"Seeing a Different Game"

Leaders “See a Different Game”

When I was young, I was an avid reader of The Sporting News, a weekly newspaper whose tagline was “See a Different Game.”

Its promise was simple: by analyzing baseball statistics in new ways, it could uncover insights that ordinary box scores did not reveal. Some of the data came from official Major League Baseball statistics. But some of the most interesting insights came from amateur statisticians in the Society of American Baseball Researchers.

The lesson stayed with me throughout my career: leaders who find better data or analyze existing data differently can get superior results by seeing a different game.

Every business leader agrees in principle that data matters. But the real opportunity lies in finding data and signals that others overlook.

Today that challenge has become even more important. Audited financial statements still provide an essential minimum standard for reporting, but they are increasingly too limited to detect the deepest trends in a business.

Look at the edges of the bell curve

Many business analyses focus on averages. But some of the most valuable insights come from the outer edges of a bell curve.

I learned this when studying research from Dartmouth showing healthcare spending across more than 300 Medicare regions. On one axis was spending, from low to high. On the other axis were outcomes.

The surprising finding was that higher spending did not consistently produce better outcomes. This raised an important question: when does the relationship between spending and outcomes break down?

That insight helped us redesign our self-insured healthcare program. By investing more in prevention, navigation services, and incentives for cost-effective providers, we were able to improve health outcomes while reducing costs.

The lesson was clear: averages often hides the most important insight.

We applied the same thinking elsewhere. A handful of our inventors were extraordinarily successful in generating patents that produced large infringement royalties. One of them, Ron Sansone, secured a patent that eventually resulted in a $400 million royalty payment by Hewlett-Packard.

When we studied what these inventors did differently, we found something surprising. They were not necessarily better engineers or innovators. They were better predictors of where entire industries would eventually innovate and therefore where companies would most likely unknowingly infringe their patents.

The same statistical thinking helped us understand why some telesales customers retained postage meters after trial periods while others cancelled. A surprising pattern emerged: customers who opened the box quickly, installed the meter, and printed postage the first day were far more likely to remain customers.

A simple outbound call shortly after delivery increased retention dramatically.

The bell curve turned out to be a powerful tool for discovering insights hiding in plain sight.

Timeline analysis can reveal hidden trends

Another lesson is that portfolio averages can hide dangerous trends.

Shortly after I became CEO in 1996, analysts from Capital Group, our largest shareholder, questioned whether our equipment leasing business was earning an adequate return.

Our CFO assured me the portfolio exceeded our cost of capital. But the comment bothered me.

Instead of analyzing the entire portfolio, I asked the team to examine only the most recent two years of lease transactions. The results were troubling. Profit spreads were declining, asset values at the end of leases were falling, and tax advantages were shrinking. New leases were producing returns below our cost of capital.

The overall portfolio still looked healthy because older leases were profitable. But the hidden trend line was deteriorating. We began exiting that business, completing the process by 2006.

Without examining the timeline of new transactions, the problem would have remained hidden.

Pay attention to small anomalies

Sometimes the most valuable signals come from small behavioral anomalies. We noticed that about 250,000 customers were requesting emergency postage meter refills, paying a $25 fee each time.

At first this made little sense. These mailroom managers worked inside large organizations whose treasury departments could easily provide funds at far lower cost.

But the behavior suggested something deeper. When we studied the pattern, we discovered that many mailrooms preferred our financing because it gave them more control over their own spending and avoided internal chargebacks from corporate treasury departments.  This gave us confidence that extending lines of credit to long-term customers could be successful and scalable.

What began as a puzzling data point eventually led to a $200 million financial services business.  

Small anomalies often signal unmet needs.

Risk often hides in the future, not the past

Financial statements are excellent at reporting what has already happened. But they are less effective at measuring future risk.

John Miller, a colleague of mine on the Eaton Board and a retired oil company executive, taught me the value of applying a risk-adjusted cost of capital to major strategic decisions.

We eventually applied this method to our leasing business. Instead of evaluating expected profits alone, we discounted future cash flows using higher rates to reflect risk.

Once we did that, the case for exiting the business became compelling. When we ultimately sold the portfolio, our stock price rose 10 percent in a single day.

A similar lesson emerged in aircraft leasing. During the late 1980s, favorable tax rules made aircraft leases extremely attractive. Many companies, including Pitney Bowes, entered the business.

But these were 24-year commitments. Few companies adequately considered the risk of catastrophic disruptions over a 24-year period, especially given the troubled domestic airlines history. Bankruptcies were frequent and credit losses were staggering over time.

As a result of reduced air travel after 9/11, the airline industry collapsed into multiple bankruptcies in 2002. Pitney Bowes took a $213 million charge, while GE Capital recorded losses exceeding $4 billion.

The problem was not poor accounting. The problem was that accounting rules capture probable and estimable losses, not catastrophic risks which could not sized sufficiently to meet accounting requirements and whose timing could not be predicted.

Looking beyond financial statements

Today we are entering a new era.

Artificial intelligence makes it possible to analyze forms of data that never appear in financial statements or Management Discussion and Analysis sections of annual reports.

For example:  customer complaints in forums and social media, developer activity in software ecosystems, employee sentiment and talent flows, emerging competitive narratives, and supply chain disruptions

These signals often reveal changes in the business environment months or years before they appear in financial results.

Financial statements answer the question:

What happened financially?

But leaders increasingly need to understand:

What is changing in the system around us? What weak signals suggest emerging opportunities or risks? Which capabilities inside the organization are strengthening or weakening?

Building early warning systems

The challenge for modern leaders is to build parallel sensing systems alongside GAAP accounting. AI now makes it possible to monitor customer behavior, ecosystem health, talent flows, technology adoption, reputation dynamics, and competitive moves in near real time.

But the most important signal often remains human judgment.

At a pivotal meeting one year into my career as an operating executive, CEO George Harvey asked a simple question about the shipping products we leased to customers:

“Are these products that our customers want to buy or lease?”

The room fell silent. One manager finally offered a hesitant answer.  That hesitation told George everything he needed to know. Six weeks later he reorganized the business. Posing one question had become an early warning system.

Today, when a product or service struggles, I often ask a similar question:  “What would it take to make this a “must-have” rather than a “nice-to-have”?” The discussion that follows usually reveals signals we were not seeing before.

And that, ultimately, is what leadership is about:  learning to see a different game.