I have discovered, over the years, that I’m a spectrum thinker. On white boards and bar tables and with wild air gestures, I always seem to be explaining how there are two opposing endpoints and why I’m only interested in this or that part of the area between them.

So it is perhaps unsurprisingly that over the past few months of evangelizing the idea of a Chief Behavioral Officer and talking to companies both large and small about how psychology fits in their business, I’ve started to see a spectrum in how application is happening.

On one end is Insight. The function typically lives in data science/analytics/whatever the heck we are calling it these days and reports to the head of that area. The primary inputs tend to be variables that are already instrumented, and the primary output is typically some sort of report that indicates a potential surface area for change, with some more ambitious companies also including a few recommendations for high-level potential intervention. This report is handed off to whoever controls the variable itself (marketing, product, ops, etc.), while the Behavioral Scientist returns to the data puddle to investigate something new.

This is where the bulk of the job openings are at the moment: Allstate, Amazon, Facebook, you name it. In some ways, the descriptions often sound like a hybrid of user research and data science, with the goal expressed as “We want to understand our users’ behavior, particularly where it is irrational”. Understand is the key word; this role is about the why of human behavior. Certainly there is an implicit belief that the understanding will lead to better behavior change, but the actual change lives elsewhere, with whoever owns the lever that may need pulling.

Contrast that with the other end of the spectrum, Intervention. This function is focused on the actual changing of human behavior and seems to be living in Strategy/Innovation/Global Services. While this role may touch data, it doesn’t seem to have analysis at the core of its function (think SPSS instead of R) and if paired with a solid data team, may not actually be doing much data work at all. The output isn’t a report but rather an intervention that has been experimented and iterated until it can be shown to reliably change a behavior and is ready for scale.

Similar to Insights, there is still a handoff at the scaling point, where the intervention is handed off to the relevant team for ongoing ownership, but relatively speaking, the Intervention function is picking up the ball later (after an insight) and carrying it farther (a scaleable intervention exists).

There have been comparatively fewer roles I’ve seen here, in part because Insight already fits into existing structures (Data Science reports on trend, someone else pulls the lever), whereas Intervention requires creating a new step in between. But I believe that this is a little like the recent pseudo-bifurcation of data science as analytics (BI, Insight teams, etc.) and data science as product (machine learning, AI, etc.). A conversation with an insurance company recruiter sticks in my mind: “We’d love to be doing intervention, we just don’t think we are there yet, so we’re starting with insights.”

Is this spectrum rigorous? Absolutely not, and every company is thinking about it differently. There is no science here, only an attempt to pattern match the signal out of the noise. But I think that in order for behavioral science to catch up to data science in terms of corporate understanding, it behooves us to start understanding how to use a common vernacular. Executives need terms they can buy in to and recruiters need roles they can recruit for.

One potential option is to recognize the commonality of the two roles by keeping a single title, Behavioral Scientist, but emphasizing differing job requirements and responsibilities. Speaking very broadly, I’ve seen more postings use “behavioral economics” as a requirement when they are looking for Insight and “behavioral design” when looking for Intervention, although both of those terms are about the modification of other fields to incorporate psychology rather putting psychology at the center.

In my conception of CBO, both insight and intervention are needed. I’ll admit that I’m biased toward the intervention side, since my expertise is mostly in the building of things, but look at Bing in the Classroom: there were initial insights (“School search volume is lower than expected”, “Curiosity is not the root cause”) that allowed for the intervention. Ditto GetRaised (“Women are significantly underpaid”, “Women are less likely to ask for raises and less likely to get them when they do”).

But as with data science, we must resist the urge to simply relegate behavioral scientists to insight functions. There is a natural tendency to look at the black box of human behavior and long for understanding.  But in reality, business is driven by the ability to change behavior, so to not apply science directly to the intervention design seems foolhardy.  Regardless of which is more needed, however, the predicting of behavior and the modification of behavior are related but not the same, and should not be painted with a single brush.

Side note: For years, I resisted calling myself a feminist. Typical arguments about humanism and striving for equality not being gendered and blah blah blah. And now it is in my damn Twitter bio. Similarly, for years I’ve resisted the term behavioral design. Science is so important to me, it is hard to leave it out. And yet as people increasingly use behavioral design to differentiate from behavioral economics, it may be something to consider. I’m not convinced enough to yet start using the term in a self-applied way…but I’m tempted. Particularly because I distinctly don’t want to spend the rest of my life predicting behavior; I want to create it.

Earlier this week, I bombed a talk at the Professional Convention Management Association’s Education Conference.  This is actually fairly rare for me: because I love the science, it is normally easy for me to talk fluently and authentically.  This week, though, I just couldn’t get it together.  So I’m going to do something I try not to – write what I should have said.

Behavior Change
Before I melted down, I did a pretty decent job of explaining at least the basics of competing pressures.  But I missed a few key points that are worth surfacing.

First, because of the natural tendency to focus on promoting pressures, there is a great deal of whitespace on the inhibiting pressure side.  But it is not just because of our focus that this remains true.  In my M&M example, you’ll notice that the promoting pressures tended to be heterogeneous: one person wants to eat M&Ms because their blood sugar is low, another because they are delicious, another because they’re a bit sad and need a delightful moment.  But the inhibiting pressures tend to be homogeneous: we are all affected by cost, availability, etc.  Thus, while strengthening a promoting pressure may help a select few, weakening an inhibiting pressures tends to help everyone.  Thus, on a dollar-for-dollar basis, the money you spend making things easier to do will have higher ROI than those you spend on making people want to do things in the first place.

Another benefit of focusing on inhibiting pressures is that it helps move away from a nuclear arms race for attention.  To a behavioral scientist, the limited resource in the world isn’t time or money but mental energy.  So when we go the traditional route of trying to maximize share of mind (“If you only attend one conference this year, it should be X”), we force all conferences to compete with each other for a fixed and limited resource.  On the other hand, if instead what we do is make things easier and more mentally efficient, we actually unlock greater potential.  If I can provide the same value, but at half the mental, then that creates new focus that can be used to attend another conference or take another action at the same conference.

In many ways, this is really a plea for focus.  Conferences have become choice overloaded, a sort of fear-of-missing-out hell where everyone feels like everyone else is getting more value than they are.  I’m not saying any of the following products should exist, but imagine if they did.

What if, instead of making me try to find friends and make dinner plans, your conference app automatically set me up for dinner with people you thought I might enjoy?  This might sound a little crazy but let me tell you about an experiment I did once.  We built an app that advertised itself as doing one thing: sucking in all your personal data, then recommending the absolute best place for you to have lunch.  You logged in with Facebook and got a recommendation, but on the backend, we didn’t actually personalize at all.  We simply pulled randomly from a list of restaurants with good ratings that were nearby.

People loved it.  They said it knew them so well, marveled at how much it could tell just from their Facebook data (this was a few years ago, so people might not be surprised now), and how much better it made choosing food.  We can so often get obsessed with the idea that if we can’t do things perfectly (deliver on those promoting pressures), they aren’t worth doing.  But sometimes, just holding value constant and making things easier is worth it.  Everyone has to eat.  Don’t make them choose where and who with unless they want to.

Another example is around increasing representativeness of speakers.  So often, I hear from organizers who say “Well, we just don’t get that many proposals from women” or “There just aren’t that many underrepresented folks that want to talk”.  Bullshit.  There is a huge difference between not wanting to talk and not feeling like you are the right person to do so.  What if you ask your existing speakers (particularly if you’re paying them) to run a quick speaker training for attendees who might want to try speaking next year?  Or create speaking slots with lower barriers to entry, like lightning talks of two minutes, talks that are responses to a prompt, etc.  There are a million ways to reduce inhibiting pressures here and you’re the real experts – it simply starts with not accepting the status quo.

Conference organizing is a tremendously difficult creative endeavor.  And as with many such things, those who are responsible for it frequently resist the notion that data can be helpful, in part because it feels like it may destroy the creative impulses that take a good conference and make it a great one.

I want to bring a different perspective.  As a behavioral scientist, data is one of my primary tools.  And yet my job, which can be summed up as the designing of interventions that change behavior, remains highly creative.  Rather than removing the need for ingenuity, data allows me to spend more time actually doing the creative work I like doing.
First, data tells me where to look, not just for behaviors that can be changed but also at what levers might be most effectively pulled to do so.  For example, let’s pretend I have a behavioral goal around connecting (a frequent topic at PCMAEC), something like “All attendees will leave the conference having met three new people that they talk to at least quarterly for the next eight quarters”.

Without data, I have no idea how close or far I am from that goal.  But more importantly, data isn’t just a scorecard.  A data-driven perspective on what is already happening allows me to spend more time on the why and thus on the how of change.  For example, by understanding who is already meeting the connection goal at my conference, I can investigate what is different between that group and those who aren’t connecting, and then design an intervention that bridges the gap.  But to do that, I need a data-driven perspective on what is already happening.

Data also unlocks new features and products that wouldn’t otherwise be possible.  Think about trying to address the frequent manager question of “Where should I send my employees to get professional development?”  In a world without data, the best we can do is persuasive marketing: employees get sent to whoever tells the best story.  But with data, the story can itself be driven by verifiable truths.  A manager can decide what variables matter, like where their competitors are sending people, the ratings, novelty, and diversity of speakers, average seniority and background of attendee, etc. and then make a decision based on their priorities.  That’s powerful and it will make for better conferences.
Even if none of that convinces you, data is here:  Sponsorshipped, Feathr, and others aren’t going anywhere.  Conference sponsorship and attendance is the last unmetriced frontier of marketing, and as we’ve seen across other industries, you can expect that sooner or later, everyone will be using data to evaluate this spend.

But if you’re confident that you’re producing a great conference, that should be empowering.  The only people who should fear data are those who are actually bad at what they do.  In every industry where data has been embraced, spend has gone up for those who do it well.  Think of it this way: Google would never have built Google Analytics if they thought it was going to drive down spending on Google Ads.  Putting clarity into the value of both sponsorship and attendance is an opportunity to show how importance conferences actually are.

So when the startups come knocking, give them the right feedback to control your own data destiny.  Because you can influence how this all plays out but only if you lean in rather than out.

Paying Speakers
People are often surprised to learn that I don’t belong to a speakers bureau or charge speaking fees (don’t judge me by my PCMAEC appearance; I’m normally providing more value).  But I want to argue that not only will this becoming increasingly common, it will also become the dominant norm, and it will change the entire conference industry.
Before trying to prove this, it is important to first clarify my policy.  I do allow conferences to pay for my travel, so that speaking doesn’t actually cost me money.  And if they already have a budget and are paying all speakers, I’ll ask them to donate the money to a domestic violence shelter in that market, as Sanders/Wingo recently did for me in El Paso.  So there is still some budget changing hands here, although I’ll pay my own travel if the audience is important or unique enough.

I also have to own some privilege here.  One of the reasons that I can afford not to charge for speaking is because I choose not to make speaking my job and have had financial success in other areas.   I build things for a living and that is literally the only thing I allow people to pay me for.  And as a white dude, I don’t have to make conferences pay me in order to have them take me seriously.

This is real and important.  Many of my underrepresented speaker friends have had horrendous experiences, from being denied a private space to nurse in to being asked to write extensive blog posts that white male speakers weren’t to extra “content vetting calls” before they were allowed on stage.  Nobody should have to ask to be paid simply so they can be taken seriously and the speaker community is small; when conference organizers engage in these kinds of activities, they lose both access to premium speakers and risk potential public exposure and the corresponding loss of attendance and sponsorship.

Issues of respect aside, there are several reasons I see speaker fees going away.  Let’s start with a logical axiom: all things being equal, the speaker who doesn’t charge can make more appearances than the speaker who does, simply because more people can afford to put them on stage.  And now more than ever, getting on stage has downstream monetizable effects.  As the working world moves increasingly away from execution-focused tactical work to knowledge-focused strategic work, demonstrating an ability to be strategic and thoughtful is what gets you a high paying job.  Even if I charged for every speech I did, it would pale in comparison to my actual salary, which is in part based on demonstrating the competencies that I show on stage.

So there is a simple economic motive to not charge for speaking, if getting on stage elevates the chance that you will take some other higher value action, like booking someone for consulting, hiring them into your company, etc.  This is different from a stage action in a non-monetizable field.  For example, if you want me to dance, you do have to pay me, because dancing doesn’t lead to Chief Behavioral Officer.

More than just speaker economics have changed, however.  In the traditional conference format, you were paying a significant amount of money specifically to gain access to a speaker.  But this was developed in a pre-internet era, where people were unable to get free, high-quality content with the same ease.  Fifty years ago, the only way to understand my view on a competing pressures model was to attend a lecture in which I spoke about it.  Now, you can just go watch a free YouTube recording of one of my talks or read my book due out next year, and you can get access to my knowledge easily and for a fraction of the cost.

But despite MOOCs and other methods of potentially accessing knowledge, people still go to college in droves.  Why?  Because self-motivating is hard.  You could go watch a YouTube video of me giving a talk but it won’t be affective in the same way watching it live will.  Because speakers respond to audiences, every live talk I give is different.  And there is still an important part of learning that requires human interaction, not only during the speech but after.

To put it differently, at PCMAEC, people identified the two dominant reasons people attend conferences: learning and connection.  On YouTube, you can’t ask me a question or grab a drink and introduce yourself.  You can’t walk out shaking your head and talking to someone you just met who was sitting next to you about how terrible I was, then exchange business cards.  Yes, YouTube has commenting and I could do a Q&A video, but there is a very real difference between computer mediated interactions and the ones we experience in person.  Indeed, as one person so eloquently put it on Twitter, because they are a remote worker, they now go to conferences just to be around people.
But what does this have to do with the death of speaking fees?  Well, if we accept that the movement is away from conferences that are simply about providing access to knowledge and toward a more interactive form of both learning and speaking, the monetary value of speakers will eventually decline as the conversations we have before, during, and after, and the corresponding connections we make around those conversations, become the primary value driver for attendees.  If you think of speakers as simply the fodder for that connection, then their individual attractive power lowers.

Now that doesn’t mean speakers are valueless; in the way that a star professor can attract students to a university, a star speaker can certainly drive ticket sales.  But if the value of each individual speaker to do so is going down, and the ancillary value that speakers harvest from simply speaking is going up, at some point those cross over.  Couple this with the fact that more people can actually be trained to become better speakers as equitable access to education continues, you’ve got a recipe for the end of speaker fees.
To look at it differently, think about sponsored speaking slots.  At the moment, companies pay big money to essentially buy mainstage speaking time, because they recognize the brand halo that it has: not only can your smart exec talk about your product, people also respect the company for employing said smart exec.  But companies could easily pursue an alternative strategy, like we did at Microsoft.  One of the smart folks on my team had the brilliant idea of simply removing inhibiting strategies to grabbing the speaking slots we didn’t have to pay for, by training smart people to be better speakers, helping them apply for slots, and then paying for their travel costs.

So if the many smart people get trained on better speaking and you end up with a plethora of amazing speakers who are willing to do it for free because they can find other ways to monetize, why would any conference organizer reasonably pay speaking fees?  Instead, they can use that cash to democratize access by paying for travel and double-down on actually respecting speakers’ time and effort.

A Final Note
It is important to end by recognizing the graciousness of the PCMAEC audience.  As is my habit, I was entirely authentic onstage and noted that I was having trouble – after asking for an extra round of applause to give myself a moment to recenter, the crowd graciously obliged and afterwards many of them said they thought it was just shtick because the talk itself wasn’t bad.

But trust me…it isn’t shtick.  The talk wasn’t good.  But hopefully at least some of what I would have said came through in this article and we can all go make better conferences.  Because that’s what really matters.  Now more than ever, people need to come together, to debate and learn and connect and just generally cause trouble while opposing the status quo.  As conveners, conference organizers are far more than functionaries – making sure the drinks are cold is table stakes, but the real work worth doing is the behaviors that remain changed days and weeks and months after attendees go home.  Done right, conferences can change the world.

Side note: As everyone is painfully aware (because I won’t shut up about it), the lack of representation on stage pisses me off. I’ve tried a variety of small experiments to tackle this, but I’m ready to step it up a notch. Using some of the advance money from my book, I’ve hired someone to manage a small project we’re calling Speakershipped.  The idea is very simple: if you are an underrepresented speaker, we will essentially act as your free speaker bureau. I will personally help you uplevel your speaking skills, we’ll construct a bio and several proposed talks, and then we’ll actively pitch you to conferences. This will come at no charge to you and you’ll be in complete control over where you want to speak, what your acceptable parameters are (they have to pay for travel, main stage only, etc.), and how you want to appear. It is my hope that by taking a more active role than the traditional “let’s make a list of underrepresented speakers” approach, we can see much faster change.  If you are interested in speaking or are a conference organizer willing to accept pitches, please email and we’ll get started.

Recently I’ve been working to evangelize the use of behavioral science in business, which often means working my network to get to the top levels of companies so I can do a bit of evangelism. And in doing so, I’ve learned something interesting: I don’t know corporate leaders. I don’t even know people who know corporate leaders.

You could argue that the first-degree gap isn’t surprising. After all, I came from academia and specialize in a field that is only newly being applied at scale in the corporate world. But it isn’t like I am generally unconnected: if you name a successful startup person, particularly in NYC, it is a fair bet that I either know them or am one degree away. I give about fifty talks a year, all across the world. I’ve got social media followers. Hell, you’re reading this!

And the gap isn’t unique to me. It isn’t only that I don’t know many people at the top of corporates, but due to the glory of LinkedIn, I know that I’m not even a hop or two away. There is something bigger going on here and I think it is at the root of a serious problem in increasing the efficiency of innovation.

If you look at who is running the top three levels (CEO, C-level direct reports, and their direct reports, the Baby C’s) of the Fortune 1000, a pattern emerges. Take my old boss, Satya Nadella of Microsoft, as a sort of archetype. Graduated from college in 1990, goes to work at Sun, joins Microsoft in 1992, finishes his MBA in 1997, appointed CEO in 2014.

Now admittedly, that’s cherry picking. But look at Nadella’s top level: minus the folks just acquired from LinkedIn, only one has ever worked at a startup (which was acquired by Microsoft in 1997). They all follow a fairly similar path: corporate, MBA, more corporate or consulting, then pick a company and spend 10+ years there. Along the way, you meet a bunch of other people doing the same and you all employ each other as you move around the companies.

Entrepreneurs have a template as well. You work at a startup, you found a startup, you fail or get acquired or get big…repeat. And just like the corporate folks, you meet a cohort of people who you value and they become the tribe that you recruit from and party with. They become your friends.

Neither of these two templates are bad on their own. But what this difference in path creates is a gap in social circle. And in a world where social circles create innovation because of creative collision, that’s a real problem.

Take recruiting as an example. I’m acutely aware of how limited my brain is. When a founder asks “Do you know a good X?”, I generally think back over the people I’ve talked to recently and who I have coming up, because that’s about all my puny memory can hold, a month’s worth of people.

I have to imagine that is probably how it works for corporate folks as well. When they see each other for drinks and ask those casual recruiting questions, I’m sure they suggest people who are top of mind for them. And because that social circle was formed over years in a corporate environment (and MBA programs; for entrepreneurs, there is probably an incubator cohort bias), they regurgitate other corporate people, just like I did for other startup people.

Again, this isn’t terrible on its own, except that we need crosspollination. Corporates need more innovation from entrepreneurs. Entrepreneurs need to do more business development deals with corporates. And as research points out over and over again (mostly because we continue to do nothing about it), diversity of viewpoint makes for stronger, more profitable companies.

I’m sure that at the very highest levels, this gap probably isn’t as real; Reid Hoffman probably knows plenty of CEOs and Satya likely knows plenty of founders. Although I do wonder how personal those connections are – at the backyard BBQ, is there really a mix? Does Indra Nooyi invited Matt Salzberg over for coffee?
And maybe that’s the challenge, the go forward action: we all take a second to find someone on the other side of the gap, in a similar role, and invite them to have a drink or a coffee and just talk about the areas of mutual overlap. Or the Yankees. I’m not sure it matters that we talk about business so much as we simply take the time to get to know each other.

And yes, I promise to do it as well: I just sent Mauro Porcini a note.

Side note: Because this was on my mind, I tried a quick Twitter question, asking my followers if anyone could think of an example where a Fortune 100 that wasn’t recently a startup hired a startup person into their C-suite, other than through acquisition. Grand answer? A massive blank. Nobody could think of one. And even if one or two trickle in after this post, it is telling that this isn’t on the tip of our tongue.
Shouldn’t it be common? Take a serial entrepreneur; if they have two or more exits, do we seriously think they can’t contribute meaningfully at the top levels of a company? If we want innovation, we better start hiring for it.

One of the most consistent questions I’ve been asked over the years is where to find good product people (a term I use because nobody can seem to decide if they are product managers, project managers, or some other exotic variant). Most companies recognize the value that a highly-skilled product person can bring, so they are in high demand, and yet definitions of the role itself vary: It sometimes emphasizes deciding and scoping what will be built, sometimes managing the process of building, and sometimes owning the iteration back and forth between the two.

So unsurprisingly, when you look at the best product people out there, they don’t come from any consistent background. Some are former engineers or designers, others are subject matter experts, and some just seem to take to it naturally. But with a role so vague, it is hard to predictably find the right people to fill it.

And education isn’t helping. There are very few full programs that train product people and not even many good courses that do more than teach potential tools. So I’ve always struggled to answer the question, because if there is no consistent training and no consistent definition of what they should be doing, how can I possibly tell you where to find them?

Which naturally means I’m going to now try to do that. Recently, I’ve become increasingly convinced that the answer to Product is Science, and the answer to finding good product people is to look to scientists and those with training running experiments in a lab setting. There are a couple of reasons for this.

As I wrote briefly about recently, scientists are trained interventionists. The whole process of science is looking at the world as it currently exists and all we know about it, and then theorizing variations and potential outcomes. Which, I’d argue, is exactly what good product people should be doing. When you build a product, you are fundamentally trying to change some sort of behavior in the world. You hopefully have some understanding of what currently happens and why, what you want to happen and why, and then you experiment with methods that bridge the gap.

That’s exactly what scientists do on a daily basis. And they are highly familiar with the iterative process that it takes to get there and generally not frustrated by it. To have succeeded in science, they must necessarily have become accustomed to and patient with the universal truth that most experiments fail to produce the theorized outcomes (or often any discernable outcome at all). Behind every paper with a few studies in it, there are often a dozen failed experiments from which the scientist learned something about what didn’t work and then got busy creating another attempt at one that did.

And scientists are already trained to evaluate the data that helps in that iteration. While most are certainly not full-fledged data scientists, they are accustomed to looking at the statistical output of an experiment, evaluating the results, and then iterating to the next version. They can run regressions, are familiar with at least one statistical package, and don’t need to run off to a separate team to understand what worked and didn’t.
They are also accustomed to cooperation and advice, so when they do need help, they are likely to go get it.

In academic science, it is virtually impossible to go it alone: The process as it currently stands requires an advisor. Rare is the entirely independent scientist, alone in a tower trying to resurrect their personal Frankenstein. Scientists are trained in a tradition of teams, with lab meetings, plenty of white boards, and discussion and debate. It isn’t a solo enterprise and that natural emphasis on collective action translates incredibly well into the modern development environment.

And you can actually hire whole teams because we already have lots of scientists. One of the benefits of discovering that we can repurpose scientists is that we don’t have to wait for the development, iteration, and subsequent years of operation of specific training programs for product people. While science certainly has challenges and we radically need to increase both the raw number and diversity of those that enter it, there is still a wide pool to draw from: According to the National Center for Educational Statistics, we get about 300K new grads a year with some form of science degree.

But perhaps more important than intervention, iteration, experimentation, data science, and abundance, we have orientation. Scientists understand the random. They won’t simply abandon a method because it doesn’t initially produce outstanding results. They’ll tinker with it, even run it again to make sure some outside variable didn’t interfere. Successful scientists, by definition, are those who don’t give up, who refuse to be discouraged.

Yet good scientists are in love with the problem, not the solution. They have only the lightest attachment to the experiments themselves and a strong passion for discovering the overall truth of the subject. I’ve never seen any group so ready to be flexible in method while holding steady on desired outcome, which is the literal meaning and whole point of a pivot.

And this is essential to good product. The number one reason companies fail is because they refuse to let go of product elements that are not driving the desired behavioral outcomes. Innovative products are by definition initially unknowable. The right solution must be teased out through experimentation and exploration. And to do that, we need trained tinkerers. To run product, we need scientists.

Side note: Yes, I fully recognize and embrace that one of the reasons I love the ideas of scientists as product people is because my background is science. But science is not without flaws. One of the reasons I left academia was because I felt (and feel) that academic science has lost sight of application. When I was shopping for PhD programs, the legendary social psychologist Tom Gilovich was the head of Cornell’s program and he called late one afternoon. “We’ve talked and we really want you,” he said, “but I just want to make sure that you’re clear on what we do here. You keep talking about applying psychology and we’re a research program – that’s what we do here, all day, every day.”

Tom saw something that I wasn’t yet mature enough to see, that despite my early successes as a researcher and love of expanding the knowledge of the field, I was never going to be happy with just research. My year as a Cornell PhD was one of the worst of my life and I was incredibly glad when it was over and I left to become Head of Product and Lead Scientist at Thrive. I probably needed that year to discover for myself the truth about a research-only focus but kudos to Tom, for calling it advance.

It is a strange time to be a behavioral scientist in business.  When I left my PhD program almost ten years ago to focus on real-world applications, I spent the majority of my first few years explaining over and over what behavioral scientists actually did.  Now, I regularly get inbound requests to speak at large companies and books by Adam Grant, Dan Ariely, Jonah Berger, Angela Duckworth, Amy Cuddy, Barry Schwartz, and others sell millions of copies.  Executive confidence that behavioral science is both valid and interesting has seemingly never been higher.

And yet hiring behavioral scientists to explicitly apply what they know has remained somewhat rare.  When I left Microsoft, I got far more recruiter inbound based on my background in startups and venture than I did in behavioral science, despite being considerably better at the latter.  And while there are a few corporate behavioral science groups, like Om Marwah’s burgeoning team over at Walmart, Steve Wendel’s at Morningstar, Charlotte Blank’s at Maritz, Prasad Setty’s at Google, and Jeff Helzner’s at AIG, by and large the Fortune 1000 seems to love behavioral science without actually applying it.

There is a fundamental gap here.  How can executives believe in behavioral science, espouse its virtues and recommend its writings, and yet not be investing in its application to their own companies?  Surely it can’t be all TED talks and no desire to see it work?

And we need it to work.  We live in a world where ~70% of people are fundamentally disengaged with the work they do (and that number hasn’t meaningfully changed in 15 years) and despite record highs in health and wealth, personal happiness is actually declining in the United States.  We are doing better but feeling worse and our current approaches to addressing that aren’t working.  If we want change, we need to employ people trained in and focused on behavioral outcomes.

So why isn’t that happening?  To me, the gap isn’t a failure of business but of behavioral scientists, myself included.  If we accept that executives across industries want to incorporate behavioral science into their companies, then we need to remove the barriers to implementation until it is applied repeatedly and at scale.  We need to demonstrate the kind of problems to which behavioral science can be applied and create clear processes for that application.

We’ve done a bad job so far.  When I was preparing to write this post, I asked some of my fellow scientists if they wanted to contribute examples of their work and methods.  The overwhelming response was “we wish we could”.  Because talking about human behavior and its explicit modification remains an area of corporate secrecy.  We trumpet the results but not the methods.

So I want to give an example from my last job at Microsoft and to call out the potential for a replicable process that could underlie the application of behavioral science at scale.  Because I believe every large company should have a Chief Behavioral Officer and team actively applying behavioral science to identify and improve human experiences, both internally and externally.  And I believe that showing how we do it makes it easier to do.

When I first came to Microsoft and looked at Bing, one of the things I heard anecdotally was that search volume was lower than expected in schools.  The working theory seemed to be that kids simply weren’t curious enough and didn’t know that search could be used to satisfy curiosity, and there were vague plans to run a marketing campaign encouraging curiosity-based search in schools.

So I got my hands on query volume logs and ran some regressions to confirm the deficit with data, creating a metric of “searches per student per day”, then went to go visit some classrooms.  Unsurprisingly, there didn’t appear to be a problem of inquisitiveness: anyone who has interacted with kids for more than a few minutes knows there isn’t a lack of curiosity.  Instead, three big inhibiting pressures stuck out, inherent in teachers’ concerns about search: a vague and unspecified fear about how student data was being used, advertising in the classroom, and potential exposure to adult content.  Plus teaching search itself was a grey area.  Was it a media skill, best left to librarians?  How did it really fit in the daily rhythm of a class?

After the classroom visit, I got a few people in a room and used a Competing Pressures model to design Bing in the Classroom: ad-free, safe, private search that was free to schools, coupled with daily mini-lessons that fit in existing classroom models.  We helped Engineering to build it, acquired a small budget to publish lesson plans, and convinced large districts to sign up in advance of the launch.

The net result?  A 40% increase in student searches at participating schools and an additional 15% at home, larger than any single previous product innovation at Bing.

Bing in the Classroom isn’t the only project I’ve built that changed behavior at scale.  One of my startups, personal finance tool Thrive, got bought by LendingTree because we proved that we could increase people’s credit scores by 20 points in six months through behavioral finance.  And GetRaised, a tool we built that helps women figure out if they are underpaid and what to do about it, has helped tens of thousands of women earn over $2.3B in raises by changing the approach from encouraging women to “lean in” (as if they don’t already want fair pay) to simply making it easier to act.  I can rattle off a laundry list of behavioral science interventions that have worked.

But of all the things I’ve built, Bing in the Classroom feels most like a process that can be repeated inside corporations to change behaviors of value.  And that’s the key.  I believe behavioral science can be used to affect a myriad of business problems that have proved resistant to other efforts: the hiring and retention of the underrepresented, the modification of products and services to work across cultures, entrenched user experience problems like payment and support.  But if we want the chance to address them, we need a replicable process for applying behavioral science to these problems.

It starts with theory-based observation.  Behavioral scientists, like all scientists, are trained to look for the systems that underlie outcomes.  And all undergo statistical training, often fairly advanced, in order to try to extract the signal of behavioral patterns from the noise of the human condition.  For Bing in the Classroom, it wasn’t enough to believe something generally about kids and search; we needed data to confirm it and observation to understand it.  Without those, we would be stuck running brand campaigns about curiosity.

Then there was the design process.  I use a Competing Pressures model and that is only one of many options, but what the methods share in common is a firm grounding in the science of intervention and behavior change.  Behavioral scientists are fundamentally interventionists, rather than operators, because the experimental process itself is about taking how people normally behave and then proving that a change in environment or interaction or situation results in a replicable change in behavior.  The goal from the outset is to alter the status quo.

That orientation is very distinct from the processes we teach to those whose primary job is operational.  Think of it as the difference between a test (atheoretical evaluation, which tells you that the green button is better than the red one) and an experiment (theoretical evaluation, which tells you why the green button is more likely to result in an outcome than a red one, and thus allows generalization to purple, blue, and orange).  Because behavioral scientists don’t just care about what but also why, they can better create interventions that change behavioral outcomes in more permanent and integral ways.

This focus on the why of human behavior also brings an openness to a wider range of potential interventions than most operators.  Unsurprisingly, when asked to generated solutions, people gravitate to those within their locus of control.  Marketers tends to find marketing solutions, like a curiosity campaign, and the engineers find technical ones; for Bing in the Classroom, they wanted to build out a special desktop client to be individually installed on each student machine (school district IT people, you can thank me for vetoing that outright).  Behavioral scientists bring something novel to table because their domain is behavior itself; all levers are fair game.

A removal from operational components also creates another uniqueness for behavioral scientists: The ability to let go.  There is a part of the Bing in the Classroom story that is rarely told.  Once it was launched and scaled to around 10M users, with the query volume increase proved, I stepped away and handed the program over to Marketing.  Indeed, the two marketers who took the handoff were named Marketers of the Year by the CMO; I was the one who nominated them.

While many at Microsoft thought this was odd, my scientist friends instinctively understood, because this is a unique facet of scientific research.  When a theory is proved, it becomes part of the corpus of knowledge that belongs not to the researcher but to the field as a whole, and researchers immediately transition to the next area of investigation.

This is fundamentally different from what we instill in operators, who are generally rewarded for retaining ownership when something is successful and thus change only when forced.  By constantly working toward solution and transition, behavioral scientists can be a potent force for change.  And politically, HR, marketing, and other disciplines have everything to gain and nothing to lose, because any positive lift created remains with the operators and the behavioral science team moves on.  This is why I like internal NPS as a potential measure of the efficacy of a behavioral science team: if you are doing your job correctly, other teams will recommend working with you.

Taken collectively, there is a replicable process here: theory-based observation coupled with data science leading to intervention-focused behavioral design, with a build-test-refine cycle and then a handoff of an empirically validated program that is ready for scaling and operation.  It can be applied to novel human experience problems that operational teams have had difficulty solving because it brings an entirely new perspective and is distinctively apolitical.  The team and expenses are small because your only costs are human capital and the potential upside can be directly linked to profit.

And that is an important word: profit.  In a world where businesses at scale have done everything in their power to maximize earnings, behavioral science is an untapped well.  Imagine the efficiency that comes from addressing the 70% of the workforce that is disengaged.  Or reversing the negative consumer happiness trend in the United States.  For the majority of large businesses, improving the human experience of a company is where the next massive increase in profit will come from.

There will be a first CEO willing to commit a few million dollars to hiring a CBO and team to drive change in their business.  But it is on behavioral scientists to make this work easier to embrace.  To show structure, process, and results.  Not a skunkworks of product ideas thrown against a wall, but a disciplined approach to behavior change.  Not tests but experiments.  Science, well applied.

Side note: One of the chief difficulties of enlarging the presence of behavioral science in business is the tendency toward secrecy.  Governments, including both the US and UK, have behavioral science teams that are transparent about their process and success.  Yet business lag behinds.  At Microsoft, we discussed my hiring but never noted that Bing in the Classroom was a direct result of a behavioral science experiment.  If we want to close the gap, we need to do so in public.  So if there are other behavioral scientists willing to contribute case studies to a followup post, send me a note.

Editors: This post would not be possible without the input of both other behavioral scientists and businesspeople, all of whom I am lucky to count as friends.  Thanks to Stefanie Sugar and Dominic Price, William Leach, Kevin Brilliant, Lauren Woodman, Kara Silverman, Jonah Berger, Adam Grant, Om Marwah, Steve Wendel, Jeff Helzner, Josh Wright, Michael Butera, Val Tsanev, Charlotte Blank, Dan Storms, Erik Johnson, Avi Karnani, Bill Cromie, Carson Miller, Andres Glusman, Michael Norton, and Kelly Peters.

At coffee with a male friend, I was talking about a female colleague that he really wants to work with. He was reviewing his pitch with me and he kept emphasizing his ability to bring her hard problems to solve. “That’s great,” I replied, “but she’s really more interested in doing original research on new solutions than on solving hard problems.”

He looked at me like I was nuts, which is honestly not an uncommon reaction when I say things. But to me, there is a very real difference between our attraction to problems and our attraction to solutions.

For example, I’m a hard problems person. While I’m interested in the new research that emerges in behavioral psychology, it pales in comparison to my interest in applying behavioral science to the problems of the world. Even if what that boils down to is utilizing the same basic principles over and over again. This may explain why I end up giving the promoting/inhibiting pressures talk over and over and over.

What gets me excited is the execution layer, designing and delivering something that moves the needle. If I can use a solution I designed before, so much the better; that just means I have more time for the next problem on down the line.

The love of new solutions is a different beast. I have an amazing engineering friend who delights in solving the same problem in new programming languages, just because she can. If she runs out of new languages, she just makes up some artificial constraint and then tries to solve the problem again. I once saw her try to program something using only the keys in the home row on her keyboard. She failed…but she had a lot of fun doing it.

Certainly there are correlations: it is often true that a hard problem requires a new solution. But I’m not as convinced that the predilection goes both directions or is always balanced. At the end of the day, something will always attract you more and knowing what floats your boat can actually be helpful in thinking about the right roles to choose. One of the reasons that Chief Behavioral Officer is so appealing to me is that it applies an existing body of knowledge against a diverse set of problems. Had new solutions been my focus, academia probably would have been a better home than it ended up being.

It is also worth thinking about as a manager, particularly in terms of attracting and rewarding talent. I’ve talked extensively about the importance of work worth doing and knowing what someone views as meaningful is a huge part of that. Trying to lure or reward someone with hard problems when what they want is new solutions may mean that you miss out.

Feels like we could probably make a test for this fairly easily. Create a challenge, let someone complete it, then offer them a choice between a new challenge or repeating the same one with the requirement of an original solution. We can add that to the long list of “Things Matt Wallaert will eventually build”. It is starting to be a very long list.

Side note: I sometimes get the feeling that no matter what I’ve thought about, someone else has already thought of it. I’m willing to bet that somewhere, someone has talked about pretty much every subject I’ve ever brought up in a blog. And after writing this, I realized that maybe the reason that doesn’t bother me is that I don’t actually care about novelty, just challenge. Forget the sexy product/service/blog post, I just want the one that solves the problem so we can move on to the next thing.

At Microsoft, one of the common cultural tropes was that no meeting ever started on time; being perpetually late was an accepted norm. And while plenty has been written about productivity loss and respect and all the other downsides of being late to a meeting, I want to talk about something slightly different. I’d like to suggest that constantly running late actually is the cause, not the symptom, of one of the largest problems of modern companies: the tendency not to fix known problems.

Let’s start with some science. In a 1973 experiment by Darley and Batson, a group of seminary students were recruited to participate in a study they were told was about measuring religious beliefs. After filling out religiosity questionnaires in Building A, they were told to go to Building B to do one of two tasks: give a talk about seminary jobs or give a sermon on the parable of The Good Samaritan. In addition, students were told they were either a little early, right on time, or running late.

Here’s the twist: on the way from Building A to Building B, the researchers had planted an actor who appeared to be in respiratory distress, slumped on the ground while coughing and groaning. That was the true measure of the study: when confronted by an opportunity to help the sick, what determined whether students would stop to help or step over them and keep going?

The first possible determinant of who stopped was how the seminary students viewed religion and themselves. Perhaps those who resonated with the social mission of Christ or believed deeply in service might be more likely to render aid? Alas, no. Despite people’s mistaken belief that goodness is some sort of mostly internal attribute, helping or not didn’t seem to be based on anything they measured about a person’s beliefs or character.

The second possibility was that what the student was going to speak on would persuade them to stop. A quick reminder on the Parable of the Good Samaritan. When asked about God’s desire for kindness, Jesus tells the story of a man who is beaten and robbed, then left by the side of the road. A priest, and then a priest’s assistant, see the man and don’t stop to help. A third man, the titular Samaritan, was part of an ethnic group that should have hated the beaten man. But it is he who stops to heal and help, taking the beaten man to a nearby home and paying for his care. And it is this Good Samaritan, says Jesus, who is going to heaven.

So surely this will work. Here these seminary students are, about to go give a talk about the importance of helping those in need. Surely this, of all possible interventions, will make them stop. But, of course, it doesn’t. Whether the student is going to give a talk about seminary jobs or saving people has absolutely no effect on whether they help.

What does matter is the most dramatically simple of interventions: how rushed do they feel? If they felt early, they stopped to help. If they were running late, they stepped right over the wounded actor and kept moving.

I’ll admit to always being a little amused by the image of the highly pious student rushing off to give a talk about helping people while literally stepping over someone who needs help. Rather like The Mayor in Chocolat, it is hard not to see the tragic comedy in such a juxtaposition between our stated intentions and our actions. And I’m not laughing at them, as much as with them: I’ve been the modern equivalent of the hurried seminary student, a hundred times over.

For those that have spent some time with my Competing Pressures Design framework, another way of stating this result is that the promoting pressures (personality and religious doctrine) are massively outweighed by the inhibiting pressure (feeling rushed). It is one of the reasons I love this experiment: it is such a vivid depiction of how much environment matters.

I would argue that most modern employees want to be good: at our jobs and as people. We recognize that human experience matters and want to be positive bosses and coworkers and employees. We want our customers to feel honored and respected and get what they need. And we want to do the right thing. The promoting pressures are not the problem.

But there is a reason we are late to meetings. Most workers will tell you that they are constantly hurried, with never enough time to do everything. We all feel like we’re running late, all of the time, and this is one of the single most powerful inhibiting pressures around. Although I started by talking about Microsoft, I’d argue the problem is even worse at startups, where you are always racing your funding runway and mostly feeling like you’re losing.

Much of modern innovation work (particularly innovation consulting) is predicated on the idea that people simply can’t see problems, that they are unable to respond to potential disruption because they don’t believe that it will happen. I don’t share that belief, but even if you do, there is no denying that most businesses have a large number of visible problems that go unsolved. And what I’m suggesting is that the reason those problems go largely unfixed is not for lack of ability or empowerment or permission, but simply because we are running late for what we believe we supposed to be doing, the omni-meeting.

The sad thing is that it doesn’t have to be this way; we don’t always have to step over problems and rush on. One of the reasons corporate hackathons and startup team retreats are so successful is that create an unhurried space in which people can fix things that bother them. These are often falsely labelled “passion projects”. Certainly some people use those events to pursue something about which they are highly passionate, but if you look closely, many of the projects are simply working on known problems that they’ve felt too busy to fix.

This is one of the reasons bug bashes exist: they are a form of explicitly setting aside time to fix known problems. But they are sadly limited to engineering. What if the marketers bug bashed, optimizing all those campaigns and fixing systems and addressing all those things that have been silently driving them crazy? I have trouble thinking of a department this wouldn’t benefit. Imagine line assembly workers taking time out just to work on the process of the line itself. The benefits vastly outweigh the costs.

For leaders, this is largely about accepting ambiguity in time usage and embracing autonomy. Google started there with the 20% time, but as Marissa Mayer so aptly put it, the dirty little secret of Google’s 20% time is that “it’s really 120% time.” Unhurried is really a cultural value, at the end of the day, and the best programs have to be buttressed with clear and unambiguous top-down messaging that prevent it from becoming simply an ask for employees to do more.

And for the individual, this is something around which we have control. We don’t have to wait around for some vague corporate overlord to permission us to take a step back and reevaluate how we think about time. Because it isn’t just being busy or not; it is about feeling busy or not.
When I was a kid, my dad taught me to recall often the first line of the Desiderata: “Go placidly amid the noise and the haste, and remember what peace there may be in silence.” Over the years, I’ve frequently lost sight of that mantra and it is one of the reasons I have taken the last several months off, after leaving Microsoft, to concentrate on family.

Even without working, I find myself filling every minute. And yet the unfilled minutes, the blank spaces on my calendar, are the true moments that I breakthrough. Because they do get filled. An empty space on my calendar is not an empty space in life; more often than not these days, those blank spaces are really time with my son. It is true, #YOLO, but that isn’t a command to fill every moment; indeed, it is the precise opposite, an invitation to step back. And we all need to work on remembering that.

Side note: There are so many people I love that I haven’t taken the time to just see and talk with, completely without agenda, because of the pressing demands of people with direct and articulated need. If you are one of those people, I apologize. Please send me a note and let’s have an unhurried lunch.

It feels like every few years, the startup community figures out that they have a shortage. First it was engineers, then product people, then UX, and lately everyone has been coming to me asking if I know any good salespeople.

Now that might be the result of the rise in enterprise software but even SMB products are recognizing that they’re going to need a ground game and asking about how to build sales teams. And rightly so: whether it is through marketing or sales, in a world crowded with competition for our attention, even the best products need someone to bring them to market.

Here’s the problem: there aren’t enough salespeople to go around. And it is our fault.

Every shortage has had its unique contributors. There weren’t enough engineers because STEM education efforts hadn’t yet begun (and I’d argue this shortage continues because we failed to welcome women and minorities into engineering until very, very recently). We ran short on product people because it was a new discipline and nobody really knew where to find them or what to look for. Ditto for UX.

With salespeople, though, it is our culture that is at fault. And if we want to have enough to power the startups of the future, we have to make some fundamental changes in how we talk about sales and its experts.

Before I talk about culture, I want to dispel the myth of compensation as the cause. Certainly money matters. Because we have trained sales people to expect compensation that is at least somewhat commissioned based, the lifetime value of a product directly affects who you can get to sell it. If an advertising or finance sales gig can net you seven figures, startups tend to look less attractive.

But look at all the startup engineers. While it is mathematically true that the expected value of a career at a Fortune 1000 is higher than that of startups, they still leave big companies to go to startups in droves and plenty of new graduates end up there as well. The lure of being closer to the product, having more control, with greater connection to users and to the meaning inherent in the work captures plenty of attention. So clearly compensation isn’t everything.

Therein lies the much deeper issue. Borrowing Werner Vogel’s conception of startup folk as either missionaries or mercenaries, we have created a culture where we only allow salespeople to be mercenaries. “Salesperson” has become startup language for “necessary evil”. They are the lowest of the low, highly paid but never loved.

Even customer support gets more respect than sales. Fancily renamed into customer advocates, customer service is seen as the voice of users, feeding back into the great product cycle of launch and revise. They may be paid dramatically less than salespeople but they are given far more cultural credibility. Couple that with a low barrier to entry and rarely do you hear complaints that we can’t find enough customer advocates.
Cultural credibility matters. If the consistent message of startups is that they are the place you go for meaning, and we deny salespeople access to a meaning orientation, we are essentially denying them access to startups. So experienced salespeople don’t leave big companies and young people don’t go into sales roles.

I don’t blame them. If you can be a customer advocate, or a product manager, or really anything else, why would you pick a career where you will be consistently denigrated? Rather like young black men who opt out of continuing their educational journey because of the bleak prospects beyond, we can’t fault salespeople for acting rationally and refusing to enter into a world that constantly accuses them of being hired guns.

If we want to change this, we need to make a different set of cultural choices. We need founders to highlight and celebrate outstanding salespeople who helped them on the path to outsized exits. We need to start meeting salespeople where they are and genuinely wanting to learn more about their craft as part of broadening our own skillset. And product people and marketers need to actively solicit the expert opinion of salespeople and incorporate their feedback into design choices.

But most importantly, we must allow them to be missionaries. Just like any other startup role, salespeople have a particular skillset that they can choose to apply in a variety of ways. If we want to compete for their attention and convince them to choose to use that skillset on behalf of our products and services, we need to disentangle all our assumptions about motivation and personality from those skills. This isn’t Glengarry Glen Ross; nobody is getting murdered for sales leads. Good salespeople can help us save the world but is on us to invite them to do so.

Side note: If you are looking for salespeople, my best advice is to ask other salespeople if they know anyone who is looking. Homophily is still in play – birds of a feather really do flock together. And for my money, look at Mormons, psychology students, strippers, and anyone else who has looked public shaming in the face and made the shamers blink. Once you’ve had a thousand doors closed in your face when you started talking about God, or stood in a train station trying to get people to fill out surveys for hours, or counted on your ability to earn tips while naked to make ends meet, you don’t mind a little bit of cold calling and aren’t ashamed to make the ask.

The morning after Donald Trump was elected our president, I gave a talk at a women’s leadership conference put on by Thomson Reuters. During the Q&A, the highest voted question was “How do we think about what happened last night?” This is the answer I gave.

There are a number of ways to approach the election, and I’m sure there will be both scientists and pundits who comment at length in the coming days. So let me speak from a personal place.

I love Hemingway, and particularly For Whom The Bells Tolls. There is a quote that has always stuck with me: “The world is a fine place and worth fighting for…”. At the end of the movie Se7en, Morgan Freeman’s character shares this quote, adding “I believe in the second part”.

I still believe the world is a fine place, that people are essentially good, and that we will continue up and to the right in the long term. But if you cannot convince yourself of that, remember that the second part remains true. Good or bad, this is worth fighting for. This is not a time to flee to Canada; we need fighters, here and now. We need you.

Republicans used to say “Love it or leave it”. I love it. So I’m staying.

This doesn’t mean I’m giving up my right to be opinionated. To note that we elected (not they, we) a man who makes openly sexist and racist comments. To not be OK with that. To be angry.

But I’m reminded of the story of C. P. Ellis. I grew up in rural Oregon and though I have been away these many years, I am indelibly a country person in ways that are difficult to describe. Across race and class and gender last night, no demographic was so important as where you live: cities voted for Clinton, everyone else for Trump.

They are not idiots. They are not all racist, or sexist. They are a part of my family (both metaphorically and in actuality) that deeply and powerfully feel that they have been marginalized. They are experiencing the pain and loss that comes from that. And they voted accordingly.
C. P. Ellis was an avowed racist and head of the Ku Klux Klan in his town. When his town desegregated the schools, he was invited to co-chair the committee, alongside a black woman named Ann Atwater. And because she did not reject him but embraced him, because she treated him as intelligent and worth talking to, he left the Klan and eventually became a labor organizer. And her lifelong friend.

One of my friends texted me this morning and said “Just scary to think how we have no idea who our neighbors are.” Maybe that needs to change. Maybe it is time to find out. We need to get to know each other at a deep and personal level, and to take the extraordinary step that is empathy and compassion.

That’s easy for me to say, as a white man with wealth. I am probably better off, individually, than I was yesterday on lots of measures, if much poorer as a husband, father, and member of a civilized society. I recognize the gall of saying to those who now face an even more uphill battle against racism and sexism “Open your arms and embrace they who have put you here”.

I also know that if we want change, short of bloody revolution, compassion is our greatest hope. The Ballot or The Bullet. I cannot abide the bullet. I want to live here. So we elected Trump together – now what? Who will we be? What will we do? We get to decide, each of us, today and tomorrow and for the next four years. Love it or leave it. Understand or reject. It is a decision we must all make carefully.

Side note: Today reminds me of power outages. Or 9-11. Or any of the other events, both large and small, that cause people to rally together and to recognize the common human decency that lives in most everyone. I have this amazing little boy that I love; not one given to prayer, or God, I pray we all do the right thing. Oh, how I pray.

Recently, Tinder has a public reaction on Twitter to an article in Vanity Fair. The brief gist is that the Vanity Fair writer, Nancy Jo Sales, wrote about Tinder as contributing to a negative hookup culture. Tinder wrote a long series of tweets in response about all the good, positive interactions people have on their platform. Notice how I have left out all the judgment words (don’t worry, it won’t last). But I did so deliberately because regardless of what you think of Tinder, hookup cultures, or Nancy Jo Sales’ reporting, the real winner here should be science education.

Judgement time. The Vanity Fair piece was actually not very good (despite Wired referring to it as “excellent”). The Tinder response was also not very good. But they were both not very good for the same reason: selection bias. And focusing on how that bias plays out is actually where some use might come out of this public meltdown.

In the VF piece, the interviewees seem to almost all be young men and women who are actively using Tinder. The women seem to think it contributes to a hookup culture that devalues them; the men seem to agree. Yet therein lies the key problem: by talking only to people who are currently using the app, Sales is actually interviewing the “failures”, also known as a negative selection bias.

If the point of Tinder is for people to find long-term romantic relationships, than the majority of the people on the app will be people who are not yet successful at doing so. Thus, if you want to explore the potential effect of the app on finding a mate, you have to interview people who are no longer on the app and find out why they left. Is it because it was all hookups and they tired of it? Or is it because they no longer need the app because they found someone?

Tinder seems to be trying to point this out in their Twitter rant, but they go too far and end up falling victim to the other end of the spectrum, the positive selection bias (sometimes referred to as the survivorship bias). They mostly focus on people who should have theoretically left their platform: marriages. A “shit ton of marriages”, according to them.

If you throw enough people at a situation, usually some of them are going to come out with positive results. But if you only look at those with positive results, you ignore the disappeared failures who either left and didn’t have positive results or are still around and failing. Tinder specifically calls out how they are relying on the stories users tell them, which they call #SwipeRight stories.  Given the interaction pattern of the app, that presumably means just the positive ones.

The real pity is that avoiding the positive and negative selection bias should have been relatively easy. You could interview people in all four quadrants (current user/positive, current user/negative, ex user/positive, ex user/negative). Even this is a sort of selection bias, however: there are plenty of people who get married or have bad relationships entirely without the aid of technology. Tinder is taking credit for the marriages, Sales is blaming them for the hookups, but in reality both happen in populations that have never touched Tinder. So what you really want is to understand how the populations that use Tinder and those that don’t differ. And even then, you can’t blame that difference entirely on Tinder. After all, they might have been different to start with in a way that made them choose to be on the platform or not.

Now clearly, journalism isn’t science and Twitter certainly isn’t science. And we can debate whether either of those two mediums have a responsibility to uphold some sort of ethics. But science education means that we don’t have to rely on the ethics of others. All the spectators reading the article and Tweets, with a bit of STEM education, can actually recognize the biases for themselves and avoid perpetuating them. And I sincerely hope that someone uses this debacle as a reason to increase science education funding. Wouldn’t that be lovely?

Side note: It is always amazing to me how quickly people treat changing sexual and relationship norms as if they are the end of the world. In the 90s, divorces became more common. And yet people still fall in love and we actually just worked really hard as a country to make sure that all people have the right to marry. It is far from the death of marriage. I don’t know if hookup culture is meant positively or negatively (probably depends on whether you are actually hooking up or not), but there is one thing I know: it is not the end of love.