whether it’s “Orange is the New Black” or “House of Cards.” The same laws of storytelling apply—if it’s a good story, it’s a good story. Mine just have to be based on fact. I can’t make it up.

The Chief Innovation Officer’s 100-Day Plan

Congratulations! Your energy and track record of successfully launching high-impact initiatives scored you a plum role heading up innovation. Expectations are high, but some skeptics in the organization feel that innovation is an overhyped buzzword that doesn’t justify being a separate function. So, what can you do in your first 100 days to set things off on the right track?

Over the past decade we’ve helped dozens of leaders through their first 100 days. Based on our experience, augmented by in-depth interviews with a few of the most seasoned practitioners with which we have worked, we suggest that innovation leaders put the following five items on their 100-day punch list.

Spend quality time with every member of the executive committee. This should go without saying, but it’s vitally important to develop relationships with the CEO, business unit leaders, and other key executives to understand the company’s strategy, so that the innovation approach and projects you pursue align with overall corporate goals. Brad Gambill, who over the past few years has played a leading role in strategy and innovation at LGE, SingTel, and TE Connectivity, believes the first 100 days are an ideal time to “ask dumb questions and master the basics of the business.” He particularly suggests focusing on the things “everyone else takes for granted and thinks are obvious but aren’t quite so obvious to people coming in from the outside.” So don’t be afraid to ask why a decision-making meeting ran the way it did or challenge the wisdom of pursuing a certain strategy or project.

It is particularly important to understand these executives’ views of two things – innovation’s role in helping the company achieve its growth goals and your role in leading innovation. Is innovation intended to improve and expand the existing business, or is it meant to redefine the company itself and the industry in which it operates? Do executives expect you to establish and incubate a growth businesses, act as a coach to existing teams, or focus on establishing a culture of innovation so that new ideas emerge organically?

As you invest time with top executives, you should begin to understand the organizational relationship between your innovation work and the current business. Are leaders willing to give up some of their human and financial resources to advance innovation? Are you expected to recruit a separate team from within and beyond the company? Or are you expected to spin straw into gold by working without dedicated resources? Will leaders support you if you propose radical changes to people, structures, processes, and roadmaps, or are you supposed to change everything but in a way that no one notices?

Zero in the most critical organizational roadblocks to innovation. Chances are, you won’t get the same answers to these questions from everyone you talk to. Those areas where executives disagree with one another will define the most immediate (and often the most fundamental) challenges and opportunities you’ll face in your role.

As quickly as possible within your first 100 days, therefore, you will need to understand where the fault lines lay in your company. Pay particular attention to the three hidden determinants of your company’s true strategy – how it funds and staffs projects, how it measures and rewards performance, and how it allocates overall budgets. A clear understanding of where leaders’ priorities fail to match what the company is actually funding and rewarding will help you identify the biggest hurdles to achieving your longer-term agenda, and where short-term workarounds are required.

Define your intent firmly but flexibly. You don’t need to have all the answers perfectly formulated from the beginning. But you should have a perspective – even on Day 1 – regarding how your role as the innovation leader can help the organization achieve its overall strategy. Look for ways to stretch the boundaries of current innovation efforts, but remember you are not the CEO or CTO. You need them to want to support you, not worry that you are gunning for their jobs. Gambill suggests one way to build this trust is never to bring up a problem without also proposing a solution. The CEO “has lots of people who know how to point out problems; it is important to establish yourself as a problem solver and confidant as quickly as possible.”

Determine how you plan to balance your efforts between developing ideas, supporting initiatives in other parts of the organization, and creating an overall culture of innovation. Those are related, but distinctly different, tasks. Don’t get too rooted to your initial perspective. Be as adaptable in your approach as you will be when you work on specific ideas.

Develop your own view of the innovation landscape around the company. Colin Watts, who has played a leadership role in innovation and strategy functions at Walgreens, Campbell Soup, Johnson & Johnson, and Weight Watchers, suggests getting a “clear market definition ideally grounded in customer insights.” Companies tend to define their world based on the categories in which they compete or the products they offer. However, customers are always on the lookout for the best way to get a job done and don’t really care what industries or categories the solutions happen to fall into. Understanding how customers make their choices often reveals a completely different set of competitors, redefining the market in which your company operates, its role in the market, and the basis for business success.

Watts also suggests zeroing in on the adjacencies that have the potential to shape your market. As he notes, “There is no such thing as an isolated market anymore.” Through an innovation lens you are likely to see early signs of change that the core business might have missed.

Develop a first-cut portfolio of short and longer-term efforts, with a few planned quick losses. A key component of your job, of course, will likely be to advance a set of innovation initiatives. Some may already be in progress. There may be a backlog of ideas waiting to be developed. Or the raw material might be a bit rougher, existing primarily in people’s heads. Regardless, in the first 100 days you want to come up with a clear view of some of the specific things on which you will plan to work. Some of these might be very specific initiatives, like identifying product-market fit for a new technology. Some might involve investigating broader areas of opportunity (for example, “wearables”). Some may involve developing specific capabilities. One specific capability Watts suggests building as an “investment that will pay back for years to come” is a “fast and cheap way to pilot ideas and products.”

Savvy innovation leaders place some long-term bets that they start to explore while also quickly addressing some more immediate business opportunities to earn credibility. If your portfolio is all filled with near-in ideas, some people in the core organization might naturally ask why they can’t do what you are doing themselves. And you are probably missing the most exciting and possibly disruptive ideas in your space. But if the portfolio is filled only with further out ideas, you run the risk that organizational patience will run out as you do the long, hard work of developing them.

When considering quick wins, don’t avoid quick losses. True innovation requires an organization to stop avoiding failure and see the benefits of learning from it. But failure remains very scary to everyone. Have enough things going on that you can tolerate a quick loss without damaging your overall pipeline. As Watts says, “You may be able to do it fast or do it cheap or do it reliably but not likely all three.” Make sure that you and your executive sponsors loudly and proudly celebrate the first project you stop when it becomes clear it won’t work.

That feels like a lot for 100 days, and it is. Innovation has the power to positively transform an organization, but no one said it was going to be easy.

Too Many Marketing Teams Are Stuck in the Past

Many marketing organizations are still operating like it’s the 1990s — or even earlier. Duplicative marketing teams exist within the same company across multiple product lines. Digital marketing teams are centralized yet isolated from the broader organization. Marketing groups are splintered into communications, consumer marketing, brand marketing, and digital marketing units with no common thread in strategy and execution.

Over the past dozen years, I have participated in both the infusion of digital capabilities into traditional marketing organizations and the establishment and maturation of digital marketing organizations at Disney, J.Crew and, now, Conde Nast Entertainment where I am VP of marketing-digital. Based on this experience, I see five areas that need to change in order for marketing to function effectively in the digital age.

Internal structure: Most marketing teams are organized by either functional expertise (such as social media marketing or marketing analytics) or brand. To be a successful digital marketing organization, your team needs to be organized by functional expertise rather than by brand, project or platform in order to deliver coherent, integrated campaigns across all consumer touchpoints. The customer who is a fan of your brand’s Facebook page should receive a more personalized email newsletter after visiting your website. She should be given a personalized promo code in her email to shop at your brick and mortar store based on her online shopping history, and later, be reminded with a push notification message on her mobile phone when the promo code is about to expire so she can take advantage of it online.

My team at J.Crew was organized by function such as affiliate marketing, paid search, email marketing, and search engine optimization. At Conde Nast Entertainment, my team is also organized by function across social media, paid advertising, earned/owned media, insights/analytics, and audience development. Each functional expert is responsible for all 12 brands that we work on. This structure is effective in a multi-brand environment with a centralized marketing team because each brand benefits from deep functional expertise as well as consistency across touch points.

Functional alignment: Many marketing organizations suffer from a failure of cross-functional collaboration. For example, IT decisions that affect marketing may be made without a thoughtful analysis about the resulting user experience beyond page load speed and server uptime. New product features may be introduced into an e-commerce site without understanding how they will impact traffic conversion rates and average order value.

Digital marketing teams need a seat at the table so they can infuse digital-first marketing insights into product and technology planning. Website feature changes should not be released without thoughtful analysis of the potential impact on traffic. Email marketing templates should not be altered for design reasons without a/b testing the impact of the change on click-through rates. Website page title changes should not be based solely on editorial considerations but also search engine optimization competitiveness.

Meritocracy vs. hierarchy: In traditional marketing organizations, job responsibilities and titles are hierarchical and rarely fluid. Each role is clearly defined and limited in scope. The new digital marketing organization thrives on a less hierarchical structure with more flexibility and an emphasis on meritocracy. Your Email Marketing Manager may also happen to be an expert in Instagram. Hence, your next email campaign may be highly integrated with social media. At Conde Nast Entertainment, digital marketing execution sometimes falls to whoever on my team can figure out the best way forward first.

When technology and consumer behavior patterns are changing so quickly, there may not be time to wait until the person assigned to the campaign gets around the task.

Data-driven decision making: Compared to digital organizations, traditional marketing organizations have a longer feedback loop on their campaign performance and results of their go-to-market strategy. In digital organizations, immediate data allows marketers to be smarter and faster in their decision-making. It is time to capitalize on the marriage of traditional and digital marketing data. Digital marketing insights can guide the strategy of traditional marketing and verse versa.

At J.Crew, I would determine my paid search marketing investments and choose which clothing product categories to drive online demand based on in-store sales data. For example, if the mint green cashmere sweater is a top category seller at stores in New York City, I would shift my paid search advertising to concentrate on relevant keywords, and target by geography and remarketing lists to customers in similar zip codes as they shop through search engines.

Governance: A few forward-thinking organizations are doing without a chief marketing officer, and instead have given the job of leading marketing to a chief digital officer. The question of who owns digital marketing in an organization is often uncertain. Accountability for digital revenue, digital product innovation, omnichannel strategy, and online audience growth blurs the line between many traditional roles from marketing to technology to product development to strategy.

Organizations that seek to be more digitally focused should first ensure alignment at the top between vision and execution. The CEO’s vision must prioritize digital marketing innovation. The execution of the vision could be governed by either a Chief Digital Officer or a CMO. Having this a CDO role could make sense if the company is revenue and product focused in an advertising supported business model. The CMO role could make sense if the company is consumer and content focused because of the specialized knowledge required to drive an effective traffic and audience strategy.

What’s next? As a marketer, I have witnessed two camps of organizational transformations in the digital age.

Camp one is characterized by cycles of digital misalignment across the company. The company makes significant investments in digital marketing, infrastructure, product design and technology to optimize digital performance, only to gut everything and start over again every two to five years. This is not only disruptive to the people in the organization but also to your company’s bottom line.

Organizations in the second camp define a pivotal moment at which they will become a digital-first company with a commitment to invest in digital marketing, technology infrastructure, and digital talents. From this point on, the organization reorganizes its workforce, strategy roadmap and investments to build a new marketing organization that fully integrates traditional and digital marketing in a sustainable way.

Algorithms Make Better Predictions — Except When They Don’t

Predictive analytics is proving itself both powerful and perilous. Powerful, because advanced algorithms can take a near-unlimited number of factors into account, provide deep insights into variation, and scale to meet the needs of even the largest company. Perilous, because bad data and hidden false assumptions can seriously mislead. Further, algorithms cannot (yet, anyway) tap intuition — the soft factors that are not data inputs, the tacit knowledge that experienced managers deploy every day, nor the creative genius of innovators.

So what should managers, especially leaders, do? The obvious answer is employ both computer-based programs and your own intuition. In this post, I’ll use a series of simple plots to explain how to tap the potential of predictive analytics, sidestep the perils, and bring both the data and your good judgment to bear.

To start, consider the figure below, “Performance since 2008,” a quarter-by-quarter time-series plot of results on a variable of interest (for example, it could be sales of a particular item, estimated resources to complete a certain project, etc). We need to predict performance for the first quarter of 2015 (1Q15).

A quick glance only might yield, “Wow, I don’t know. Performance is bouncing up and down. How would I even guess?”

After staring at the plot a bit longer, most individuals (and all good analytics programs) will spot seasonality: down in first quarters, up in thirds. The next figure is a simpler plot, featuring first quarters only.

This plot suggests that the first quarter is pretty mundane; except for 2014, performance is tightly contained in a 91 to 93 band.

So what’s the prediction for 2015’s first quarter? As the figure, “Potential Predictions for First Quarter, 2015,” depicts, I can argue for at least three:

“We should expect 1Q15 to be like most first quarters. There were several huge snowstorms last year, so 2014 was an anomaly.” Perhaps the explanation of a seasoned veteran who’s learned it’s best to under-promise and over-deliver.
“2014 is the new normal. We got a one-time boost because we turbocharged the knurdle valve.” Perhaps the prediction and explanation of an engineer who is proud to have improved a piece of the variable in question.
“We started a new trend in 2014 and should expect to see similar gains in 1Q15.” Perhaps the prediction and explanation of a new product manager, aiming to score points with the boss, who is demanding across-the-board improvements.

The quandary here underscores the importance of algorithms. I have no doubt that each of these managers is smart, well-meaning, and doing his or her best. But at most, only one of them is “right.” One in three is an excellent batting average in baseball, but hardly up to the demands of competitive business. Algorithms offer distinct advantages. Good ones are unemotional and (largely) apolitical. They don’t care that it is best to under-promise and over-deliver or that the new boss is particularly demanding.

At the same time, they’re capable of digging deeper. They can help evaluate whether the weather really played a factor in 2014 and take weather forecasts into account in predicting 2015. Similarly, they can seek evidence for the “new trend” in the second quarter and in similar variables. They can also search for possible causes. (Note: Algorithms can only detect correlation, though. Individuals must work out causation.)

In a related vein, good predictions should feature ranges, such as 94.9 ± 2.4. To see why this is, take a look at the figure below. The plot features two cases, one exhibiting low variation (in gray — note that all past values are between 94 and 96), the second relatively higher variation (in blue — values here range from 90 to 100). In both cases the mean is 94.9.

Now suppose 1Q15 comes in at 98 (the hypothetical dot in the figure). This should come as a surprise in case 1 — 98 is far above anything that’s happened in the past. Not so in case 2 — several past values are greater. Thus prediction ranges (94.9 ± 2.4 for case 1 and 94.9 ± 9.5 for case 2) help managers understand just how close they should expect actual performance to be to the point prediction (94.9).

Calculating these ranges is quite technical. Few can do it by eyeball alone. But for good computerized algorithms, it is a snap.

These three abilities — to take emotion and politics out of the prediction, to seek deeper insights, and to quantify variation — are powerful, and leaders should seek to leverage them. That being said, managers should not be seduced into thinking that predictive algorithms are all-knowing. They are not.

Algorithms only operate on the inputs they’re provided. Snowstorms affect many things and may lie at the heart of the boost in the first quarter of 2014, as mentioned above. But if weather is not part of the algorithm, the suspected explanation cannot be taken into account.

Algorithms can also be remarkably sensitive to bad data. Consider the result if you were to change one data value by dropping a decimal place (e.g., a 95 became 9.5). The resulting prediction interval changes from 94.9 ± 2.4 to 91.4 ± 50, setting a trap for the unwary. At first glance, one might not challenge the 91.4 ± 50 and use it without too much thought. The impact, from preordering too much stock to missing an opportunity to reserving too little resources for completing an important project, may go unnoticed as well. But the costs can add up. Bad data is all too common and the impact on predictions can be subtle and vicious. At the root of the financial crisis, bad data on mortgage applications led banks to underestimate the probability of default — an issue that cascaded as those mortgages were packaged into complex products.

In addition, algorithms are also based on assumptions that may effectively be hidden in arcane technical language. For example, you may have heard, “We’ve assumed that variables are independent, homoscedastic, and follow normal distributions.” Such language can camouflage an assumption that is simply not true, since the terminology can scare people off from digging deeper. For example, the assumption that mortgage defaults are independent of one another held true enough (or didn’t matter) for a long time, until pressed in the run-up to the financial crisis. As Nate Silver describes in The Signal and The Noise, this led those who held the mortgages to underestimate risk by orders of magnitude (and exacerbating the data quality issues noted above).

Thus, you should never trust an algorithm that you don’t understand. The same applies for the input data. The only way to truly understand the algorithm is to ask (either yourself or data scientists) a lot of questions. You need to understand the physical reality that drives the variables you’re interested in and the explanatory factors you’re using to predict them. You need to understand the real-world implications of the assumptions.

More than anything, you need to know when the algorithm breaks down. Plots like the one below help. The figure presents the time-series for the “one misplaced decimal” situation I referenced above. I’ve also added the upper and lower prediction ranges (technically, this is a “control chart,” and the ranges are lower and upper control limits respectively). It is easy enough to see that 3Q12 was very strange indeed. There might be an explanation (i.e., bad data), or it may be that the underlying process is unstable. This is the key insight smart managers really seek. Until they know, smart managers don’t trust any prediction.

This picture also underscores the need to invest in data quality. Over the long run, nothing builds better predictions more than knowing you can trust the data. Conversely, there is nothing worse than having a meeting about the implications of 1Q15’s predictions degrade into a shouting match about whether bad data stymies everything.

Finally, you must develop a keen sense of smell for predictive analytics, the data, and your own intuition. Trust your intuition and use it to challenge the analytics and the data, and conversely, use them to train your intuition. If something just doesn’t “smell right,” become very, very skeptical.

Good algorithms make better predictions than people most of the time — except when they don’t. If you’re fighting the first half of this claim, you need to get over it. Stop thinking of the algorithm as your enemy. And if you doubt the second half, prepare for some very harsh surprises.

Get Over Your Fear of Sales

When you graduate from college with a degree in communication studies and rhetoric, the business world can look very confusing. Unsure of where I fit in, I explored options. Many friends suggested sales. I was doubtful. I worried that being in sales would not carry the prestige and credibility I so badly wanted as I started my professional career. I was also having a hard time getting excited about selling any particular product.

Then I interviewed with a partner at a (then) Big Six consulting firm. He talked about an opportunity to work on “leveraging the most important assets in the firm— its people.”

“That sounds terrific,” I said, thinking that matched my interests, “What function is that?” The partner replied, “Human resource management.”

My 22-year-old self thought, How cool is that? It even has management in the title. That sounded way better than “Sales Rep for Acme Company.” Off to San Francisco I went to be a human resource management associate at a Big Six consulting firm.

After two years in this position, I had an epiphany. I was at an expense-account business lunch with a senior partner and an audit associate. The partner liked us both and remarked that we both had a lot of talent. He went on to say, “The main difference is that you (pointing to the audit associate) generate revenue, and you (pointing to me) are overhead.” Two of the three people at the table had a quick laugh, and my job search began as soon as we returned to the office. I came to a stark realization that day: sales is at the heart of every commercial enterprise and that being the revenue-generating engine of a business was actually a good thing. Maybe even something to be proud of.

In truth, though, I was afraid of sales. The perception. The quotas. I hated the idea of having to be pushy.

I’m hardly the only one with this misconception of sales. Twenty years later, with two stints as an executive vice president of sales along the way, I often see that despite the obvious need to sell their products, many companies encounter some form of resistance to “sales.” Ironically, this unwillingness to own and embrace a sales culture frequently comes from within the sales team itself. I hear sales professionals say, “I don’t really sell. I help clients make a buying decision.” Or “My job is more of being a consultant to my clients.” And my favorite, “I’m not in sales, I’m in business development.” Even professionals who have dedicated their careers to sales are afraid of sales. Or at least, they’re afraid of the label.  Why?

I’ve come to the conclusion that at least part of that fear stems from the persistence of an anachronistic definition of selling and a complete misunderstanding of what successful sales professionals actually do.

Many people equate sales with making people buy things they don’t want, don’t need, and can’t afford. That perception likely emerged from the days, at the turn of the 20th century, when hucksters and peddlers were among the few sales jobs on the U.S. census, and unfortunately this image still persists in some professions. The proverbial used-car salesman springs to mind.

But today there are over 28 census codes that reference professional sales specifically, many of which require tremendous expertise. For instance, a client of mine in the medical device industry employs sales professionals whom doctors consult about the proper application of their product while they are in surgery. Take that in. A doctor asking a sales professional questions during surgery. This is not your father’s salesman.

When I’m called on to help an organization with a sales transformation, I quickly gauge the culture and begin to address counterproductive beliefs that are holding them back from getting the performance they want. There are three key steps to overcoming a negative sales culture: You need to help them see that:

If you operate on the assumption that people will benefit from using your products and services, then sales is entirely about helping others. Done well, selling today is helping people identify and address their needs in order to achieve their goals: to improve efficiency in a business, to make something easier, to live a better life in retirement, to be safer, live longer, and so forth. In this way, sales is not simply an appendage of the organization responsible for distribution, but the conduit for showing how your clients benefit from your products or services.

How you sell is a vital part of the value you create for the customer. While conducting research and observing my own sales teams, I’ve sat in on over 1,000 meetings between sellers and buyers, and one of the things I’ve observed is that successful salespeople don’t “pitch” and they don’t “close.”  That is, they don’t prattle on about how great their offerings are, and they’re not pushy (what some have called the “spray and pray” method). This may sound like heresy to many sales professionals, particularly those who cut their teeth in sales before the 1990s. But it is true.

What they do instead is engage in a mutual dialogue about what a client is trying to accomplish, and then apply the solutions offered through their products or services to the client’s needs. The very best ask smart questions, helping clients to see problems they didn’t even know they had or opportunities around the corner.

One of the best examples of I’ve seen of this was a sales rep for a major daily newspaper. Her job was to sell ad space in a highly competitive market where advertisers had ever-increasing alternatives to newspaper space. I had the opportunity to observe several of her sales calls as part of a consulting assignment for the paper, and I immediately noticed how little she talked versus how much she encouraged the client to speak.  She told me that her objective was to help the client see why advertising with her newspaper would help him grow his business, and she asked insightful questions. When she did talk about advertising options, she focused specifically the ideas the client expressed. The meeting lasted only 45 minutes because she didn’t spend any time talking about features or benefits that weren’t relevant. At the end, she simply expressed an interest in working with the prospect, offering two or three suggestions on how they might proceed. He opted to receive a proposal and agreed to review it the next week. How she sold her product was key to her success as one of the top five sales reps in the company.

Every employee is selling in some capacity — even if they don’t think they are — so they might as well get good at it. In his book To Sell Is Human, Dan Pink indicates that more than 40% of our professional time is spent selling. Not only selling the company’s products or services, but selling ideas, approaches, or a particular way to solve a problem. I have written here before about how sales professionals create value with customers. Your ability to create value, is inextricably linked with your ability to sell, no matter what position you’re in.

When I work with professionals in customer service or IT who bristle at the idea of being in sales, I emphasize that done well, sales and service are very alike, though one is typically proactive, and the other is reactive. While that difference is not trivial, consider that the outcome of a good service experience and a good sales experience is the resolution of some problem a customer has, or the identification of some opportunity for improvement.

Don’t run from away from sales  as I used to. Update your thinking to the 21st century. Sales is the engine powering all business. And sales professionals are the ones driving the train.