Generating Focused Ideas Through Practical Empathy
A webinar for Waggl Thought Leadership
This webinar builds awareness of cognitive bias when it comes to being “data-driven,” and clarifies the differences between the problem space and the solution space. I introduce mental model diagrams, behavioral audience segments (thinking styles), and show how these artifacts (which are created and updated in the problem space) act to power your solution space idea & design generation. Waggl is a service company that has created a platform to crowd source employee feedback and align on action. Webinar: Generating Focused Ideas Through Practical Empathy (43 minutes. My section begins at 4:00 and ends at 46:45.) Transcript below.
transcript
Alex: Welcome, everyone. I’m very excited to be here and have Indi here with us today. I’ve spent a lot of my own career in the realm of design, innovation and strategy and thus to host Indi in the context of our Waggl Thought Leadership is really a treat. I know many of you on the line are already familiar with Indi and her work and you may in fact be UX practitioners yourselves. But let me also do a proper introduction for everyone. In a nutshell, Indi is an independent data scientist. She’s a researcher and an author of two highly respected books on the subjects of Mental Models and Practical Empathy. She’s also co-founder of Adaptive Path which was a pioneering UX agency that led the user experience field for many years and was ultimately acquired by Capital One. Indi got her start as a software engineer studying at Cal Poly, San Luis Obispo, and it was in her early work in this capacity that she began to observe a gap between how engineers and creators of products worked — primarily from assumptions about users or consumers and what they were trying to accomplish. In other words, oftentimes the product or service created didn’t match up to what people actually needed or wanted. So, this launched Indi down a fascinating path of research practice, deep thinking and written contribution to a field of user-centered design. And today, in addition to writing and research, she works with innovation strategy teams inside large and small enterprises to teach them the skills of problem space research for product strategy. This includes her seminal work on developing empathy. With that introduction, let’s get started and segue to say we are thrilled to have Indi here with us because Waggl also shares a deep focus and appreciation for the power of listening, and deep understanding of people which is absolutely important and valuable for product design, and equally important within organizations to build strong and healthy cultures. So, without further ado, Indi, thank you so much for being here today and over to you.
Indi: Alex, what a great introduction. Thank you so much. My ears are burning. I was really interested, when I met Michael (Papay, co-founder of Waggl) a couple of years ago, in the Waggl philosophy of being able to really get a little bit deeper into what is going through people’s minds. I’m very happy to be joining up with you guys to do this. Welcome everybody and let’s get right down to it. I’m going to start off talking about some work that I did for an airline. I was embedded in the team — helping them, mentoring them, teaching them how to do this — but also supporting them as they went through 8 different sets of research. So, we did 8 different projects on it. Along the way, of course, I got to know them very well and they would throw at me some of the other data that they were dealing with. This is an example of some of that other data that they were being given.
This was the UX team and the product owners. They were primarily interested in sort of interacting with all of the “silos” across the airline and grappling with the idea of being heard and letting people know what they were up to. People would send them things like this and say, “Okay, look at that. That statistic that stands out here is that far more females check their bags than males do. So, what can we do about it?” So, they instantly jumped to this idea that, “Oh yeah, females pack more because they’ve got more shoes. They can’t take their makeup through security and so, of course, they’ve got to check it in. So, let’s offer them discounts on their second checked bag and maybe show some ads for pink luggage.” (Wah-wah-wah. Sad trumpet sound) So, we’re jumping right into ideation based on a piece of data. Woe, to all the women who are not necessarily falling into that assumptive group and being pestered with pictures of pink luggage. The point here is that assumptions are pernicious because you don’t know that you’re making them. There’s something that’s going on subconsciously and I’ve been working to give people tools to bring them to consciousness level, to become a little bit more aware of them. Also, become aware of the way that you’re using the data. Most of the time, and this is actually really funny, but it is truly much of the time, when I see companies using data, they’re using it in an incorrect way. For example, here is a very fun website that’s called Spurious Correlations (by Tyler Vigen).
As you can see, clearly because people in Maine are not eating as much margarine, their divorce rate is going down. So, let’s do something about getting margarine off the market, or something like that, to support marriages. This guy has a website that’s full of this stuff. You’ll have at least 15 minutes worth of fun paging through them. But, take a look at this.
This is a study done by a Berkeley researcher, Paul Piff, a number of years ago. It was just after all the 99% protests. What he did was he stood at a crosswalk — he had his researchers stand at a crosswalk and positioned one of his students as if they were about to cross the crosswalk and then they noted down which makes of car stopped and which makes of car did not stop. And you know what his conclusion was? The expensive cars don’t stop; therefore, the rich are greedy. Shame on the rich.
So, this is a particular kind of cognitive bias. It falls under correlation-is-not-causation. This phrase is something that you probably heard in university. You might not have remembered hearing it, but now you’re going to remember it, especially because of the Spurious Correlations. It’s just so much fun. If you were to actually talk to the drivers about why they stopped, or why they didn’t stop, you would get different kinds of thinking, none of which actually had to do with greed or wealth.
That one on the upper right is the one that’s less than ethical, or moral, or whatever, if you’re going to be judgmental about it. (“I didn’t want to wait; I’m faster than a ped; I was going somewhere more important …” But most of the reasons are completely different than wealth.
So, one of the things that you want to be aware of are these cognitive biases. There’s something called confirmation bias. That’s when you’re looking at evidence, like Paul Piff did, and you decide to make it fit a belief. (Or actually, possibly what he was doing was trying to get grant money and get attention and using the 99% protests as a way to get that attention. I understand that, but not the conclusion he came to.) There’s also something called the sharpshooter fallacy. There’s something called post-hoc fallacy. There’s something called the common belief fallacy. It’ really important to be aware of these things. To be aware of the way that you interpret and use your data.
So, back to the data that the team I was working with at the airline had to grapple with. These people would send it to them and they’re all like, “Woo-hoo look at that big, marked difference between female and male.” And the big question is, well wait a minute, why did they segregate the numbers up by gender? Why did they report them by age? Why didn’t they report them by destination? Or the duration of the trip? The airline would have that data. And while we’re being silly, why not by the height of the person because maybe they can carry a bag and a short person can’t? Or, number of alcoholic drinks and they don’t want to drag a bag. I don’t know, right. This is silly. I’m saying this in a sort of hyperbolic way because there’s pretense going on.
Erika Hall has written about it, about how it feels to count things. So, if you do a survey, or if you’re counting data, counting feels objective and truthful and you can trust it. So, we’re going to use that feeling of trust to move forward and make our business decisions.
It’s also a kind of comfort from risk, from the fear of making the wrong choice with your business decisions. So, you try to be data driven. So many companies say, “Oh yes, we’re data driven; we have big data and data visualization. This is all very important. Scientific rigor is extremely important. It’s a very core philosophy of our business culture.” And yet, two leaders in their fields — like Shawn Davis up at Microsoft — he’s like, “Why do we call it data driven? I mean it sounds like we’re letting the data make our decisions for us. Why can’t we have the person in the loop? Let’s called it data informed and then we’re going to make the decision.”
Another leader, Karl Fast, he’s also talking about how the idea of science is culturally defined as natural science, like physics, like chemistry. These are things where you do experiments to prove things. You come up with a hypothesis. You do an experiment. You prove it and you get your peers to try it themselves and validate it, or not. And this is not what business is about. This is not what we are doing when we are building things. Building things is called, in the academic world, artificial science. And there’s a different way of measuring in artificial science. There’s a different way of making yourself comfortable and giving yourself a way to trust your decisions.
These two guys (Jakob Nielsen and Don Norman), you might have heard of them, they wrote an article way back in 2004 that was talking about how statistical research is not necessarily scientific or credible, and I think that’s really important. We’ve been saying this over and over again, and yet we’ve constantly run into people, our clients, who are still sort of blind to the way that they’re using data because it’s convention. This is the way that everybody else talks about it and that’s the way it’s done. When we do that, we end up with experiences that actually cause harm.
I don’t know if anyone has recognized this picture? This is a picture of Rebecca Meyer. This a Facebook Year-in-Review, looking back at your year. “Look at this beautiful picture of your daughter.” Eric is her father. And this was the year that she got cancer and that she died, the day before her 6th birthday. So, seeing something like this was very harmful to Eric. But Eric is a very intelligent guy and instead of just lashing out, what he did was he wrote a really informed article, in December 2014, about how our algorithms are not reaching out broadly enough to consider other scenarios, other instances or uses or approaches that people might have when they come to your tool. He also mentioned, “It’s not a special disease of young, inexperienced programmers.” That is right. It’s convention. We are just in such a big hurry to get our products out and to support people, even internally, that we do a generalized job of doing it, according to a generalized understanding, filled with assumptions about of our audiences.
So, what I’m out to do is help people understand that simplifying is a mistake. Dumbing-down to like a generalized version of an audience is not something that we have to do. It’s only something dictated by our understanding of speed and our feeling that we will lose market share, or lose profit because we are taking the time to understand something — as opposed to looking at it the opposite way and say, hey, if we are taking time to understand something more deeply we’re going to gain market share. We’re going to get audiences we had never thought about and we will increase our profit.
So, this is where I want to introduce the concepts of solution space and problem space. You’ve probably heard these terms before and they get thrown around in sort of a very fuzzy way. First, I’m going to show you a layout of design thinking.
This is the Stanford Design School approach. IDEO was also one of the designers of this. The idea is that first you take time to empathize with people and then define what you learned and ideate, prototype and test. The ideate-prototype-and-test then might then loop back to either redefine or re-empathize or re-ideate. Christina Wodtke wrote an article in Medium that sort of restates what these things mean for UX people and for product owners. So, it’s context analysis. It’s sense making of that data that you get from the context. It’s idea generation, product definition and idea validation, again.
Here’s another fuzzy way that people are looking at it. Dan Olsen, an author, actually runs a product manager meetup down on the San Francisco peninsula. In his book (The Lean Product Playbook), he has this diagram where he’s talking about the problem space being a section of this loop. So here, you’re trying to understand a problem, the problem that your solution is trying to solve, so that you can make your solution solve it better.
And I think the fuzzy part about this is that the problem space isn’t about generating ideas. Understanding the problem space will help you generate ideas and those new ideas will help your solution solve the original problem, but when you’re looking at the problem space you’re turning away from your organization and away from the solution that you’re all caught up in — you’re turning toward a person and trying to understand their intent or their purpose.
Here’s my diagram, or at least the initial version of it.
We’ve got the loop (on the left) that’s spinning, that’s always spinning, that’s always under deadline. It’s about the ideation and pulling things together and testing them and delivering them and all of that. But we have a separate spinning cycle that’s in the problem space. So, on the solution space side you’re looking toward your organization. You’re looking toward all the things that you do to support people internally and externally. In the problem space, you don’t care about your organization. You’re not an employee of your organization. You are a human and you’re interested in finding out what makes people tick. Not with respect to your organization, but with respect to their purpose.
(Here is a more updated version of that same diagram.)
So, I like to put this little definition up here that a user is someone with a relationship to your organization. A lot of people are like, “Oh yeah, we don’t use the word user. We say member.” Or, “We say passenger.” You know all those other words — those are all other words for “user.” They’re all still in the solution space. They’re all still people who have a relationship to your org.
So, a person is a human being who has a purpose or an intent. It’s a larger purpose or intent. In a way, it’s not tied to technology. It’s not tied to tools. It’s not tied to brands. It’s something you could have asked your great grandmother. We can go back in time because humans have been thinking and solving problems and getting things done and getting amazing things done for centuries and centuries. So, there’s no reason why we couldn’t, like if we had a time machine, go back in time and do the same kind of research that I do now with respect to our forebears.
What I am proposing is that we are spending too much time thinking about the tasks and the goals of the user and we need to add, in addition — I think it’s fine to think about the tasks and goals — but we need to add some time to understand the purpose or the intent. Let me give an example. A task might be “book a flight” and the goal might be “I want to take a trip to the Grand Canyon.” But my purpose is that my Mom has never seen the Grand Canyon. She’s getting older and I want to bring her up to the edge of the Canyon and give her that amazing moment. That’s something different. Now, because my Mom has certain phobias … she also can’t get around very well, she lives in a different city than I do — how am I going to make this trip happen? There’s a lot more thinking that goes in there and yet I probably would have done this even 100 years ago. (If I were wealthy enough.) It doesn’t matter what the technology is. I’m still going through this kind of thinking. So, this is the example that I like to use to help clarify what I mean.
Back to my diagram where I’ve separated the solution space and it’s spinning cycle from the problem space and the cycle of looking at people — looking away from your organization. Looking away from technology, away from systems, away from everything you do to support people internally or externally and just thinking of them as a person who is trying to accomplish something. They’ve got a purpose. You’re trying to be in a learning mode, a beginner’s mind. It’s something where you’re very curious about them and you’re not trying to solve anything, for right now.
It starts with listening. While you’re listening, you’re using empathy to develop a connection with someone. Then you use a different kind of empathy for analyzing all of that data -the contextual analysis that Christina was talking about — to come up with some artifacts that you will then use to affect your solution space.
I want to just put in a couple of words about that curiosity mindset. Here I’ve got the quintessential picture of the toddler and we all know what toddlers are good at, right? They’re good at asking, “Why?” But, that’s not why I put this picture up here. The reason I put this picture up here is because toddlers are not embarrassed about what they don’t know. Think about it. When you are with a toddler and they’re asking you all of these questions, they’re not sort of trying to hide the fact that they’re uninformed. They’re just curious. They want to know, and they’re trying to see how things go together and what things lead from one place to another. So, this is why I put this slide up here. This is the most important slide to this whole deck: this curiosity mode; this beginner mind … this “I’m not embarrassed about what I don’t know.”
So now, all of that said, now you turn back to your organization. You turn back to the solution space. This is when you can use this information to innovate. You can use this information also for a number of other things, but innovation is what we’re talking about during this call today. And that affects different parts of the solution space cycle. It affects ideation. It affects the kinds of designs that you’re making. It affects the way you’re going to deliver them.
When you’re in your stand-up meeting, or when you’re designing with cross-departmental collaboration, however you’re doing these things, you’re already slightly aware that you are not the people that you’re designing for. And so, you try to explore other perspectives. You try for edge cases or “stress” cases. You try for different scenarios. But, time and time again, I see my clients when they’re trying for these different scenarios and these edge cases, they’re making them up. They’re totally imaging them based on anecdotal information, as opposed to actually going out and listening to people.
So, when you are trying to innovative, (the creative process is super well researched and well understood), when you’re trying to innovative, basically you’re letting your brain come up with ideas and those ideas are coming out of the pool of your own knowledge and experiences — the things that you read, how you’re being influenced by people around you. Now, if you also fill your brain with how other people are reasoning toward a purpose, in your work the number of ideas that come out is going to be much broader. It’s just how it works.
And we all know that the way we’re doing creation, when we come up with an idea, it’s not necessarily in a meeting, where we’re like, “Okay let’s come up with a new idea.” It’s when you’re out running or it’s when you’re in the shower. Or it’s when you’re just waking up or going to bed. You’ve got a notebook by your bed. I talk to a lot of people, like, “Where do you get your ideas?” These are very common answers to that. I think Einstein said, “You want to fill your brain with a bunch of concepts and let them sort of rub together in your brain.” Eventually something will pop out and another thing will pop out and another thing will pop out. Your teammate will have other things popping out and you’ll get to rub those ideas together within the collaborative brain of the whole team and let more things pop out with time. (Which kind of describes Waggl as a tool.)
The idea is that when you get some stories from real people, those can also become the things that changes an organization’s direction. You may have read about people talking about how powerful stories are. If you are inventing your stories, if you’re playing make-believe and making them up, it is not as powerful as actually having stories. You may have had experience with made-up personas before and you’re probably nodding your head.
The other point that I want to make here is it’s really circular reasoning to use your understanding of how people use technology to come up with new ideas. Innovation is going to be much broader if you’re understanding people as humans, if you’re understanding their purpose and their inner reasoning, than if you’re simply understanding that thin layer of how they’re using different systems right now.
So, back to this diagram. What I’ve done is I’ve added in those hexagonal bits from the design thinking model and you can see that that E (for Empathize) is there. It’s not there in that solution space. The E is all of the problem space. And what I’m arguing is that you don’t need to do the problem space all the time. You do it once a year. You do it once every couple of months. With the airline team, we did 8 studies over the course of 16 months and that was a complete anomaly. None of my clients have ever done this. None of the other people who have been using these problem space techniques that I’ve been talking about for decades have ever done that. It’s always been a much slower spinning cycle. It’s something else entirely and it’s not a part of your solution spinning cycle. And what’s coming out of — I mentioned artifacts earlier — what’s coming out of this, you can do it in a number of ways and get different things out of it, but the things that I pull out of it are a mental model diagram (also called an opportunity map) and behavioral audience segments (also called thinking styles). These are then referenced when you’re ideating. The behavioral audience segments then support the way that you’re making your design come together. They support the way you’re pulling the delivery together. So, these are like — in a way, they’re not a part of the actual arrow, they’re supporting the things that you’re doing within that arrow, over there in the solution space.
And, in honor of the recent solar eclipse (21-Aug-2017) I have drawn up a new diagram. I think it’s a little bit better.
You may not only have only one solution space. You may have a lot of different projects going on, trying to affect different systems, internally and externally. Problems based research is like the sun. It’s shining on those systems and it’s giving them the energy that they need, the knowledge and the understanding that they need to actually create life. So, yes you’re creating life on the little planets, but without the sun you’re not going to be able to do that very well. So, this is a little bit of an apt analogy in that it really separates out those two things. The E (empathize) in the Design Thinking diagram is not a part of a product loop. It is the sunshine that is shining the energy onto all of these other efforts that you’re making.
Let me quick give you an introduction to mental model diagrams and to the behavioral audience segments. Mental model diagrams are like a skeleton in that they form the bones off of which you will hang the flesh of your solution thinking — your design thinking. So, in a way this skeleton, the bones have to be really strong. Also, you don’t have to have all the bones there. I often go back a year later and help clients fill in some more bones and then a couple of years later fill in more bones.
But eventually we’re going to have a skeleton — you can even start with a sketchy skeleton — a bare bones skeleton — but we’re going to have this artifact that’s called a mental model diagram. This is the top half of the mental model diagram. This is the skeletal part, the bones part.
And what it contains is the deep understanding of what’s going through people’s minds and hearts as they’re attempting to fulfill a purpose. So, this particular data is from a study I did about near-miss accidents. This first part is “Recognize I am in a dangerous situation.” The next part is “Get safe again.” It contains towers like, “I want to behave in a smart way so I can get out of this dangerous situation safely.” “I reach out mentally to others for help, to get out of this situation.” In that latter one, at the very bottom, “Pray to my dead brother to help me through this incident.” So, there’s all sorts of coping things that people do. There’s all sorts of thinking and reasoning, inner thoughts that are going through people’s brains. There’s also emotions that are going on. Like, “I feel panicked about what I should I do after the bracket hit my windshield.” So, there’s panic going on. There’s all sorts of other emotions as well. There are also things called guiding principles which are kind of the operating instructions that we all have at our core, that we learned and sort of built upon from when we were kids. It’s part of how we make our decisions. The boxes in the towers contain:
- Inner thinking
- Emotional reactions
- Guiding principles
This other section is “Find out if anyone was hurt.” In it appears “Feel relief that I, or others, were not hurt.” “Worry that I might have hurt someone.” “Reassure people that I’m not hurt,” and on and on. This is only a tiny section of it. The idea is to get into this inner thinking.
Look how deep this is. This is what people have told us during our data collection, as we’re listening to them about what went through their mind during a near miss accident that they had. All this data actually becomes quite a long skeletal diagram. And then what you can do is you can hang things, or align things, beneath those towers. Those are alignment of the services that you produce — internal/external. Alignment of different features that you might have. You might also tag them with respect to maybe who owns them, or how they’re being delivered. The top part of the mental model diagram you can see here, the skeleton part, has all these colors in it. So, we’re depicting which kinds of thinking styles or behavioral audience segments are doing what in these towers.
Here’s another example. This one was for the group that is in charge of helping employers understand what the law is about around hiring people with disabilities.
They have the mental spaces here in yellow, orange and green. And then the bottom part, what they were doing was lining up — what they were doing in support of the towers that appear in the top. And you can see there are big gaps. And there are places where there is a lot of support. And there are places where there is a little bit of support, weak support. So, this gave them all sorts of opportunities to say, “Hey, you know maybe we should come up with some ideas around such and such.” Again, they’ve decorated it with the type of behavioral audience segment.
In this next case, we’re actually aligning competition. This was a developer’s network and we looked at the competition to see how they were doing. You can see where the competition is taking care of people and where this particular corporation is not. And so, this gives them ideas for direction. It’s a little bit more strategic.
When there’s flesh on that skeleton, then you turn to the mental model diagram over and over again, whenever you come to the ideation phase of your cycles on all those planets that are cycling the sun. You go through and you get insights out of it. You pull insights. You do markup with different stakeholders to get different people’s approaches. You do prototypes. This is how you’re using this data. You’re referencing it to do the things that you normally do during this idea generation phase, to make sure you’re supporting specific approaches, not just generalizations.
Let me introduce behavioral audience segments, or what I call thinking styles. It comes from the same data. So, I make a mental model diagram and then I turn around and I make the behavioral audience segments.
So, let’s take a look. On the left are marketing segments.
You’ve got Lily and Ken — this is a marketing segment actually from a university I worked with. They have low grade point average, so we think of them as “less than serious about their academics.” Here’s Robert with a high grade point average. Here’s Georgia, she’s older; she has what they like to call, “lots of other life experience.” And then here’s Michael; he’s low income and so they thought he was worried about how to get in and stay in. We did our problem space research and none of this is true in terms of the scope of deciding where to go to college. In fact, if you look at those descriptions, most of the details are demographic. So, what they’re doing is they’re imagining someone’s thinking style out of a demographic, which just doesn’t work.
Instead, what we found out, was that nobody we spoke to about deciding between colleges was worried about how to get in and stay in, no matter how much income they had. (Later studies might turn up this thinking, but we did not encounter it.) There were people who had a calling. They were passionate about the topic. This isn’t about demographics. It’s more about the thinking that goes through someone’s head as they are considering which university to attend. I think several of the people who fell into the “passionate about the topic” area, they were going to go into the nursing program because of experiences that they had in their own life. One young woman had grown up in Bosnia and been through the war there and really had great respect for the medical profession and really wanted to become a nurse. “I have a calling.” Another woman had just helped her father through the end of his life. She had been an accountant at a department store for 30 years and she’s going back to school because she has a calling. She was with her father in the convalescent home and just was bowled over by the way the nurses were taking care of people. She said, “I have to do this. I’ve been doing the wrong thing all of my life.” Notice that the demographics of age don’t apply to the thinking style (unless the context is about age).
Demographics are not thinking styles. I’ve got a write-up that I did on Medium that’s called Describing Personas that talks more about this, which you might want to check out.
Here’s another example.
This is Healthwise. They provide all the online content for your health insurance company and for websites like WebMD. They’re also making packages for behavior change, like helping people quit smoking. These are some packages that they designed for people trying to lose weight. First what they did was they went out and did problem space research. They turned to people as humans and said, “What are you trying to accomplish here? What’s your purpose? And what went through your mind as you did this?” And it turned out, there were three different thinking styles with respect to losing weight. Healthwise came up with three different packages, each with a different editorial tone and components that spoke to the different thinking styles that these people had.
So, where does all this data come from? It comes from something I call a listening session. It’s not an interview. It’s active listening. You don’t go in with a list of questions. What we try to do is tell the person we’re interested in this area of their life. “What was everything that went through your mind the last time that you did this?” And we let them take us where it is important.
It’s just like going on a tour in a city. Even though you may have a passion for church architecture, you’re not going to interrupt the tour guide and ask questions about the church down the street if the tour guide is talking about the history of some uprising that was happening on this corner here. She was never going to mention the church. Yeah, she could tell you about the church, but it’s not something she had planned on talking about within the realm of the tour she was giving. This is a good analogy for what a listening session is.
In the listening session, we try to gather deeper understanding. We’re not looking at the things that you get out of the typical interview. We’re not looking at preferences. We’re not interested in opinions. We’re not looking at explanations or statements of fact. What we’re interested in is what were those actual thought bubbles coming up inside your brain? What were you talking to yourself about in there? What were your reactions? What were your guiding principles? How did those last two affect the decisions that you made? Where did those last two come from? What caused them? And that’s how we get the data.
In order to go to that deeper layer, you need empathy. I want to define empathy for you. No wait — I want to show you there are many forms of empathy. There is no one definition of empathy. And there are still so many people out there trying to come up with one definition. (I was at a conference yesterday and the MC was talking about empathy in terms of emotional contagion.) There are lots of different kinds of empathy with different uses. All of these are valid. This research actually has come from very well-known and understood studies in the psychology field. What I’m interested in to help you within your work are the last two — affective empathy and cognitive empathy.
- emotional contagion
- personal distress
- mirrored empathy
- empathic concern (compassion)
- self empathy
- empathic listening
- cognitive empathy
First, let me just define what emotional contagion is. There’s this book by Paul Bloom called Against Empathy that’s influencing a lot of people. He’s all like, “Oh you know empathy. You get exhausted trying to bathe yourself in other people’s emotions and that makes it really hard for you to make a moral decision.” And “We should really focus on compassion.” Yes, I agree with compassion, but what I’m interested in is before we take the action, let’s understand. Let’s gather more information. Let’s build that understanding in our brain so that we can come up with more ideas.
Emotional contagion is something that movie directors use a lot, or authors use a lot. I don’t know if you’ve watched Up or The Little Prince? Have you ever cried in a movie? This is emotional contagion. Also, have you ever hosted a dinner party where you kind of set up a playlist. You’re like, “Oh, I want to set the mood for my party.” That’s emotional contagion. These are the two supportive uses I can think of — usually emotional contagion is used manipulatively. Not always. So, you want to be aware of what that motivation is. (support or manipulation)
Empathic listening is also called plain “empathy,” and empathy is not emotional contagion. Empathic listening was really well demonstrated in the Pixar movie, Inside Out, when there were three characters going on a quest. One of those characters is Joy (the yellow-dress one) and the other one is Sadness (the blue-face one) and then there’s this pink elephant. They’re hurrying along and the pink elephant loses something really dear to him and Joy tries to tickle him to make him cheer up so they can (quick!) get back on the quest. It’s not working. He’s still sitting there. Sadness sits down next to him and says, “You know, I’m sorry that they took that thing from you. It was something that you must have loved.” And the pink elephant starts to talk about it for a little bit, cries a little bit and literally 42 seconds later says, “Let’s go do this thing.” That’s what empathic listening can help you with.
Empathic listening in your work is supporting another person through an inner process — like a listening session. Like having a stranger ask you your inner thinking. You need emotional empathy to build rapport in a listening session and help that person trust you, so that they can talk about these deeper things.
Cognitive empathy is all about consciously cultivating understanding of other perspectives. It’s about being able to understand how another person thinks, reacts, and makes decisions based on guiding principles. It’s that inner voice chattering away as they try to accomplish a larger purpose or intent. “If I decide to quit my accounting job and take nursing classes, I’ll need to set up at least two years of backup funds. My cousin did something similar when she was in her 40’s, and she got stuck without money and ended up having to sell her house.” Cognitive empathy is also about finding patterns between people and building a solid understanding of the breadth of people’s thinking and their own shifting thinking styles. These become a powerful guide for your product focus. You can help people accomplish their purpose in a way that really supports them, as opposed to generally supports them.
One last thing — I just want to mention the health of your organization and some current events. (Photo of James Damore, ex-Google employee.) Yes, this was the writer of that memo that was saying that women just don’t have the cognitive chops to be engineers. So, it’s still out there a lot (cognitive bias relating to data, assumptions based on convention, exclusion based on assumptions). How are we going to reach a better state within our organizations? We want to reach this thing called an inclusion mindset where instead of believing that other people are inferior, you believe anyone can have good skills. Instead of judging and assuming, you’re aware when you’re about to make (or in the middle of) a judgment or an assumption, and you can stop yourself. There’s no stopping the judging or assuming, that’s kind of the way our brain works, but you can become aware of it, and then when it happens you can do something about it. Like: find out more, do a listening session with someone. True, the inclusion mindset is sort of like fuzzy-bunny, rainbows and unicorns. We want to get there, and it’s really hard. And instead we end up with people who write memos about what they believe to be true. (“I wish you’d had a boss or a mentor who could have helped you to find a more productive way to express yourself, and to challenge some of your views.” Kim Scott, An Open Letter to James Damore)
We also end up with all sorts of different ways of trying to help people. There are ways to understand other people within the workplace, like Myers-Briggs and other personality profiles. I heard somebody refer to this as “astrology for business” which I thought was awesome because what’s happening here isn’t achieving our rainbow goal of believing anyone can have skills. Instead, what we’re doing is categorizing and labeling. This is increasing your assumptions and your judgments, not decreasing them. So, it’s a little bit of a fallacy there. It’s a little bit of a worry.
What we really want is to embrace the idea that there is endless diversity of human thinking styles. We’re not going to be able to support all of them, but we can identify them and support the ones that we’re really good at supporting. And it’s supporting in a very special way as opposed to a generalized way. And then that’s going to help you come up with your abundance of ideas.
My message to everyone is to take time, do that listening. Take time to understand people.
That is where I want to end. Thank you very much. I hope this has been insightful. I have two books out there. Ask me for a discount code at Rosenfeld Media. I also put a version of Practical Empathy on Audible. Practical Empathy is going to teach you how to listen and it’s going to teach you how to turn that data into those mental model diagrams. You can also get the book on Amazon. I’ve got a website with a lot of information on it and reference stuff. I write a newsletter every now and then. I do coaching andall of that sort of thing. And I’m also available to help with your research in small ways and large ways, within every organization, to try to actually make this happen. I think we have a chance to do it.
Thank you very much.
Alex: Indi, that was fantastic. Thank you so much. I’m sure I speak for everyone that it was a great rhythm of you taking us up to the top, looking from the top of the mountain, sharing some conceptual and important patterns and trends and then diving down and helping us see what that felt like, both from a tangible, practical work point of view and also really lifting our ability to observe and start to think more concretely about things that we’re observing as well. I have a couple of questions for you and then we also have a couple of audience questions as well.
I want to pick up right where you left off. It struck me that active listening, as you describe it in your work, is very powerful because it creates a personal and lasting connection between the listener and the listenee. It consequently establishes, at least temporarily, at that moment in time, a trust and investment in that listener/listenee relationship. Why is active listening such a powerful device in today’s context, in discourse around diversity and inclusion?
Indi: Yeah, super powerful. (Note, I like to say listener/speaker.) I think that it’s so powerful because partially, it’s not done very widely. So, when suddenly you have someone interested in listening to you, you are more than willing to work with them, as opposed to closing up and saying, “He’s just a misogynist. I don’t want to deal with him.” If he is a misogynist, but he still takes time to listen, then I’m willing to work with him. I’m willing to look past these things and try to build something different together. So, I think that’s one of the reasons. Listening is also — it’s got to be a very, very human reaction, going all the way back to when you were a little kid and you’re like “Mommy, Mommy, could I….” or “Mommy, look at this” and Mommy is all like “not now,” or “don’t interrupt.” And people just aren’t interested in listening to you and so you grow up with this idea that you’re only going to be listened to in certain circumstances and you try to generate those circumstances. And they are few and far between. When you get home, after the end of the day, you’re with the people that you live with and you’re like, “Oh, how was your day?” Do you really listen? Do they really answer? Do you go deep? Some people do. Some people don’t. It’s a very powerful thing when you are listened to, in terms of forming connections.
Alex: I’m reminded of my daily routine with my own daughters after school. “Tell me about your day.” I’m not sure if I’m entirely and always active listening.
Indi: Right!
Alex: Exactly!! One more — you started the session today talking a bit about data and how the larger an organization becomes the more reliant on data it becomes until one day the organization is suddenly data driven. That is not just about customers and markets, but also about their own employees, which as you say can be pernicious at worst and impersonal at best. So, what are some successful ways that you’ve seen companies, in particular, resisting the trend to convert everything to data?
Indi: (Note: I misunderstood the question during the webinar. Here is a closer answer.) True, collecting data about any group of humans, especially quantitative data that is meant to represent thinking or motivations and will be used to decide how to treat someone, is something to approach with care and an open mind for the fact that any one human can change categories a number of times a day, based on context. I think maybe the worry is also what I was talking about in terms of interpreting data, those cognitive biases. I think it’s really important. There’s been talk about emphasizing the skills of being able to do critical thinking, being able to recognize these things, being able to understand sort of logical and philosophical arguments. So, you can see when people are sort of making a mistake and not necessarily point it out, but offer alternatives, or additional things. I think people have a lot more success when they don’t say, “no,” but say, “yes, and”. Sure, we can offer pink suitcases to all the women who are trying to book a flight, but, “Yes, and, let’s also look into the other circumstances that we can understand about this person. See if we might be able to support those other circumstances.”
Alex: Makes sense. I love that. Okay, we have a couple of questions from our audience. Somebody writes, our industry seems to be leaning towards a prototyping culture a lot lately. Mr. Alan Cooper recently tweet-stormed about this subject and one of the main takeaways I had was that when one chooses to test and prototype several ideas also get left behind and aren’t ever considered or taken ahead into further investigation. Any advice how to better choose what to prototype or test?
Indi: Ah, yes. So, that’s basically why I am trying to get more people to do more problem space research is to understand and align, get a better look at where you stand with respect to what people are trying to accomplish. And that helps you choose. That helps you circle areas and look into some of the thinking that’s going on and say, “This is some thinking that sounds really important to people, should we do it?” Or maybe, “It’s not our expertise.” Or, “It would take us too much to tool up to do that sort of thing — it’s not a great idea for our organization.” So, the choice of ideas to test can also come from the way that things align in that mental model diagram, along with the ideas themselves. The problem space is not giving you ideas. Remember the ideas happen in the solution space. The prototyping happens in the solution space and these are the things that help you. They give you the energy and confidence to decide. They give you the layout of what you’ve heard in reality and you can compare it to your business reality and make decisions that way.
Alex: How do you think about emotional intelligence as it relates to your empathy style of idea generation?
Indi: Emotional intelligence is, in one way, being much more self-aware and aware of others. So, it is a skill that I don’t think we teach our kids and I highly recommend this. Let me give you this example. My neighbor is a meditation teacher, and she talks about emotions as weather. I’ve actually heard other people talk about it too. You can’t stop your emotion and you can’t tell somebody else to stop having emotions. It’s like rain. It’s raining — the emotion is happening. But, you can recognize that it’s raining and you can do something about it. You maybe go indoors. Or, maybe you can enjoy the rain. Or, you can see it’s raining on someone else and you can offer them an umbrella (offer to hear them, make them feel heard). So, it’s that recognition, it’s that mindfulness that the emotions are not things to turn off. Another psychology person — I was on a panel with him, Joseph Lee — he likes to use the two versions of the Star Trek movie as an example of emotional mindfulness. In the early one, I think it ends with Kirk, full or emotion, going into the neutron chamber to save the day, even if he is going to die, and Spock is on the outside not feeling emotion. Whereas in the newer version — and the older one was based on 60s style level of emotional awareness — the newer version has Spock going in there, recognizing the emotion of saving others, of sacrifice, and acting on it. So, it’s recognizing emotions in a different way. I think Joseph is saying we are in a slightly different culture now about recognizing emotions and being more mindful of them.
Alex: Excellent. Thank you so much for taking those questions. We are just about at the top of the hour. Indi, I just want to thank you tremendously for this great hour that you spent and the time and effort you put to developing these thoughts which, as I said, really are resonant for us here at Waggl. Of course, we think a lot about lifting the human voice up and out through organizations and making that a really important part of conversation and decision making and how organizations are run. And making that ultimately a much more human and relatable place.
