4
LEADERSHIP
The Deciding DELTA Factor
IF WE HAD TO CHOOSE a single factor to determine how analytical an organization will be, it would be leadership. If leaders get behind analytical initiatives, they are much more likely to bear fruit. Leaders have a strong influence on culture and can mobilize people, money, and time to help push for more analytical decision making. A decade ago, when we did our first study on how companies build analytical capability, we concluded that one of the most important prerequisites is having leaders who care about analytical decision making—a conclusion that we still stand behind today. 1
However, there is one revision we’d like to make to our earlier thinking, writing, and speaking: we focused, perhaps too narrowly, on analytical leadership from CEOs. Now, there is no doubt that to be a full-fledged analytical competitor, you need the CEO in your corner. However, there is also no doubt that almost any employee can move an organization in a more analytical direction. To be sure, an impressive title and massive resources help, but in this chapter we’ll discuss how a variety of people who aren’t CEOs can make their organizations more analytical and fact based.
We’ll begin by focusing on the typical attributes of analytical leaders—the traits that help them move their organizations toward analytical decision making. If you want, you can probably use these as a sort of assessment to see how you—or someone you know—stacks up as an analytical leader. Some of the attributes are more likely to be found among high-level executives such as CEOs; others are more appropriate for lower- and midlevel managers and individual contributors.
Included in this chapter are four case studies of analytical leaders at different levels of organizations: the head of an analytical department, the head of a business function, a business unit head and entrepreneur, and a CEO/president team. We discuss how each leader exhibits many of the analytical leadership traits we’ve laid out. Even this broad range of leaders by no means covers the variety of leadership possibilities; almost anyone can help an organization to become more analytical, including individual contributors.
What Analytical Leaders Do
Analytical leaders at every level of the organization exhibit some common traits. Of course, almost no leader has them all, and different leaders have the traits in different proportions. But before talking about how they’re embodied in real people, we think it’s useful to lay out these attributes in the abstract.
Analytical leaders tend to demonstrate the following behaviors.
Develop Their People Skills. Analytical leaders need to have good people skills—a trait that is not as obvious as it sounds. Many highly analytical people seem to prefer computers and data to people; they don’t sympathize, empathize, or communicate well with others. Why should they, if humans don’t even come with a SORT function or an easily searchable interface? But if you don’t have good people skills, you’re not going to be a good leader of any type—including analytical.
Push for More Data and Analysis. The core responsibility of an analytical leader is to set the expectation that people will make decisions based on data and analysis. If someone comes to you with a recommendation that appears to be based on intuition, you’ll push back if you’re an analytical leader. You may encourage the miscreant to gather more data, relate it to some other data, perform a correlation analysis, or (assuming certain capabilities) build a multivariate probit regression model. If you let people get away with sloppy logic and uninformed intuition as their primary decision tools, they won’t naturally move toward analytics and facts—tools that are harder to gather and use. Most people need a bit of urging to move in the analytical direction.
Hire Smart People, and Give Them Credit for Being Smart. One of the most important functions of analytical leaders is to hire smart analysts. Many companies in industries that have not previously been very analytical find themselves with relatively few people who can do serious analytical work, so they have to be brought in. Persuading quantitative MBAs or PhDs to work in places like Harrah’s or Sears that have never had such people is a tall order. Once they are hired, good leaders provide a stimulating and supportive work environment for analysts and give them credit for the work that they do. We’ve all seen managers who present others’ analyses in meetings as their own. These are not good analytical leaders—or good leaders at all.
Set a Hands-on Example. Analytical leaders aren’t hypocrites. They lead by example, using data and analysis in their own decisions. This doesn’t mean that they have to know all the details of Chi Square Automatic Interaction Detection (affectionately known as CHAID) or structural equation modeling. But they do need to have the same passion for fact-based decision making that they want to inspire in the people they lead. Occasionally, they’ll feel the need to get their hands dirty and mess around with data and brainstorm with analysts themselves. They’ll do so because they like analytics and because they want others to emulate their example.
Sign Up for Results. As Joe Megibow of Hotels.com points out, it’s common to find middle- and lower-level analysts who complain about the lack of analytical leadership in their organizations. If only their work were appreciated! If only someone understood how important they were! But there is something they can do to take leadership. They can commit themselves to achieving a specific result in the part of the organization they serve or control. If they’re in direct mail, they can take responsibility for a certain level of promotional lift. If they’re in Web metrics, they can increase page views. If they’re in supply chain, they can reduce inventory by a specified level. This will advance the analytical orientation of the organization overall, and probably get the person who signed up for the result a promotion if it’s achieved. That’s just what it did for Megibow, who was recently promoted to the senior analytical role at Expedia, the parent company of Hotels.com.
Teach. Analytical leaders are patient teachers of applying analytical perspectives to business. Sometimes they teach actual analytical techniques. At other times they gently guide employees and colleagues into more rigorous thinking and decision making. If you’ve received the best kind of teaching from analytical leaders, you may not even be aware that you’ve been taught—all of a sudden you have stretched your capabilities, and you think you’ve done it on your own.
Set Strategy and Performance Expectations. Good analytical leaders know that analytics and fact-based decisions don’t happen in a vacuum. In order for people to know where and how to apply their analytical skills, they need a strategy for their business, function, and even department. What are we trying to accomplish? Which goals will analytics help to achieve? After setting the strategy, analytical leaders need to define a set of performance targets for their organizations and direct reports to achieve. Defining the metrics will itself drive the organization in a more analytical direction and motivate employees to begin using analytical tools themselves.
Look for Leverage. Since analytics can been applied to a variety of business problems, it’s important to focus them where it makes a difference. Strong analytical leaders know where to find leverage—where a small improvement in a process driven by analytics can make a big difference. A simple example would be in retail, where a small improvement in profit margin or lift gets multiplied across many sales. One of the analytical leaders we describe in the following profiles, Tom Anderson, says that he looks for a “multiplicative” business—one in which a small analytical advantage gets multiplied through several different factors that drive business success.
Demonstrate Persistence Over Time. Analytical leaders have to be “pluggers”—people who work doggedly and persistently—because changes that apply analytics to decision making, business processes, information systems, culture, and strategy hardly happen overnight. Even once they do happen, leaders have to continually revise and update their analytical approaches. So if you want to be an analytical leader, be patient and be prepared to work at it for the long haul.
Build an Analytical Ecosystem. Analytical leaders can rarely go it alone in building analytical capabilities. Instead, they have to build an ecosystem consisting of other leaders in their business, employees, external analytical suppliers, business partners, and so forth. The networks supply talent, advice, resources, tools, and solutions to common problems. In effect, leadership comes not from individuals, but from a network of analytical leaders across organizations.
Work Along Multiple Fronts. Analytical leaders know that no single application or initiative will make their company successful. Thus, they proceed along multiple fronts with a portfolio of projects. Some initiatives may have a greater technical focus, while others may involve more human or organizational analytical capabilities.
Know the Limits of Analytics. Good analytical leaders know when to use their intuition. They blend art and science in decision making. They use analytics whenever possible but can also see the big picture. Some aspects of business—for example, detecting major shifts in business models and customer value—require the human brain.
Case Studies of Analytical Leaders
To make these abstract attributes concrete, we present four case studies of analytical leaders at different organizational levels, each obtained through interviews with the relevant individuals. Highlighted in italic type are the attributes we delineated in the preceding list (just in case you’ve forgotten them already).
Shannon Antorcha, Analytical Department Leader,
Carnival Cruise Lines
Shannon Antorcha is a direct, enthusiastic provider of analytical database marketing capabilities at Carnival Cruise Lines, which is the flagship brand under Carnival Corporation, the world’s largest cruise operator. She’s been at Carnival for ten years, starting in revenue management and moving to lead the six-person database marketing function in 2006. Carnival isn’t yet what we’d call an analytical competitor, but it has made enormous strides in its analytical capabilities, and Shannon has been a major contributor to the organization’s progress.
Shannon has worked to hire smart people in her function. “Everybody has a different skill and a different set of relationships,” she says. “We play good cop and bad cop with the different groups where we have strong relationships; if one member of my group has a strong relationship with, say, the IT function, she’ll be the good cop, and I’ll be the bad cop, always asking for more from them.” She also attempts to build an analytical ecosystem by forming relationships with IT, other groups in the marketing function, the chief marketing officer, the CEO, and external providers of software and services such as SAS.
Her group often digs in and sets a hands-on example for how to use analytics. Shannon notes:
When I joined Carnival nine years ago, it was evident to me that the organization was very operationally focused. We made many business decisions with highly tuned intuition (i.e., it just feels right given business experience) or on a cost basis (i.e., it will save money this year versus last). As one of the first members of a highly analytical revenue management start-up team, we had a daunting task ahead. A data warehouse was a relatively new concept, as were data mining, analytics, and BI. This needed to be built from the ground up, practically overnight. I have learned that individuals or business units need to accept responsibility for their deliverables. For example, being an active participant in IT projects from inception keeps the projects on time, on budget, and within scope. Our philosophy is to be involved in an IT project from the beginning. We whiteboard architecture with the IT team, while looking at table structures and doing data discovery exercises. We follow that up by being proactive through the development stage and even into system testing. Following this approach, when it comes time for the users to accept the solution that was built by IT, there are no surprises. At Carnival, I was one of the first to participate with IT in this way. In a short time, we have built a robust data warehouse and we have increased demand for analytical insight throughout the organization.
Shannon tries to teach other business functions about what’s possible with analytics: “If you’re going to be a change agent, you have to educate people and help them understand what you’re trying to do. Eventually you will get their buy-in.” When she came to Carnival, Shannon says, she was impatient and less diplomatic than she is now. Over time, she has improved her people skills through awareness, working to improve, and coaching from mentors.
Shannon sees developing more analytical capabilities at Carnival as a long-term initiative. She has exhibited persistence over time: “We just keep plugging away at it—at times there is overwhelming support for our theories and ideas. At other times, we meet with resistance. I just wait it out.” Her group also works along multiple fronts, but organizes its projects within a strategic context: “We juggle over a hundred different initiatives at one time. What is important is to keep a pulse on the strategic vision of our leadership team to ensure we are prioritizing these initiatives in alignment with that vision.”
When asked how she measures success, Shannon describes improvements in relationships, noting, “We’re no longer perceived as a weird species.” A second measure is the growing demand for her group’s services: “We’ve shown what we’re capable of doing, and now there is more demand than we can address. We’ve got a long way to go with analytics, but we have made great progress.”
Greg Poole, Business Function Leader, Talbots
Greg Poole arrived at Talbots, a leading retailer of classic women’s clothing, only six months before we interviewed him. He fills the new position of executive vice president and chief supply chain officer. He had previously held supply chain roles at retailers Ann Taylor and Gap, as well as retailers in Europe.
Greg says that analytics are “part of his DNA.” He likes to work from a position of fact. Talbots has not historically been very analytical in its decision making, and Greg feels that he and other members of the management team were brought into the company in part to inject a more analytical orientation. The entire leadership team is new to Talbots, and their goal is to turn around the company’s performance in an extremely difficult economic climate. Greg plans to build an analytical ecosystem, working in particular with the new CFO, the new head of a financial planning group in the merchandising organization, and an external consultant he’s using to gather and analyze data. He’s also bringing key suppliers into the network by informing them of Talbots’ financial position and giving them greater visibility into orders and business processes. He takes every opportunity to teach the rest of the organization about the analytical transformation he believes is necessary and has already presented to multiple groups around the company and to suppliers.
One of Greg’s earliest moves was to set strategy and performance expectations with the leadership of the supply chain organization. The three-pronged strategy is to focus on product quality, improve speed to market, and improve the sourcing cost position. He defined specific numeric targets in each area, and Talbots has already achieved some of them. Each product category owner also has targets, particularly in the area of margin improvement. Greg himself has signed up for results by offering a margin improvement to the company’s board of directors. He is working along multiple fronts by having analytical objectives for each strategic goal. For example, to help meet the cost position goal, he and his sourcing managers conduct “fact-based negotiation” with suppliers, improving metrics and analyses of supplier costs, relative prices paid, and price/volume curves.
Greg is continually pushing for more data and analysis, and is setting a hands-on example in doing so. One wall of his office is covered not with the usual inspirational posters and clipped Dilbert strips, but with charts and graphs. At each meeting of the supply chain leaders, he communicates key metrics, many of which have never been used before at Talbots. He is, however, aware that analytical progress will require persistence over time. At one of his previous firms, he says, it took three years to make the kind of transition he’s planned for Talbots, and that firm had substantially more resources. Fortunately, he feels that there are plenty of low-hanging opportunities for improvement.
Greg clearly has strong people skills, and seems to know the limits of analytics. While he’s introducing a large number of new supply chain management and analytical approaches to his organization (and to their suppliers), he is careful not to overwhelm the group or force them to use any particular approach. By setting a good example and creating a performance context, he hopes that managers and employees will see that using analytics is the only way to succeed.
Tom Anderson, Division Head and Entrepreneur
Tom Anderson is a confident executive with a straightforward manner. He’s been an analytical executive at several different firms, and knows that his analytical skills are one of his key strengths as a manager and leader.
After receiving an MS in Management at MIT, Tom worked for McKinsey & Company as a consultant, primarily to the financial services industry. He says that he always sought out the most quantitative modeling-oriented client projects in that role. After becoming a partner at McKinsey he left for Capital One, the highly analytical credit card and consumer financing business, to head the young adult (customers twenty-five and younger) business unit. He explains, “Capital One appealed to me because of the ‘information-based strategy’ approach they take. It was a lot of nonhierarchical problem solving. Anyone could suggest new analytical approaches, regardless of their level.”
Capital One had a strong testing culture, but he did not view testing as an end in itself. “If you’re allowed to do whatever test you want, something is bound to stick. But in the long run that’s not sustainable. Every test costs money. I felt we needed to track the impact and the rollout, and ensure that we achieved value,” Anderson remembers. He constantly pushed for data and analysis, but he told employees, “Before you do the analysis, write the document with the charts about the results, the why, and what you would do with it.”
Though Capital One had a lot of analytical people, he still had to demonstrate some of the analytical approaches he advocated. “You have to become a teacher,” he notes. “Some people already have the problem-solving capabilities, and you have to teach them the math. Others know the math, but don’t know how to apply it to business problems.” At times he set a hands-on example by doing the analysis himself, and showing others what he had done.
The young adult business did very well under Anderson’s leadership. It had historically made about $20 million in profit each year. “After about six months figuring out the business,” he says, “we made $70 million the next twelve months.” But Anderson wanted more autonomy, so he asked to take over a medical financing business that Capital One had acquired. He signed up for results with Capital One’s CEO Rich Fairbank, promising to turn the business around. The medical finance business was losing millions a year; when Anderson left, it was making tens of millions a year and loan originations were up 150 percent year over year.
Tom seeks opportunities where there is no single “silver bullet,” but the chance to work along multiple fronts. “The beauty of analytics,” he suggests, “is that you find lots of things that can be incrementally improved.” He looks for leverage where analytics can make a major difference to performance: “If it’s a multiplicative business, as medical finance was, and you can improve each factor—the number of doctors times the number of patients times the percentage that seek financing—by 10 percent, it’s huge.”
Tom left Capital One to lead a start-up business called UPromise, in which consumers receive money toward college costs when they spend money at certain businesses. Again, he sought leverage. The business had been focused only on acquiring new members, but he extended the scope to the percentage and dollar volume of purchases by members through UPromise. Eventually UPromise was sold, and Tom had little desire to stay at the acquiring company—in part because its leaders were not analytically oriented.
In each of these businesses, Tom attempted to hire smart people. The key to finding good people, he believes, is the combination of analytical and people skills. He believes he has both, and he seeks both in those who work for him. Some of his people left the Capital One young adults unit to join him at the medical finance business. Some also left Capital One to join him at UPromise. He’s still keeping track of some of them for his next venture, which will undoubtedly have an analytical orientation.
Jim and Chris McCann, CEO and President, 1-800-Flowers.com
Jim and Chris McCann are brothers who together run the 1-800-Flowers.com business. Jim is the founder, chairman, and CEO; Chris serves as president. The company originated as a single florist shop in New York and is now the world’s leading florist and gift company, with more than $700 million in revenues and a database of more than 35 million customers. In addition to its 1-800-Flowers.com consumer floral business, the company has also become a leading player in the gourmet food and gift baskets category with such brands as Fannie May Confections, The Popcorn Factory, and Cheryl & Co. cookies.
Jim and Chris are both analytical leaders, but their leadership priorities and styles differ markedly. Jim notes: “Chris is much more analytical than I am. I’m ten years older than Chris, and he’s learned from my mistakes. I have gotten more analytical over time, but I’m a florist and a social worker by background; neither group is known for its analytics!” Nevertheless, Jim’s focus on emotion as well as analytics is a strong asset in a business built around gifting and celebratory occasions.
Chris concurs: “We have created a good working style. Jim’s decision-making style is much more intuitive. I may think he’s crazy with an idea, but he keeps driving it. I want to see the analytics. It helps us to avoid mistakes, but we’ve realized that Jim is usually right with his intuition.” Chris also notes that they use data differently: “Jim’s style is to gather a little bit of data, and then begin to move quickly. I’m more oriented to having greater amounts of data, and then making a commitment. But I may stick with it for too long; Jim is better at seeing trends.” Clearly the two executives know the limits of analytics as well as those of intuition; both also have good people skills as well as an orientation to numbers.
The two brothers divide up strategic priorities. They have three strategic themes for the current economy: focusing on customers, reducing costs, and innovating for the future. Chris is heavily focused on the first two, which are amenable to better understanding with data and analysis. Jim is more focused on innovation, which is more intuition-friendly. Together they are clearly setting a strategy and performance expectations for analytical work.
Chris sees one aspect of his role as pushing for more data and analysis. He says, “We have a culture of analytics and testing. I say, ‘I know what you think—tell me what you can prove.’ We also subscribe to that comment, ‘In God we trust; all others bring data.’” Jim supports these maxims, though he may come up with an initial idea through personal relationships or observations. For example, he realized that one of the company’s products was perhaps too expensive when his son, a successful hedge fund employee, told him that he couldn’t afford to send it very often. But before taking action, the company gathered data to support that hypothesis.
When the McCanns decide to buy a company, they view analytical skills as one of the capabilities that the parent brings to the new brand. However, they don’t assume that their capabilities are always superior. Chris notes: “When we acquire a company we usually move them as quickly as possible into our analytical environment. But we bought a gift baskets company that had better merchandising and planning capabilities than we did, so we adopted many of their practices.”
In addition to using analytics in their own businesses, they are building an analytical ecosystem in the broader floral industry. They own the BloomNet network, which sends flower orders to local florists for fulfillment. They have substantial data on what flowers are ordered under what conditions, and they are beginning to share the data and analysis with florists and even flower growers.
The brothers McCann are clearly working along multiple fronts with regard to analytics. They have analytical approaches to operations, finance, customer relationships, and several other aspects of the business. In marketing, they attempt to combine an intuitive understanding of customers with the data and analyses that describe them; they use “personas” to embody market research in specific hypothetical individuals. “Tina,” Jim observes, “is a persona representing one of our best customers; she loves gifting, and views it as part of maintaining relationships. Having a name and a set of attributes for her is much more meaningful to our people than the demographic averages on which she is based.”
They believe that hiring smart people with analytical skills is critical to their success. “As we look at talent now,” Jim relates, “senior people must have analytical capability. They have to embrace data.” Not everybody has to be purely analytical, however, as Chris notes: “We have some very creative merchants and designers who are great at what they do. We try to complement them with analytical support where needed.”
It is perhaps unusual to find such complementary orientations to decision making in two brothers. But the McCanns argue that other companies can emulate their style with two executives who aren’t related: “We think there should be two at the top in every company,” Jim says—and Chris agrees.
Of course, the four examples of analytical leaders described in this chapter cannot represent the full range of approaches and styles. They do illustrate, however, that analytical leaders embody a variety of traits, and that analytical orientation is a critical aspect of leadership that should be more widely recognized in contemporary business.
It should also be clear from these examples that analytical leaders are not one-dimensional, numbers-obsessed cyborgs with stunted intuition. Instead, they are well-rounded individuals with both analytical and people skills. A good analytical leader is simply a good leader in general who happens to have a strong analytical orientation.
“Analytical leadership” is not a well-understood topic in the management literature, but if you speak with people at any level who are trying to make their organizations more analytical, they’ll attest to its importance. What they may not realize is that analytical leadership is not just the province of the CEO and the organization’s senior-most executives, but of any manager or individual contributor who seeks to make an impact.
Leadership Through the Stages
Rather than lay out a methodological approach to leadership in the chapter thus far, we’ve shown attributes and personalities. But next, we describe the different leadership environments as organizations move through the stages of analytical capability. A summary of these changes is given in table 4-1.
From Stage 1 to Stage 2. If your organization has analytical leaders at stage 1, they are likely to be both quite low in the leadership hierarchy and quite dissatisfied with the “analytically impaired” status of their employer. The best way to get from stage 1 to stage 2 is for leaders in business functions to emerge or be hired. They don’t need to toot their analytical horns a lot, but they do need to get analytical projects going and achieve some business results, so their organization will start noticing. The projects need only to achieve significant value—no loud huzzahs are necessary yet—so the stage will be set for the next level of leadership roles.
Moving to the next stage: Leadership
| From stage 1 Analytically Impaired to stage 2 Localized Analytics |
From stage 2 Localized Analytics to stage 3 Analytical Aspirations |
From stage 3 Analytical Aspirations to stage 4 Analytical Companies |
From stage 4 Analytical Companies to stage 5 Analytical Competitors |
| Encourage the emergence of analytical leaders in functions and business units. |
Create a vision of how analytics will be used in the organization in the future, and begin to identify the specific capabilities necessary. |
Engage senior leaders in building analytical capabilities, particularly in the areas of data, technology, and analytical human resources. |
Encourage leaders to be very visible with their analytical capabilities, and to communicate with internal and external stakeholders about how analytics contribute to success. |
From Stage 2 to Stage 3. In stage 3, organizations develop analytical aspirations. How do they develop them? Through the attitudes and actions of leaders. The aspirations at this point should go beyond improving particular functions; they should aim to benefit the entire organization. Therefore, the leaders who must step up must at least control major business units, but ideally work as senior executives of the entire enterprise.
Their job is to create a vision for how analytics will transform their business in the future—perhaps not possible today, but in, say, two or four years. Leaders of a pharmaceutical or health care organization could address how their businesses will be transformed by personalized medicine. Investment firms’ leaders might address how investment decision support might change their business and relationships with customers. The idea behind such a vision is that it would inspire more aggressive analytical activity and provide a conceptual umbrella for disparate projects that need to be coordinated.
From Stage 3 to Stage 4. Stage 4 is about building capability and resources—not vision, but execution; not talk, but action. Leaders at this stage are diligently working away on analytical projects and infrastructure, even though their firms may have other strategic priorities. Senior executives are insisting on fact-based and analytical decision making, and building it into the organization as one—but not necessarily the most prominent—cultural attribute.
The roles of functions that support analytics are particularly important at this stage. CIOs build the technology and data infrastructure for analytics. Line managers and human resource executives ensure that people with analytical backgrounds are hired and retained. Functional leaders, who built standalone analytical applications at earlier stages, align with other executives and create cross-functional, integrated applications. Analytical leaders at lower levels of the organization communicate and coordinate with each other, anticipating a time when analytics will become a central feature of their firms’ strategies.
From Stage 4 to Stage 5. Stage 5, with its visible-to-all analytical competition, is a “coming out party” for firms that have been at stage 4. The firm’s analytical capabilities move front and center and are displayed to the world. Leaders who have quietly pursued analytical activities need to become expert communicators about their work, both internally and externally. They not only have to do great analytical work but also have to persuade customers, investors, and even the press that their analytics provide a significant advantage. And since analytical competitors can never rest, leaders at this stage need to resist complacency and static analytical tactics. Continual review of analytics and their fit to the organization is required (as we discuss in chapter 9). It takes talented leaders both to celebrate the analytical achievements of their organizations and to spur them on to further exploration and growth.
Keep in Mind…
• Leaders should set a good example of analytical activity themselves.
• Analytical and fact-based decisions should be rewarded, and their absence rebuked.
• Leaders must find and nurture other people and organizations who can help their firms develop analytical capabilities.
• Leaders mustn’t let smart analysts snow them with analytical methods and tools that the analysts themselves can’t explain in straightforward terms.
• Leaders don’t rely totally on analytics in decision making; good decisions are often a mix of art and science.
• Leaders must find and nurture other people and organizations that can help their firms develop analytical capabilities.