UX 研究的清算时刻已至 | Judd Antin(Airbnb、Meta)
The UX Research reckoning is here | Judd Antin (Airbnb, Meta)
Performing User-Centricity
Judd Antin: User-centered performance refers to customer obsession or user-centered practice that is symbolic rather than focused on learning. It’s hugely common, I would argue. It’s work we do to signal to each other how customer obsessed we are, not because we want to make a different decision. If your listeners are like, “I don’t do that.” I’m like, “Think about it for a second. This is extremely common.” Every time a PM comes to a researcher at the end of a product process and says, “Can you just run a quick user study just to validate our assumptions,” that’s user-centered performance. It’s too late to matter. We got to ship it. What they want is to check the box. One of my big mantras was, “We don’t validate, we falsify. We are looking to be wrong.” Many PMs, many designers are not in that place. They do not want to be wrong. They’re looking to validate, and that’s user-centered performance.
Introducing Our Guest
Lenny: Today my guest is Judd Anton. Judd helped build the user research practice at Facebook. He was a longtime head of research at Airbnb, and his direct reports have gone on to lead research teams at Figma, Notion, Slack, Robinhood, Duolingo, Fair, and other amazing companies. These days, Judd spends his time consulting, helping companies with organizational challenges, product strategy, design, research, hiring, onboarding, and crisis management. In our conversation, we unpack a conclusion that Jud has come to recently about how the user research field is going through a reckoning and what needs to change both within the user research field and how companies leverage user research going forward.
Judd shares what the user research field has gotten wrong over the last decade, how PMs and designers rely on user research too often, and to answer the wrong questions, where user research will continue to provide significant value, and how to best leverage your researchers, why it’s important for researchers to think about the business goals more versus just what the users need, what to look for when you’re hiring a user researcher, how PMs can be better partners to researchers, and also a phenomenon that I love that Judd describes and often witnesses, that he calls user-centered performance, where everyone acts like they care about the user, but they’re just doing it for show and already know what they want to do. This episode has a lot of spicy takes and will probably upset some people, but Judd is sharing some real talk, here, that I think we all need to hear. With that, I bring you Judd Antin after a short word from our sponsors.
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Judd Antin: Lenny, thanks for having me.
Starting With Airbnb
Lenny: It’s my pleasure. So we actually worked together at Airbnb for many years. And as I was preparing for this, I realized how many of the people that you managed went on to do amazing things. So I’m just going to read a list of people that worked for you and what they do now. We had Matt Gallivan, who now leads research at Slack. We have Janna Bray, who leads research at Notion, Celeste Ridlen, who leads research at Robinhood. Rebecca Grey, who leads research at Fair, Hannah Pileggi, who I think was leading research at Duolingo, Louise Beryl, who leads research at Figma, and then Noam, who was leading research at Wealthfront. I think he moved on to something else. What a fricking crazy alumni community and group from this one team that you hired and incubated.
Judd Antin: No, I’ve never looked at that list, but I’ll tell you, I have been so privileged to work with all these amazing humans. I can’t take credit for it. They’re just outstanding people. And I’m glad the diaspora is out there, because these people, rock stars.
Reactions to the Article
Lenny: Okay. The main reason that I wanted to do a podcast episode with you is that you wrote this piece that was titled The User Research Reckoning is Here, which I understand caused quite a stir in the research community and I think adjacent communities. And let me just read one of your takeaways at the top of your post to give people a sense of what it was about. You wrote, “The user research discipline over the last 15 years is dying. The reckoning is here. The discipline can still survive and thrive, but we’d better adapt, and quick.” Before we get into the meat of the piece, could you share a bit about just the reaction to this piece and maybe if it was a surprise and what you expected would happen when you put this out?
Layoff Warning Signs
Judd Antin: Yeah, I was definitely surprised. I wrote it because I wanted to start a conversation about something I was thinking about. I didn’t really know who would read it. And in the end it turned out a lot of people read it. I learned that using the word reckoning may have been a mistake because it inspires a lot of drama in a conversation that I wanted to be really productive and positive. Overall, I would say, though, that the response was very positive. It seemed to resonate with a lot of people who reached out to me. I spent a lot of time talking to teams, to designers, to researchers, but there were also a ton of critiques.
I would say some of it was like people thought I was throwing research or researchers under the bus, like, “It’s researchers’ fault. We’re doing it wrong.” Which I don’t believe at all. And that I wasn’t taking responsibility as a research leader or a design leader myself. And the most interesting one I would say was the anti-capitalist crew, because one of my points that we’ll talk about is that I think researchers need to be more profit focused. And there are a lot of people out there who, I think they think that’s not cool or not research’s job, and I’m like, “Well, what are we doing then, if we’re not helping businesses succeed?” But that was the most surprising critique, for sure.
End of Zero-Interest Rates
Lenny: I’ve worked with some of those people who are just like, “Why are we growing? Why do we focus so much on growth? Why do we need to grow this business?”
Reintegrating UX Research
Judd Antin: Yeah.
Lenny: So I get that.
The Vicious Cycle
Judd Antin: Maybe it’s the wrong industry for them.
Identifying Great Researchers
Lenny: Yeah. I’m not a fan of that. Okay. Let’s actually dig into the meat of your message, and the big takeaway, and the conclusion of what you’re finding is happening in user research. And I know a lot of this comes from a lot of user researchers have been laid off at a lot of companies. It was one of the hardest hit teams. And so I think a lot of this comes from that. So yeah, so let’s just start big and then see where it goes.
Judd Antin: So yeah, everybody who’s paying attention has noticed that there have been a bunch of layoffs. And I think back in the summer I was thinking, “Listen, this seems to be hitting UX and UX research particularly hard. Is there something going on? Is there a bigger picture?” The reason I use the word reckoning is because to me that’s like, “Hey, a moment to take stock.” And triggered by the fact that a lot of wonderful humans may have lost their jobs, and many more are afraid of losing their jobs. And so if it’s a sign, the fact that research has been hit so hard, it’s a sign of what? And so the thesis of my article is really, it’s a sign that maybe the system is a little more broken than we think and that research is not driving the value or impact that it should or could. And that’s for a bunch of reasons, I think. Some of it is stuff that research can do better, and a lot of it is how research is integrated and positioned in companies.
And at the root of all that, I think, is that we’re just doing too much of what I would consider the wrong type of research. And what I mean by the wrong type of research is I have this framework, and it’s in the article, macro, middle range, and micro research, at least three ways to talk about it. And it’s pretty simple, the intuition of what those are. So macro research is big picture, strategic, business focused, forward-looking innovation, look at the market, look at competitors, long-term research to understand where the product should go next, stuff like that.
And then you have micro research, which a lot of really technical usability falls into this, all the beautiful stuff that researchers do to enable a really high quality, excellent, pixel perfect thing to go out the door, laser-focused research to understand AB test results, stuff like that. And then you have this middle range, which is this blobular place where the research questions are middle altitude and a lot of the core, let’s say user understanding questions fall here. And a lot of what research is doing is research in that space. It’s, “Let’s take a group of people and ask some questions about how they think, feel, behave, how they’re using a product or not using a product.” And it’s just this devastating mix of really interesting to many, including researchers, and not impactful enough for the business. That’s the core thesis. Researchers do it because it’s interesting, but honestly, and a thing we should talk about, Lenny, is researchers also do it because it’s the kind of work we most often get asked to do.
Researchers Need Business Orientation
Lenny: Yeah. That’s exactly what I was thinking. As a PM, that’s what I want to get answers to, is like, “How should we think about this one product?” And I totally get this.
Judd Antin: Yeah. The questions turn out to be really interesting and there are many cases at many companies where it’s super impactful. But the problem with those types of questions is they tend to, they trigger all the worst stuff that researchers experience. So they yield results which are interesting, but sometimes hard to operationalize. They trigger the post hoc bias, really, really, really well where a lot of people can say confidently like, “Oh, that was obvious. We knew that already.” And they fulfill this need for us to feel and be customer obsessed, user-centered, without changing anything. So doing too much of that research to me is a symptom of a broken system, and where companies are really different from each other. I heard from so many after this article and they’re like, “Well, my company and my industry is like this or not like this.”
But in tech, we spent the last many years hiring, hiring, hiring researchers, but maybe, I’m sure most of your listeners are familiar with the idea of a ZIRP. Maybe it was a zero interest base phenomenon, where it was okay when the money was easy, to hire researchers, even though we were not setting them up properly. We’re were going to set them up to fail. We set them up as a service function. We didn’t know what research was for. We didn’t know how to really drive impact with it. And that’s where the reckoning comes from. It’s like that era is over. Research, I think, is more crucial than ever. Good, great researchers are more impactful than ever. But it’s in a new space. We’re in a new space now.
Performing User-Centricity
Lenny: I want to make sure people understand this framework. And specifically, how would you best describe the difference between this middle range research and macro research?
Judd Antin: Middle range research is usually focused on a more specific set of research questions or a constituency. So if macro is like, “Let’s understand the overall competitive landscape. Let’s do a concept car type project where we really look ahead. Let’s get involved with strategic planning,” which is a wonderful thing for researchers to do, do TAM studies, other things like that, that stuff lives in the macro space.
The middle range space is like, what’s a good example? “We want to know how Airbnb hosts feel about their payment options.” That’s a really interesting, reasonable question. And we can go out and do research on that, but it’s not that specific. It’s not really targeted at a business problem yet. It could be. Maybe that’s a result of the research, but it yields these middle range insights in which we’ve learned things like, “Well, hosts want flexibility about their payment options.” I’m making this up. And that’s a good example where it’s like, “It’s not that that’s not an interesting set of questions, it’s just not quite pointed enough.” And it’s not framed in the language of the funnel, or the business strategy, or the OKRs. It’s not quite enough aligned enough to that. It’s too blobular in that middle level and it ends up not driving impact.
Limits of Mid-Range Research and Intuition
Lenny: I think it also leads to a lot of the things as you described, people don’t like about research. It delays everything. You have to wait for the research to be done to have an answer, to make a clear decision. It also creates this issue that people complain about, that PMs and product teams don’t want to just make a decision on their own. They’re like, “I will get this additional data point and make sure research tells us this is the right answer instead of just trusting there.” God, I guess maybe along those lines, this might be going off a little track, but what’s your advice there for, say, product managers or PMs or product teams to not necessarily rely on research for that middle research?
Judd Antin: I think the reason why so many PMs ask for those middle range questions is because they haven’t really gotten deep with their researcher in a way which can leverage it for maximum impact. So if the question is like, “Hey, Judd, you just pointed out a bunch of problems, can you be more solutions oriented?” Well, the solution is simple but not easy to me. It’s that we need to restructure the way we make products in a way which integrates research much more fully. It looks like consistent relationships in which researchers, and the work, and the insights they provide are a part of the process from beginning to end.
And I think, Lenny, you as a PM, that’s how you worked. I remember you, I know who you worked with. You worked with great researchers. But honestly, most product processes are not that way. And so that’s when research is a service function. It gets called in right at the end. It’s reactive in the sense that a researcher in the room listening and participating in the conversation could have a ton of impact on framing exactly the right question that will drive maximum business impact, maximum product improvement at that moment, and then go do it quick, and get back, and we’re onto the next. But they weren’t there, the relationship wasn’t there. They’re not engaged in the project from the beginning. And that’s the number one root of the problem. As long as research is a service discipline, I think we’re going to be stuck in this spot.
Wisdom of the Crowd
Lenny: When people might be hearing this, on the one hand, it’s research has been not as helpful to teams as they thought, and researchers have been spending time on the wrong thing. On the other hand, your advice is integrate research from the beginning, make them more involved throughout. And I think that might confuse people. How should people think about, like, “Research is actually more important? You should integrate them more deeply.”
Judd Antin: There’s a vicious cycle that’s been happening, is from where I sit, and this is what I hear from many, many researchers and research leaders, which is a lot of companies hired a lot of researchers with great intentions, didn’t quite know how to integrate them. And UX research is a newer discipline, so maybe that’s not surprising. We’re still learning how to use it. “Cool, let’s evolve.” But a lot of companies hired these people, but they hired them into kind of like a service discipline, very reactive, not in the room, not integrated in the way I said.
And so they had less input on the questions to ask, or they’re included, but only at the end. And then they’re unable to build those direct relationships, to be there in the room to actually drive the questions and insert insights. Because a good researcher is like the repository of insights you need for growth, but they’re not there. They don’t participate in the decision. So they end up doing research. They have jobs to do, so they do research that is too reactive, it doesn’t matter, and then it’s less impactful. Executives conclude that therefore researchers are not as impactful and then they get sidelined or laid off and the cycle continues.
So I think the short circuit is the constant engagement. If you take a great researcher and you insert them consistently in a product process, I feel confident that researcher will drive a product improvement, metrics impact, growth, all the things that you want to see as a PM and a product leader. It’s just that’s the exception, not the norm these days.
The Researcher-PM Relationship
Lenny: This may be a hard question to answer, but when people hear, “If you have a great researcher, here’s how you approach it.” What are signals that your researchers is great versus not great? What are some things people could look for to tell them, like, “Oh, maybe I have the wrong researcher on my team.”
Immense Value of Micro Research
Judd Antin: The best researchers I think are first of all, multi method. The first iteration of user research was primarily a qualitative discipline. But a strong opinion that I have is that is largely one of those models that needs to evolve. It’s not that qualitative user research is no longer important. It’s that the best researchers have five tools. I think they have five tools. And those five tools are number one, what we would call formative or generative user experience research. So looking ahead, innovation focused, really open-ended, maybe more ethnographic, “Let’s go out into the field and talk to host and guests on Airbnb. Let’s see people using our product in the field,” stuff like that. So that’s formative.
The second type is evaluative, so more like usability testing. The third tool is a basic rigorous survey design. It’s the best scaled way to get responses from communities small and large. You can get a lot out of really well crafted surveys. But to do that, you have to have the fourth tool, which is applied statistics, the best research, know a little bit of stats. You can’t interact in a world of AB testing without knowing basic statistics.
And then in the old version of this, the fifth tool was SQL, because I think good researchers need to be able to run their own queries. These days, so much of that is dashboarded, that the fifth tool may now be prompt engineering, which is a thing we could talk about, but I think maybe that’s the fifth tool is somewhere, is it technical skills that fall in between querying your own data, understanding it very well in companies that are awash with data and then interacting with generative AI.
Balancing Mid-Range and Micro Research
Lenny: Amazing. That’s such a cool list. Okay, so just to playback, formative, generative, innovative skills to think bigger and come up with new ideas, usability.
Judd Antin: Yep.
Breaking Research Stereotypes
Lenny: Yeah, usability. How did you describe it? I have a different word, here. Evaluate? Evaluative?
Judd Antin: Evaluative, right.
What A/B Testing Can and Can’t Tell You
Lenny: Okay.
The Fabricated Henry Ford Quote
Judd Antin: So we’re evaluating products and doing more. Really that’s the micro level of research.
Lenny: Survey design, being really rigorous about it, applied statistics, and then SQL/dashboard/prompt engineering.
”We Already Knew That”: Hindsight Bias
Judd Antin: Right.
User Research and Mental Models
Lenny: Maybe just one last question along this thread, also a big question, but any advice for how to evaluate these skills/interview for them? I know this is its own deep topic, but any advice for someone trying to find this person?
Judd Antin: I’ve interviewed hundreds or thousands of researchers, and the way I usually approach that is you want a researcher who’s got a Swiss army knife, because if all you have is a hammer, then everything looks like a nail. And so if you give in the context of an interview, let’s say, a researcher, a pretty juicy, open-ended research question, and you want to see how they handle it, and a good answer is usually multi-method. We’re not going to handle it in any one way. We’re going to say, “Well, here’s a couple of ways we could deal with this. Here’s how we could do this in a day, or a week, or a month.” We usually don’t have a month, but sometimes big research projects go on for that long. “And here are the different sets of methods that we can use.”
So see where they go. It’s actually pretty simple. Most researchers are deeper in one than the other, and sometimes you can make up for those five tools with the team. So you have experts who are t-shaped, but maybe deeper in one or several of those ways. But when I built a team at Meta and at Airbnb, that was my goal, is individually as researchers build up those tools and then as a team build deep expertise that would fill all the gaps.
Moving Past the Reckoning
Lenny: Coming back to the main premise of your post, one of your big takeaways is, “Researchers need to be much more business oriented, thinking about what helps the business versus the user.” Which I think to a lot of researchers will feel really weird. Can you just talk about your takeaways there?
What Companies Should Do
Judd Antin: So much of user experience practice, not just research, but design too, is focused on empathy and very user-centered. This is beautiful. I’m not saying that we should abandon that. I think what I’m saying is there’s an overlapping event, where you have the user and profit or the business. And what researchers need to do is be way more explicit about finding that overlap. So one thing, when researchers ask for advice, they’re like, “Well, what should I do to be more business or profit focused?” I say something like, “Did you read the last quarterly report, If it’s a public company? Did you listen to the shareholder call?” And they’re probably like, “No, it’s full of a bunch of language I didn’t quite get.”
And I’m like, “Yep.” So there you go. That’s the language you need to learn. Scour your Google Drive folder, your internal folder and look for all of the documents that are about this quarter, or this halves, or next half strategy. What are the OKRs? Understand the metrics and the conversion funnel, know it back and forward, because then what you’re doing is you’re proposing, if you’re in the active conversation, you’re saying, ‘Cool, I hear you asking that research question. I’ve identified this is exactly the spot in the funnel where I think we need to do work. There’s an opportunity here. Or that competitor is eating our lunch with this group of users. I know that because I read the competitive report and I understand it deeply.’” So those are skills that some researchers have and a lot are building these days, but historically, last 15 years, it hasn’t been a thing we’ve been as focused on, and I think that’s an evolution that needs to happen.
Lenny: I think a lot of PMs listening to this are going to be like, “Hallelujah.” This is exactly what I’ve been trying to convince people of. It’s what I’ve been trying to convince my researchers of, and design often falls into this.
Insights Behind the One-Way Mirror
Judd Antin: But Lenny, the opposite is true, too, because you got to take the average PM who lives in that land, all day, every day, and what they do is not in the Venn. I think those are people who are also performing customer centricity and performing user-centeredness a lot, when they’re really not interested. And so this is not about researcher. This takes two sides. Fixing this broken system takes everyone, researchers, PMs, designers, everyone at a company, but also the way that organization is structured, and integrating itself in a different way. Everybody’s got to come to the table.
Optimal Allocation of Researchers
Lenny: Such a good point. And you have this actual term that you call user-centered performance, where it’s the performance of being user-centered. Can you talk about that and then just what advice you’d give to PMs that, hearing this, are like, “Yes, I love everything you’re saying,” and then not realizing maybe they’re too far in that extreme?
Judd Antin: User-centered performance is a term I made up, because it’s fun to make up terms. And it refers to customer obsession or a user-centered practice that is symbolic rather than focused on learning. So it’s hugely common, I would argue. It’s work we do to signal to each other how customer obsessed we are, not because we want to make a different decision. And if your listeners are like, “I don’t do that.” I’m like, “Think about it for a second.” Because this is extremely common. It shows up in explicit ways and implicit ways.
So explicitly, I would say every time a PM comes to a researcher at the end of a product process and says, “Can you just run a quick user study just to validate our assumptions?” That’s user-centered performance. It’s too late to matter. That PM is not interested in being wrong at all. It’s too late in the game for that. We got to ship it. What they want is to check the box. So any check the box style research is a wild example of user-centered performance.
I would argue every researcher has probably had to do executive listening sessions because a lot of PMs, founders, product people, but designers, too, they want to get close to the customer. And so, like, “Can I do some focus groups? I want to be there. I want to ask them questions.” This is 97% performance. It’s well-intentioned, but it isn’t focused on learning. It isn’t going to drive better outcomes or more impact.
And then there’s all these implicit ways that people engage in that kind of user performance, too. A lot of it comes down to cognitive biases, confirmation bias, ego. One of my big mantras was, “We don’t validate, we falsify. We are looking to be wrong.” That is the mindset you should use when you’re approaching insights and research. “I want to be wrong. I want you to do research that shows we were off base in the following ways. Tell me exactly how and why in a way that allows me to fix it quickly.” But many PMs, many designers are not in that place. They do not want to be wrong. They’re looking to validate. And that’s user-centered performance.
Self-Empowerment for Researchers
Lenny: Oh, man. I think a lot of people are hearing this and feeling exposed.
Judd Antin: Exposed.
The Problem With NPS
Lenny: I feel like you’re like this Deep Throat person coming from sharing these things people don’t want to talk about at the office.
Why CSAT Beats NPS
Judd Antin: I know.
Lenny: There’s this quote in your post I’m going to read. “Product managers love to ask for middle range research that they can use to justify decisions they’re reluctant to make on their own. User designers love to ask for middle range research because it fits their model of what proper design process should look like. Executives love to ask for middle range because they don’t really understand what research is for, and helps them do performative user-centeredness. In the end, they will decide based on their own opinions.”
Limits of Product Walkthroughs
Judd Antin: There is an important place for intuition in product development, of course. The best designers, researchers, product people develop strong intuition for the product. But you got to understand, intuition is where all of those biases lie. It’s where all your blind spots are. And what great insights people do, what great researchers do when you’re next to them all the time, is they’ll expose you. I don’t have to be the Deep Throat, because you have somebody who’s professional job is … Keeping you honest is probably the wrong way to put it, but as somebody whose capabilities are about expanding your horizons, making it so that your intuition is constantly improving, you don’t have to rely on it when your intuition and the evidence sort of collide in a way that either affirms or falsifies the product decision you made. Now something really good is happening.
And the other thing that is inherent in that quote is I, at Airbnb, wore many hats over the years. I was head of research two different times. I was head of design for guest products. And my last job was I was head of the design studio, so UX research, UX design, writing, localization, they all reported up to me. So I’ve seen this from many disciplinary angles in the UX field. And researchers aren’t the only ones who are guilty of this. I would say design has a ton of performance. And it comes from the fact that we have figured out user-centered design, this process, or design thinking, which IDEO popularized. Like, “That’s what we’re supposed to do, right? Bezos told us that we, as PMs, had to be customer obsessed. So that’s what we’re supposed to do.”
It’s a really common and damaging thing when we don’t genuinely have that growth learning mindset, and it’s easy to sideline researchers. We don’t need them in that situation. We’ve got our guts. Isn’t the gut where a great PM, a great founder needs to have that gut? And they do, but they need to be open to the fact that your gut, is limited, and biased, and narrow, and wrong sometimes.
Rapid Fire Q&A
Lenny: The two sides of this is trust your gut opinion, “I don’t need research, I don’t need data. I have opinions, and my own experience, and I’m going to use the product, and let’s just go with what feels right to me.” Versus pure data-driven research driven for designers that are maybe listening for product managers. Do you have any advice for just where to fall on that spectrum and just how to best leverage research to inform that opinion?
Judd Antin: Yeah, I taught a class at UC Berkeley this semester on leadership, and we talk about that a lot, because great leaders develop intuition. It’s the pattern matching part of experience, where you develop heuristics which allow you to make good judgments even if you can’t quite explain where that judgment came from. That’s what the gut is. But it’s also, like I said, where bias comes from, where all the cognitive biases, there’s a list of 151 of them on Wikipedia, I won’t name them, but all those thorny things that lead us astray, the behavioral economists and social psychologists study, those live in the gut. And so the advice is when you are looking to check your gut, you have to do that thing. A lot of your listeners have probably read Thinking Fast and Slow, System 1, System 2. Right?
Lenny: I have it here, right under my laptop, actually, holding up my laptop screen.
Judd Antin: That’s so appropriate, Lenny. So the secret is not that sexy. It’s System 2. So you engage that slow, methodical process in which you do analytic thinking as a means of checking your gut. Slow in the grand scheme of things. Slow meaning not a split second decision, not like months of analysis. That’s not what I mean.
The other thing you can do, and there’s really great research on this, is you bring in the wisdom of the crowd. So the wisdom of the crowd is a phrase a lot of people are familiar with, and it works in a specific situation. The wisdom of the crowd works when the people involved with the decision are bringing diverse sources of information and judgment to the table. Obviously, if everybody has the same sources of information, then it doesn’t matter how many people are out there. So if you want to check your gut, get a bunch of different guts together, get a bunch of different people in the room who can bring evidence and intuition to bear, and have an open, direct end kind conversation in which we might disagree. You know who’s great at that? Researchers
Lenny: Leading those discussions essentially, and getting a bunch of people’s opinions.
Judd Antin: Yeah, this is the structural solution I’m talking about, Lenny, is like, “I never asked for research teams to have their own separate OKRs.” I said two things, “Number one, what’s the teams? Shouldn’t the PMs, the engineers, the designers and the research, everybody should have the same set of metrics for success because either we’re doing it together or we’re not.” And then I said, “My metric for success is when they won’t have that meeting without you.” That’s my metric for success. If they cannot have that decision making meeting without the researcher there, that means you’ve developed influence, strong, trusting relationships, you’re an active participant in the process, not just somebody who provides input into someone else’s process. And that is when researchers can have huge impact.
Lenny: I think of the PM role in a similar way, even though people won’t have these meetings with PMs, because they’re often at the center of lot of the stuff, but you want to be a PM that people want on their team. There’s a lot of teams that are like, “We don’t want and PMs, we don’t need product managers. They just get in the way.” And I find that that’s only the case when the product manager’s not great, and not really good at their job, because most great PMs just make everyone’s life easier.
Judd Antin: They do. The grease, I-
Lenny: The grease.
Judd Antin: … love it.
Lenny: You mentioned also, before we started recording, that the biggest challenge for user researchers is in their relationship with their product manager. Can you speak to that and what you’ve seen there?
Judd Antin: I’m wary of overgeneralizing, but I can tell you that from my experience and from what I hear, the product research or product insights relationship is one of the most challenged. And I think it comes from the fact that fundamentally, many researchers are just not included in the process that PMs are running. And then, actually, I did some asking around before this podcast, and so I thought, “There are some tropes that researchers have about PMs that are worth PMs knowing, just like four or five of them, the things that researchers know PMs say, which drive us nuts because they’re not true.”
So the first one is that research just slows us down. Research is too slow. This is bullshit. A great research team can do research in a day, a week, or a month. It just depends on what you want to get out of it, like, “How much detail do you need? How many people do we need to talk to? What is the depth or breadth? Do we need to go to seven different countries to talk about our constituencies in Latin America?” Well, that’s not going to happen overnight, but we don’t often need that. The other way to look at that is that is it slower to get it wrong and fix it than to take a hot second to do the work to get it right the first time? So that’s BS. Good research doesn’t slow us down, it speeds us up.
Lenny: And also just along those lines, a big part of your premise is you don’t need to do as much research as people are doing, like this middle research that a lot of the time is put into.
Judd Antin: Yeah. Research can go super fast. I think especially, so the macro level research, I hope what it is tied to things like annual planning processes. We did a thing at Airbnb several years that we called, it was like Insights 2019, Insights 2020. They were concept car projects. And we spent quite a long time synthesizing the entire year’s worth of insights from every place we could get them and then developing with designers and engineers like a concept car for five years in the future. So that’s a long process.
But the micro level, there’s so much business value to be derived there, so much business value, and it can go so fast, Lenny, it can go so fast. You can have results in 48 hours on these things. We did a thing at Airbnb. There’s a famous story which I’ll only tell in the abstract, because I don’t want to out anything, but we call it the multimillion dollar button. And basically we did research which revealed that people weren’t going down the purchase funnel because they were afraid. The calls to actions on the button was making them afraid that it would initiate a purchase when really it was just taking the next step.
We changed the text on the button with help from our amazing content design, our UX writing team. We basically changed seven characters and made Airbnb millions of dollars, because what we found out was really simple. It was just like, “Hey, this button feels scary. The CTA on the button feels scary.” So that’s a great example of how micro … And that happened in like 48 hours, we would discover that insight, or overnight, basically. And we were like, “Hm, maybe we should test some other CTAs.” We did the conversion, we added like 1%, which is really, really hard to do. So that’s a quick example of how that type of quick research can drive a huge amount of business value.
Lenny:
So just to make this even clearer, I think this middle research zone is the stuff that does slow people down, I imagine. It’s like, “What are the challenges hosts have with payments on Airbnb?” What you’re basically saying is, “Spend your time doing the micro stuff like usability research and then the bigger stuff that’s part of overall planning. That’s part of the planning cycle. It’s not like every project you’re working on, you need to have a whole research project on.”
Judd Antin: Exactly. The micro research should be much more common. A lot of researchers think that that’s scut work, that usability is something junior researchers do. I completely disagree. I think we need to get back there as an industry and be like, “When you make a product easier to use, when you discover problems with functionality, business metrics we care about will go up.” I’ve seen it happen. But that’s not just work for interns and new grads, that’s for sure.
And then the planning process, absolutely. If we’re integrated from beginning to end, we can help. And the thing about that middle range, I think you’re right. That’s the stuff that makes the stereotype that research is slow, and a lot of times it’s also because it’s just not pointed enough. The researcher can also say in that moment, “I have studied the business plan. I know exactly where, I’ve seen the metrics trend, I have an idea about exactly where that’s going to go.” We still need to do that middle range research. The question is valuable, but it’s now very pointed and the time is worth it.
Lenny: Amazing. Okay, I want to cure the rest of these tropes.
Judd Antin: Okay, research is too slow is the first one. The second one, I can do my own research. Why do I need researchers? And that’s true, as product people, I hope you are engaging with customers and listening well. But no offense, garbage in, garbage out. The thing is, anyone can talk to a user. That does not constitute research or insights work because one user can be powerful, but one user can be idiosyncratic. And a researcher knows how to get to the heart of that really quick. They know how to take that conversation, and understand, and situate it in a way which means like, “Sure, democratize research. That’s happening. There are tools out there that will let anybody get customer feedback, voice a customer type stuff.” But a researcher is there to help you turn garbage into something that’s not garbage and avoid the bias that can come from you just reaching out to your cousin’s family and then doing whatever they thought you should do to the product. So that’s the second trope.
The third one is AB test everything. And AB tests are great, but one of my most painful things to do is to sit in a room full of PMs and data scientists who have just seen the results of an experiment that flipped a stat sig, and then they’re like, “Cool, I was significantly down over this course of time for these users.” And then they just start speculating about why that is, because the AB test rarely tells you why it changed in the way it did. And then this endless flywheel of AB testing goes and I’m like, “Hey, you don’t have to guess. I know somebody who can get you an answer or at least evidence that addresses the question of why did we see the test result we did in a very short amount of time? Or you could use your customers as Guinea pigs, and throw more experiments at them over and over, and spend a long time on it, and come to the same place in the end.”
Lenny: I think a similar critique that PMs often have is AB testing is conclusive scientifically, statistically, user research is just talking to a bunch of people. Why would I trust that? What is your best way to help PMs realize that this is actually very valuable data and you should listen to it? It’s not just, you know, a story here and there.
Judd Antin: Yeah. No, I think they’re both right. AB testing is as close as we can get to making causal claims about products. Research is usually not oriented towards making causal claims or it should not be, but those causal claims rarely tell you how and why things happen. And if you want to not make that mistake again in the future, you need to know how and why. If you want to build a better product in a way that doesn’t just answer this narrow question that an AB test answered, you need to know how and why. And so you need both. Beautiful partnerships between data scientists and research and insights people are, I think what we’re going to see in that next evolution. And if you set that virtuous cycle up, if you set up the engagement where those people are involved from the beginning, you don’t make those mistakes. You get the causal relationship, which is valuable for one reason and the hows and whys, which are valuable for other reasons.
Lenny: Awesome. Okay. I think there’s two more tropes you had.
Judd Antin: One of them is a simple one, which is like everyone loves to quote that it turns out a totally apocryphal Henry Ford quote about, “If I’d asked my users.” It turns out to the best of our knowledge, he did not say that. And-
Lenny: Really? What?
Judd Antin: Yeah, I know. Isn’t that sad?
Lenny: I didn’t know that.
Judd Antin: I know. Sorry to burst your bubble, Lenny.
Lenny: Oh, wow.
Judd Antin: Who was-
Lenny: Does anyone say anything? I feel like every quote is-
Judd Antin: Is apocryphal, now? I know.
Lenny: Yeah. What is reality? Geez, can we? Well, let’s just-
Judd Antin: Okay, maybe he said that. He certainly believed that. That’s what the historians say. But the reason that makes researchers so angry is because that’s not research. That’s not what researchers do. A researcher who’s going to ask customers what they want is a bad researcher. You need a different researcher. I’ve never done that in my career. No one on my team has ever run a study that’s like that. So that just makes researchers mad.
And then the last one is about post-hoc bias. It’s, “We knew this already. That was obvious.” And I think a lot about this book, which I would recommend to your listeners. The author is a sociologist at UPenn named Duncan Watts, and the title is Everything is Obvious If You Already Know the Answer. And it’s about hindsight bias. He makes the argument that we rely too much on intuition, heuristics, and pattern matching in a way that is inappropriate to our experience. And it’s like it leads us astray. It’s like a form of self gaslighting. And it happens because we end up selectively remembering things and then constructing narratives around them in a way which makes us feel like we already knew that, when we in fact did not.
And he talks about this other, one of those cognitive biases called the narrative fallacy, which is the idea that people love to make convenient, simple stories about the past. If I asked you about your career, Lenny, and how you got to be this amazing podcast host, you’d be like, “Well, let me tell you about this series of events.” And we do that. It’s part of how we make sense of our lives and the information around us, but it would probably be a lie in the sense that we all twist the evidence we have to fit the narrative we want to be true, because it’s simple, and lovely, and makes us happy.
Lenny: This is going to sound self-serving, but I find I’m the opposite. I’m like, “I have no idea how this all came about. Here’s some things that happened, and somehow I ended up here.” But maybe I’m being very modest and try to not give myself any credit.
Judd Antin: That’s beautiful.
Lenny: Thank you for these tropes, by the way. This was fun. I didn’t know you were going to do that. So that’s a fun, little collection we’ve got, here.
Judd Antin: Thanks.
Lenny: I wanted to ask about, there’s this tweet by Patrick Collison that I’ve brought up a couple of times on this podcast, that I think is really interesting. And his tweet is this, “In my opinion, the best product will stem from a very strong mental model of the domain and user. User research can help you get such a model and validate it along the way. But it’s important to view the syllogism of UXR as model of user research, to improving your mental model of the user, to what product you should build versus user research tells you what product to build.” Does that resonate in any way thoughts on that way of thinking about user research?
Judd Antin: Yeah, there’s a double-edged sword we talk about a lot in the research community, which is about making recommendations for design. So the best research doesn’t leave it at that. It tells you, and it’s like the what, the so what, and then the then what. But the problem with that is some researchers go too far in the other direction, where they’re like, “We ran this study, it yielded these insights, and therefore this is what we should build.” And everyone else on the team is like, “Whoa, whoa, whoa. Glad to hear your thoughts on the matter, but there’s a lot going on here. Maybe we should talk about it.” And that makes perfect sense. That’s a failure of communication.
And I think that speaks to the thing that Patrick is saying, is like, “Good research can sometimes tell us exactly what the problem is and exactly how to fix it.” An example of that is the multimillion dollar button I told you about. But in a lot of the bigger picture questions, especially the macro ones and maybe also the really pointed middle range ones, the point isn’t really, “This is exactly what we should do and this is exactly what we should build.” It is, “Let us develop a framework which is based on actual evidence, and then together as a team figure out how we want to experiment our way to a successful product.”
Lenny: To close the loop on this specific thread, what is your advice to teams, researchers to help move out of this reckoning, and to move forward, and help the field, both from a user researcher perspective and also from just a company that maybe laid off a bunch of user researchers or is trying to decide what to do with their researchers?
Judd Antin: Thank you for asking. I think I said to you earlier, and I feel some pressure as maybe the first conversation that you’ve had specifically about research on this podcast.
Lenny: Yeah, I think so.
Judd Antin: And I want to help. I believe so much in this discipline of research and insights, and I think when I said, “The UX research discipline of the last 15 years is dying,” I didn’t mean that I think research is dying, far from it. I think that there’s a version of it, which we’re now moving past and into a new version. We’re going through an evolution, as many do. And so the question for me is like, “How can researchers, and the companies, and the other people with whom they work create a new version, a different version, an evolution, which is hugely impactful for the business?”
And so the advice I’d give to researchers about that is develop diverse research skills. Remembering the five or five and a half tool list that I mentioned earlier, really go deep on that business knowledge, so speaking the language of product, and business, and metrics, and understanding exactly how to use your insights like a scalpel, building those strong relationships, which is not a thing that researchers can do by themselves. It requires two-way engagements, and also in a way which allows researchers to do fewer things better.
So most researchers that I know are working on teams where they’re like, “I’m the only researcher, and I have seven PMs and 20 designers, and I’m trying to do 10 projects.” And no one’s going to do a good job that way. So researchers have to learn with their partners about how to say no and focus on the most important things. But that’s only half of it, right? That’s the research side.
I have two thoughts about what companies should be doing. The first one, it’s a little bit of an aside, but not really. One thing I learned by through the responses to the article was everybody came out of the woodworks from the variety of insights disciplines that are out there. Because I come from a tradition of user experience research or user research, but there are many insights disciplines in many industries, and they all wanted to claim one type of research or another, and say, “Oh, well, we overhear in consumer insights or market research have been doing that well for years.” And there are many insights disciplines. And generally I think creating silos is stupid.
Actually, I’m curious what you think, because here’s the number one thing I heard when I joined Airbnb and you were there, is I did it a quick listening tour where I talked to a bunch of product people. And they all said the same thing. They were like, “Listen, we have all these different people throwing insights over the transom. And it’s great. We want to hear from the data scientists, from the product specialists, from the customer service people, and the voice of the customer, whatever, all that stuff. But they’re all coming over the side and we don’t know what to make of it. It’s too much.”
And that, as much as anything, is an argument for companies to stop siloing research disciplines. So when I joined Airbnb, I set out to create an integrated insights function where it’s like, “Let’s do UX research, let’s talk about the market and competitors when we have to. Let’s integrate smartly with data science functions. Let’s integrate all the stuff we’re getting from customer service feedback.” We brought over what was then the NPS program and said, “Hey, if we’re getting customer feedback there, let’s all just use it all to fuel this one insights machine.” So that’s the first piece of advice I’d give companies.
And the second one, without being a broken record, is to think differently about the broken cycle. So integrate researchers into a unified, lean process. So if the researcher is not there from beginning to end, if there are not strong relationships between product people and design people at every level, engineering people at every level, and somebody who’s their insights partner, we’re going to fall back into this problem where we’re just a service discipline, we’re not extracting the maximum value, it comes too late, we don’t know what questions to ask, we’re ignorant about what research can do. And so creating that integrated, lean process where a researcher is arm in arm from the beginning, is the most important advice I’d give.
Lenny: That last piece may be the answer to this next question, but the question is how can product managers be better partners to user researchers/get more leverage out of user researchers?
Judd Antin: I think that is in many ways the answer, making sure that they are creating a process for the product, for their products, that it integrates user researchers and insights from beginning to end. Also, being willing to partner with the research on the roofless prioritization. I used to say that, “A full plate for a researcher was probably three things, two big projects and a small project, like a side project. More than that, your researcher is probably not doing a very good job. And a project may take 48 hours. That’s okay. But so they need your help to prioritize, they need you to participate. Great PMs will take the time to be with researchers to go into the field, even to travel.” Did you ever do that, Lenny?
Lenny: I did. I went with Louise, who introduced, we came up with this, basically told me to chat with you about this topic.
Judd Antin: Thanks, Louise.
Lenny: Thanks, Louise. We did a whole tour to Paris, our whole team, or the leads of our team went to Paris to do a bunch of focus groups and a bunch of user research behind actual mirrors. I’ve never done that before that trip, and it was amazing. We learned a ton.
Judd Antin: Can I tell you a quick story about behind the mirror?
Lenny: Please.
Judd Antin: This is back from when I was at Facebook. And there was the high times there, it was like 2012, ‘13, and newsfeed is really taking off, ads are going into newsfeed. And I was a leader of a team that was working among other things on how to address post quality. Like, “How do we think about what’s a good post and how do we get feedback about it?” And so there was a team of engineers that thought, “One thing that you can do on Facebook is hide a post.” So they were like, “This is easy. Let’s look at the posts that are hidden the most and use that as the signal of what’s a good post on Facebook?” Seems reasonable. And something tripped me on this one. And so I did two things.
So the first thing I did is I looked at the distribution of hiding by user, and found out that it’s power law distributed, like everything on the internet. There are a few people on Facebook who hide a ton, and then most people don’t hide at all. And so then what we did was, we call these super hiders, we called them super hiders. And so we said, “Let’s find super hiders around the office, and we’ll get a super hider in, and we’ll do a really traditional user interview.” We just wanted to see. So literally the first person who walked in, I remember, because this was a person who had those fingernails that are so long, you don’t know how they can do touchscreens, but they did. They were amazing at it. And it was one of those rooms with the glass. And I insisted that the ENG directors, the product people, and they were willing, whatever.
So everybody’s behind the glass, and I’m there with them, and the excellent researcher is in the room, and they come in, and we’re just doing a traditional think aloud study. And so they go, “Hey, can you open up your Facebook app? We would just love to see what your experience is like.” So they open up Facebook, and were looking, and they look at the first story, and they hide it. They go to the second story and they hide it. And this went on for a while.
And she’s definitely using Facebook, but every time she’d finish with a story, she’d hide it. And the people in the back room were starting to chatter. And they’re like, “Wait, what? What is happening right now?” And like the good researcher that this person was, they let it continue, and they’re like, “Whoa, can you tell me what you’re thinking right now?” Come to find out that she was like, “Well, I hid that story because I’d seen it already.” The model she was going for was inbox zero, which was sad, because it was infinitely scrolling. She would never get there.
And the reason I liked that story is because the people in the back room had their minds blown. It was not that we assumed that was common behavior, like this person could have been unique, but it was enough, because those people were there, experiencing the research, that N of one allowed them to burst their own bubble and realize, “Okay, we can’t think so naïvely about hides as a signal anymore.” And we came up with a better solution.
Lenny: That is an awesome story and such a good example of you don’t need statistical significance to get massive insights. One example just gives you a, “Wow, this might be exactly what’s happening. Let’s go validate that.” Versus, like, “We are confident, 100%, this is what happened.” I love that. It reminds me actually in the mirror study that I was talking about in Paris, there’s a Facebook element to it, too. We were trying to convince hosts how to feel more comfortable accepting guests who are booking instantly. And one of our theories was if they were connected on Facebook, they would be more comfortable letting someone book instantly.
Judd Antin: Yeah.
Lenny: And we’re just like, “Hey, what if you were to connect Facebook and see if they’re friends?” And everybody in Paris was very afraid of connecting and giving Facebook any data, way ahead of what the US hosts were feeling.
Judd Antin: Yeah.
Lenny: So it just made it very clear nobody wants to actually give Facebook any data. So it was very anti-Facebook at that point.
Judd Antin: Yeah, that’s so interesting. Germany and France were always our bellwethers for what the rest of the world would be thinking with data privacy concerns.
Lenny: Oh, man. Okay, a couple more things. A lot of this started with a lot of layoffs within user research. And I think between the lines, there’s a sense of teams don’t need as many researchers as they hired during the ZIRP era. I think a question in everyone’s mind is just like, “How many researchers do we need? What is a good ratio?” I imagine there’s not a simple answer, here, but just what’s your general advice to companies of how many researchers is it right?
Judd Antin: This is a thing I’ve thought a lot about, especially in my role as the head of the design studio, that was my fundamental question. It’s like, “You have all these writers, designers, researchers, how do you structure them, how many, and where, and who works on what?” And the organizing principle for me was always relationships. You know you have enough when the people who need to have a concept research partner have them. And I would much rather create pain in that situation than spread someone too thinly. So my advice was always like, “Don’t try to create a researcher to cover this entire product space. Pair a researcher up with somebody who’s going to involve them in a consistent, engaged process, and let them go to work, and see the impact they’re going to have, but protect their time. And then other people are like, “Wait a second, that person’s doing great work. I want some of that.”
And creating that pain for them, because it’s a pain of loss, is the number one way to grow headcount. That’s how I always approached getting more headcount, was not arguing abstractly for why research is important, but by asking partners who wish they had it, to do the arguing for me. And so you’re right, there isn’t a clean answer for like, “Hey, this is the right ratio,” because it really depends on the nature of the product. Like, “Is it a early stage product? Is it a late stage product? Are we talking about a startup or a late stage company?” But I would argue there’s always room for a researcher. Lenny, I’ll tell you, and I used this in a keynote talk I gave lately. You published recently a list of, I think it was about 20 B2B companies and their first 10 employees. Do you remember doing that?
Lenny: Absolutely.
Judd Antin: Do you remember how many researchers are anywhere on that list? I’ll give you a hint.
Lenny: Not too many.
Judd Antin: It’s between zero and two. It’s one. There’s one researcher on that list, anywhere. Anywhere. And that’s messed up to me. Now, look, it’s just these 20 companies, and each is in their own space, so I’m not going to overgeneralize. But a researcher can drive incredible value no matter what stage a company is at, because a good researcher makes you go faster, not slower, and they drive impact because they answer questions which are impossible to answer in any other way. That’s true if you’re a startup. It’s true if you’re a late stage company. Now, if it’s your first 10 employees, one researcher is going to go a long way. As you grow, making sure that you’re matching up researchers so that they have strong partners in the key parts of the business is the best way to figure out if you have enough.
Lenny: Interesting. So your advice is, as you’re starting a company, your pitch is that you’ll have a lot more leverage and move faster hiring a researcher versus generally an engineers, but you’d be trading off, essentially. That’s what most of the hires end up being.
Judd Antin: I am reluctant to overgeneralize, and I would say I know many founders who are in startup mode are like, “I know what I need to build. The problem is that I need people who can help me execute.” And I think that’s right. And so everything’s a trade off. But remember, imagine that you could have that Swiss army knife at your disposal. Maybe you’ve got an MVP out the door, and you’re looking to make your first major iteration, or like many startups, you need to pivot. This is where it’s like, “Hey, you don’t have to do that alone.” We deify startup founders who pivot appropriately, but I think that is what we would might call moral luck, where we deify the ones who got it right, and even though they made exactly the same decisions as the one who got them wrong.
And the fact of the matter is, if you have an insights person with you who has that Swiss army knife of tools, you’re not in it alone. You don’t have to guess. Ultimately, it will still come down to a tough decision that you and founders have to make, but you can have evidence that bears on that decision, which you wouldn’t be able to get any other way.
Lenny: To close out on this, and I have just a couple more questions on this thread, I think one of your big messages to researchers is, “You can be empowered. It’s up to you to do the right sort of research and to move your career in the right direction, not become a researcher people don’t need.” And there’s this quote that you have at the top of your post, where a lot of the reaction, or I guess the way you put it is, “I know what you’re thinking, they just don’t get it. We’re so misunderstood. Our plight is to deliver insights that users use to drive business value while we’re forgotten, never driving the roadmap, no seat at the table, consistently miscast, only to be laid off in the end.” And what I’m hearing from you is like, “You can change that. You can push back on doing research that isn’t actually contributing.” But let me ask you, what’s your lasting, I don’t know, advice you would leave researchers with to be successful?
Judd Antin: Yeah, it’s tough to be operating in a broken system, and so I feel that response, where you feel kind of powerless, but I think that’s not likely to lead us past this moment to the next evolution of research. So that’s where it’s like I don’t blame any researcher at all for being in the spot they’re in. It’s been a tough go. However, crying about our lot is not going to get us anywhere. So I think the point of the article for me, and this is advice I give companies all the time when I do consulting with them, is like, “Hey, we can set this up in a different way, which responds to the current environment in a way which will drive a huge amount of impact.” Now, that takes companies making the right choices. It also takes researchers owning up and developing skills, pushing back, understanding what research can have the most value, developing the skills, and the knowledge, and language around the business, becoming more influential, being excellent communicators.
It’s one of the things I would evaluate the most in hiring, especially research leaders, because I needed them to show and teach by example, is like isn’t just rigorous research. It’s like if a tree fell in the forest and no one was there to hear it, you need to communicate it effectively, and you need to do it in a way that’s appropriate to the audience. Because If I’m talking to you, Lenny, it’s different than I’m talking to Brian Chesky at Airbnb. And so I got to be able to give that presentation effectively, and get right to the heart of it, and speak the right language. And so if you’re a researcher, it’s not hopeless. Actually the discipline, the future is so bright, and we can help it along by continuing to develop these different skills as companies build a model that’s more inclusive.
Lenny: Awesome. Okay. I have one just random, tangential question about NPS. You have strong opinions about NPS, and I just wanted to hear your perspective on the value of NPS, your experience with NPS.
Judd Antin: Yeah. I do have a strong opinion about NPS. I like to say, “NPS is the best example of the marketing industry marketing itself.” And the problem is this threatens many people’s livelihoods, because there’s an entire industry of consultants and software providers that want you to believe NPS is a useful and accurate metric. The problem is, the consensus in the survey science community is that NPS makes all the mistakes. So it’s a garbage in, garbage out problem. So the likelihood to recommend question is bad for a whole variety of reasons. So it’s bad because it’s a zero to 11 scale. It’s bad because it’s usually unlabeled. So we label the polls, but that’s not the gold standard for research. It’s bad because it’s 11 items.
And there’s a couple of problems with that. Number one, we find that precision goes down after five items on average, maybe seven. Number two, especially on mobile, if you’re taking this survey, what percentage of those options are below the fold? We are not going to get accurate survey data. And so from a survey perspective, it’s really bad. There’s also this intuition, which is like, “How likely are you to recommend Windows 11 to your friends and family?” I am not a person who goes around recommending operating systems. The question is fundamentally flawed.
The argument is that that question is a good indicator of loyalty, but there’s a really simple solution, Lenny. Customer satisfaction, a simple CSAT metric, is better. It has better data properties, it is more precise, it is more correlated to business outcomes. I wanted to prove this. This is something that survey scientists know and marketers don’t want you to know. And so we did the work with Mike Murakami, who led survey science at Airbnb, and he’s still there, great researcher. And we basically redid all that work to find out if all that stuff was true just for Airbnb. And it is. It’s simple. Don’t ask NPS, ask customer satisfaction
Lenny: And the customer satisfaction question, what’s the actual question for people to make sure?
Judd Antin: Overall, how satisfied are you with your experience with Airbnb? Or it could be some version of that, which is like, “Overall, how satisfied are you with your experience with customer service when you had a problem?” So there could be a more specific version of that question, but those questions have better properties. And a lot of people say, “Well, hey, everybody’s using NPS. So at least it gives me a benchmark because I can compare my NPS to industry NPS.” The problem with that is the research shows that NPS is idiosyncratic, so it goes up and downs in ways that we don’t understand, and there’s a lot of inconsistency in how it’s asked, that creates variations in the data, which means it’s not apples to apples, so you can’t even compare your NPS meaningfully to somebody else’s.
Lenny: I love these hot takes. I’m curious to see who comes out of the woodwork, too, when-
Judd Antin: People are going to be so mad, Lenny.
Lenny: I love that. I think, yeah, I’ve heard this many times and people don’t talk about it. Okay. Is there anything else you want to share or leave people with before we get to our very exciting lightning round?
Judd Antin: Can I? Yeah, I want to add one thing if I could, because this has come up on your podcast a few times recently, which is about the idea of people doing their own product walkthroughs. So should a PM just rely on their own dog fooding of the product and their own walkthrough to figure out how to fix it? And a couple of times recently this has come up, and I think the consensus seems to be, “Yes, this is a good thing.” And I have a contrarian opinion there, too, which is that I think it is really important for everyone to dog food their own products. The problem is related to relying on your intuition about those products, which is the thing most PMs have trouble with is realizing, “You are nothing like the user. You are nothing like them in ways that will bias the way you think about what’s good and bad in your product in ways that you can’t necessarily recognize. Some things with a product, some problems with a product, you need a pulse to recognize.”
And most good PMs that I know have a pulse and so cool. But a lot of them require context of use, priorities, constraints that you just don’t have and you can’t imagine purely on the basis of your own usage. So what I think that means is that you should definitely dog food your own product. Doing product walkthroughs to identify lists of potential issues is a great thing to do. Prioritizing that list, figuring out which ones are more or less a problem, and for whom is an area where you should be extremely wary of relying on your own opinion, expertise, or intuition when you are dog fooding your own product.
Lenny: Thank you for sharing that. It’s definitely come up a bunch on this podcast, so I think that’s an important lesson for people to take away. Anything else before we get to our very exciting lightning round?
Judd Antin: I appreciate you, Lenny. Thanks for having me on.
Lenny: I appreciate you, Judd. Well, with that, we’ve reached our very exciting lightning round. Are you ready?
Judd Antin: I am ready.
Lenny: What are two or three books that you’ve recommended most to other people?
Judd Antin: I recently read a business book by Barbara Kellerman called Bad Leadership. And what I love about it is that we spend a lot of time talking about good leaders, and she really dives into the worst leaders and what makes them bad leaders in a way that I think is really valuable for everybody.
I’d also recommend, I read a lot of fiction, so two recommendations, there. One, a recent Pulitzer Prize winner, Demon Copperhead by Barbara Kingsolver. It’s an outstanding read that also is really sad, and moving, and illustrative, especially if you want to understand rural poverty. And then completely other side of the fiction spectrum, if you’re interested in science fiction, which I am, read the Murderbot Diaries, it’s about a sarcastic killer robot. And who doesn’t love them?
Lenny: I love these fiction recommendations. I feel like we need more of these on the podcast, so thank you.
Judd Antin: Yeah. Everybody goes to business books.
Lenny: Yeah, absolutely. What is a favorite recent movie or TV show that you really enjoyed?
Judd Antin: We recently watched The Last of Us and it blew our mind. I watched it after I played the video game after long last, if you are a person who plays video games and you haven’t played The Last of Us, play it. If you don’t know, the show is based on the video game, not the other way around.
Lenny: Do you have a favorite interview question you like to ask candidates that you’re interviewing?
Judd Antin: Think of a topic that you had to explain lately that was the most complex, and then explain it to me like I’m five. And there are a lot of ways to vary that question, but the reason I like it is because I think, and I’ve asked this question to VP and C-suite candidates in multiple disciplines, and sometimes it’s related to a conversation, like I might ask them to explain something complicated about quantum computing, or music theory, or it could be a complex business decision, but I want to see if somebody can break a complex problem down in a really simple way, and give me an intuition for it in a short amount of time. I think that is a differentiator between good and great for many people.
Lenny: Do you have a favorite product you recently discovered that you really like?
Judd Antin: Yeah, this is a really weird one, but my whole family started indoor rock climbing recently, and there’s a challenge you have when you top rope, which is that you’re looking up all the time. So they make these glasses, which are called belay glasses, and they have an angled mirror embedded in the lens, so that you can look straight ahead, and the view you see is up towards the person who you’re belaying. And I just thought that product is so perfect for that. That’s a niche problem and there isn’t a better way to solve it.
Lenny: Do you have a favorite motto that you often come back to, that you share with friends either in work or in life that you find useful?
Judd Antin: Yeah. This is going to seem like pandering, Lenny, but I don’t know if you remember a conversation that you and I had, it must’ve been eight years ago. I remember where we were sitting. And it was about stoicism. Do you remember this? Anyway, we had this conversation.
Lenny: I don’t, but I was into stoicism for a while.
Judd Antin: I know you were, because we talked about it. And so the motto comes from stoicism, which is basically, “Focus on the things you can control and ignore the rest.” And a lot of people think of this as the serenity prayer or the serenity saying, that was a 20th century invention, but Epictetus was writing about this BC, and I think about it all the time. So much of the stress, and pain, and worry that we have in life comes from things we can’t control. So I try to let those things go.
Lenny: Amazing. I learned that lesson from 7 Habits of Highly Effective People, and just the importance of thinking about these circles of you can control, you can influence, and you have no control over, and there’s no reason to think about those other things.
Judd Antin: Absolutely.
Lenny: Judd, this was everything I hoped it would be. We got into some really good stuff. I’m excited to hear how people react. Two final questions, where can folks find you if they want to learn about what you’re up to, actually share what you’re up to these days, and how people can find you, and then also how can listeners be useful to you?
Judd Antin: Yeah. Thanks for asking those questions. People can find me at juddantin.com. That’s the best way to find out what I’m up to. These days, I’m a consultant. I help people with UX strategy, org design, and crisis management. Somehow I love dealing with other people’s dumpster fires, and I’ve found that I’m constitutionally good at it somehow. So juddantin.com is the place to find out. I also write. I write a medium post that you can find at onebigthought.com. And you’ll find a lot of the topics we talked about today, including the original post that started this at onebigthought.com. If there’s one thing I could ask your listeners to do is to get next to your researcher. I just think if you build those relationships and involve a researcher and insights person early and often, beautiful things will happen for you and for the business. So that’s the thing everyone can do for me.
Lenny: I love that. I’ve always done that. I loved my researchers that I’ve worked with, many of them reporting to you, and so beautiful takeaway. Judd, thank you so much for being here.
Judd Antin: Lenny, thank you. It’s been a pleasure.
Lenny: Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcast, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.
Glossary
| English | 中文 |
|---|---|
| applied statistics | 应用统计 |
| belay glasses | belay glasses(保护眼镜,攀岩用) |
| bellwether | 风向标 |
| Brian Chesky | Brian Chesky(Airbnb 联合创始人) |
| broken record | 老调重弹 |
| causal claims | 因果判断 |
| cognitive bias | 认知偏误 |
| concept car | 概念车(喻指前瞻性原型) |
| confirmation bias | 确认偏误 |
| content design | 内容设计 |
| conversion funnel | 转化漏斗 |
| CTA (Call To Action) | CTA(行动号召) |
| customer obsession | 客户痴迷 |
| design thinking | 设计思维 |
| dogfooding | dogfood(吃自己的狗粮,喻指团队亲自使用自家产品) |
| dumpster fires | 烂摊子(喻指混乱失控的局面) |
| Duncan Watts | Duncan Watts(宾夕法尼亚大学社会学家) |
| Epictetus | Epictetus(古希腊斯多葛派哲学家) |
| evaluative research | 评估性研究 |
| falsify | 证伪 |
| formative research | 形成性研究 |
| garbage in, garbage out | 垃圾进,垃圾出 |
| gaslighting | 煤气灯效应 |
| generative research | 生成性研究 |
| gold standard | 金标准 |
| Guinea pig | 小白鼠 |
| gut (intuition) | 直觉 |
| heuristic | 启发式方法 |
| hindsight bias | 后见之明偏误 |
| inbox zero | 收件箱归零 |
| listening tour | 倾听之旅 |
| macro research | 宏观研究 |
| mental model | 心智模型 |
| micro research | 微观研究 |
| middle range research | 中距研究 |
| moral luck | 道德运气 |
| MVP | MVP(最小可行产品) |
| narrative fallacy | 叙事谬误 |
| NPS (Net Promoter Score) | NPS(净推荐值) |
| OKR | OKR(目标与关键成果) |
| org design | 组织设计 |
| over the transom | 不请自来地涌来 |
| Patrick Collison | Patrick Collison(Stripe 联合创始人) |
| post hoc bias | 事后偏见 |
| power law distributed | 幂律分布 |
| prompt engineering | 提示工程 |
| reckoning | 清算时刻 |
| scut work | 苦差事 |
| serenity prayer | 宁静祈祷 |
| silo | 部门壁垒 |
| stat sig (statistical significance) | 统计显著 |
| stoicism | 斯多葛主义 |
| Swiss army knife | 瑞士军刀(喻指多面手能力) |
| System 1 / System 2 | 系统 1 / 系统 2 |
| T-shaped | T 型(广度加深度的人才模型) |
| TAM (Total Addressable Market) | TAM(总可达市场规模) |
| think aloud study | 出声思维研究 |
| Thinking, Fast and Slow | 《思考,快与慢》 |
| top rope | top rope(顶绳攀登) |
| under the bus | 推出去挡枪 |
| user-centered performance | 以用户为中心的表演 |
| UX Research | UX 研究 |
| UX writing | UX 文案 |
| virtuous cycle | 良性循环 |
| voice of customer (VOC) | 客户之声 |
| wisdom of the crowd | 群体智慧 |
| ZIRP (Zero Interest Rate Policy/Phenomenon) | 零利率政策(现象) |
Reformatted by reformat_english.py
UX 研究的清算时刻已至 | Judd Antin(Airbnb、Meta)
以用户为中心的表演
Judd Antin: 以用户为中心的表演,指的是那些象征性的、而非以学习为目的的客户至上或以用户为中心的实践。我认为这种现象极为普遍。我们做这些工作是为了向彼此表明自己有多么以客户为中心,而不是因为我们想要做出不同的决策。如果你的听众觉得”我才不会这样”,我会说,“想一想吧,这极其常见。“每次当产品经理在产品流程末端跑来找研究员说”你能不能快速跑一个用户研究来验证我们的假设”——那就是以用户为中心的表演。已经太晚了,来不及产生任何影响。产品要上线了。他们想要的就是打个勾。我的一大信条是,“我们不做验证,我们做证伪。我们在寻找自己出错的地方。“许多产品经理、许多设计师并不在这个状态。他们不想出错,他们想要验证——而这就是以用户为中心的表演。
嘉宾介绍
Lenny: 今天的嘉宾是 Judd Antin。Judd 帮助建立了 Facebook 的用户研究体系,曾长期担任 Airbnb 的研究负责人,他的直接下属后来分别领导了 Figma、Notion、Slack、Robinhood、Duolingo、Fair 等优秀公司的研究团队。如今,Judd 从事咨询工作,帮助企业应对组织挑战、产品战略、设计、研究、招聘、入职培训和危机管理等方面的问题。在我们的对话中,我们深入探讨了 Judd 最近得出的一个结论——用户研究领域正在经历一场清算时刻,以及该领域内部和企业未来如何利用用户研究都需要做出哪些改变。
Judd 分享了过去十年用户研究领域做错了什么,产品经理和设计师如何过度依赖用户研究、且往往是为了回答错误的问题,用户研究在哪些方面会继续提供重要价值,以及如何最好地发挥研究员的作用,为什么研究员应该更多地关注业务目标而不仅仅是用户需求,招聘用户研究员时应该看重什么,产品经理如何成为研究员更好的合作伙伴,还有一个我很喜欢的、Judd 描述并经常见到的现象——他称之为”以用户为中心的表演”:每个人都装作关心用户,但其实只是在做样子,心里早有主意。这期节目有不少辛辣观点,可能会让一些人不舒服,但 Judd 说的是真心话,我认为我们都需要听听。
从 Airbnb 说起
Lenny: Judd,非常感谢你来做客,欢迎来到播客。
Judd Antin: Lenny,谢谢邀请。
Lenny: 是我的荣幸。我们其实一起在 Airbnb 共事了很多年。在准备这期节目的时候,我意识到你带过的那么多人后来都做出了了不起的成绩。我来念一份曾经在你手下工作过的人以及他们现在的职位——Matt Gallivan,现在领导 Slack 的研究团队;Janna Bray,领导 Notion 的研究团队;Celeste Ridlen,领导 Robinhood 的研究团队;Rebecca Grey,领导 Fair 的研究团队;Hannah Pileggi,我记得她之前在领导 Duolingo 的研究团队;Louise Beryl,领导 Figma 的研究团队;还有 Noam,之前领导 Wealthfront 的研究团队,我想他后来去了别的地方。你招聘和培养的这个团队,竟然产生了如此令人惊叹的校友群体,简直不可思议。
Judd Antin: 我确实没有这样梳理过这份名单,但我要说,能与这些出色的人共事是我的荣幸。这不是我的功劳,他们本身就很优秀。我很高兴这些人散布在各处,他们都是明星。
文章引发的反响
Lenny: 我想和你做一期播客节目的主要原因,是你写了一篇题为”The User Research Reckoning is Here”的文章,据我所知,这篇文章在研究社区以及相关领域引起了不小的轰动。我先读一下你文章开头的一个核心观点,让大家感受一下这篇文章的内容。你写道,“过去十五年的用户研究学科正在走向消亡。清算时刻已经到来。这个学科仍然可以存活和繁荣,但我们最好迅速做出适应。“在深入文章内容之前,能不能先谈谈这篇文章的反响?是否出乎意料,以及你发布时预期会发生什么?
Judd Antin: 确实出乎我的意料。我写这篇文章是因为想就我思考的一个问题展开对话,并不确定谁会读到。结果最终有很多人读了。我发现使用”清算时刻”这个词可能是个失误,因为它给我本想推动的富有建设性和积极性的对话注入了太多戏剧性。不过总体来说,反响还是很正面的。它似乎引起了很多人的共鸣,许多人主动联系了我。我花了很多时间与团队、设计师、研究员交流,但也有大量的批评。
有些人认为我在把研究或研究员推出去挡枪,好像在说”这是研究员的错,我们做错了。“我完全不这么认为。还有人认为我没有承担自己作为研究领导者或设计领导者应负的责任。我觉得最有意思的批评来自反资本主义阵营,因为我们后面会谈到的一个观点是,我认为研究员应该更加关注利润。有很多人觉得这不酷,或者不应该是研究的工作,我的回应是,“那我们到底在做什么,如果不是在帮助企业成功的话?“但这确实是最令人意外的批评,毫无疑问。
Lenny: 我和那样的一些人共事过,他们就是会说”我们为什么要增长?为什么如此关注增长?为什么我们需要发展业务?“
裁员的信号
Judd Antin: 也许他们入错了行。
Lenny: 是的,我不太认同那种态度。好吧,让我们真正深入你文章的核心内容,看看你在 UX 研究领域发现了什么,最大的结论和要点是什么。我知道这其中很大一部分来源于很多公司的 UX 研究员被裁掉的现实,这是受冲击最严重的团队之一。所以我想很多讨论都是由此而来的。那我们就从宏观开始聊,然后看看会走到哪里。
Judd Antin: 好,所有关注这行的人都注意到了大规模裁员的发生。我记得去年夏天我就在想,“听着,UX 和 UX 研究似乎受到的冲击特别大。是不是有什么更深层次的问题?是否有更大的图景?“我之所以使用”清算时刻”这个词,是因为对我来说,它意味着——“嘿,这是一个停下来盘点反思的时刻。“而触发这一点的,是很多优秀的人可能已经失去了工作,还有更多人正在担心失去工作。如果说这是一个信号——研究受到如此严重冲击这件事本身是一个信号——那它说明了什么?我这篇文章的核心论点就是,这个信号表明,也许整个系统比我们想象的更加破碎,研究并没有驱动它本应该、本可以驱动的价值或影响力。原因有很多。其中一部分是研究自身可以做得更好的地方,而很大一部分则与研究在公司中的整合方式和定位有关。
而在这一切的根源上,我认为我们只是在做太多我所谓的”错误类型的研究”。所谓的错误类型的研究,我有一个框架,文章里也有——宏观研究、中距研究和微观研究,至少可以从这三个层面来讨论。这三者的直觉非常简单。宏观研究是关于大局的——战略性的、面向业务的、前瞻性的创新——看市场、看竞争对手、做长期研究来理解产品下一步该往哪走,诸如此类。
然后是微观研究,很多非常技术性的可用性研究就属于这一类——研究员为了让一个真正高质量、精益求精、像素级完美的产品顺利上线所做的一切精美工作,以及为了理解 AB 测试结果而进行的精准聚焦的研究,诸如此类。再然后就是这个中距研究——一个模糊的地带,这里的研究问题处在一个中间海拔,很多核心的,比如说用户理解方面的问题就落在这里。研究工作大量集中在这一空间。就是——“我们找一群人来,问一些关于他们如何思考、感受、行为的问题,他们如何使用或不用某个产品。“这是各种因素的毁灭性混合——对很多人(包括研究员自己)来说非常有趣,但对业务来说影响力又不够。这就是核心论点。研究员做这类研究是因为它有趣,但说实话——这也是我们应该讨论的一点,Lenny——研究员做这类研究也是因为这最经常是他们被要求去做的工作。
Lenny: 是的,这正是我在想的。作为 PM,我想要得到答案的恰恰就是这类问题——“我们应该如何看待这个产品?“我完全理解这一点。
Judd Antin: 没错。这些问题确实非常有趣,在很多公司的很多场景下也极具影响力。但这类问题的问题在于,它们往往触发了研究员所经历的最糟糕的情况。它们产出的结果很有趣,但有时很难落地操作。它们非常容易引发事后偏见——很多人会笃定地说,“哦,这很明显嘛,我们早就知道了。“而且它们满足了我们感觉自己是、也被认为是客户至上、以用户为中心的那种需求,却不需要改变任何东西。所以在我看来,做太多这类研究就是一个系统破碎的症状,也是公司之间真正产生差异的地方。我在文章发布后收到了很多反馈,很多人说,“嗯,我的公司和我的行业是这样的,或者不是这样的。“
零利率时代的终结
但在科技行业,过去这些年我们一直在招人、招人、招研究员,但也许——我相信你的大多数听众都熟悉 ZIRP 这个概念——也许这是零利率政策时代的现象,当资金充裕的时候,招研究员没问题,即使我们没有为他们搭建好正确的体系。我们其实是在让他们走向失败。我们把研究设定为一个服务职能,我们不知道研究是用来做什么的,我们不知道如何真正用它来驱动影响力。这就是”清算时刻”的由来——那个时代结束了。我认为研究比以往任何时候都更加关键,优秀的研究员比以往任何时候都更具影响力。但它处在一个新的空间里。我们现在在一个新的空间里。
Lenny: 我想确保大家理解这个框架。具体来说,你怎么最好地描述中距研究和宏观研究之间的区别?
Judd Antin: 中距研究通常聚焦于更具体的一组研究问题或一个特定的用户群体。如果说宏观研究是——“让我们理解整体竞争格局,让我们做一个概念车式的项目来真正展望未来,让我们参与战略规划”——这对研究员来说是极好的事情——做 TAM 研究,以及诸如此类的事情,这些都属于宏观空间。
中距空间的话,举个例子——“我们想了解 Airbnb 房东对他们的支付选项有什么感受。“这是一个非常有趣、合理的问题。我们可以出去做这方面的研究,但它不够具体,也没有真正对准一个业务问题。它有可能对准,也许研究的结果会指向一个业务问题,但它产出的是这类中距洞察——我们学到了诸如”嗯,房东希望支付选项更灵活”之类的结论。这是我编的。这就是一个很好的例子——并不是说这些问题不有趣,只是它们的指向性还不够。它们不是用漏斗的语言、业务战略的语言、OKR 的语言来表述的,与这些的对齐度不够。它们在那个中间层面过于模糊,最终无法驱动影响力。
Lenny: 我觉得这也导致了你描述的很多人们对研究的不满——它拖慢了一切。你得等研究做完才能得到答案,才能做出明确决策。它还造成了人们经常抱怨的一个问题——PM 和产品团队不愿意自己做决策,他们会说”我要先拿到这个额外的数据点,让研究告诉我们正确答案”,而不是直接凭判断做决定。顺着这个思路说——也许有点跑题了——但在这方面,你对 PM 或产品团队有什么建议,让他们不必非得依赖研究来做那种中距研究?
重新整合研究
Judd Antin: 我觉得很多 PM 之所以问那些中距问题,是因为他们没有真正与研究员深入合作,从而将研究的价值发挥到最大。所以如果问题是——“嘿,Judd,你指出了一堆问题,能不能更偏解决方案一点?“对我来说,解决方案很简单但不容易——我们需要重构产品开发的方式,将研究更完整地整合进来。它的形态是这样的:建立稳定持续的合作关系,让研究员、他们的工作、以及他们提供的洞察,成为从始至终整个过程的一部分。
Judd Antin: Lenny,你作为 PM,我觉得你当时的工作方式就是这样的。我记得你,我知道你和谁合作过。你和优秀的研究员一起工作。但说实话,大多数产品团队的过程并不是这样的。于是研究就变成了一个服务职能——到最后才被叫进来。它是被动的,而如果研究员在房间里倾听并参与讨论,本可以在当时就产生巨大的影响力——精准地框定出能够带来最大商业影响、最大产品改进的正确问题,然后快速去执行,回来汇报,接着推进下一步。但他们不在场,关系没有建立起来,他们没有从项目一开始就参与。这就是问题的头号根源。只要研究还是一个服务型职能,我觉得我们就会一直困在这个困境里。
Lenny: 人们听到这些可能会觉得矛盾——一方面,研究对团队的帮助没有预期那么大,研究员花时间在做错误的事情上;另一方面,你的建议是从一开始就整合研究,让他们更深入地参与。我觉得这可能会让人困惑。大家应该怎么理解——“研究其实更重要?应该更深度地整合他们?“
恶性循环
Judd Antin: 从我的角度,以及我从许许多多研究员和研究负责人那里听到的来看,一直在发生一个恶性循环。很多公司出于很好的初衷招聘了大量研究员,但并不太清楚如何整合他们。UX 研究是一个相对新的学科,所以这也许不令人意外。我们仍在学习如何使用它。“好的,让我们继续进化。” 但很多公司把这些人招进来,却把他们放在一个服务型职能的位置上——非常被动,不在讨论的房间里,没有按照我说的那种方式整合。
所以他们对”该问什么问题”的发言权更少,或者虽然被纳入了,但只是在最后阶段。然后他们就无法建立那种直接的合作关系,无法在房间里真正推动问题的提出和洞察的输入。因为一个好的研究员就像是你成长所需洞察的知识库,但他们不在场。他们不参与决策。所以他们最终还是在做研究——他们有工作要做,所以他们做的研究过于被动,无关紧要,影响力也就更低。高管因此得出结论,认为研究员的影响力不大,然后他们被边缘化或被裁掉,循环继续。
所以我认为打破这个循环的关键就是持续参与。如果你把一个优秀的研究员持续地嵌入到产品开发流程中,我有信心这个研究员会推动产品改进、指标提升、增长——所有作为 PM 和产品负责人希望看到的东西。只是现在这成了例外,而不是常态。
如何识别优秀的研究员
Lenny: 这个问题可能很难回答,但当人们听到”如果你有一位优秀的研究员,应该这样做”时,什么信号能区分一个研究员是优秀的还是不够好的?有什么东西可以让人们判断——“哦,也许我的团队里放错了研究员。”
Judd Antin: 我认为最优秀的研究员首先具备多方法能力。用户体验研究的第一波迭代主要是一个定性学科。但我有一个强烈的观点——这很大程度上是那些需要进化的模型之一。这不是说定性用户研究不再重要了,而是最好的研究员拥有五种工具。我认为他们有五种工具。第一种是我们所说的形成性或生成性用户体验研究(formative/generative user experience research)——向前看,聚焦创新,真正开放式的,可能更偏田野调查性质的。“让我们到实地去和 Airbnb 的房东与房客聊聊。让我们看看人们在实际场景中如何使用我们的产品。“诸如此类。这就是形成性研究。
第二种工具是评估性研究(evaluative),更像是可用性测试(usability testing)。第三种工具是基本的严谨的问卷设计。这是从大大小小的社区获取反馈的最好规模化方式。精心设计的问卷可以获得大量洞察。但要做到这一点,你必须拥有第四种工具,就是应用统计(applied statistics)。最好的研究员懂一些统计。在一个 A/B 测试的世界里,你不可能在不懂基本统计的情况下运作。
在旧版本中,第五种工具是 SQL,因为我认为好的研究员需要能自己跑查询。现在很多数据都已经在仪表盘上了,所以第五种工具可能变成了提示工程(prompt engineering)。这个我们可以之后再聊,但我认为第五种工具可能在技术技能这个范畴里——介于自己查询数据、在数据丰富的公司里深入理解数据、以及与生成式 AI 交互之间。
Lenny: 太棒了,这个清单很酷。好的,让我复述一下——形成性、生成性、创新性的技能,去想得更大、产生新想法;可用性……
Judd Antin: 对。
Lenny: 可用性。你怎么描述的来着?我这里用了另一个词——评估(evaluate)?评估性的(evaluative)?
Judd Antin: 评估性的,对。
Lenny: 好的。
Judd Antin: 就是对产品进行评估,做得更深入。这其实就是研究的微观层面。
Lenny: 问卷设计,要非常严谨;应用统计;然后是 SQL / 仪表盘 / 提示工程。
Judd Antin: 对。
Lenny: 沿着这条线也许再问最后一个问题,也是个很大的问题——关于如何评估这些技能、如何面试考察这些技能,你有什么建议吗?我知道这本身就是一个很深的话题,但对于想要找到这样一个人的人来说,有什么建议吗?
Judd Antin: 我面试过成百上千位研究员。我通常的做法是——你需要一个拥有瑞士军刀的研究员,因为如果你手里只有一把锤子,那所有东西看起来都像钉子。所以在面试场景中,比如你给研究员一个相当有吸引力的、开放性的研究问题,你想看他们如何应对——好的回答通常是多方法的。“我们不会只用一种方式来处理。我们会说,这里有几种可以应对的方式。我们可以用一天、一周或一个月来做。“我们通常没有一个月的时间,但有时大型研究项目确实会持续那么久。“这里是我们可以使用的不同方法组合。”
所以看他们会往哪个方向走。这其实很简单。大多数研究员在某一个方面会比其他方面更深入,有时候你可以通过团队来弥补这五种工具的不足。所以你有 T 型人才——专家在某一个或几个方向上更深。但当我在 Meta 和 Airbnb 搭建团队时,我的目标就是——让每个研究员个人逐步建立这五种工具,然后作为团队建立深厚的专业能力来填补所有的空白。
研究员需要以商业为导向
Lenny: 回到你那篇文章的核心观点,你的一个重要结论是——“研究员需要更加以商业为导向,思考什么能帮助商业而不仅仅是用户。“我觉得对很多研究员来说这会感觉很奇怪。你能谈谈你在这方面的看法吗?
Judd Antin: 用户体验实践中有太多内容——不仅仅是研究,设计也是——都聚焦于共情,非常以用户为中心。这很美好。我并不是说我们应该放弃这一点。我想说的是,存在一个交集——用户与利润(或商业)之间的交集。而研究员需要做的,就是更加明确地找到那个交集。所以,当研究员来寻求建议时,他们会说:“我该怎么做才能更加以商业或利润为导向?“我会说类似这样的话:“你读过上一季度的财报吗?如果是上市公司的话。你听过股东电话会议吗?“他们可能会说:“没有,里面全是我不太能理解的语言。”
我会说:“没错。“那正是你需要学习的语言。翻遍你的 Google Drive 文件夹、内部文件夹,去找所有关于本季度、本半年或下个半年战略的文档。OKR 是什么?理解各项指标和转化漏斗,烂熟于心。因为这样你才能在主动参与对话时提出建议:“好的,我听到你提出的那个研究问题。我已经确定,这正是我认为我们需要在漏斗中进行工作的那个环节。这里存在一个机会。“或者”那个竞争对手正在蚕食我们在这群用户中的份额。我知道这一点,因为我读过竞品报告,而且深入理解了它。“这些是一些研究员已经具备的技能,很多研究员目前也在培养。但从历史上看,过去 15 年里,这并不是我们一直关注的重点,我认为这是一场必须发生的进化。
Lenny: 我想很多听这个播客的 PM 会说:“哈利路亚。“这正是我一直试图说服大家的事情。这正是我一直试图说服我的研究员的事情,设计也常常落入同样的境地。
Judd Antin: 但是 Lenny,反过来也一样。因为你要考虑到那些整天、每天都生活在那个领域的普通 PM,他们做的事情其实也不在那个交集中。我认为这些人很多时候也在表演以客户为中心,表演以用户为中心,而他们实际上并不真正感兴趣。所以这不只是研究员的问题。这是双向的。修复这个破碎的系统需要所有人——研究员、PM、设计师,公司里的每一个人——还需要组织架构以不同的方式进行重组。每个人都必须坐到谈判桌前。
以用户为中心的表演
Lenny: 说得太好了。你造了一个术语叫”以用户为中心的表演”,指的是表演出以用户为中心的样子。你能谈谈这个概念吗?对于那些听到这里、觉得”你说的一切我都赞同”的 PM,你会给他们什么建议?也许他们没有意识到自己走到了另一个极端?
Judd Antin: “以用户为中心的表演”是我编的一个词,因为造词很有趣。它指的是一种符号化的客户痴迷或以用户为中心的实践,而非以学习为导向的实践。我认为这非常普遍。它是我们为了向彼此发出”我们多么痴迷客户”的信号而做的事情,而不是因为我们想做出不同的决策。如果听众们心想:“我没有这样做。“我会说:“再想想。“因为这极其常见。它以显性和隐性的方式表现出来。
显性地来说,我觉得每次一个 PM 在产品流程的末期来找研究员说:“你能不能快速跑一个用户研究,验证一下我们的假设?“——这就是以用户为中心的表演。已经来不及了,这件事已经不可能产生影响了。那个 PM 根本不打算接受自己是错的。在这个阶段已经太晚了,我们必须发布产品。他们想要的是打勾交差。所以任何打勾交差式的研究,都是以用户为中心的表演的典型例子。
我觉得每个研究员大概都不得不做过高管聆听会,因为很多 PM、创始人、产品人员,设计师也是,他们想接近客户。所以他们会说:“能不能做几个焦点小组?我想到场。我想问他们问题。“这 97% 都是表演。出发点是好的,但它不以学习为目的。它不会带来更好的结果或更大的影响。
然后还有各种隐性的方式,让人们也在进行这种以用户为中心的表演。很多可以归结为认知偏误、确认偏误、自我意识。我有一句常说的话:“我们不验证,我们证伪。我们在寻找自己出错的地方。“这才是你面对洞察和研究时应有的心态。“我希望自己是错的。我希望你做的研究能证明我们在以下几个方面是偏离的。请确切地告诉我,以什么方式、为什么,好让我能快速修正。“但很多 PM、很多设计师并没有处在那个状态。他们不想被证明是错的。他们在寻求验证。这就是以用户为中心的表演。
Lenny: 天哪。我觉得很多人听到这些会感到被揭穿了。
Judd Antin: 被揭穿了。
Lenny: 我觉得你就像那个”深喉”一样,曝光了人们在办公室不愿谈论的事情。
Judd Antin: 我知道。
中距研究与直觉的局限
Lenny: 你文章中有一段话,我来读一下。“产品经理喜欢索要中距研究,用来为那些他们不愿独自做出的决策辩护。设计师喜欢索要中距研究,因为这符合他们心中关于’正确设计流程应该是什么样’的模型。高管喜欢索要中距研究,因为他们并不真正理解研究是做什么的,而这有助于他们进行以用户为中心的表演。最终,他们会根据自己的意见做决定。”
Judd Antin: 直觉在产品开发中当然有它重要的位置。最优秀的设计师、研究员、产品人都会对产品发展出强烈的直觉。但你必须理解,直觉正是所有偏误藏身之处。它是你所有盲点所在。优秀的洞察人员会做的事情——当一个优秀的研究员一直陪伴在你身边时——他们会揭露你。我不需要当那个”深喉”,因为你身边有人,他的职业职责就是……”让你保持诚实”可能不是最准确的表述,但他的能力在于拓展你的视野,让你的直觉不断提升,这样你就不必单纯依赖直觉。当你的直觉和证据在某个产品决策上发生碰撞——无论证实还是证伪——这时真正好的事情就发生了。
在那段话中还隐含着另一层意思。我在 Airbnb 多年来身兼数职。我两次担任研究负责人,也曾担任旅客产品设计负责人。我的最后一个职位是设计工作室负责人,所以 UX 研究、UX 设计、文案、本地化,都向我汇报。所以我在 UX 领域从多个学科角度观察过这一切。研究员并不是唯一对此负有责任的一方。我认为设计领域同样存在大量表演。其根源在于我们已经确立了一套以用户为中心的设计流程,或者说设计思维——IDEO 将其推广开来。“这就是我们应该做的,对吧?贝佐斯告诉过我们,作为 PM,必须对客户保持痴迷。所以这就是我们应该做的。”
当我们没有真正具备那种成长型学习心态时,这是一种非常普遍且有害的现象,而研究员也很容易被边缘化。在这种情况下我们不需要他们。我们有直觉。一个优秀的 PM、一个优秀的创始人不是正需要那种直觉吗?是的,他们需要,但他们也需要接受这样一个事实:你的直觉是有限的、有偏见的、狭隘的,而且有时候是错的。
Lenny: 这件事的两面是:一面是相信直觉,“我不需要研究,不需要数据。我有自己的观点和经验,我自己也会用这个产品,就按感觉对的来。“另一面是完全靠数据驱动、研究驱动。对于正在收听的设计师或产品经理,你有什么建议?在这个光谱上应该落在哪个位置,如何最好地利用研究来辅助自己的判断?
Judd Antin: 这个学期我在 UC Berkeley 教了一门关于领导力的课,我们经常讨论这个问题,因为优秀的领导者会培养出直觉。直觉是经验中模式识别的那部分——你发展出一套启发式方法,让你能做出好的判断,哪怕你无法完全解释这个判断从何而来。这就是所谓的”直觉”。但正如我之前所说,直觉也是偏误的来源——所有那些认知偏误,维基百科上列了 151 条,我不会一一列举——但正是那些让行为经济学家和社会心理学家研究不休的棘手东西,那些把我们引入歧途的东西,都藏在直觉里。所以我的建议是,当你想要检验自己的直觉时,你必须去做那件事。你的很多听众可能都读过《思考,快与慢》,系统 1、系统 2,对吧?
Lenny: 我手边就有一本,就垫在笔记本电脑下面,正好撑着屏幕。
Judd Antin: 太贴切了,Lenny。秘密其实没那么性感。答案就是系统 2。你要启动那个缓慢的、有条理的过程,通过分析性思维来检验你的直觉。“慢”是相对整体决策而言的——不是说瞬间做出的决定,也不是说要花几个月去做分析,我不是那个意思。
群体智慧
另一件你可以做的事——这方面有非常出色的研究——就是引入群体智慧。“群体智慧”这个词很多人都很熟悉,但它在特定条件下才有效。群体智慧发挥作用的前提是,参与决策的人带来的是多元化的信息来源和判断。显然,如果所有人的信息来源都一样,那不管有多少人参与都不管用。所以,如果你想检验自己的直觉,就把各种不同的直觉聚在一起,把各种不同的人召集到同一个房间里,让他们拿出证据和直觉来讨论,进行开放、直接、坦诚的对话,即使我们可能会产生分歧。你知道谁最擅长做这件事吗?研究员。
Lenny: 本质上就是让他们来主导这些讨论,汇集各方意见。
Judd Antin: 对,这就是我说的结构性解决方案,Lenny。比如,“我从不要求研究团队有自己单独的 OKR。“我说过两件事:“第一,什么是团队?PM、工程师、设计师和研究员,大家应该有同一套成功指标,因为要么我们一起做,要么就不算数。“然后我说:“我衡量成功的标准是——他们开那个会不能没有你。“这就是我衡量成功的标准。如果他们没办法在研究员不在场的情况下做决策会议,那就意味着你建立了影响力,建立了牢固的信任关系,你是流程中的积极参与者,而不仅仅是给别人流程提供输入的人。这才是研究员能够产生巨大影响的时候。
Lenny: 我对 PM 角色的看法也很类似,虽然大家不会说”开会不能没有 PM”——因为 PM 通常就处在很多事情的中心——但你希望自己是大家都想拉进团队的 PM。很多团队会说,“我们不要 PM,我们不需要产品经理,他们只会碍事。“但我发现这只在产品经理不够优秀、不太擅长自己工作的时候才会出现,因为大多数优秀的 PM 会让所有人的工作变得更轻松。
Judd Antin: 确实如此。润滑剂,我——
Lenny: 润滑剂。
Judd Antin: ……特别喜欢这个比喻。
研究员与 PM 的关系
Lenny: 你在录音开始之前还提到,用户研究员面临的最大挑战在于他们与产品经理的关系。你能谈谈这个话题以及你的观察吗?
Judd Antin: 我不想过度泛化,但我可以告诉你,根据我的经验和我的了解,产品研究或产品洞察(与 PM 的)关系是最受挑战的关系之一。我认为根本原因在于,很多研究员根本没有被纳入 PM 所主导的流程之中。实际上,我在录这期播客之前专门做了些打听,我想,“研究员对 PM 有一些刻板印象,值得让 PM 们知道。大概四五条吧,都是研究员知道 PM 会说的话,而这些话让我们抓狂,因为它们根本不是事实。”
第一条是:研究只会拖慢我们。研究太慢了。这是胡扯。一个优秀的研究团队可以在一天、一周或一个月内完成研究,完全取决于你想从中获得什么——“你需要多少细节?我们需要跟多少人谈?是深度还是广度?我们需要去七个不同的国家了解我们在拉丁美洲的用户群体吗?“那当然不可能一夜之间完成,但我们往往不需要做到那种程度。换个角度看——做错了再去修复,和花一点时间把事情一次做对,哪个更慢?所以那条就是胡扯。好的研究不会拖慢我们,它会加速我们。
Lenny: 顺着这个思路,你的一个核心前提也是——你不需要做人们以为的那么多研究,很多投入在那类中距研究上的时间其实没必要。
Judd Antin: 对。研究可以非常快。尤其是——宏观研究,我希望它是与年度规划流程挂钩的。我们在 Airbnb 做过一个持续了好几年的项目,类似于”Insights 2019""Insights 2020”这样的概念车项目。我们会花相当长的时间,把整年的洞察从所有能获取的渠道进行综合,然后和设计师、工程师一起开发一个五年后的概念原型。那是一个很长的过程。
微观研究的巨大价值
但在微观层面,可以创造巨大的商业价值——巨大的商业价值,而且速度可以非常快,Lenny,真的可以非常快。这些事情你 48 小时内就能出结果。我们在 Airbnb 做过一件事。有一个著名的故事,我只抽象地讲,因为我不想泄露任何东西,但我们称之为”百万美元按钮”。基本上,我们做了一项研究发现,用户没有走完购买流程,是因为他们害怕。按钮上的行动号召让他们担心点击后会直接发起购买,而实际上那只是进入下一步。
我们和出色的内容设计团队、UX 文案团队合作,修改了按钮上的文字。我们基本上只改了七个字符,就为 Airbnb 赚了数百万美元,因为我们发现的其实非常简单——就是”嘿,这个按钮让人感觉有风险。按钮上的 CTA 让人害怕。“这就是微观研究的一个绝佳案例……而且整个过程大概就 48 小时,我们就发现了那个洞察,基本上一夜之间。然后我们想,“嗯,也许应该测试一些别的 CTA。“我们做了转化测试,提升了大约 1%——做到这一点真的非常非常难。这就是快速研究如何驱动巨大商业价值的一个生动案例。
中距研究与微观研究的平衡
Lenny: 所以为了让这一点更清楚,我觉得这个中间研究地带确实是会拖慢人们的部分。比如,“房东在 Airbnb 上支付方面有什么困难?“你基本上是在说,“把时间花在微观层面的东西上,比如可用性研究,然后再做更大的、属于整体规划一部分的东西。那是规划周期的一部分。并不是说你做的每个项目都需要配套一整个研究项目。”
Judd Antin: 没错。微观研究应该常见得多。很多研究者认为那是苦差事,认为可用性研究是初级研究员干的事。我完全不同意。我认为作为一个行业,我们需要回归到这一点——“当你让产品变得更好用,当你发现功能上的问题,我们关心的商业指标就会上升。“我亲眼见过。而且那绝对不仅仅是实习生和应届毕业生的工作。
然后在规划流程中,绝对如此。如果我们从头到尾都参与其中,就能帮上忙。关于那个中距研究,我觉得你说得对。正是那些东西造成了”研究很慢”的刻板印象,而且很多时候也确实是因为研究问题不够聚焦。研究者在那个时刻也可以说,“我已经研究过商业计划了。我清楚知道指标的趋势走向,我对未来的方向有自己的判断。“我们仍然需要做中距研究。那个研究问题是有价值的,但现在它非常聚焦,投入的时间是值得的。
破除关于研究的刻板印象
Lenny: 太棒了。好,我想把剩下那些刻板印象也逐一破除。
Judd Antin: 好。“研究太慢”是第一个。第二个是”我自己也能做研究,我为什么需要研究者?“这话说得也对——作为产品人员,我希望你们确实在与客户互动,认真倾听。但恕我直言,垃圾进,垃圾出。问题在于,任何人都能跟用户聊天。但这并不构成研究或洞察工作,因为一个用户可能很有说服力,但也可能只是个案。而研究者知道如何迅速触及核心。他们知道如何处理那段对话,理解它,并将其置于正确的语境中——也就是说,“当然,民主化研究,这正在发生。市面上有工具可以让任何人获取客户反馈,做客户之声之类的事情。“但研究者的存在是为了帮你把垃圾变成不是垃圾的东西,避免你只是去找表亲一家问问,然后照他们觉得产品该怎么改就怎么改所带来的偏误。这是第二个刻板印象。
A/B 测试能告诉你的和不能告诉你的
第三个是”一切都要 A/B 测试”。A/B 测试很好,但我最痛苦的事情之一就是坐在一个满是 PM 和数据科学家的房间里,他们刚刚看到实验结果达到了统计显著,然后说,“好,在这段时间内对这群用户来说显著下降了。“然后他们就开始猜测为什么会这样,因为 A/B 测试很少能告诉你它为什么以那种方式变化。然后这个无尽的 A/B 测试飞轮就转起来了,我就说,“嘿,你不用猜。我认识一个人,他能在很短的时间内给你一个答案,或者至少给你针对’为什么我们会看到这样的测试结果’这个问题的证据。否则你就只能拿你的客户当小白鼠,反复对他们做更多实验,花很长时间,最终到达同一个地方。”
Lenny: 我觉得 PM 们经常有的一个类似批评是,A/B 测试在科学上、统计上是结论性的,而用户研究只不过是跟一群人聊天。我凭什么信任那个?你最好的方式是什么,来帮助 PM 意识到这其实是很有价值的数据,他们应该认真对待?而不只是,你知道的,这里一个故事那里一个故事。
Judd Antin: 是的。不,我觉得他们两边都对。A/B 测试是我们目前最接近对产品做出因果判断的方法。研究通常不是——也不应该是——以做出因果判断为导向的,但那些因果判断很少告诉你事情是如何以及为什么发生的。如果你不想将来再犯同样的错误,你需要知道如何以及为什么。如果你想要以不仅仅回答 A/B 测试所回答的那个狭窄问题的方式打造更好的产品,你需要知道如何以及为什么。所以你需要两者兼备。数据科学家和研究与洞察人员之间的美妙合作,我认为是我们在下一阶段进化中将会看到的。如果你建立起那个良性循环,如果你建立起那些人从头就参与的协作方式,你就不会犯那些错误。你获得了因果关系——它因为一个原因而有价值——以及”如何”和”为什么”——它们因为其他原因而有价值。
Lenny: 太好了。好。我觉得你还有两个刻板印象。
杜撰的亨利·福特名言
Judd Antin: 其中一个很简单,就是每个人都爱引用那句——结果发现完全是杜撰的——亨利·福特的名言,关于”如果我问用户的话……”。据我们所能确定的,他从未说过那句话。
Lenny: 真的?什么?
Judd Antin: 对,我知道。很令人伤心吧?
Lenny: 我不知道这个。
Judd Antin: 我知道。抱歉打破你的幻想,Lenny。
Lenny: 哇哦。
Judd Antin: 谁来着——
Lenny: 还有人说过什么真话吗?我觉得每句名言都——
Judd Antin: 都是杜撰的了?我知道。
Lenny: 是啊。什么是真的?天哪,我们能不能——好,我们就——
Judd Antin: 好,也许他说过。他确实相信这一点。历史学家是这么说的。但让研究者如此愤怒的原因是,那不是研究。那不是研究者做的事。一个会去问客户想要什么的研究者是一个糟糕的研究者。你需要换一个研究者。我的职业生涯中从未做过那种事。我的团队中从未有人做过那样的研究。所以那只会让研究者生气。
“我们早就知道了”——后见之明偏误
最后一个刻板印象是关于事后偏见的,就是”我们早就知道了。那不是很明显吗。“我经常想起一本书,我想推荐给你的听众。作者是宾夕法尼亚大学的社会学家 Duncan Watts,书名是《Everything is Obvious If You Already Know the Answer》。讲的就是后见之明偏误(hindsight bias)。他的论点是,我们过度依赖直觉、启发式方法和模式匹配,而这种依赖与我们的经验水平不相称。这会把我们引入歧途。就像一种自我煤气灯效应(self gaslighting)。之所以会发生,是因为我们最终选择性地记住一些事情,然后围绕它们构建叙事,让我们觉得自己早就知道了,而事实上并没有。
他还谈到了另一种认知偏误,叫做叙事谬误(narrative fallacy),即人们喜欢为过去编造方便、简单的故事。如果我问你的职业经历,Lenny,你是怎么成为这么厉害的播客主持人的,你大概会说:“好吧,让我给你讲讲这一系列事情。“我们确实都会这样做。这是我们理解自身生活和周围信息的一种方式,但这很可能是一种谎言,因为我们都会扭曲手中的证据来迎合自己想要相信的叙事,因为那样简单、美好,而且让我们开心。
Lenny: 这听起来可能有点自我标榜的意味,但我发现我恰恰相反。我总是觉得,“我完全不知道这一切是怎么发生的。这里发生了一些事情,然后不知道怎么的我就到了现在这个位置。“不过也许我这只是过于谦虚,不想给自己任何功劳。
Judd Antin: 这挺好的。
Lenny: 顺便谢谢你分享这些刻板印象,很有趣。我不知道你会讲这些。所以我们这里收集了一组有趣的总结。
Judd Antin: 谢谢。
用户研究与心智模型
Lenny: 我想问一下,Patrick Collison 有一条推文,我在这个播客上提到过几次,我觉得非常有意思。他的推文是这样的:“在我看来,最好的产品源于对领域和用户非常强大的心智模型。用户研究可以帮助你建立这样的模型,并在过程中不断验证它。但重要的是要把用户研究的逻辑链条理解为:对用户研究的建模,到改善你对用户的心智模型,再到你应该构建什么产品——而不是用户研究告诉你该构建什么产品。“这种对用户研究的理解方式,你怎么看?有什么共鸣吗?
Judd Antin: 有的。我们在研究社区里经常讨论一个双刃剑问题,就是关于为设计提出建议这件事。最好的研究不会止步于发现,它会告诉你——是什么、所以呢、然后怎么办。但问题在于,有些研究者会在另一个方向上走得太远,他们会说:“我们做了这项研究,得出了这些洞察,所以我们应该这样构建产品。“然后团队里的其他人会说:“等等等等,很高兴听到你的看法,但这里面涉及的东西很多,也许我们应该讨论一下。“这完全合理。这是一种沟通上的失败。
我认为这也印证了 Patrick 说的那一点——好的研究有时候确实能准确告诉我们问题是什么以及如何修复。我之前提到的那个价值数百万的按钮就是一个例子。但在很多更大的问题面前,尤其是宏观层面的问题,也许还包括一些非常具体的中距研究问题,关键并不在于”我们应该精确地这样做,精确地构建这个”。而在于”让我们建立一个基于实际证据的框架,然后作为团队一起想办法,通过实验走向一个成功的产品”。
走出清算时刻
Lenny: 把这个话题收个尾,你对团队、研究者有什么建议,帮助他们走出这场清算时刻、继续前行、推动这个领域发展?既从用户研究者的角度,也从公司角度——也许有些公司裁掉了一批用户研究者,或者正在纠结如何安排他们的研究人员。
Judd Antin: 谢谢你问这个问题。我之前跟你说过,作为你播客上第一次专门讨论研究的对话,我感到一些压力。
Lenny: 对,我想是的。
Judd Antin: 而我想要帮到大家。我对研究和洞察这个学科有着极大的信念。当我说”过去十五年的 UX 研究学科正在消亡”时,我并不是说研究正在消亡——远非如此。我认为有某个版本的研究,我们正在超越它,进入一个新版本。我们正在经历一次演变,许多领域都经历过这样的过程。所以对我来说,问题是:“研究者、公司,以及与他们合作的其他人,如何共同创造一个新的版本、一个不同的版本、一次演变,从而对业务产生巨大的影响力?”
所以我给研究者的建议是:发展多元化的研究技能。回想一下我之前提到的五个或五个半工具的清单,真正深入地掌握业务知识——学会用产品、商业和指标的语言说话,准确地理解如何像手术刀一样精准地运用你的洞察,建立稳固的关系——这也不是研究者单方面能做到的,需要双向的互动——同时以一种让研究者能够少做一点但做得更好的方式来运作。
我认识的大多数研究者所在的团队都是这样的情况:“我是唯一的研究者,我有七个 PM 和二十个设计师,同时要做十个项目。“这样没有人能做好。所以研究者必须和他们的合作伙伴一起学会说不,聚焦在最重要的事情上。但这只是一半,只是研究者那一半。
公司应该做什么
关于公司应该做什么,我有两个想法。第一个有点题外话,但其实也不是。我通过文章收到的反馈中学到的一件事是,各种洞察学科的人都纷纷冒出来了。因为我来自 UX 研究(或用户研究)的传统,但很多行业里有很多不同的洞察学科,他们都想要声称某种研究类型是自己的领域,会说:“哦,我们在消费者洞察或市场研究这边早就做得很好了。“洞察学科确实有很多。总的来说,我认为建立部门壁垒(silo)是愚蠢的。
实际上我很好奇你怎么想,因为这是我在加入 Airbnb 时听到的头号反馈——你当时也在那里。我做了一个快速的倾听之旅,跟很多产品人员聊了聊。他们说的都一样:“听着,我们有各种各样的人把洞察往我们这边扔。这挺好的。我们想听数据科学家的、产品专家的、客服人员的、客户之声的,所有这些东西。但它们全都从四面八方涌过来,我们不知道该怎么处理。太多了。”
这一点,和其他任何事情一样,都在论证公司应该停止将研究学科割裂开来。所以当我加入 Airbnb 时,我着手创建了一个整合的洞察职能——就是:“让我们做 UX 研究,该讨论市场和竞争对手的时候就去讨论。让我们与数据科学职能进行智能整合。让我们把从客服反馈中获取的所有信息整合起来。“我们把当时负责 NPS 的项目也拿了过来,说:“既然我们在那里收集客户反馈,那就让所有人都来利用这些信息,为这一台洞察机器提供燃料。“所以这是我会给公司的第一件建议。
第二件,虽然有点老调重弹,就是要换一种方式思考那个断裂的循环。把研究者整合到一个统一的、精益的流程中。如果研究者没有从头到尾参与其中,如果产品人员、设计人员、工程人员在各个层级没有与他们的洞察伙伴建立稳固的关系,我们就会重新陷入那个老问题——我们只是一个服务性的职能,我们没有提取出最大价值,洞察来得太晚,我们不知道该问什么问题,我们不了解研究能做什么。所以创造那种从头开始就与研究者在同一战线并肩作战的整合性精益流程,是我能给出的最重要的建议。
Lenny: 最后这点可能正好回答下一个问题——产品经理怎样才能成为用户研究员更好的合作伙伴,或者从他们身上获得更大的杠杆效应?
Judd Antin: 我认为在很多方面确实如此——确保他们为产品建立一套从头到尾整合了用户研究员和洞察的流程。另外,也要愿意在优先级排序上与研究员坦诚合作。我以前常说:“一个研究员满负荷的状态大概是三件事——两个大项目加一个小项目,类似于副业那种。超过这个数量,你的研究员很可能做得不够好。一个项目可能只需要 48 小时,这没问题。但他们需要你帮助排序优先级,需要你参与其中。优秀的 PM 会花时间跟研究员在一起,一起到现场去,甚至一起出差。“你做过这种事吗,Lenny?
Lenny: 做过。我跟 Louise 一起去的——就是她介绍我们认识的,基本上也是她让我来找你聊这个话题的。
Judd Antin: 谢谢 Louise。
Lenny: 谢谢 Louise。我们整个团队去巴黎做了一次完整的调研之旅,或者说是团队的负责人去了巴黎,做了很多焦点小组,也做了很多在单向镜后面的用户研究。在那次旅行之前我从没做过那种事,体验太棒了。我们学到了很多东西。
Judd Antin: 我能讲一个关于单向镜后面的快速故事吗?
Lenny: 请讲。
单向镜背后的洞察
Judd Antin: 这还是我在 Facebook 时候的事。那时候是黄金期,大概是 2012、2013 年,News Feed 真正起飞,广告开始进入 News Feed。我当时带领一个团队,工作之一就是如何评估帖子质量——比如”我们怎么判断什么是一个好帖子,怎么获取相关反馈?“当时有一组工程师觉得:“在 Facebook 上你可以隐藏帖子。这很简单——我们看看哪些帖子被隐藏得最多,把它当作衡量帖子好坏的信号不就行了?“听起来挺合理的。但我对这件事有种直觉上的不安,所以我做了两件事。
第一件事是,我查看了用户隐藏行为的分布,发现它是幂律分布的——跟互联网上的一切一样。Facebook 上有极少数人隐藏大量内容,而大多数人根本不隐藏。于是我们把这些人叫作”超级隐藏者”。我们说:“让我们在办公室附近找几个超级隐藏者,把他们请进来,做一个非常传统的用户访谈。“我们就是想看看。我还记得走进来的第一个人——她的指甲特别长,你都不明白她怎么用触摸屏的,但她就是用得很溜,厉害得很。那是一个带单向镜的房间。我坚持让工程总监、产品人员都到后面的观察室去,他们也愿意配合。
所有人都在单向镜后面,我也跟他们在一起。那位优秀的研究员在访谈室里面和参与者坐在一起,我们做的就是传统的出声思维研究。他们说:“嘿,你能打开你的 Facebook 应用吗?我们就是想看看你的使用体验是什么样的。“于是她打开 Facebook,看着第一条动态,把它隐藏了。看第二条,又隐藏了。就这样持续了一段时间。
她确实在使用 Facebook,但每看完一条动态就会把它隐藏。后面观察室的人开始骚动起来,“等等,什么?现在到底在发生什么?“那位研究员很专业,她没有打断,而是让这一切继续下去,然后说:“哇,你能告诉我你现在在想什么吗?“结果发现,她说的是:“我隐藏那条是因为我已经看过了。“她追求的模式是收件箱归零——这其实很可悲,因为那是无限滚动的信息流,她永远也归不了零。
我喜欢这个故事的原因是,后面观察室里那些人的认知被彻底颠覆了。并不是说我们假设这种行为很普遍——这个人可能是特例——但这已经足够了,因为那些人亲临现场、亲历了这场研究,这个 N=1 的案例就足以打破他们自己的思维泡沫,让他们意识到:“好吧,我们不能再那么天真地把’隐藏’当作信号了。“于是我们想出了一个更好的解决方案。
Lenny: 这个故事太棒了,而且是一个绝佳的例子,说明你不需要统计显著就能获得巨大的洞察。一个案例就能让你意识到:“哇,可能就是这么回事,我们去验证一下。“而不是说”我们百分之百确信就是这样”。我很喜欢。这其实让我想起我在巴黎提到的那个单向镜研究,里面也有一个 Facebook 的元素。我们当时试图说服房东如何更放心地接受即时预订的房客。我们的一个假设是:如果他们在 Facebook 上互相关联,他们会更放心让别人即时预订。
Judd Antin: 对。
Lenny: 我们就问:“如果你们连接 Facebook 看看是不是朋友呢?“巴黎的所有人都非常害怕连接并给 Facebook 任何数据,远比美国的房东们更警惕。
Judd Antin: 对。
Lenny: 所以这就很清楚了——没有人真的想给 Facebook 任何数据。那时候的反 Facebook 情绪很强。
Judd Antin: 是的,这很有意思。德国和法国一直是我们判断世界其他地区数据隐私态度的风向标。
研究人员的合理配置
Lenny: 天哪。好,再聊几个问题。我们这个话题的起点是用户研究领域的大量裁员。我觉得字里行间传递出一种感觉:团队并不需要零利率政策(现象)时代招聘的那么多研究员。我想每个人心里都有一个疑问:“我们到底需要多少研究员?合理的比例是多少?“我猜这没有简单的答案,但你给公司的总体建议是什么——多少研究员才是合适的?
Judd Antin: 这件事我思考了很多,尤其是我在担任设计工作室负责人这个角色的时候,那是我最根本的问题。就是:“你有这么多文案、设计师、研究员,你怎么组织他们,安排多少、放在哪里、谁做什么?“对我来说,组织原则始终是关系。当那些需要概念验证层面的研究合作伙伴的人都有了匹配的研究员,你就知道数量够了。在这种情况下,我宁愿让一些人分不到研究员,也不愿意把某个人铺得太薄。所以我一贯的建议是:“不要试图让一个研究员覆盖整个产品空间。把一个研究员和某个产品伙伴配对,让这个伙伴把他们纳入一个持续的、深度参与的流程中,让他们放手去干,看看他们能产生什么影响——但要保护他们的时间。“然后其他人就会说:“等等,那个人干得很出色,我也想要。”
而让他们承受那种痛苦——因为那是一种失去的痛苦——恰恰是扩充编制的头号方法。我争取更多编制一贯的做法,从来不是抽象地论证研究为什么重要,而是让那些渴望拥有研究支持的合作伙伴替我去争取。所以你说得对,这个问题没有干净的答案,比如”嘿,这就是正确的比例”,因为这确实取决于产品的性质。比如,“是早期产品还是成熟期产品?我们说的是创业公司还是成熟企业?“但我想说的是,始终有研究员的位置。Lenny,我告诉你,这是我最近一次主题演讲中用过的例子。你前不久发过一份列表,大概是 20 家 B2B 公司和它们的前 10 名员工。你还记得发过这个吗?
Lenny: 当然记得。
Judd Antin: 你还记得那份列表里有多少研究员吗?我给你一个提示。
Lenny: 不太多。
Judd Antin: 在零到二之间。是一个。整个列表上只有一位研究员,任何位置都算。这让我觉得不对劲。当然,这只是 20 家公司,每家都有自己的领域,所以我不想过度泛化。但一个研究员无论公司处于什么阶段都能创造巨大的价值,因为好的研究员让你走得更快,而不是更慢;他们推动影响力,因为他们回答的是用其他任何方式都无法回答的问题。创业公司如此,成熟企业也如此。当然,如果是你的前 10 名员工,一个研究员就能发挥很大作用。随着公司成长,确保研究员与业务关键部分有强有力的合作伙伴配对,这是判断人员数量是否充足的最佳方式。
Lenny: 有意思。所以你的建议是,创业初期,你的主张是招一个研究员比一般招一个工程师能获得更大的杠杆、跑得更快,但这本质上是一种取舍。大多数招聘到最后都是这样。
Judd Antin: 我不太愿意过度泛化,我确实认识很多处于创业阶段的创始人会说:“我知道要建什么,问题是我需要能帮我执行的人。“我觉得这没错。所以一切都是权衡。但请记住,想象一下如果你身边有那把瑞士军刀(喻指多面手能力)。也许你已经推出了 MVP,正在进行第一次重大迭代,或者像很多创业公司一样,你需要转型。这时候关键就在于:“嘿,你不必独自做这件事。“我们神化那些成功转型的创业公司创始人,但我认为那或许就是我们所说的 moral luck——我们神化那些恰好走对了的人,即使他们和那些走错了的人做出了完全相同的决策。
事实是,如果你身边有一位拥有那整套瑞士军刀工具的洞察人员,你就不是一个人在战斗。你不必靠猜。最终,这仍然会归结为你和创始人们必须做出的艰难决策,但你可以拥有支撑那个决策的证据,而这是你用其他方式无法获得的。
研究员的自我赋能
Lenny: 关于这个话题,我还有最后几个问题。我觉得你对研究员传达的一个重要信息是:“你可以获得主动权。这取决于你去做正确类型的研究,把职业引向正确的方向,而不是变成一个别人不需要的研究员。“你文章开头有一段话,很多反应——或者你大概是这样写的——“我知道你们在想什么:他们就是不懂。我们太被误解了。我们的命运就是交付洞见,让用户拿去推动商业价值,而我们自己却被遗忘,永远无法影响路线图,没有一席之地,一再被安排错角色,最后还被裁员。“而我从你这里听到的是:“你可以改变这一切。你可以拒绝做那些并不真正产生贡献的研究。“但我想问你,你对研究员获得成功最核心的、持久的建议是什么?
Judd Antin: 在一个有问题的系统中运作确实很难,所以我理解那种反应,那种无力感。但我认为这种心态不太可能带领我们走过这个时刻,进入研究的下一个进化阶段。所以我完全不责怪任何研究员身处当前的处境。这一路确实艰难。然而,抱怨我们的命运不会带我们走到任何地方。所以对我来说,那篇文章的核心观点——也是我给公司做咨询时始终在说的——就是:“嘿,我们可以用一种不同的方式来组织这件事,它能回应当前的环境,同时产生巨大的影响力。“当然,这需要公司做出正确的选择。同时,也需要研究员自己承担责任:发展技能,学会拒绝,理解什么样的研究能创造最大价值,培养关于商业的技能、知识和语言,变得更有影响力,成为出色的沟通者。
这是我在招聘时最看重的方面之一,尤其是招聘研究负责人,因为我需要他们以身作则来示范和教导——不只是严谨的研究。就像一棵树在森林里倒下,如果没人在场听到——你需要有效地传达研究结果,而且要针对受众采用合适的方式。因为如果我在跟你 Lenny 说话,那和我跟 Airbnb 的 Brian Chesky 说话是不一样的。所以我必须能把那场展示做好,直击要害,说对语言。所以如果你是一名研究员,情况并非没有希望。事实上,这个学科的未来非常光明,我们可以通过持续发展这些不同的技能来推动它前行,同时公司也在构建一个更具包容性的模型。
NPS 的问题
Lenny: 好的。我还有一个比较发散的问题,关于 NPS(净推荐值)。你对 NPS 有很强烈的看法,我想听听你对 NPS 的价值和使用体验的观点。
Judd Antin: 是的,我对 NPS 确实有强烈看法。我常说:“NPS 是营销行业自我营销的最佳案例。“问题在于,这威胁到了很多人的生计,因为有一整个由顾问和软件供应商组成的行业,他们希望你相信 NPS 是一个有用且准确的指标。但事实是,调查科学界的共识是 NPS 犯了所有该犯的错误。这是一个 garbage in, garbage out(垃圾进,垃圾出)的问题。“推荐意愿”这个问题本身就有很多毛病。它用的是 0 到 10 的量表,这就有问题。它通常不加标注。我们给民调加标注,但那不是研究的金标准。它有 11 个选项,这也有问题。第一,我们发现超过 5 个选项——也许 7 个——精确度就会下降。第二,尤其在移动端,如果你在做这个调查,那些选项有多少比例在屏幕下方看不到的区域?我们不可能获得准确的调查数据。所以从调查方法的角度来说,它确实很糟糕。还有一个直觉上的问题:“你有多大可能向你的朋友和家人推荐 Windows 11?“我不是一个到处跟人推荐操作系统的人。这个问题从根本上是 flawed 的。
CSAT 优于 NPS
Judd Antin: 他们的论点是那个问题是忠诚度的良好指标,但其实有一个非常简单的解决方案,Lenny。客户满意度,一个简单的 CSAT 指标,就更好。它的数据属性更好,更精确,与业务结果的关联度也更高。我想证明这一点。这是调查科学家知道但营销人员不想让你知道的事情。所以我们和 Mike Murakami 一起做了这项工作,他曾在 Airbnb 负责调查科学,他现在还在那里,是一位优秀的研究者。我们基本上重新做了所有那些工作,想验证这些结论对 Airbnb 来说是否成立。确实成立。很简单——不要问 NPS,问客户满意度。
Lenny: 客户满意度的问题,具体是怎么问的,方便确认一下吗?
Judd Antin: “总体而言,您对 Airbnb 的使用体验有多满意?“或者类似版本,比如”总体而言,当您遇到问题时,您对客服体验有多满意?“所以可以有更具体的版本,但这些问题的属性更好。很多人会说:“嗯,大家都在用 NPS,至少它给了我一个基准,因为我可以把自己的 NPS 和行业 NPS 做比较。“问题在于,研究表明 NPS 是特异性很强的——它以我们无法理解的方式上下波动,而且提问方式存在大量不一致,这些不一致会在数据中产生变异,这意味着根本不是苹果对苹果的比较,你甚至无法有意义地把自己的 NPS 和别人的进行比较。
Lenny: 我喜欢这些犀利观点。我很好奇谁会跳出来,当——
Judd Antin: 人们会很生气的,Lenny。
Lenny: 我就喜欢这样。我觉得,对,这个我听过很多次了,但大家都不公开说。好的。在我们进入非常精彩的快问快答环节之前,你还有什么想分享或者留给听众的吗?
产品走查的局限
Judd Antin: 可以吗?对,我想补充一点,因为最近你的播客里好几次谈到这个话题,就是关于人们自己做产品走查的想法。一个产品经理是否应该仅仅依赖自己 dogfooding 产品、自己走查来判断怎么修复问题?最近几次讨论中这个话题都出现了,看起来大家的共识是”是的,这是一件好事”。而我在这个问题上也有一个反面意见,我认为每个人都应该 dogfood 自己的产品,这确实很重要。问题在于依赖你对这些产品的直觉——大多数产品经理难以意识到的是:“你和用户完全不一样。你和他们在方方面面都不一样,这些差异会以你未必能察觉的方式偏见你对产品优劣的判断。产品中有些问题,你需要有脉搏才能感知到。“而大多数优秀的产品经理确实有脉搏,这很好。但很多问题需要使用场景、优先级、约束条件等上下文,而这些是你不具备的,也无法仅凭自己的使用来想象。所以我认为这意味着你当然应该 dogfood 自己的产品。通过产品走查来列出潜在问题清单是一件好事。但对那个清单进行优先级排序,判断哪些问题更大或更小、对谁而言是问题——在这个环节,你应该极其警惕在 dogfood 产品时依赖自己的观点、经验或直觉。
Lenny: 谢谢你分享这个。这个话题确实在播客里出现了好多次,所以我觉得这对大家来说是一个重要的收获。快问快答之前还有别的吗?
Judd Antin: 感谢你,Lenny。谢谢邀请我来。
Lenny: 感谢你,Judd。好了,我们已经到了非常精彩的快问快答环节。准备好了吗?
Judd Antin: 准备好了。
快问快答
Lenny: 你最常向别人推荐的两三本书是什么?
Judd Antin: 我最近读了一本 Barbara Kellerman 写的商业书,叫《Bad Leadership》。我喜欢它的原因是,我们花大量时间讨论优秀的领导者,而她真正深入研究了最糟糕的领导者,以及是什么让他们成为糟糕的领导者,我觉得这对每个人都非常有价值。我还想推荐,我读了很多小说,所以有两个推荐。一个是最近的普利策奖得主,Barbara Kingsolver 的《Demon Copperhead》。这本书非常出色,同时也非常悲伤、感人、有启发性,尤其如果你想了解农村贫困问题的话。然后是小说光谱完全不同的另一端——如果你对科幻感兴趣,我也是,去读《Murderbot Diaries》吧,讲的是一个嘴毒的杀手机器人。谁会不喜欢呢?
Lenny: 我太喜欢这些小说推荐了。我觉得播客里需要更多这样的推荐,谢谢你。
Judd Antin: 对,大家都推荐商业书。
Lenny: 确实。你最近最喜欢的电影或电视剧是什么?
Judd Antin: 我们最近看了《The Last of Us》,简直震撼。我在玩了游戏之后才看的——如果你是玩游戏的人还没玩过《The Last of Us》,去玩吧。如果你不知道,这部剧是根据游戏改编的,不是反过来。
Lenny: 你有没有最喜欢的面试问题,喜欢问候选人的?
Judd Antin: 想一个你最近不得不解释的最复杂的话题,然后把我当成五岁小孩来解释给我听。这个问题有很多变体,但我喜欢它的原因是——我在多个职能领域的 VP 和 C 套件候选人身上都问过这个问题,有时候跟对话内容相关,比如我可能让他们解释量子计算或音乐理论中某个复杂的东西,也可能是一个复杂的商业决策——但我想看一个人能否在很短时间内把一个复杂问题拆解得非常简单,并给我一个直觉性的理解。我认为这对很多人来说是区分优秀与卓越的分水岭。
Lenny: 你最近发现的最喜欢的产品是什么?
Judd Antin: 有一个,挺冷门的。我们全家最近开始室内攀岩,当你在 top rope 的时候有一个挑战,就是你一直得仰头往上看。所以他们做了这种眼镜,叫 belay glasses,镜片里嵌入了一个角度镜,这样你就可以直视前方,但看到的画面是你上方正在保护的那个人。我就觉得这个产品对那个需求来说太完美了。那是一个非常小众的问题,而且没有更好的解决方案。
Lenny: 你有没有一个经常回到的座右铭,会跟朋友分享的,不管是工作上还是生活中的,你觉得特别有用的?
Judd Antin: 有。这听起来像是在讨好你,Lenny,但不知道你还记不记得你和我有过一次对话,大概是八年前。我还记得我们坐在哪里。那次聊的是斯多葛主义。你还记得吗?不管怎样,我们有过那次对话。
Lenny: 我不记得了,但我确实对斯多葛主义感兴趣过一段时间。
Judd Antin: 我知道你确实感兴趣过,因为我们聊过。而这个座右铭正是来自斯多葛主义,基本上就是:“专注于你能控制的事,忽略其余的。“很多人认为这是宁静祈祷,或者宁静箴言,那是 20 世纪的发明,但 Epictetus 在公元前就写过这些了,我一直在想这件事。我们生活中那么多的压力、痛苦和担忧,都来自于我们无法控制的事情。所以我试着放下那些东西。
Lenny: 太棒了。我是从《高效能人士的七个习惯》中学到这个道理的,就是思考这些圈子的重要性——你能控制的、你能影响的,以及你完全无法控制的,而那些你无法控制的事情根本没必要去想。
Judd Antin: 完全同意。
Lenny: Judd,这次对话完全达到了我的期望。我们聊到了很多好东西。我很期待听到大家的反应。最后两个问题:如果大家想了解你正在做的事情,可以在哪里找到你?顺便也分享一下你最近在忙什么,以及大家怎么找到你。另外,听众们怎么才能帮到你?
Judd Antin: 好的,谢谢问这些问题。大家可以在 juddantin.com 找到我,那是了解我动态最好的方式。目前我是一名顾问,帮助人们解决 UX 战略、组织设计和危机管理方面的问题。不知道为什么,我特别擅长处理别人的烂摊子,而且我发现自己天生就适合干这个。所以 juddantin.com 是最佳去处。我也会写东西,我在 Medium 上写文章,可以在 onebigthought.com 找到。你会发现我们今天聊到的很多话题,包括最初引发这一切的那篇文章,都在 onebigthought.com 上。如果我能请你的听众做一件事,那就是——靠近你的研究员。我只是觉得,如果你建立起这些关系,并且早早地、频繁地让研究员和洞察人员参与进来,美好的事情就会发生在你身上,也会发生在业务上。这就是大家都能为我做的事。
Lenny: 我很喜欢这一点。我一直都是这么做的。我很喜欢和我合作过的研究员们,其中很多人向你汇报过,所以这是一个很棒的收获。Judd,非常感谢你来参加节目。
Judd Antin: Lenny,谢谢你。这是我的荣幸。
Lenny: 大家再见。非常感谢收听。如果你觉得这期节目有价值,可以在 Apple Podcast、Spotify 或你喜欢的播客应用上订阅节目。也请考虑给我们评分或留下评论,因为这真的能帮助其他听众找到这个播客。你可以在 lennyspodcast.com 找到所有往期节目或了解更多关于节目的信息。下期再见。
术语表
| 原文 | 中文 |
|---|---|
| applied statistics | 应用统计 |
| belay glasses | belay glasses(保护眼镜,攀岩用) |
| bellwether | 风向标 |
| Brian Chesky | Brian Chesky(Airbnb 联合创始人) |
| broken record | 老调重弹 |
| causal claims | 因果判断 |
| cognitive bias | 认知偏误 |
| concept car | 概念车(喻指前瞻性原型) |
| confirmation bias | 确认偏误 |
| content design | 内容设计 |
| conversion funnel | 转化漏斗 |
| CTA (Call To Action) | CTA(行动号召) |
| customer obsession | 客户痴迷 |
| design thinking | 设计思维 |
| dogfooding | dogfood(吃自己的狗粮,喻指团队亲自使用自家产品) |
| dumpster fires | 烂摊子(喻指混乱失控的局面) |
| Duncan Watts | Duncan Watts(宾夕法尼亚大学社会学家) |
| Epictetus | Epictetus(古希腊斯多葛派哲学家) |
| evaluative research | 评估性研究 |
| falsify | 证伪 |
| formative research | 形成性研究 |
| garbage in, garbage out | 垃圾进,垃圾出 |
| gaslighting | 煤气灯效应 |
| generative research | 生成性研究 |
| gold standard | 金标准 |
| Guinea pig | 小白鼠 |
| gut (intuition) | 直觉 |
| heuristic | 启发式方法 |
| hindsight bias | 后见之明偏误 |
| inbox zero | 收件箱归零 |
| listening tour | 倾听之旅 |
| macro research | 宏观研究 |
| mental model | 心智模型 |
| micro research | 微观研究 |
| middle range research | 中距研究 |
| moral luck | 道德运气 |
| MVP | MVP(最小可行产品) |
| narrative fallacy | 叙事谬误 |
| NPS (Net Promoter Score) | NPS(净推荐值) |
| OKR | OKR(目标与关键成果) |
| org design | 组织设计 |
| over the transom | 不请自来地涌来 |
| Patrick Collison | Patrick Collison(Stripe 联合创始人) |
| post hoc bias | 事后偏见 |
| power law distributed | 幂律分布 |
| prompt engineering | 提示工程 |
| reckoning | 清算时刻 |
| scut work | 苦差事 |
| serenity prayer | 宁静祈祷 |
| silo | 部门壁垒 |
| stat sig (statistical significance) | 统计显著 |
| stoicism | 斯多葛主义 |
| Swiss army knife | 瑞士军刀(喻指多面手能力) |
| System 1 / System 2 | 系统 1 / 系统 2 |
| T-shaped | T 型(广度加深度的人才模型) |
| TAM (Total Addressable Market) | TAM(总可达市场规模) |
| think aloud study | 出声思维研究 |
| Thinking, Fast and Slow | 《思考,快与慢》 |
| top rope | top rope(顶绳攀登) |
| under the bus | 推出去挡枪 |
| user-centered performance | 以用户为中心的表演 |
| UX Research | UX 研究 |
| UX writing | UX 文案 |
| virtuous cycle | 良性循环 |
| voice of customer (VOC) | 客户之声 |
| wisdom of the crowd | 群体智慧 |
| ZIRP (Zero Interest Rate Policy/Phenomenon) | 零利率政策(现象) |
此文档由 AI 分片翻译(translate_long_document)