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Quants Can Be Funny, Too: Marin's Sam Wilcke Talks Comedy and Data

Former Standup Comedian Takes Creative Approach to Analyzing Data

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Sam Wilcke, Marin Software
Sam Wilcke, Marin Software
Sam Wilcke once did eight or nine stand-up comedy shows a week. Today he's director of analytics at Marin Software, the digital ad management firm that opened a promising IPO in March.

"I was not doing this as a hobby," said Mr. Wilcke of his stand-up career, noting he spent about 10 hours each day writing and four hours a day getting to and from shows around California. He gave up his comedic pursuits when an Achilles tendon injury prevented him from traveling as much as stand-up demanded.

Mr. Wilcke got his chemical engineering Ph.D. from University of California, Berkeley, where he used Magnetic Resonance Imaging to help improve the lifetime of lithium batteries.

However, it was his work analyzing pricing data that propelled him to where he is now. For most of his career, Mr. Wilcke has handled large pricing-data sets, helping publishers and marketers determine the ideal ad rates or bid prices for digital ads. He got his start in pricing in '06 at DemandTech and later moved on to Microsoft-owned Rapt Digital.

He moved to Marin in 2011, tasked with managing bid pricing for search. "I created the analytics team here," he said.

His mission was to improve client campaign performance in areas beyond bidding. He's shifted away from day-to-day analytical work in his director role, and now manages a team of 13 people in Chicago, New York, San Francisco, and the UK.

"A lot of my day is goal setting and career development," said Mr. Wilcke.

Does he miss digging into the numbers? No, Mr. Wilcke noted, explaining that he doesn't get nostalgic. "I am looped into some analyses here and there but ... the new world of managing with executives and the team is so engaging," he said.

AdAge: You were serious about a stand-up-comedy career before you moved on to work with digital-marketing data. Is there anything about your approach to doing comedy that is similar to how you tackle data?

Mr. Wilcke: Absolutely! The punch line of a well-crafted joke cannot be the most obvious conclusion, yet it must make sense. When crafting a joke, we look at all possible conclusions, even ones that may not be initially obvious, but would make sense when viewed in a subtly different way. Similarly, when looking at data sets, we are often looking for all possible conclusions, in addition to the obvious. Often times, the most valuable conclusions turn out to be the less obvious ones, the ones that require looking at the data from a totally different -- but equally valid -- perspective.

AdAge: How has search data changed since you got your start at Marin? Do you spot patterns in search data that you saw while working with pricing data?

Mr. Wilcke: More and more search-engine marketers are looking at their revenue-generating conversions with increasing complexity. Rather than thinking of a simple 'conversion' event, more marketers are thinking of 'conversion funnels' (for example registration, initial purchase, follow up purchase).

Because there is a larger delay -- often time weeks or months -- between a click and the revenue-generating conversions or purchases, but no delay between a click and a registration, marketers make tradeoffs in choosing which conversion events to value when calculating bids. When I started in search, most marketers were just thinking of a single conversion event and managing to a cost-per-acquisition target.

Bids are effectively prices. So, my background in price optimization directly translates to bid optimization, which is effectively the same process.

AdAge: You've moved from data-crunching to a role that's focused on managing a large team of people. Do you find that you have trouble keeping up with new analytics techniques?

Mr. Wilcke: No. The analytical techniques I tend to see and employ in the business world are far simpler than those I employed in science. What I do find challenging is being able to devote enough time to perform enough analyses on a given data set. Like I said earlier, we often approach a data set from many angles. Even if we are performing simpler analyses, these will take significant time to cycle through many of them.

AdAge: What educational fields of study and professional backgrounds help develop the best data people?

Mr. Wilcke: We have hired MBAs, math majors, economics majors, scientists -- in the hard sciences, such as physics and chemistry -- and, most recently, an English major who happened to have strong math skills.

We look for people with three skills: analytical/logic skills, people skills and communication skills. Generally, these skill sets can be found across many disciplines, but are difficult to find in abundance in a single person. Traditionally, we take about six months to find each new hire (and go through hundreds of resumes and dozens of candidates that we bring in for interviews).

Mr. Wilcke: What's the coolest or strangest type of data set you've ever worked with?

AdAge: One of my favorite data sets I worked with was looking at checkout carts to determine which products to lower prices on to greatly increase profitability. For example, if you placed shower bases on sale, it would lead to a very large building project where people bought shower enclosures, sealant, paint, floor tiles, etc. If you put the shower enclosure on sale, people were more inclined to keep the project cheap, though, and do the bare minimum. In other words, some consumers need to replace their shower enclosure. If you made the enclosure cheap, they would replace only the enclosure. If you made the shower base cheap while the enclosure was regularly priced, however, they would jump in and redo a bunch of work on their bathroom, spending significantly more money on more profitable items.

This shows a way that we can use data to not only model consumer behavior, but also to influence it in ways that are more profitable. Not all our findings were intuitive, but we did still measure a positive impact from our pricing strategies based on these non-intuitive findings.

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