Why Physical Science Startups Need Social Scientists

Physical scientists, from physicists to biological engineers, are great at solving problems that deal with the laws of physics. However, those of them who own or lead startups often also feel compelled to try solving problems of business strategy, usually by themselves. And since the concept of doing business is founded upon profitably selling one's solutions to human decision-makers, this begs the query:

Can physical scientists optimally answer the questions common to competing in business without the help of social scientists?

I’ve seen a number of physical science startups face this apparent quandary in their marketing, especially where customer data appears immediately actionable. That said, I thought I’d tackle the question head on, because knowing the answer could help such startups make better business decisions, particularly in marketing.

First, They’re Actually Sciences.

First and foremost, it’s important to understand that the social sciences are indeed sciences and that, no matter how “special” a market appears to be, its decision-makers are still very much human. Social scientists use the scientific method to conceptualize logically valid tests for their hypotheses and to return reliable, unbiased results. Their studies generally center on finding statistically significant relationships (i.e. they’re not by chance) between variables (e.g. stimulus XYZ positively influences purchasing of product 123), and such studies have been going on for decades.

Consequently, even if you don’t conduct your own primary research, social scientists are now collectively versed in hundreds of scientific findings -or proven phenomena- that could readily apply to your business strategy. And the phenomena they know to look for and apply largely depend on what kind of a social science practitioner they are, be they a marketing researcher/strategist, economist, etc.

No Group is Immune to Science.

One such phenomenon demonstrates that no group is entirely immune to their humanity, not even those who are supposed to be the utmost objective of experts. In a study of over 1,000 parole decisions made by eight experienced judges in Israel over the course of 10 months, Danziger, Levav, and Avnaim-Pesso (2011) found that a judge’s willingness to grant parole was significantly moderated by how long it had been since they had taken a snack or lunch break. The judges began their days granting parole about 65% percent of the time, and that rate would steadily decline –to near zero- until they had taken their next break. Then, the judges’ rate of granting parole went right back up to 65% and repeated the decline until the next break.

You’ll note that not only was this decline due to non-ideological factors, but more pertinently (to this discussion), it was partially due to the repetitive nature of this tragically life-changing task. This decision fatigue is real, predictable, and just one phenomenon that a social scientist should know and leverage for their high tech employer or client. Think about how this knowledge can be used to systematically improve conversion rates or, in more Machiavellian applications, cause market members to make less prudent decisions (ever try subscribing to web hosting?!).

In another example of our immutable humanity, Townsend and Shu (2010) found that, when experienced investors weren’t primed to the notion of aesthetics potentially influencing their judgments, their ranking of a firm’s annual report, versus that of other firms, was significantly influenced by how “pretty” that report was. A financial or tech firm’s social scientist (e.g. their marketing exec/strategist) might know about this effect and thereby advise their employer on the need to wield it when producing financial reports for stakeholders.

Sure, You’ve Got Data, but Do You Have Insights?

Sure, the kinds of behavioral data you need to make business decisions are now abundant and approachable. Still, not everyone can analyze and/or interpret this data correctly. While it’s true that many physical scientists are adept at inferential statistics, it seems reasonable to assume that they aren’t as practiced on the specific kinds of statistics or variables we use to analyze the behaviors of and between humans. To that end, while nobody is free from bias, social scientists have to regularly and logically prevent such biases from entering their interpretation of behavioral data and/or study conceptualization. That's largely because of the "black box" nature of the mind and how that forces social scientists to deduce away, in written form, other possible explanations for their findings.

One such fallacy I’ve seen some smart, good-meaning physical scientists pursue is the notion of surveying their customers for insights into how they should develop new products and/or hone their promotions, usually with the hope of increasing their market share (i.e. “marketing”). A social scientist would quickly recognize that this firm is attempting to sample their customer base, which logically cannot accurately represent their market’s demographics, behaviors, or preferences. Such respondents only represent a specific mix of demographics, behaviors, and preferences that led to purchasing from this firm. Therefore, such a survey might help a firm squeeze out a bit more revenue from their customers or from the few similar market members they’ve yet to engage. However, if this firm truly wants to increase their market share, they actually have to draw their insights from a random –and sizable- sample of their market.

Social scientists also know how to test for –or prevent- the influence of exogenous social variables (i.e. variables that are thought to be outside the specific system studied). Plus, they plainly should know more of them, especially in their area of practice (e.g. marketing or buyer behavior). This can prove very valuable where a firm’s marketing analytics display some fully unexpected behavioral patterns.

For instance, an electronics manufacturer could believe that they have done everything right with respect to promoting their supposedly in-demand product within the media and channels they control, yet their target market is simply not buying. Why would that be? Well, the engineers who devised this product might be surprised to learn that, outside of sheer utility, social factors such as peer-reference group judgments could be limiting their potential to convert interest in the product into actual sales of the product. Their product or brand could be considered "amateur" or "too good to be true" by a reference group (e.g. theI.E.E.E.), causing prospects to fear trying it.

A social scientist, like a qualified marketing researcher/strategist, would know how best to test for the above and can then formulate strategies to moderate -or capitalize- on whatever they find. For example, they could plan and execute native advertising campaigns that address a market's concerns over a product and/or systematically capitalize on the market's concerns over competing solutions!

CHECKSUM(Social_Sciences)

In sum, whatever the reason(s) some physical scientists may have for considering business strategy without the expertise of a relevant social scientist, it seems it'd be wise of them to rethink doing so. They might be surprised to see that their social science colleagues had indeed studied one or more sciences in their 6-10 years of schooling. For, just as physical scientists can wield physical phenomena to identify or produce physical effects, social scientists can wield social phenomena to identify and produce behavioral effects. And these effects can lead to some beautifully physical outcomes, like higher profits and longer-term competitiveness.

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Jude Calvillo is the Co-Founder of a Stealth-Mode Startup in the Behavioral Sciences. He’s also a marketing researcher, strategist, and interactive producer at Sovereign Market, whose past clients include SRI International, Medtronic (Corevalve), and Insight Investments. He holds a Masters in Communication from the Johns Hopkins University and a Bachelors in Political Science from UCLA.