445. From Insights to Action: Behavioral Science at Lyft with Irrational Labs

Summary

Podcast Summary: The Brainy Business | Understanding the Psychology of Why People Buy | Behavioral Economics

Episode: 445. From Insights to Action: Behavioral Science at Lyft with Irrational Labs
Release Date: November 14, 2024
Host: Melina Palmer
Guests: Evelyn Gosnell & Dr. Isabel McDonald from Irrational Labs

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Introduction

In episode 445 of The Brainy Business: Understanding the Psychology of Why People Buy, host Melina Palmer delves into the intricate world of behavioral economics with Evelyn Gosnell and Dr. Isabel McDonald from Irrational Labs. This episode focuses on applying behavioral science to real-world business challenges, exemplified by a recent collaboration with Lyft.

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Guest Introductions

Evelyn Gosnell serves as the Managing Director at Irrational Labs. With a decade-long tenure, Evelyn transitioned from product management at brands like Christian Dior to behavioral science, leveraging her expertise to enhance product experiences and drive business outcomes for tech giants such as Lyft, TikTok, and Google.

Dr. Isabel McDonald is a senior behavioral scientist at Irrational Labs, holding a PhD in Public Policy from Harvard University. Her passion lies in utilizing behavioral economics to develop technology tools for social good. Isabel's background includes consultancy roles at Mercer Consulting and a postdoctoral position at UC Berkeley's Haas School of Business, where she influenced Fortune 500 companies on talent strategies and employee motivation.

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Case Study: Lyft's Women Plus Connect Feature

The core of the discussion revolves around a project Irrational Labs undertook with Lyft to boost the adoption of their Women Plus Connect feature—a program designed to match female drivers with female or non-binary riders, enhancing safety and comfort for both parties.

Project Overview

• Objective: Increase opt-in rates for the Women Plus Connect feature among Lyft drivers.
• Challenge: Despite positive qualitative feedback and initial city-based success, national rollout saw lower-than-expected opt-ins.

Evelyn Gosnell explains, “Lyft came to us and said, can we use behavioral science to make sure drivers are really aware of this option and those that want to have opted in?” (07:15).

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Methodology and Experimental Design

Irrational Labs approached the challenge by:

1. Behavioral Mapping: Analyzing the user journey to identify psychological barriers impeding opt-in rates.
2. Message Crafting: Developing 20 potential messages incorporating various behavioral tactics.
3. Selection and Testing: Narrowing down to three key messages for A/B testing against a control group.

The Three Treatments

1. Neutral Message (00:59)

   • Content: Clearly explained the feature without any behavioral nudges.
   • Effect: Led to a 97% increase in opt-ins compared to the control group.
   • Quote: “Women Plus Connect is this new feature that increases your likelihood of matching with female or non-binary rider drivers...” (07:54).

2. Confirm Settings Treatment

   • Content: Prompted drivers to confirm their current settings, subtly nudging them to opt in.
   • Effect: Achieved the highest increase with a 173% rise in opt-ins.
   • Quote: “Looks like you are not opted in to Women Plus Connect. Is that correct? Tap to review.” (07:54).

3. Concreteness Treatment

   • Content: Provided specific scenarios where the feature would be beneficial, making the benefits tangible.
   • Effect: Resulted in an 83% increase in opt-ins and also led to drivers working more hours, enhancing overall engagement.
   • Quote: “Driving late, try Women Plus Connect and you can match with more women and non-binary riders at night or anytime.” (07:54).

Isabel McDonald reveals, “The confirmed settings treatment had the most opt-ins... but the concreteness message was the only one that changed drivers' behavior and actually caused them to drive more hours with Lyft.” (12:07).

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Insights and Discussions

Effectiveness of Behavioral Treatments

The treatment messages, though varying in approach, all significantly outperformed the control. The confirmed settings message was most effective at increasing opt-ins, demonstrating the power of prompting users to verify their current choices. However, the concreteness treatment not only boosted opt-ins but also had a secondary effect of increasing drivers’ engagement with the platform.

Melina Palmer highlights, “The concreteness of it makes it come alive and makes the value of it even more kind of robust.” (17:03).

Balancing Business Goals and Behavioral Science

Evelyn emphasizes the importance of aligning behavioral interventions with overarching business objectives. For Lyft, while increasing opt-ins was primary, promoting more driving hours was also crucial.

Evelyn Gosnell: “It's about thinking carefully about how you set up an experiment... ensuring you measure not just the immediate outcome but also related behaviors.” (15:19).

Framing and Mental Models

Isabel discusses the significance of framing messages concretely to shape users' mental models, making the benefits more tangible and actionable.

Isabel McDonald: “Making things more tangible helps them think about when they might use this feature.” (16:04).

Challenges in Op-acing Behavioral Interventions

Crafting effective messages within constraints (e.g., character limits in push notifications) requires meticulous planning and testing. The team navigates these challenges by iteratively refining their approach based on data-driven insights.

Evelyn Gosnell: “Every word needs to work really hard for you... designing compelling messages within tight constraints is a critical and fun challenge.” (19:02).

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Key Takeaways

1. Specificity Enhances Engagement: Concrete messaging not only drives initial opt-ins but also fosters deeper engagement with the product.

2. Behavioral Mapping is Crucial: Understanding the entire user journey helps identify and address specific pain points or drop-off moments.

3. Alignment with Business Goals: Behavioral interventions must consider broader business objectives to ensure holistic success.

4. Iterative Testing Yields Insights: Continual experimentation and willingness to test unexpected hypotheses can lead to significant breakthroughs.

5. Uncomfortably Specific Goals Drive Success: Defining precise, measurable behaviors facilitates effective intervention design and accountability.

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Conclusion

Melina Palmer wraps up the episode by emphasizing the power of behavioral frameworks like the 3B framework (Identify a Behavior, Reduce Barriers, Amplify Benefits) and the importance of setting uncomfortably specific goals to drive meaningful change. She encourages listeners to apply these principles to their own businesses and share their experiences.

Melina Palmer: “What might it look like if you were to come up with something that's so specific it makes you feel a little uncomfortable? How might you design an initiative to help you achieve that goal?” (48:57).

Evelyn Gosnell adds, “Try it out... and make sure you're living in your end user's shoes to truly design for their needs.” (46:15).

Isabel McDonald: “Being uncomfortably specific helps streamline ideation and aligns teams towards clear, actionable objectives.” (43:50).

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Notable Quotes

• Evelyn Gosnell (07:15): “Lyft came to us and said, can we use behavioral science to make sure drivers are really aware of this option and those that want to have opted in?”

• Isabel McDonald (12:07): “The confirmed settings treatment had the most opt-ins... but the concreteness message was the only one that changed drivers' behavior and actually caused them to drive more hours with Lyft.”

• Melina Palmer (17:03): “The concreteness of it makes it come alive and makes the value of it even more kind of robust.”

• Isabel McDonald (16:04): “Making things more tangible helps them think about when they might use this feature.”

• Melina Palmer (48:57): “What might it look like if you were to come up with something that's so specific it makes you feel a little uncomfortable? How might you design an initiative to help you achieve that goal?”

• Isabel McDonald (43:50): “Being uncomfortably specific helps streamline ideation and aligns teams towards clear, actionable objectives.”

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Final Thoughts

This episode underscores the transformative potential of behavioral science in business contexts. By dissecting real-world applications, such as Lyft's Women Plus Connect feature, Evelyn and Isabel demonstrate how nuanced, data-driven strategies can lead to substantial improvements in user engagement and business outcomes. Listeners are encouraged to adopt these insights, framing their goals with precision and leveraging behavioral tools to foster growth and customer satisfaction.

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For more information and resources mentioned in this episode, visit thebrainybusiness.com. Connect with Evelyn and Isabel at irrationallabs.com.

Transcript

[Melina Palmer]

Hey there Melina. Here I'm excited to share. I'm teaching two virtual courses in Applied Behavioral Economics which are enrolling now. Advanced concepts of Behavioral Economics and Internal Communication and Change management. So if you're interested, don't delay, learn more and enroll at HBL. Like Human Behavior Lab.tamu like Texas A&M University edu. Again, that's HBL.tamu edu and click on Certificate program when you're ready. Let's start the show. Welcome to episode 445 of the Brainy Business Understanding the Psychology of why People Buy. In today's episode, I'm excited to introduce you to evelyn Gosnell and Dr. Isabel McDonald from Irrational Labs. Ready?

[Evelyn Gosnell]

Let's get started.

[Isabel McDonald]

You are listening to the Brainy Business Podcast where we dig into psychology of why people Buy and help you incorporate behavioral economics into your business, making it more brain friendly. Now, here's your host, Melina Palmer.

[Melina Palmer]

Hello. Hello everyone. My name is Melina Palmer and I want to welcome you to the Brainy Business Podcast.

[Evelyn Gosnell]

In today's conversation, I'm joined by Evelyn.

[Melina Palmer]

Gosnell and Isabelle McDonald of Irrational Labs. As Managing Director, Evelyn applies behavioral insights to help product teams drive business outcomes and customer value.

[Evelyn Gosnell]

She has worked with Lyft, TikTok, Google.

[Melina Palmer]

Airbnb, Procter and Gamble, the World Bank, Microsoft, Intuit, and indeed implemented scalable behavioral training programs at companies such as Aetna and been written about by the New York Times and Chicago Sun. She is a frequent speaker, media guest, and noted expert wherever behavioral economics intersects with real people's needs. I'm also joined today by Dr. Isabelle McDonald, who is a behavioral scientist at Irrational Labs. Isabel holds a PhD in Public Policy from Harvard University with a specialty in Behavioral economics. She's passionate about how a deeper understanding of economic and psychological forces can guide the development of technology tools for social good. She's worked with financial institutions, NGOs and government agencies to implement large scale experiments, as well as to develop tech products that help users achieve their financial and professional goals. Previously, Isabel was a consultant in the talent practice for Mercer Consulting. In this role, she advised Fortune 500 companies on developing talent pipelines, HR analytics, and compensation strategies that maximize employee motivation and productivity. Isabel also holds a postdoctoral research position at the Haas School of Business at UC Berkeley. Today we're talking about a recent project that Irrational Labs did with Lyft. Evelyn and Isabel will walk us through that project and share key insights for your business to learn from the work no matter what industry you're in, really quickly before we get into the conversation.

[Evelyn Gosnell]

I want to be sure you know.

[Melina Palmer]

That there are links in the show, notes for my top related past episodes.

[Evelyn Gosnell]

And books, ways to get in touch.

[Melina Palmer]

With all of us and more. It's all within the app you're listening to and@the brainybusiness.com 445. Now let's jump right in. Evelyn, Isabel, welcome to the Brainy Business Podcast.

[Evelyn Gosnell]

Thanks for having us.

[Isabel McDonald]

Excited to be here.

[Melina Palmer]

Absolutely.

[Evelyn Gosnell]

Yay. So, so delighted to be chatting with you both today. It's always fun when we have multiple guests on the show and we get to decide who gets to introduce themselves first here. So we'll let you do a little bit of, you know, mental math or whatnot to determine who gets to go first. But if you can share a little bit about yourself and the work that.

[Melina Palmer]

You do and we'll learn more about you.

[Isabel McDonald]

Right.

[Evelyn Gosnell]

I'm Evelyn, managing director at Irrational Labs. And one fun fact about me that I think is relevant for today is that I am a product manager turned behavioral scientist. So I've been at Irrational labs for about 10 years now. We work with lots of companies primarily in product to say, how can you use behavioral insights to make a better product, a better user experience experience. How can we drive the outcomes that both the user and the company cares about?

And so just to expand on that a little bit before we get to Isabelle, hearing about your background too is so what type of products were you managing and how did you find behavioral science?

I was actually managing more physical products. Most of our companies that we work with now are in tech, but I was at apparel for a while. I was at, many years ago, Christian Dior. So that's a whole different story in France. And so now most of it is in the, like I said in the tech space, but I almost accidentally stumbled upon behavioral science and said, wow, number one, this is fascinating and sounds really, really fun. And number two, what a powerful toolkit for us who work in product to say, why have I built. I've spent all this time and energy building this new feature that my customers said that they wanted and now why are they using it? Right. So behavioral science has clues for that, has toolkits for that. And you know, Isabel and I are happy to turn her to nerd out and share more about that with you today.

[Melina Palmer]

Oh, yes, no worries.

[Evelyn Gosnell]

We shall nerd out all the for. For our, you know, our time that we have here together. So thanks, Evelyn. Isabelle, please, yeah, share a little bit about yourself.

Sure.

[Isabel McDonald]

I am a senior behavioral scientist At Irrational Labs. I'm actually an economist by training and I discovered behavioral economics during my PhD research and I just knew instantly it, it was where I belonged. I think it, it just seemed to capture so much more of what was really happening in people's lives and how people were making decision making or how people were making decisions. And so more and more of my research took on a behavioral flavor. And then a few years ago I was thinking that what I enjoyed most about research was really working with partners and helping them interpret academic findings, helping them apply that, and then seeing that impact in product, really impacting people's lives. And so I discovered Irrational Labs. And it was also just this moment of clarity, of feeling like I had found my place. So been here ever since.

[Melina Palmer]

I love that.

[Evelyn Gosnell]

And so I know Evelyn, you shared a little bit about the, you know, the work that Irrational Labs does as far as, you know, some of the sectors you work with, tech and things like that. Can you share a little bit more about, I guess, your methodology, your approach, like what's different about Irrational Labs from, you know, some of the other options that might be out there as people are saying, hey, I've got, I got.

[Melina Palmer]

Products like what do I do? Right?

[Evelyn Gosnell]

Yeah, I mean, and maybe we jump right into an example. We, we recently worked with Lyft and I think it's a, it's kind of makes the process come alive. If we kind of walk through having a business challenge, how can you then apply the psychologies to understand? You almost do kind of a behavioral mapping to say what psychologies are in play for this particular part of the user experience, what is affecting the user, how, why aren't they doing X and what make, what might we do using our toolkit to nudge them to do X. So maybe we turn to Isabel to share a little bit more about, about this case study that we're very excited about.

Love it.

Sure.

[Isabel McDonald]

So Lyft came to us. They had recently launched a new feature for both drivers and riders. It's called Women plus Connect. And essentially if you opt in to this feature, it will increase the likelihood that you as a female driver would be matched with a female rider. And they had done some qualitative research on this. Drivers writers were loving it. They launched it in a few cities and the qual research was showing just really strong interest and appreciation for this feature. But when they rolled it out nationally, they felt like not as many people had opted into the program as they expected. I imagine this is a pretty common thing that people would expect experience of you're hearing that people want this, users are asking for it, but then they don't actually opt in. So really, what is going on there? And Lyft came to us and said, hey, is there a way that we can use behavioral science to make sure that especially drivers are really aware of this option and those that want to have opted in. And so we said, sure, happy, happy to take this on. Really excellent opportunity to apply behavioral science. We then go through a whole brainstorming process where we probably came up with 20 different messages that we could send drivers that would incorporate different behavioral tactics. We never lack ideas for potential interventions. But then we narrowed it down through this whole process of ranking and getting all of our behavioral scientists to weigh in on what they think would be the most impactful things. We narrowed it down to three messages that we could take forward to actually test with drivers. So the three treatments that we ended up with. The first was a very neutral message where we just wanted to clearly concisely convey what is this feature and what do drivers need to do to opt into it. So it just said pretty clearly, you know, Women plus Connect is this new feature that increases your likelihood of matching with female or non binary driver riders and you can just tap here to opt in. So we call that our neutral treatment since it's really just conveying the benefit to drivers. The second treatment that we ended up with, we'll call it the Confirm Settings treatment. We have found in, in other projects, a really great way to capture people's attention is to make a statement and then ask them, is this correct or do, is there something you need to do to update this, verify this? So, so what the actual text was, we said, looks like you are not opted in to Women plus Connect. Is that correct? Tap to review. And, and so that was our, our second treatment, the Confirmed Settings. And then the third one, we'll call it the concreteness treatment. And we wanted to be really specific for drivers about when and why they they might use this feature. So it said, driving late, try Women plus Connect and you can match with more women and non binary riders at night or anytime. And you know, tap to turn this on. So those were the three messages and we randomized all of the drivers that had not opted in to Women plus Connect already into receiving one of these three messages or a control group where we didn't send them any message. And we saw what happened in terms of who actually clicked on the messages and opted in. So at Irrational Labs, part of our process is always to vote on what we Think the winning treatment will be in advance. We think this is such a fun part of the process and really helps with learning because once you see the answer, you'll always say okay, I knew that was going to happen.

[Evelyn Gosnell]

Right?

[Isabel McDonald]

So I guess if you're listening, you know, pause for a moment, think neutral message, confirmed settings or concreteness. What do you think Juan? But we'll do a drum roll and I'll reveal the answer. It was actually the confirmed settings treatment had the most opt ins, the most clicks, opens and opt ins it was about 173% increase in new drivers who opted in relative to the control group. So really big change. And we compare that to for the neutral message it was about a 97% increase and then concreteness was an 83% increase. So all of them were pretty effective. The confirmed settings one was just even more effective. But there was a twist because the outcome that we were initially interested in was really can we get drivers to open this message and can we get them to opt in in and really solving this behavioral problem of attention. And so that was the core thing we were looking at. But what we found was it was actually the concreteness message was the only message that changed drivers behavior and actually caused them to drive more hours with Lyft. And so that was really interesting because it helped us understand that maybe there's the trade off between getting people's initial interest in this new feature that you've launched, but the reason that you're launching this new feature presumably is about people's engagement overall with the product. And it was actually a different message, the concreteness treatment that was the only one that had that change in behavior. So, so really interesting results and a really nice partnership with Lyft to be able to launch this and then help them kind of interpret, analyze, understand what to do to move forward with these for sure.

[Evelyn Gosnell]

And I think it's interesting too like you said, so the concreteness of the three things you tested beyond the control, it had just like a little bit less like. So it was the quote, worst performing.

[Melina Palmer]

In the initial thing that you were looking at. Right.

[Evelyn Gosnell]

Just based on. But it actually did had this secondary benefit that is actually really important that it's important, you know, tracking some of this of whether you got more people to, you know, driving more hours is important for, for Lyft as well and making sure that there, there can be more matches in the women plus connect and beyond if it was, was it getting them to drive at all times more or specifically like at night or, or things that you were kind of looking at measuring for that all times more.

I think this is one of those, this is why behavioral science is so fun and so powerful, right? It's, it's about thinking and aligning with the company in this case on like what are the most important metrics for them, right? What is the stated goal which is like increasing opt in into this beautiful feature that makes the overall experience better for people. Like people have said that they really wanted this.

[Isabel McDonald]

Cool.

[Evelyn Gosnell]

So let's increase opt ins.

[Isabel McDonald]

Cool.

[Evelyn Gosnell]

Let's, let's use our behavioral science toolkit and come up with solutions there. But it's also having the right conversations and understanding the business that we're working with closely enough to understand their overall goals, of course are to increase drivers driving as well. So this is kind of balancing these two things really thinking carefully about how you set up an experiment. What are your DVs, what are the most important things? And then of course even measuring it over time. Not just like we could, you could you start with like early days, you get curious, oh, what's the click rate, what's the, you know, and then it's like then what happens next? So I think it's, it's all about being very thoughtful and measured in our approach of how we, how we collaborate with these companies.

[Isabel McDonald]

I think there's an interesting thing about concreteness or just being more specific. I've seen this gut reaction that some people have that they're afraid to be really specific, give specific examples in product because they worry, okay, if that specific example doesn't apply to this particular user, then is that going to be a turn off for them? So in this case, if you are a driver who never drives at night, is this message really not going to work for you? Because that doesn't apply to you specifically. But what I think is true about concreteness and being more specific is it just makes things more tangible for drivers and it helps them think about. Even if this specific example doesn't apply to you, what are the times that you drive and when might you use this? And just making that more tangible I think is what really translates to that behavior change that we saw.

[Evelyn Gosnell]

Definitely. And so just looking here, so if we were to expand a little bit on the language that went into formulating some of these. Right. So like looking at that concreteness example, right. So it's saying driving late match with.

[Melina Palmer]

You know, more women and non binary.

[Evelyn Gosnell]

Riders at night or anytime with women plus connect.

[Melina Palmer]

Right.

[Evelyn Gosnell]

So if we say it's not only at night.

[Melina Palmer]

Right.

[Evelyn Gosnell]

But you picked Something very specific to tag onto to where we're able to go in there. So you could have said, you could have said something else that would have also been concrete. Did you test any other sort of formats on that, that before you landed on it being the at night versus something else that would have also been concrete?

[Isabel McDonald]

Yeah, I mean I think that could be a really interesting extension of an experiment. For sample size reasons, we were constrained in the number of things that we could test. But every single word that we put into a push notification, an email. We've gone through it so many times as a team, had all the conversations with the legal team, the copy editors, everybody who's going to be involved in the process before we actually launch an experiment has had something to say. But. But it was a really nice partnership with the Lyft team to continue to be advocating for. Okay, here are the specific terms that we think are really powerful and that we think need to be in this final message that gets sent out. So always a huge process that goes into actually launching these experiments.

[Evelyn Gosnell]

Yeah. The hardest slash, most fun. You can see where my bias slides. Like hardest fun is those pop ups, those notifications. Like you have like two lines of text and so every word needs to work really hard for you. So not only the wording but the sequence. Do you do an emoji? All of these things are, are very, very thought through.

Yes, that's. I was going to actually expand on the same thing, Evelyn of say, like, I think this is such a powerful test for someone because it's also. So when I talk with people about framing things, right? So we say, okay, write the, you know, all the best things about it, what you wanted to do. You can write it as a paragraph, right? You get to start of like, why would we do this? And what comes into that. And then if you only have 25 words, what would you say? And if you only have 10 words, what would you say? If you have a certain number of characters, how would you do it? And you only get to play, you know, it used to be is you looking at tweets and stuff and fitting the 140 characters or whatever it is, right. That you know. So if you go into that process and you can't bold or you can't do this or you can't do that, you know, how would you make this be compelling enough to stand out beyond.

[Melina Palmer]

Other push notifications that someone might be getting?

[Evelyn Gosnell]

Because of course, as a driver for, for Lyft, I'm sure you have so.

[Melina Palmer]

Many notifications coming up of like, do.

[Evelyn Gosnell]

You want to take this ride? Do you want to do this? And you like maybe sometimes you're just ignoring them and so things could get missed. And so how does this thing stand out but still feel consistent? There are just so many variables and when you have so few characters like you said Evelyn, I'm sure that's actually a really fun process for people to try to do a test when you have so little room to work with.

Yeah. And you hit the nail on the head there with the, the idea of okay, so if we're just self serving and we're purely just saying I all I'm solving for is women plus connect and I need to increase the opt in rate and that's all I'm going to do. That's one world. That's not how real companies operate though. Right. They have, they are thinking about the driver as they should holistically. There's lots of teams trying to ping them with messaging. Hey, there's an incentive to go do this truck ride. There's incentive to do. There's all kinds of messaging. So if you, we thought deeply about this especially for the confirmed settings. One where it's like oops, our psychology that we were trying to lean into there is being like oops, you're not opting into this. Are you sure? Like hint, hint. You probably should be like that's what we were leaning into and we probably could have done it even more effectively in terms of just pure numbers to just if you're trying to draw attention. I mean you just mentioned like Twitter. Right. It's like anything attention grab, fear and oh, error state. There's something, oh, go fix this. Right. We could have been even more aggressive in our attention seeking or attention grabbing even is probably the right word. But that would have not been correct for the overall context of how drive. Like so those are really important discussions to have. And this is again going back to the awareness of the, the company and the their bigger landscape of what they're. They're solving for.

Yeah. And definitely like you said, the bigger circumstance, you know that what on the academic side of the field. Right. You're testing such nuance of just this.

[Melina Palmer]

One little teeny tiny thing in its.

[Evelyn Gosnell]

Vacuum space to see what we can learn. And in this applied side, like you said with real business it's like, yeah, we want to increase opt ins.

[Melina Palmer]

Why? Right?

[Evelyn Gosnell]

Like why does that matter? And why does like why does that matter? Why does that matter? And we can see that there's typically something else deeper that matters. Beyond just the opt ins. Like you want people to opt in and so that they can do what? Right. That they're going to also then be driving more. They're going to be more likely to accept, they're going to be proactively out more. Like, are there these other, is there, you know, all these future tests you can do? But when you don't have the, if you don't ask those additional questions, you can't layer that into both what you're looking at for your, your benchmarking and your testing as well as in the way that you're going to frame some of those messages.

Exactly. And what we're excited about is like these are lessons that now Lyft can take and implement in further, further marketing outreach that they do. Right. This idea of concreteness.

[Melina Palmer]

Yeah.

[Evelyn Gosnell]

By the way, let's just like pull up for a second and just think about product people in general. I think Isabelle was touching on this earlier. It's like people resist. It's like, why, you know, but my product does all these things. Why would I, why would I just say this one use case. Right. This reminds me of a project that we did many years ago with an insurance company. It was like job insurance, job loss and you could get up to $9,000 if you lost your job. And we looked at their homepage and we ended up changing it very with a minor, minor detail. We added some images, three different images, and we said John used it for paying rent and Tina used it for groceries and Maria used it for medical bills. Right. Like obviously you can use it for whatever. It's $9,000. No one's saying what do you can you like, in a purely rational world, it's better to have it unconstrained, to have freedom. You can have, spend it on whatever you like. In the behavioral science world, we know that it just helps them with that one step less of thinking of like, oh yeah, I would want to use it for my medical bills. Oh yeah, I would want to use it for this. And so we increased page over page conversion by making this, this particular change with this insurance company. And I think that's just one example of like, yeah, kind of pushing and nudging the clients that we work with to say, yeah, driving at night, but you can also try like this also applies during the daytime, but the concreteness of it makes it come alive and makes the value of it even more kind of robust.

Yeah, well, and like you said there, it's, you know, having the.

[Melina Palmer]

Or any time in parentheses.

[Evelyn Gosnell]

Right. Helps to Showcase that. But it doesn't have to be really in your face. So if wasn't there, how might things have been different? And I think what's interesting about that insurance example that you just shared also is so they're also subtly sort of priming things you might use it for that I'm sure that the insurance company would be happier that you use it for versus like, Steve went to Disneyland. Right. Like, took his. Went on this, like, whatever, you know, so by having it be things that are, you know, rent and, you know, that's helping to bridge the gap. And maybe it's something like started a small business or like there's something that they can be doing that feels more in line with what that insurance agency is probably hoping you use the money for, even though you can use it for whatever you want. So, you know, smart in that way as well.

Yeah. By the way, there's a separate experiment there with, between hedonic and functional. Like, we could, we could run that. Isabelle, what were you going to say?

[Isabel McDonald]

No? Well, we, we often use the term mental model. So you're shaping the mental model of what is this product and what you use it for. And I think the examples that you give can be really crucial in shaping that understanding. And I would guess, you know, it probably does have some impact on average on. On people's behavior of what they really spend this on.

[Evelyn Gosnell]

The big piece here, we're working a lot more in AI and I think this is the obvious problem with AI. It's like you can use it for everything. Well, I don't know what that means. Again, cognitively, there's too many things to think through. And so how do we create the mental model of use cases, both on the consumer side? So we're working a lot with. Yeah, like, how does do we get the end user to use the AI thing? But we're also working with companies on internal adoption rates of employees using it. And it's just really being number one, clear about the use cases and then number two, solving for very low friction to actually, you know, deploy it into their workflows.

Definitely. And that's like you said, AI is such a great example with that because in the it can do everything. And so I feel like I can't do anything with it. And I don't really know where to start. And yeah, I see examples where people are using AI to make really fun images or, or do whatever else that they're sharing on Instagram or something, but I don't know how to even do.

[Melina Palmer]

A really simple prompt.

[Evelyn Gosnell]

So that's very much an example of a thing I could never do and I feel like I don't want to waste the time. And it's easy to come up with all these reasons why you can't or why you'll do that later because you know, status quo is so much more comfortable in the way that I've always done things. Whereas you know, if you teach someone how they can use it to write a better, you know, it's like here's some prompts for better subject lines or these are the top three mistakes, like to make sure you don't include in your first prompt or I know I was talking with someone that was interviewing me about that AI aspect and for training and I was saying like, you know what if you do something that is totally outside of what they're doing in their day to day function.

[Melina Palmer]

So if this was for internal teams.

[Evelyn Gosnell]

And say, you know, you're going to use the AI to be a test of who can make the best most creative like short story that you're having to come up with and if the teams are using it as like a.

[Melina Palmer]

Game and it's sort of bonding and.

[Evelyn Gosnell]

You'Re coming up with this thing and you're voting but people get sort of comfortable with doing the back and forth with the AI and then I'll feel okay when I'm going to translate that experience into now how would I use that to write an email or whatever you might use that for?

Yeah, I think that's really nice insight around how do we create a longer term mental model for AI use. I think especially when it's not perfect yet it's making errors, its accuracy isn't as high as we might want it to be. So how do we increase trust from oh, this first time use, I asked it to do this. I don't like the answer. Goodbye, I'm never coming back. That's not what we want. And so how do we create within the user? How do we design the user interface in a way that nudges that longer term mental model?

[Isabel McDonald]

Yeah, I think in, in some ways we're really battling human psychology there because we know that people have this asymmetric response to something that's negative versus something that's positive. If you use AI 10 times and it gives you a perfectly acceptable answer 90% of the time, but you know, one time it's didn't then that's what you're going to remember and that's what really sticks out to people. So how do we get People to shift their memories and their overall understanding of the product. It's hard and so it's definitely an area I think we'll see a lot more behavioral science come to play in the future.

[Evelyn Gosnell]

Definitely bringing it back to some of your process. And I want to revisit, as you were saying, with the, the lift example that you like to have the, the.

[Melina Palmer]

Guessing of which ones you think are.

[Evelyn Gosnell]

Going to do best and I can't let you not share what you guessed and thought was going to do work best both yourselves as well as if the overall team kind of weighted one over the others. What, what were your, what did you bet on in that test?

[Isabel McDonald]

Yeah, so, so we love to do this and, and we always encourage people to write it down number one and ideally put some low stakes bets of, you know, a dollar, some money on the line just to really make it fun with your team and give you the opportunity to get the glory of guessing right. So I, I was really expecting the confirmed settings to win and I, I think I'm almost cheating a little bit because I had done a similar project with fintech company that was trying to get people to link bank accounts. Really common problem that comes up in fintech. We, we tried a somewhat similar intervention and it was extremely effective. So I've seen the, the power of that. But I, I really didn't expect this concreteness outcome and it's, we, we often call this hindsight bias of looking back. It makes so much sense. Of course that would be the outcome but that is really the reason that we put so much emphasis on let's put down our predictions so that we make sure that we're learning because it's almost better when things don't go the way that you thought. You typically are going to learn even more from that.

[Evelyn Gosnell]

Yeah, I always love testing that where the totally random thing that you think this is weird but let's just throw it in there. So often I've heard from people that when they're sharing it's like man, we threw this thing in and it seemed super weird but it outperformed way beyond what we expected. So I think that's the, the beauty of doing tests too is you get to try something kind of off the wall and, and often they end up doing pretty well. Um, Evelyn, had you made a, a.

[Melina Palmer]

Guess, a prediction on this one?

[Isabel McDonald]

I don't recall. We can check our record.

[Evelyn Gosnell]

Yeah, but irrational sometimes there's like lunch that's at the at stake, you know, so it's nice, you know, to make it Social as well, and fun. But we'll have to look at that. I think one thing that stands out to me here is just the importance of testing multiple conditions. So it's like we, we do have a hypothesis based approach for each condition that we're going to put in. Right. So there's a reason to believe, based on the research that this may work. And there's a certain degree of humbleness to say let's, let's. No one's tested these exact things in this exact scenario at Lyft, so let's go ahead and test it and learn. So I think one of the things we do very frequently with our clients is push. And Isabel's done this wonderfully over the last year with some of our clients is really saying, let's move the needle. She's dramatically moved the needle on like this particular, one particular company. You know, usually we test like a B, two things at a time. And now it's like they have measurably through our collaboration, testing many more at once because you're just increasing the learning. So it's about testing more things, learning faster, but then also doing a good job internally to say how do we diffuse these insights? What other teams would benefit from knowing that this particular tactic works? How do we capture that? How do we like really build this sort of experimental machine? And, and Isabel and I love working with companies and doing that.

Yeah. Experimenting is so important. And that's, you know, I'm sure that the tips I give on experiments are similar to, you know, what you would agree and like what you would tell people too is like, you gotta think.

[Melina Palmer]

Small in the things you're testing.

[Evelyn Gosnell]

Right. So if we do everything different, then it's like it's just a new thing. It's not necessarily a test in this way. Right. But we also be thoughtful about what you're testing because you can't test everything. Like you said, you have a lot of ideas and you started with 20 or more, but you have to whittle it down to make sure you have the right things that are looking at those outcomes and then be able to just test often and be learning and iterating and you know, moving on to where you can layer in those, those learnings and things. So whether this ties back specifically to Lyft or if you want to talk more, you know, general and how this works, I always love to ask people about, because I think, you know, the way that you first see a problem and we talked, we talked about this a little bit.

[Melina Palmer]

Right.

[Evelyn Gosnell]

But is often not what the actual kind of root problem is. And when you start looking at the behavioral shifts or those nudges that you want to be putting into place. And so what sort of process do you go through up front to make sure you're working on the right thing? Right. So helping. So a client comes to you and says, hey, we want help with this. And you go, well, like, let's make sure. Like, this might not be fully what we want to look at. It's probably something over here. You know, what does that process look like for you to make sure you're going to test the right stuff?

[Isabel McDonald]

Yeah. So it typically is a whole data review. We love to collect any data that. That is available, any prior reports, qualitative research, a B, test everything that we can possibly draw from. And then I think this is a common thing. We find sometimes that there is this difference that we're seeing between what we're hearing in the qual research versus what the data is telling us. And then we try and make a list, okay, what are all of our hypotheses about why that could be going on? So in this case, attention was a big one of what if people just missed the announcement? They're. They're getting too many messages on their phone, you know, but there. There are other hypotheses that we can lay out there. And then we're trying to think about how can we match up any of the data that's available to try and kind of rule in or out any of these hypotheses and narrow down our list about here. Here is where we think this is going on. And then the other part that Evelyn mentioned is we call it behavioral mapping. So every single step, every click, every decision, every time you had to go find a password or any. Anything, whether it's in the app, out of the app, we're trying to lay that out to say, okay, any data we have on where people are dropping off or where we're having issues in the flow, it could have been that, you know, people navigate or they clicked on the initial announcement, but once they actually got to the page where you opt in, there was something confusing about that. And that's where we saw drop off. You know, that wasn't the case, but it could have been. So having that behavioral map allows us to be super systematic. I think one thing that we've seen, some. Some clients will work really hard on one stage of the funnel, and they'll do a great job at that. But there's friction, you know, two steps down the line and then you're, you're losing everyone there and you're not going to get the outcome that you want. So it's really matching up all the data that we can and doing this really systematic review of the process from the user side. That's where we typically see the magic happen.

[Evelyn Gosnell]

Yeah.

What Isabelle is alluding to there is very often teams, as I'm sure you know, are organized is such as like they own this part and then they, this other team own this part.

Yeah. Super silo. Yeah.

And it's like, well, we only own this part.

[Isabel McDonald]

And we're like, but your user goes.

[Evelyn Gosnell]

Through this whole thing. So we, that's one of the very first things.

[Isabel McDonald]

We just like sign up as a.

[Evelyn Gosnell]

User doing all the things as them and we kind of encourage our, our clients to do so as well. Right. Whether the term for it by the way, dog fooding is just terrible. It's the worst. I wrote a whole article on this being like why is it called dog fooding? But just this idea of you need to be in your clients, in your end user shoes to kind of experience that is so, so critical. And then our perspective, our role is to lay on layer on the psychologies. But one thing I want to add to what Isabel was saying and to answer your question in a little bit of a different way is so often our clients come to us with a high level business problem. This is the outcome that we're trying to drive and part of our role is to help them define that as a behavior. It's a lot harder to change this overall big picture outcome. We want our clients to, you know, our end users to improve their health. How about let's get them to book their next, you know, checkup like that. That is a behavior we can drive. So it's, it's helping facilitate these discussions with them. It sounds like a simple thing like defining this key behavior that you want to change. But we found it very effective. Very. It's kind of a key step in the process to really narrow in on that and saying which of these behaviors will have the highest impact to the out the business outcome that they're, that they're looking to solve for.

So important. I was reminded in this, the way you were explaining that I remember, so when I did my first thesis, type of project or paper, which I did an undergrad thesis and I remember going to my advisor again and again and saying like it's too big, it's too big, it's too big. Rewrite it too much, you know, it's not like it became this like. So what felt like the most nuanced, ridiculous thing of like, how could I even write anything about this? It's so, it's like only over one month. And it's a certain type of. I had to do with advertisements of whether they were, you know, high kind of cognitive processing versus image based in the US versus Japan at the same time and now. And it was so like narrowed in and you think I can't write anything about this and it becomes a 30 page paper. Right. Like, and so I think that challenge, which was so frustrating when I first went through it and someone telling me like, it's not too big, too big, too big. But to say to get from. We want people to improve their health. To like, what's the, like to book, you know, that the appointment before they leave an office or something.

[Melina Palmer]

Right.

[Evelyn Gosnell]

Just to get the appointment scheduled is the one thing. And on the one side it may feel. It's definitely not like disappointing. Right. But there's a kind of a deflating moment where you realize you have to do all those things one tiny micro shift at a time. And so I always encourage people too to like reframe that mentally. To like, we have the opportunity to get there one tiny step at a time. But if we think about it so big and it's so vague, like that is a moving, invisible target that we're never going to get close to. So what do you have any tips for people as they're going to start going through this process of saying, okay, I want to try and uncover a behavioral small thing I can do? How do they know that they found.

[Melina Palmer]

One that's worth focusing on if they're.

[Evelyn Gosnell]

Starting from some sort of like big picture goal?

Yeah, I actually think it's quite empowering. I think you can't change. I think it's a, you know, again, when we work in the space, it seems obvious, but it's like you can't change behavior unless you've defined the behavior. So like that's just the first step. And so you kind of. People are very used to kind of having their. They're clear on their outcomes, they're clear on the KPI that they're being measured on and maybe even bonused on. So they have that and then we're like, okay, so how do we translate that into behaviors? And that's just a brainstorm exercise of coming up with the list. Then what's key then is to say which of these is most closely tied to this top level goal? And you're likely, if you're doing your job well as a pm, you're going to attack many of those key behaviors. It's just picking one, starting there. Okay. Going to the next one. And so I wouldn't even sweat the like, progress over perfection. Pick one, start and kind of keep going.

[Isabel McDonald]

Isabelle? Yeah, I think the, the term that we like to use is that key behaviors should be uncomfortably specific. So if it doesn't feel uncomfortable, then maybe you're not specific enough. I do think this is easier to do in teams because you can be really trying to play devil's advocate to each other of asking, okay, who specifically are we talking about? We're not talking. We're not trying to solve for every single user. We're trying to solve for this smaller population that we feel like we can really change their behavior with good design. And then, you know, when are they taking this action? Is it before or after something else? Where are they? All of these questions that you can be asking yourself to try and get even more specific. And I do think that the power of that specificity really comes through once you get to the ideation stage. It feels like you're constraining yourself, but I think it's actually so much easier to come up with ideas if you're not thinking about, you know, how do I improve health? It's where. Where do I even start there? Right. Versus how do I book an appointment or how do I get somebody to sign up on. On this specific page? You know, the more you constrain, it actually helps your ideation process. And I think I've seen that happen for, for teams and it can be really this magic moment once they dive into ideation. The, the power of those discussions getting to that uncomfortably specific key behavior really comes through.

[Evelyn Gosnell]

I love that. Uncomfortably specific is a really fun way to think about it. And I would tie that to being. It's nice to live in the, the gray and the vague of things where it feels like, oh yeah, we're going to improve health. And it's like you say, it's the invisible moving target, whereas no one can say you didn't do it. Right. So we can. There's not the same accountability that comes with the. I would assume why we say this being uncomfortably specific is like, it's very clear if we hit it or we didn't. Right. And that can feel a little bit scary. Is that sort of the, the root to that?

Yeah, yeah. Again, I think it's that it allows you to design for it. I sure there. But yeah. Then you can, then you can design an experiment, then you can measure it, then you can learn.

All of those things are also good for sure. And I just mean as somebody's trying to decide what do I mean by uncomfortable? Right. Like why would, why would I be uncomfortable with specificity? What does that mean? Right. And so I would think that that's sort of rooted in that accountability that comes with it. But we know because we can design for the right thing, then we can show that we moved the needle or we didn't and we're iterating, we're learning.

[Melina Palmer]

You know, along the way.

[Evelyn Gosnell]

Right.

I think uncomfortable comes from like the specificity. So let's say we want high school students to, you know, increase their chance to go to college or something like that. Like do better in school. Some, some sort of high level outcome like that. You would want to break that down into like doing homework, potentially doing homework from 5 to 6pm on Monday, Tuesday, Wednesday, Thursday. Like you're starting to specify. And that is the PC like. But, but, but, but what if they want to do it later at night? But, but what if they don't have homework every day? But what if that's the uncomfortableness? But it's like if you do, if we start to, and this is where, as Isabelle said, you should be having discussions with your teammates to be like, do we want it daily? So is it five to six? Like what, what is. Where are we going to land? There's some battles that should happen there. But the benefit of that is then if we're on a platform, we're, we're now designing for this. Cool. We get to send Cal invites, we get to send reminders, we get to like, you can design for the thing once you know the thing is. But if you have gotten stuck and just well, I don't want to commit to Monday, Tuesday, Wednesday, I don't want to. Then it. You're making it a lot harder for yourself as well. Was saying for that next step of ideation and designing for the solution. So I think it's empowering.

Oh for sure.

[Isabel McDonald]

Yeah. I think we can also, I think we can tell right away when we go to a homepage and it feels like the team has not had those discussions to get uncomfortably specific about what behavior they want from users. And it feels like There's a million CTAs that are taking you in all these different directions. And that's hard from, from a user's perspective to know what to do there. But when teams are clearly aligned, they know what they're asking and they know, or they have a hypothesis about what the rate path is to get users on board. That is really powerful.

[Evelyn Gosnell]

I love it. And a fun little assignment for everyone if we're going to help them, to be uncomfortably specific with their next step, is that they can go look at their own homepage or user journey, what they happen to have and to say, you know, is it clear who it's for, what they're supposed to do and how they get there, or do you have some work?

Exactly. I love it.

Awesome. Well, thank you both so much for coming and chatting with me today about this example from Lyft and more about what you do at Irrational Labs. For everyone who wants to learn more that, you know, they're ready to move forward with their project, you know, whatever that happens to be, you know, what's their best step to connect with y'all, to follow you, whatever that happens to be.

So folks interested in us can find out more [e-mail] we have case studies, we have a boot camp that we teach folks on learning more and up leveling your skills in behavioral science. We have a newsletter that gives you all kinds of updates about the events that we run.

Perfect. Well, Evelyn, Isabel, thank you again so much for joining me. It's been delightful to chat with you today.

Thank you so much for having us.

[Isabel McDonald]

This was fun.

[Melina Palmer]

Thank you again to Evelyn and Isabel from Irrational Labs for joining me on the show today. What got your brain buzzing in today's conversation? For me, I always liked to learn about the frameworks that teams are using to apply behavioral science into business. So the 3B framework stood out for me. As a reminder, those steps are to identify a behavior, reduce barriers, and amplify benefits. I also love that phrasing of finding a goal that is uncomfortably specific.

[Evelyn Gosnell]

That is so perfect and has really.

[Melina Palmer]

Stuck with me since having this conversation. As you look at your work and projects, whatever framework you're using to influence behavior, you should align it to some sort of goal. What are you trying to get people to do? How does it relate back to your business problem and focus?

[Evelyn Gosnell]

Is your goal vague enough that it feels comfortable?

[Melina Palmer]

What might it look like if you were to come up with something that's so specific it makes you feel a little uncomfortable? How might you design an initiative to help you achieve that goal? And how does making it more specific increase the chances that you will achieve it?

[Evelyn Gosnell]

Try it out.

[Melina Palmer]

And of course, come let us know.

[Evelyn Gosnell]

On social media how it worked for you. We'd love to hear about it.

[Melina Palmer]

There are links in the show notes to make it easy to connect with all three of us, along with links.

[Evelyn Gosnell]

To my top related past episodes, books, and more.

[Melina Palmer]

It's all waiting for you in the app you're listening to and@the brainybusiness.com 4. And thank you again to Evelyn and Isabelle for joining me on the show today. It was a delight to chat with and learn from you. Join me Tuesday for another Brainy episode of the Brainy Business Podcast.

[Evelyn Gosnell]

It's going to be a lot of fun.

[Melina Palmer]

You don't want to miss it. Until then, thanks again for listening and learning with me, and remember to be thoughtful.

[Isabel McDonald]

Thank you for listening to the Brainy Business Podcast. Molina offers virtual strategy sessions, workshops, and other services to help businesses be more brain friendly. For more free resources, visit thebrainybusiness.com.