Data Firm Muscles Up, Hires Data-Pro CFO

At a succession of finance jobs he’s held the past 15 years, a facility for data collection and analysis has made the difference for Steven Love, t...
David McCannSeptember 30, 2013

For Steven Love, the opportunity to become the first CFO at Evolv, a four-year-old Silicon Valley start-up, was something like love at first sight.

Evolv is a data company. It merges information from its clients’ human-resources systems, Evolv’s own database and publicly available sources into a cloud-based platform that identifies job candidates worth hiring. The platform also lets companies assess employee performance and spot the employees who are at the greatest risk of leaving.

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Count Love among the growing legions of finance chiefs who view data analysis as the key to getting the job done well, right up there with understanding what you’re selling and whom you’re selling to.

Picture of Evolv CFO Stephen Love

“Opinions can differ on what the data means. But that’s a great discussion.” — Evolv CFO Steven Love

“When I saw this opportunity I thought, wow, what this company is doing totally synchs with how I go about my job,” Love says. “That is, make sure you’ve got great data that you understand and can use to make business decisions. For our customers we’re applying that to the oldest business cycle out there: the work force. Every company has that, so we think we have a very sizable opportunity.”

So far Evolv has targeted companies with 10,000-plus employees but plans over time to drive further and further down the size scale. It will need a lot of customers to grow into a large company; revenue is now at a run rate for “the double digit millions,” says Love.

Love’s eyes were opened wide to the importance of strong data in 1999, when he joined Portal Software (since acquired by Oracle), which had been a client of Love’s at Ernst & Young.

At E&Y Love was a revenue-recognition expert, which Portal, which was going public, needed. He made the move, he says, because he’d tired of giving clients bad news after a quarter’s close. “I dealt with software companies, and software revenue recognition was complex in the late 1990s,” Love says. “Often my clients had different revenue than they thought they did. I wanted to be on the company side and work with a CFO, sales team, customers and contracts so we’d have a consistently accurate view of expected revenue and not have those surprises.”

At Portal, where Love was at first the controller, a key task related to revenue recognition was collecting and analyzing data flowing from the company’s customer contracts. The information was sliced and diced by contract size, product type, customer geography and payment terms.

Such categories are (as they were then) useful for building out a revenue-recognition history that, along with accounting guidance, determines what a company’s revenue-recognition policy can be. For example, say most of a software company’s contracts call for payment 30 days after the deal is signed, but for some customers the term is extended to six months. In the latter case, recognition of the revenue ordinarily has to be deferred for that amount of time.

But if, over time, an adequate number of six-month contracts in a particular category are successfully collected, the company’s history may be deemed sufficient to allow earlier recognition of revenue from those contracts. “It is critical to capture and monitor data points that support your revenue-recognition policy and monitor for any appropriate changes,” Love says.

It was at a subsequent employer, prescription-drug maker Connetics, where Love was vice president of finance and administration, that he had what he calls his seminal data experience.

About 90 percent of Connetics’ inventory was distributed through the “big three” drug wholesalers – AmerisourceBergen, Cardinal Health and McKesson. Early in Love’s tenure, at the beginning of 2006, these companies began to provide detailed inventory information – product on hand and shipped, by warehouse and by SKU. “Before, they had considered that their proprietary information. It was a really big change for the pharmaceutical industry,” he says.

When the wholesalers started providing the data, Connectics’ finance department noticed that inventory levels were higher at some warehouses than what was reflected in Connectics’ internal operational data. After looking into the matter, Love’s team confirmed that the external data was correct. But, because the company had previously relied on the incorrect internal data, for five years it had reported insufficient returns reserves – the quarter-end balance-sheet liability for unsold product that is expected to be returned after the quarter.

The errors were material enough that Connectics had to restate five years’ worth of financial statements. That was bad enough, but because there were no current and valid SEC filings, the company risked defaulting on two convertible debt issues. Connetics had a mere 90 days to redo the financials or else risk defaulting on $280 million of debt.

It beat the deadline by two days, which was something of a miracle because of legal distractions. “Whenever these things happen, you get parties coming out of the woodwork to sue you,” Love says.

Later Love moved to Affymax, a company that conducted clinical trials of new drugs, as its vice president of finance. Affymax was trying to gain approval for its own first product, an injectible drug that treated anemia in patients with chronic kidney disease. The company had poured 10 years of work and $900 million of capital into the effort.

When the great recession hit in 2009, Affymax had just begun a two-and-a-half-year, phase-three clinical trial program that would cost $2.5 billion, far exceeding the rest of the company’s operating expense. “It was hugely important that we had an accurate understanding of the operating expense for that program,” Love says, especially because it would have been prohibitively expensive to raise money at that time.

There are hundreds of steps in such clinical programs, each of which drives an expense. So the company scrupulously collected reams of highly detailed data on expenses related to each of the 2,600 patients that participated in the trial. The strategy worked, as the drug ultimately was approved. (The euphoria was short-lived, though. Just months after the drug, called Omontys, was commercialized, Affymax pulled the plug on it last February because several patients had died from the treatment. Affymax itself is now on life support and not expected to survive.)

At Evolv, a primary goal for Love is to scale the company for future growth. “We’ve proven that our product works and that Fortune 100 companies will buy and will expand their usage,” he says. “Now the challenge and opportunity in front of us is to prove that the Fortune 500 will too, then eventually smaller businesses.” His other big task is working up financial forecasts for the next three years.

Data analysis certainly will play a key role in both endeavors. “Data sets you free to make the best decisions possible,” Love says. “Now, opinions can differ on what the data means. But that’s a great discussion, as opposed to having to ask what data you have.”