Scott Nelson,
President & Chief Digital Officer,
Tamarack Technology

Prior to 2002, the business of baseball captured tremendous amounts of data and stats but was largely managed by intuition and gut feel. But that all changed when Billy Bean and Paul DePodesta introduced a data-based strategy to build the Oakland A’s. Moneyball: The Art of Winning an Unfair Game by Michael Lewis has become one of the most important business books of the past 20 years. Moneyball is important because it demystifies the use of data and analytics in a business that was traditionally influenced by decisions based on human judgement and emotion.

Sound familiar? How many times does one read or hear how important ‘relationships’ are to finding and acquiring customers in equipment finance? Similarly, decisions are made in equipment finance everyday where the deal parameters don’t quite fit an underwriting policy, but the deal is funded because of the relationship. However, new decision-making behaviors have emerged in the industry based entirely on the use of data. These Moneyball behaviors are practiced by leaders who do not strictly follow traditional “relationship” approaches to the business but use data to drive decision-

making. They may or may not be familiar with Moneyball concepts or be fans of Billy Bean, but they are acting on and creating success with what are now commonly called “Moneyball Moments.”

A “Moneyball Moment” is not the point in time when one realizes they should use data. Baseball had tons of stats at the time, but scouts and managers were not asking questions of the data. A “Moneyball Moment” is that point in time when a business leader recognizes that they are not focused on the right metrics to make their business the best it can be. In Moneyball, Bean embraced several such moments using data to make decisions on players usings stats like on-base percentages versus batting averages, using his best pitchers early in a game so his third-tier pitchers could be closers and playing hitters who were patient and got-on-base more versus the flashy, more well-known home run hitters. Bean made the less obvious choices, the ones that made the most sense statistically, the ones that enabled the 2002 Oakland A’s to win a record 20 games in a row.

Data-managed baseball can be a metaphor for data-managed equipment finance — “we can Moneyball this thing” to use the now common verbal form of the book title. Michael Lewis story about writing Moneyball provides a method for finding Moneyball Moments in equipment finance. First, gather operational business insights from industry leaders — make a soft immersion into “the club house.” Second, look at industry data for parallels with baseball’s Moneyball Moments. In this metaphor — equipment finance for baseball — customers are the players and the goal is to assemble the right players to build a high performing team — a reliable and profitable portfolio.

Insights from Industry Leaders

What type of customer is your favorite? Makes the most money? Is the safest for the business? Industry leaders’ answers to these questions provide insights into some of the attitudes toward customers in equipment finance as well as identify key characteristics that are not typically part of the customer acquisition process.

Favorite Customers

One of the problems with ‘your favorite’ security questions is that ‘favorite’ is both emotional and fleeting. Emotion is reflected in the relationship definitions: an appreciation of the finance company, i.e., “I like them if they like me” or a customer who effectively includes the lessor in their ‘family’ challenges and decisions.

Note that these answers do not address the question, “Will favorites perform for the business?” The definitions of favorites do not include or require data and as such lead to more subjective or intuitive decision making — a decision process that is difficult to reproduce and scale.

Most Profitable Customer

Not surprisingly, profitability immediately brings data into the discussion. Renewal percentages, transaction sizes, company sizes and contract type are easily measured and tracked in the company’s systems. An understanding of profitability highlights the opportunity data presents in finding and identifying the right kinds of customers in the data streams of the business.

Safest Customer

Safest versus Profitable customer analysis reveals an example Moneyball Moment: “Safe customers pay off because they don’t like payments” versus “Most profitable customers are those who renew.” Lewis said Bean and DePodesta realized that others in baseball were looking at the most obvious stats and not the nuanced ones. Customers that prefer to pay off a loan because they don’t like payments are safe, but customers who renew are more profitable. Question: “What do credit scores say about a propensity to renew?”

Three Moneyball Moments in Equipment Finance

Favorite and Safest versus Profitable provide insight into both sides of a Moneyball Moment — the traditional measure or method versus the most important measures that should be driving business decisions. A Moneyball approach might use data to understand and find more profitable customers rather than safe customers. It’s understanding the importance of relationships and customer behavior within the definition of a “favorite customer” while augmenting that understanding with data that highlights the most profitable. The Moneyball Moment is using data to acquire the right customers — not just any customer — to be the most successful and to “win.” What are equipment finance’s on-base-percentage versus batting-average insights?

“Make More Than We Lose” vs. “Avoid Losses at All Costs”

An equipment finance CEO once said to me, “We work hard to get funding — I don’t want to lose it.” Lewis said scouts and managers in baseball had one primary focus: “avoid humiliation.” To do this they pursued the same time-tested players as everyone else and followed the same traditional game management methods because they didn’t want to be humiliated. The obvious analogy in equipment finance is “avoid all losses.”

But what happens if we switch from avoiding losses to making more money than we lose? Three things happen when this model is run well. First, the business has a much larger TAM in any given market because the business engages risky customers who are shunned by traditional competitors. Second, the GM% on any given deal increases because this market segment is willing to pay more to get access to capital. Third, the profitability of the overall business increases because the portfolio is managed to engage risk at greater returns. The table chart below shows how this works when a portfolio is broken down and analyzed by credit tier.

Three key insights jump out of this data. First, the percentage of funding lost lags the number of deals lost showing how the business uses deal size limitations to mitigate the expanded credit tier risk — much smaller deals are made in the D tier. Second, one cannot blindly embrace high risk credits because the Adj GM rolls over. There is a sweet spot in the market beyond which the percentage of losses gets too high and the business starts losing more money than it makes in that Tier. Third, an A-credit portfolio makes safe money — the GM% and Adj GM% are equal — but engaging “winning more than losing” can enable a company the ability to make 50% more money — 29% GM versus ~19% while growing faster in a larger TAM. The key, of course, is consistently making the right decisions which is where AI solutions help increase the precision and prediction of outcomes. AI predictors can separate C-credit delinquency from C-credit losses at origination and tilt the balance to making more money.

Seek Renewal vs. Avoid Delinquency

In baseball the key to winning is runs, so Bean and DePodesto asked, “What does a player’s batting average say about runs?” They switched their focus from batting averages to runs and in turn to on-base-percentages because a player can’t score a run if they are not on base. Our interviews found the widely held belief that the most valuable leasing customers are the ones who renew. Simple math regarding acquisition costs and extended cash flows shows this to be a fact, not a belief. Yet the industry has an infatuation with delinquency and loss. Why not focus on renewals — on acquiring only customers who renew?

If renewals are the focus, two measures become critical. First, the deals must be true leases so a renewal is an option. The second part is finding customers who are more likely to renew. Perhaps it’s not surprising that D credits have a higher renewal rate as they are more likely to get true leases and more likely to stick with the deal once they get it. A, B, and C credits are statistically flat, but when one then considers the asset type, a second opportunity arises — forklifts. Forklifts renew at twice the rate of similar heavy equipment.

Credit scores and background checks do not provide much insight into renewals, but every finance company who has been in business for more than a few years has “dark data” that tells them about customers who renew. Renewal is a relatively easy prediction for an AI agent because it is a very clearly defined outcome. Renewal prediction percentages could be the on-base percentage of future successful finance companies. Perhaps a B-credit all-forklift portfolio is a winning team.

Right Applications vs. More Applications

Perhaps the most central tenant of the Moneyball story is the selection and fielding of the right team — putting the right people in the right seats. One of the more nuanced parts of the story was their preference for patient hitters. Scott Hatteberg ended the 2002 season ranked first in the American League for not swinging at first pitches. DePodesta had a simulation model of an “all Scott Hatteberg” team that scored 53 more runs per year than the $125 million per year New York Yankees at the time.

Equipment finance tends to covet “at bats and the first pitch” in order to get more applications and process them as quickly as possible. But the Favorite and Safest versus Profitable customer analysis points to the fact that origination should be about being patient and finding the right customers — not just processing more applications from more customers.
This can be done by identifying the characteristics of the right customers for the business’ strategy and desired outcomes.

Then the sales and marketing team would target and select only customers with characteristics. AI-based automation bridges the gap here by accepting as many applications as possible while only selecting the right customers. Because the AI will give a “quick no” to deals that do not fit the criteria it can, ironically, deliver the patience needed to build a portfolio of the right customers for the given business objective.

Your Moneyball Moment

Moneyball Moments are happening every day. Moneyball has been widely adopted by sports team owners and has crossed over to business leaders everywhere. But taking action when everyone else is looking at the problem differently is not easy and takes courage. But equipment finance has two big advantages to help move the business to the next level. First, Billy Bean’s example gives confidence that the process can work. Second, lessors have the data needed to understand the risk associated with changing how customers are selected and the business is run. Moneyball Moments are already visible in the industry and more examples will be seen in the future.

EDITOR’S NOTE: Interested in exploring this topic further? See the footnote below for additional reading.

¹Bean, Randy, “Moneyball 20 Years Later: A Progress Report on Data and Analytics in Professional Sports,” Forbes, Sept. 18, 2022.

ABOUT THE AUTHORS:

Scott Nelson is the president and chief digital officer of Tamarack Technology. He is an expert in technology strategy and development including AI and automation as well as an industry expert in equipment finance. Nelson leads the company’s efforts to expand its impact on the industry through innovation using new technologies and digital transformation strategies. In his dual role at Tamarack, Nelson is responsible for the company’s vision and strategic planning as well as business operations across professional services and Tamarack’s suite of AI products. He has more than 30 years of strategic technology development, deployment and design thinking experience working with both entrepreneurs and Fortune 500 companies.

Timothy Appleget is director of SaaS products at Tamarack Technology. In his role, he is responsible for the development and implementation of Tamarack’s technology services offerings around data, IoT and workflow automation. He joined Tamarack in March 2021 after more than two decades in equipment finance with Wells Fargo. He has a deep understanding of enterprise solutions in the industry built on years of experience of leading technology and business operations teams.