Rewind the clock to the early 2000s. The internet was filled with web pages to broadcast information. It was a glorified and more convenient Yellow Pages. Then Friendster, MySpace, and Facebook were built. At the inception of these products, social media was basically a directory to see what your friends were up to. It was everyone’s own personal webpage.

Quickly, these sites became new tools to communicate. In a time when communication via phone and text could be expensive, these social platforms provided new and convenient ways to connect. Yes, there was AIM, but those communications were one-to-one. Interestingly, these social platforms made communication between individuals public for all to see. They were novel in how they turned the internet from billboards into a town square.

You would post on your friend's wall for all to see. And then they would reply by posting on your wall for all to see. I liken it to the 70s and 80s when kids would roam free throughout the neighborhood for hours without parents having a care in the world. We didn’t care that other people could see our communications just like parents were fine with kids wandering the streets alone. Posting publicly was hip, cool, and the culture of the day.

However, the way friends communicate and behave on social media has changed. I am going to map out how small UX changes on social platforms transformed social media into just media. And to be clear, UX changes aren't the only thing that changes behavior—lots of factors go into it. With that said, the following UX changes paved the way for how we use social media today.

Feed

When social media was a directory and wall posts, it was difficult to discover new people. You had to know their names and be able to search for them to send a friend request. You came to platforms such as Facebook to be with the people you knew and cared about and would add new friends as you collected them in the real world.

However, for Facebook the company, this was a growth barrier because users weren’t able to easily see updates from friends.

Facebook turned to the innovative idea of the Feed, which would transform how social (and non-social) apps have been built ever since. The idea is that you would be able to see a stream of content from your friends every time you log in. Ultimately, Feed would become a growth engine to discover new people and content.

The reception for Feed was poor. However, Facebook observed that, though people complained about the change, they actually stayed on the site longer. It was an amazing product to make Facebook sticky for users.

The introduction of Feed set the stage for years down the road of users passively consuming content through scrolling and viewing instead of commenting and posting.

The Like Button

FriendFeed, a rival social network to Facebook, was the first social network to introduce the Like button. It took almost two years, but Facebook followed FriendFeed and introduced it as well.

Subscribe now

It is now universally used for many purposes, including allowing social media apps to gain important signal into what type of content and businesses resonate with users to serve more personalized content and ads.

For users, a simple click of the mouse (or tap on the phone) takes on many different meanings. Approval, appreciation, agreement, sympathy, etc. A simple 'like' removes the barrier to actually communicating with words. Again, this was another small change that eventually steered social media to be less social.

Chat & Groups

In the mid-to-late 2000s, we saw new products that allowed communication to happen in smaller, more personalized groups. Reddit launched, which allows for participation in anonymous groups or subreddits. Facebook chat (Messenger) debuted, allowing users to chat in private with their friends. WhatsApp was released in 2009 as an affordable alternative to texting. And Facebook groups, a place to gather in small communities, became available for like-minded users to congregate. These products ushered in private communication on social media.

Cultural changes also helped push users to use these new private or semi-private communication tools more than broadcasting openly. Cancel culture and political discourse became more prevalent, forcing people to take sides. The 2016 presidential election was nasty, making the discourse on social apps more fiery. Many people were turned off and didn’t want to express their viewpoints publicly due to fear of backlash.

Additionally, people have become more conscious of privacy. With data breaches and more scrutiny of Big Tech companies, users started to become more closed-off in their online profiles.

Slowly but surely, communication between friends would become more private as opposed to public.

Algorithms

Remember the invention of the Feed mentioned earlier and how it paved the way for consumption over engagement? Well, algorithms were the big driver of this behavior. Initial algorithm sorted friend content (Facebook) or subreddits (Reddit) that would be most relevant to you. As the algorithms became more complex, it then started recommending new content.

This increased the ability for users to discover new friends, groups, subreddits, etc. Algorithms keep people scrolling and increase time spent in the apps based on content you will like.

New Use Cases

With the improvement and creation of algorithms, search, and hashtags, social media has become more than a place to talk and connect with friends. It became a way to discover new trends, stay up to date on news, and find cool products. New ad types were even created to support creators (Paid Partnerships). In essence, social media became a place to be entertained, stay up to date, and participate in commerce activities.

Share

Soon users started to follow people they didn’t know, something that was uncommon when the industry was born. Users entered into parasocial relationships. The creator economy emerged and more time on social apps was spent watching and looking at the content of people users didn’t have personal relationships with.

TikTokification of Social Apps

TikTok took the world by storm and time spent was instantly sucked into scrolling short-form video. Powered by an extremely good algorithm that learned what users liked, it became an app that users could open and be instantly entertained.

TikTok also introduced fullscreen video, which didn’t allow users to see the next video before scrolling. Instead of being able to peek and see if the next video was interesting and worth watching, the UI kept users looking forward to the next video, which enabled more scrolling. Just test this out. Go to TikTok and try to force yourself to only watch the first five videos. It’s tough to not watch more.

With an incredibly entertaining product and fantastic algorithm, users spent more time consuming videos from creators instead of communicating with friends.

In the world of social media where time spent equals more ad dollars, every other social app needed to figure out ways to increase time spent or lose out to TikTok. Thus we saw the rise of Reels and YouTube Shorts. We even see LinkedIn, Substack, and X embracing short-form video now.

Apps that were once reserved for communicating openly with close friends have now turned into media giants where friends also happen to be present. TikTok is probably the biggest single reason our social apps are now media-first.

Key Takeaways

Gone are the days of public communication between friends on social. Couple these UX changes above with new cultural norms and changing user demands, and you have the new state of social media.

Some key takeaways to consider as you build your own products:

1. Even small UI/UX changes can change behavior. Consider all potential behavioral changes to mitigate anything that detracts from core metrics and the company mission. Set up guardrails to prevent unintended consequences of product changes. While making a change may move important metrics in one area, it may regress important metrics for another. Ensure that everyone is aligned and aware of the consequences before making changes.

2. However, when you discover a behavioral change that can benefit your goals, know that you may have hit a gold mine. It isn’t easy to change behavior that can meet user and business needs, so when you do, go all in.

3. Behavior change often takes time and is usually cumulative across many small changes. However, when one killer use case is introduced, the floodgates can open and behavior change may happen quickly. Think TikTok, Uber, Airbnb, etc.

You are doing user research wrong. I’m not talking about the execution of research, but how UX research is used within an organization. User research is often underutilized, leading to unrealized business impact.

Imagine this: Your product is starting to mature. The company is starting to expand into new markets and customer segments. It doesn’t come as naturally to know what to build next. You look at some of the most successful tech companies and see they all have dedicated user researchers. You think that hiring your own researcher is the logical next step.

• You hire a research all-star.

• Things seem promising at first.

• They help improve a product area and you see an immediate impact.

• But then the excitement wanes.

• People don’t know how to use the research.

• The researcher keeps asking for more budget.

• Research takes longer than you think it should and becomes a bottleneck to decision-making.

Does any of this sound familiar to you? If so, keep on reading. This post will give a high-level overview of why things go wrong, how research is misused, and how to build a research foundation integrated into your company culture and has business impact. Everything in this post can probably have a deep dive post dedicated to it. So if you feel you need more insights into any one thing, let me know!

Subscribe now

Why Things Go Wrong

Many things can go wrong when integrating user research into an organization, but they often come down to one of three foundational issues.

Organizational Challenges

Organizational silos

A researcher is siloed within the design org, or even worse, the marketing org. The function works in a vacuum and doesn’t interface with other parts of the business.

Lack of research integration into product workflows

Researchers are treated as consultants and don’t have the context on the products to dive deep into the issues to generate meaningful insights. This leads to shallow takeaways that aren’t actionable and have everyone saying, “duh.”

Slow insights (too much red tape)

There is often too much red tape around doing research. This can come in the form of getting permission for budgets, accessing users/customers considered off-limits by the sales and customer success teams, or legal and privacy making it difficult to get approvals.

Research Literacy

Lack of understanding of what research is and what it can do

Some may not have experience working with dedicated researchers and don’t understand the value it can bring. They have little understanding, and education, and haven’t seen research demonstrate anything of value.

Poor communication of how research impacts the business

Research can fall short when their insights don’t speak to business results. Too many are great researchers but have poor business acumen and little product sense.

Prioritizing certain types of data over others

Research is just one data point, but so are the data points from other functions such as data science. Some organizations may prioritize and value certain data points over research data points, which causes research to be viewed as less important.

Cultural Barriers

Lack of user-centered mindset at leadership levels

Some leaders may not have a UX background and believe user-centered products aren’t a must-have (to be fair, in some industries this is probably correct). Others may think they have gotten this far without proper research, so why start now?

A fear of uncomfortable truths

Sometimes research reveals issues and challenges that are easier to sweep under the rug. It can uncover that the team’s strategy and current thinking is misguided. Rather than fight that and convince leadership otherwise, it's easier to just not do the research and chalk a failed experiment or product launch up to valuable learnings. However, teams could have discovered these learnings much earlier if they had just done the research.

The Misuse of Research

Other times, research can be integrated into an organization correctly, but the way research is used can lend itself to not having the impact it should.

Used for Validation, not understanding

Research confirms assumptions instead of challenging them

Validating assumptions is fine in some instances. But research should also push the boundaries, develop new hypotheses, and forge innovative product direction. Otherwise, research is just going to be there to justify ideas rather than uncover new ones.

Testing solutions instead of exploring user needs

Again, testing solutions in UXR isn’t a bad thing. But if it’s not combined with exploring new needs and problems, research will be viewed as unnecessary when budgets get tight.

Performed too late in the product cycle

Research should often come first in the product life cycle, not last. If research is done throughout, you will be in a good spot. If it’s done once and doesn’t iterate, it will become stale.

Disconnected from the business

Viewed as a checkbox rather than a strategic tool

Too many teams view research as nice to have or as something they do to validate the direction of the team. Instead, it should be used to help shape strategy through the voice of the user. When it is used strategically, it can deliver new products, transform old ones, and lift the tide of an entire business.

Disconnected from decision-making

Sometimes researchers aren’t in the room when decisions are being made. Other times, research is disregarded or forgotten when during critical conversations. When user insights are front and center in decision-making, decisions will be made correctly the first time, and business/user tradeoffs will be known upfront so no surprises occur down the road.

Building a Research Foundation

The following are ways research can be utilized to its full potential in any organization.

Make research a strategic function

Involve researchers in product planning and prioritization

Research output and researchers themselves should be heavily involved in product planning. Planning should start with the user problems and opportunities that researchers have discovered and should drive the themes of the roadmap to meet business goals. Researchers shouldn’t feel like their work is constrained due to hierarchy. There should be a direct line from research to leadership so they have all the data points needed to make informed decisions. Additionally, planning should be built around research findings. The language of user problems should be built into documents, reviews, and products.

Embed researchers into product teams

At every company, researchers should be the most knowledgeable people about the product and how users use it. This can only be done by embedding researchers into product teams. They should be in sync with product managers, designers, engineers, data scientists, product marketers, etc. By embedding them, researchers can become strategic partners, not consultants. Research will naturally be brought in throughout the product life cycle when embedded in teams and show value.

Subscribe now

Communicate insights in business terms

Translate findings into the language of business

“If we do X, it will lead to Y users being happier” vs “If we do A, it will reduce churn by B% and land to C% more revenue.” It’s easier to get buy-in when research findings move the business forward.

As much as researchers are in it to make users' lives better, most products and businesses aren’t charities. Everything researchers do needs to benefit the business in the long run. Learning how to appeal to the needs of the business is essential for research to have an impact.

Business impact can come by reducing churn, increasing revenue, reducing spend to find PMF, increasing user engagement, etc. Every researcher needs to understand what metric their team/company cares about and tailor their research to move that metric in the right direction.

Use compelling narratives, not just data dumps

No one takes the time to understand and read through all the data. Researchers need to communicate like a salesperson. Understand what the business wants/needs. Speak to those wants/needs. Communicate succinctly as to what the team needs to do to address those wants/needs. That’s it. Don’t show your work upfront. This isn’t an academic paper. Throw all that in the appendix of your deck. After all, people make decisions based on narratives sprinkled with data.

Range of research

Have a balance of the type of research conducted

This article does a good job of explaining the need for multiple types of research. The article talks about 1) Strategic research, which is foundational, business-first, and future thinking, 2) Product research focused on user needs and product development, and 3) Tactical research focused on usability and optimization.

The author argues that researchers spend too much time doing #2 and should spend more time on #1 and #3. Too much research focused on preferences and concept testing limits research to be a function that gives thumbs up or thumbs down to ideas rather than one that drives business strategy and moves metrics. A careful balance of all three types of research helps maximize the potential of research to be a core part of an organization.

That’s A Wrap

Utilizing some of these tips and removing barriers takes time. It requires showing the value of research through proper execution. However once a team catches the research wave, organizations can be transformed to have a user-first mentality, which will ultimately drive sustained business value. As I mentioned earlier, each of these points can likely be its own post. Let me know if you’d like a deep dive into any of them!

Growth solves all problems. One key element of growth is knowing how to write great copy for your landing pages, ads, etc. Admittedly, I’m no copywriter. However, I do know how to understand users and that’s 90% of the battle when it comes to writing copy that converts.

You need to deeply know your users’ needs, wants, problems, and challenges. I have previously written on how to understand user pain points. If you haven’t read it, I’d recommend going and skimming it before continuing here.

Once you understand your users, you can start writing great copy. Appeal to their desires, headaches, and the way they speak. One tip is to have one user segment in mind. It’s easier to write for one person than everyone.

For the sake of simplicity, let’s imagine we are writing copy for a landing page. And more specifically, copy above the fold, meaning what a user sees on their screen before they start scrolling. It’s the most important real estate on a landing page. It is what creates the first impression of your brand. It will influence bounce rates and conversions the most.

The main purpose of great copy on a landing page is to capture attention, communicate your value proposition clearly, and guide the user toward a single, focused action.

Elements of High-Converting Copy

A landing page has three key elements: The header, subheader, and call to action (CTA).

The headline is the hook or the attention-grabber. It should focus on the outcomes or benefits your product or service provides, not the features that make your product or service great. This is why understanding the motivations and the pain points of your users is important. Use your understanding to get at their emotions.

The subheader supports or explains the header. It provides context and details on what you are offering, who it is helping, and/or how it helps. Depending on what you need to convey, it can be a one-liner or a couple of quick bullets. Most importantly, it needs to be concise enough that people will take the time to read it. If you can’t distill what you do down to a few key bullets, then you need to refine how you explain what you do

The CTA is what you want the user to do. The CTA should be action-oriented and provide enough insight into what happens next if they click. Make it difficult for visitors to say no. On ad platforms such as Facebook, you won’t be able to customize your CTA, but you can find something that works best for your product or service.

Subscribe now

Avoid These Mistakes

Avoid wording that feels high-committal. At this stage, users are weary about committing without learning more. If asking for user information on the landing page, avoid asking for too much. An email will do. Don’t ask for their full name, date of birth, and password information. Too many fields to fill out introduces unnecessary friction.

Ensure you don’t have multiple CTAs. Having one button doesn’t mean you have only one CTA. Your copy itself may be a CTA and competing with what you actually want a visitor to do. Keep the messaging simple and aligned to do one thing. If you try to boil the ocean with your copy, your visitors are going to boil their brains trying to understand your offering. Visitors should be able to succinctly share in their own words what you do and how it benefits them after seeing your landing page or ad.

Avoid generic copy. “The future of tech is here” doesn’t tell me how you solve my immediate problem. It’s classic for B2B SaaS companies to be vague in their copy. If you are making people scroll or click around to figure out what you offer, you’ve already frustrated your landing page visitor.

Examples

DoorDash

There are probably a few different reasons why one would want to be a Dasher for DoorDash. One may be to make some extra money on the side. Another may be to have more flexibility in their schedule. This landing page emphasizes the value of flexibility. Being your own boss and working your own hours is very attractive to a segment of DoorDash's Dashers, which is exactly why they speak to it on this landing page. Notice that they don’t try to talk to everyone, but instead focus on one type of user.

Branch Furniture

Picture an office manager trying to get desks and chairs for a rapidly growing team. No one wants to order from Amazon or Ikea and spend hours reading directions and putting furniture together. Branch’s copy directly addresses this huge pain point and speaks to how they solve it through professional installation. It makes a pain point in someone’s job pain-free. Additionally, the CTA explains the next steps. By clicking, you are on your way to building out the perfect office.

Twinwood Adventure

This landing page focuses on the emotional benefits of indoor skydiving. Twinwood Adventures knows they are in a competitive market. They aren’t just competing against other indoor skydiving facilities, but against anything that is adrenaline-inducing—think mountain biking, boating, cliff jumping, etc.

They know potential customers likely have multiple tabs open doing research to look for something fun to do on the weekend. By including key aspects of deciding where to spend money (e.g. price, discounts), it helps potential customers make a quick decision and book a session.

My only knock on this landing page is that it has a phone number to call and a button for a discount, which could cause confusion on how to move forward. The only reason why this isn’t too bad though is because the phone number and the discount button lead to the same place, which is booking an indoor skydiving session.

Optimizing Your Copy

Now, getting your landing page or ad right from the start is almost impossible. Create multiple landing pages and test different copy. A/B test single lines to understand what resonates more and converts. Use AI to help you make your copy more concise and capture the tone you are going for. Gather qualitative feedback to understand how people feel and comprehend your copy. The more feedback and data you can collect, the better your copy will get over time.

Remember, writing copy to convert isn’t about explaining everything. It’s not about appealing to everyone or sharing a comprehensive list of features. It's not about having a Master's degree in English. It’s about appealing to the needs of target users and getting inside their minds. The more you know what they are thinking, the better copy you will write. So spend the time upfront understanding your users and the copy you write will become exponentially better at converting.

Most agree that consistently getting user feedback to build and optimize products is essential. Whether you are at the beginning stages and have zero users or are optimizing every pixel and have billions of users, gathering data directly from the people who use your product should be ingrained into the building process.

However, I see a common mistake made by many builders. Sometimes it's a post on LinkedIn asking for feedback on a product or sometimes it's a seasoned UX researcher being too lackadaisical in their participant recruiting practice.

This post will be a quick guide to help practitioners get insights from the right people so you aren’t led astray by feedback you shouldn’t be listening to.

What Screeners Are & Why They Matter

Screeners are a set of questions used to find the right users to talk to. Think of screeners as a mini-survey to narrow down the people you talk to.

Imagine you are working for Etsy and want to explore offering small business owners loans directly instead of through your partners. As you explore this new product offering and talk with business owners in the process, you should seek out a specific type of business owner to talk to.

You wouldn’t ask someone allergic to dogs to test a new dog leash. Etsy also wouldn’t ask any business owner without narrowing it down to sellers who would benefit from and use a new loan product.

With that background, let’s examine three high-level principles of a good screener.

Principles of a Good Screener

• Targeted: Screeners are a set of questions that you have potential participants answer. Instead of broadly asking any business owner about the potential of a loan, make sure you are talking to people who would be eligible or who the product would actually benefit.

• Unbiased: The order and way the questions are asked shouldn't give away the type of person you are looking for. There are professional survey takers who know how to game the system just to get paid.

• Clear: Avoid jargon to ensure it is easy to understand.

Keeping these three principles in mind while writing questions will enable you to create a quality screener. Now let’s get tactical and actually write a screener.

Subscribe now

Writing a Screener

Let’s use the Etsy example above. Imagine we want to gauge small business owners' perceptions of a new product that provides loans directly from Etsy and is integrated into the seller dashboard.

First, we need to define who we are looking for. For this screener, we don’t need small business owners to be on Etsy already because we believe offering a loan product could bring new sellers to Etsy. We are also looking for small businesses that are already successful ($50,000+ in yearly sales) and have a desire to grow.

Let’s go ahead and start writing screener questions.

1. Which of the following would you consider yourself?

   1. An employee without key decision-making power [terminate]

   2. An employee with key decision-making power

   3. A passive business Owner [terminate]

   4. An active business owner

In this case, we are ok talking with high-level employees or active business owners. Everyone else will be eliminated from our study.

2. What was your approximate revenue over the last 12 months?

   1. $0-$10k [eliminate]

   2. $10k-$25k [eliminate]

   3. $25k-$50k [eliminate]

   4. $50k-$100k

   5. $100k-$500k

   6. $500k-$1m

   7. $1m or more

This question eliminates participants who wouldn’t be eligible for a loan. Note: I’m not an expert on Etsy’s sellers, but you may want to cap the revenue a business makes to ensure they are getting “small businesses” in their sample. All you need to do is determine what the cap is and eliminate anyone who answers above it. Similarly, you could also add a question about employee count.

3. Select all the ways you have tried to grow your business.

   1. Apply for a loan

   2. Grow a social media presence

   3. Partner with other sellers

   4. Partner with influencers

   5. Spend money on ads

   6. Grow my email list

   7. Other:______

   8. I am not trying to grow my business [eliminate]

What you are looking for in this question is that participants are actively looking to grow their business. You may want to prioritize participants who are trying several different ways to grow their business. One pro tip is to randomize the answer options and eliminate participants who select every answer.

4. Select all the ways to grow you would consider doing in the next 90 days.

   1. Apply for a loan [Must select]

   2. Grow a social media presence

   3. Partner with other sellers

   4. Partner with influencers

   5. Spend money on ads

   6. Grow my email list

   7. Other:______

   8. I am not trying to grow my business [eliminate]

This question ensures participants are open to applying for a loan. If someone wouldn’t consider going into debt to grow, then they probably aren’t the right person to speak with.

5. Which of the following platforms do you use to sell your products? Select all that apply.

   1. Etsy [50/50 split]

   2. Amazon

   3. Shopify

   4. Facebook Marketplace

   5. eBay

   6. TikTok Shops

   7. Other:______

   8. None of the above

The point is to get a good split of Etsy users vs users of other platforms. Asking the question in this way instead of just asking if they use Etsy (allowing users to self-select) also makes it easier to weed out professional survey takers.

With these five questions (plus questions about contact information if needed), you will have a list of “qualified” candidates to interview about the new Etsy loan product whose opinions and perceptions actually matter when building. Everyone else’s opinions shouldn’t be taken as seriously when building a targeted user-first product.

Where to Distribute Your Screener

Lastly, the question becomes where to distribute a screener to get people to fill it out. Where you distribute a screener depends on the needs of the study. For example, do you want to recruit current users or potential users? Churned users or users who just completed an action or behavior in your app? All these things will determine how you recruit.

With that said, the most common ways to distribute your screener are through the following:

• Current user/customer list: You can shoot them an email or create a pop-up of the screener in your product

• A third-party panel: There are self-serve tools such as UserTesting and Dscout or more white-glove services such as Ipsos or Kantar. You can select participants who are ready to talk to you from a pool of thousands.

• Gorilla recruitment: Join a Facebook group, a subreddit, or go to physical places your customers or potential customers hang out. You can post and distribute your screener there.

There you have it. Next time you do product testing, make sure you are recruiting the right people to get feedback that actually matters.

I want to share some of the differences between working in a startup vs Big Tech since I think understanding the differences can be useful to someone deciding between the two.

I have to caveat this by saying this is only informed by my own experience. Early in my career, I worked at a couple different startups and then decided to move to a FAANG and spend some time in Big Tech. I am also not a SWE, though I feel much of the differences I will highlight are transferable across functions.

I know many use the terms loosely, but when I refer to Big Tech, I’m referring to some of the most successful public tech companies out there—Google, Meta, Apple, Amazon, Netflix, Airbnb, Uber, etc.

When referring to startups, let’s just say the cutoff is around a Series B-ish—so before a company really scales in the market and operationally.

I’ll start with some of the most common questions and work my way down into some of the nuanced details of the job that differentiate the two. Without further ado, let’s dive in.

Compensation

Startups

The majority of startups can’t compete with Big Tech compensation. Most startups fail (or fail to meet expectations). Many join startups with the promise and dreams of their equity turning into millions of dollars, allowing them to retire to a beach in Fiji. But for 99% of startups, this just isn’t the reality, especially if you aren’t one of the first 10 hires or a high-ranking executive at the startup. Compensation should not be the main reason you join a startup (more on this below).

Big Tech

Compensation in Big Tech is just that—big. Regardless of function, a mid to senior-level employee with 2-8 years experience can expect to be making anywhere between $200k - $500k in total compensation. And if you are more experienced, the compensation can almost be limitless. Most at the Director-level across functions are making $1 million or more a year.

A portion of what you make will usually be in RSUs, but for public companies, RSUs are essentially cash because you can sell once it vests. Your bonus will be more than a lot of the population makes in an entire year. If you are simply optimizing your career based on earnings, Big Tech is the better option 99% of the time.

Compensation Scenario

Let’s look at a hypothetical scenario. Let’s say you make $300k/year in Big Tech. Over 10 years you will earn $3m assuming no raises or stock appreciation.

Now let’s compare that to a hypothetical startup. Let’s say you earn $180k/year and have 1% equity (which is rare, but possible if you are one of the first 10 hires). Assuming no raises, your salary will be $1.8m over 10 years.

Let’s assume the startup raises another round or two of fundraising after you join, diluting your equity shares to 0.5%. At year 10, the company sells for $100m. Your final equity payout will be $500,000. In total, your compensation will be $2.3m over 10 years.

In this scenario, if you are optimizing for compensation, it would have been $700,000 better to take the job in Big Tech instead of the startup. This also doesn’t mention all the extra perks, better 401(k) matching, optimized health benefits, etc. that Big Tech has over startups. Plus, the chances of a $100m exit are pretty far and few between, so you likely won’t ever see the additional $500,000.

Now, if your startup sold for $1B instead, your equity payout would be $5m and your total compensation would be $6.8m, beating out the compensation of your Big Tech offer. However, this scenario is extremely rare. Most startups never reach this milestone, so joining one solely for the possibility of a large payout is risky.

Work/Life Balance

Startups

I can only speak to my own experiences, but the amount of hours I put in was never as bad as what people say. Now, I was never a high-level executive when working at a startup, so I didn’t experience those pressures. But I was one of the first 30 hires at a hardware startup that was pre-revenue and still trying to find product-market fit. I had the occasional late nights. I remember testing hardware close to midnight the night before we were supposed to ship our product to alpha testers. Overall though, I felt like I still had a life outside of work.

Big Tech

Social media and LinkedIn will often talk about how cushy Big Tech jobs are. And while there are people who coast, it isn’t really the experience I had. Coasting is s thing, but you won’t get recognition and promotions. And in today’s layoff environment, you will likely be found out sooner or later.

Compared to working at startups, I probably had a more sustained busy schedule than I did at a startup. I definitely learned more about how to be able to manage attending meetings and actually getting the work done. And maybe that’s why working at a startup didn’t feel as busy—I didn’t have the block of meetings on my calendar like I did in Big Tech.

Subscribe now

Job Security

Startups

They should make an acronym in startup land—A.B.A. Always Be Applying. Startups are operated to get to the next funding round. And if the team isn’t hitting metrics, the chances of raising another round are slim. That means at any given time, a startup will have 18ish months of runway left.

My hot take is that employees who aren’t at the executive level take on more risk when it comes to job security than the founding team. Sure, maybe a founding team gave up more in opportunity cost than some lower-level employees, but they can also find a new gig quicker than someone who joins a fledgling startup right out of school. When joining a startup, ask about runway and the ability to raise the next round.

Big Tech

Big Tech has generally been known to be a bit more cushy when it comes to job security. However, that is changing quickly. You might not be as consistently scared for your job as at a startup, but there seem to be layoffs every year now and a greater emphasis on weeding out low performers. The days of joining a Big Tech company and coasting until retirement are over. You now have to prove yourself each performance cycle. There is no relying on the great work you’ve done in the past.

Learning

Startups

In a startup, it's all about breadth. You will wear many hats to get things done. You may be asked to come up with a marketing strategy having never practiced marketing in your life. Get used to using chatGPT and Google to learn on the fly. You will learn the basics of many different functions. But because you are spread thin, you won’t be able to go super deep into a particular skill set. You will have more value in a startup as a generalist than a specialist.

Big Tech

If startups are all about breadth, Big Tech is all about depth. If a startup has a team of eight building the Uber app, Big Tech has eight teams of eight building the rider experience and another eight teams building the driver experience. Teams are more specialized. You will go deep into a problem space and skillset. While you do chime in and contribute to other problem spaces and cross over with other functions, there is usually always someone there to do the job when you are ready to hand it off.

Career Growth

Startups

Career growth is often quicker in a startup. If you are exceptional, you will be handed more and more responsibilities and your titles will grow quickly. With that said, there is often title inflation in startups, so don’t be surprised if your title isn’t as fancy if you make a move to Big Tech from a startup. The downside in startups is that for some functions, there are limits to how senior you can grow. Lots of functions end up reporting to the product or engineering orgs, so unless you are one of those two functions, you may never make it to the VP or C-Suite level.

Big Tech

There is plenty of room for growth in Big Tech. Most functions have people at the VP level. The challenge is that it will often take decades to get to that level if you are starting out as an entry-level employee.

A positive of Big Tech is that there is usually an IC (individual contributor) career path and a manager career path. You can have just as much influence and pay as either without feeling like you have to do something you don’t want to do (e.g. manage people).

Titles in Big Tech often don’t reflect the reality of your seniority. Many Big Tech companies hide the level you actually are, so you have the same job title (e.g. software engineer) until you get to a director level (for ICs). This could make it more difficult to signal on places like LinkedIn all the experience and exceptional work you do. With that said, the brand name of a Big Tech company often helps when you are looking for your next gig.

Subscribe now

Impact

Startups

Startups are fighting for their lives. So every decision and project you deliver can feel highly impactful and meaningful to the success of the company. Startups will help you feel important. When people talk about having lots of impact in a startup, they are talking about the impact on the business itself. However, if you think you will have an impact on society and users at scale, startups aren’t your answer.

Big Tech

Big Tech is basically the exact opposite of startups in terms of impact. It can be easy to feel like a cog in the wheel at times. You are easily replaceable and it’s not the end of the world if you ship on Tuesday or Wednesday. However, the work you do can impact millions or billions of people. Oh, you only fine-tuned the relevance algorithm of Google Search? You just make the search experience for 90% of the world’s internet users better.

Work Environment

Startups

Startups are more unorganized. Work doesn’t fit into a box. Much fewer processes exist. Teams are small, which means you have to cover areas that don’t have a dedicated hire. It also means you get really close to your colleagues to build lots of trust and throw the politics out the door. Because there are fewer processes, you just have to figure it out and make things happen.

There are fewer recurring meetings. They instead happen on a more ad hoc and unscheduled basis. You will just turn to the person sitting next to you and ask a question. The next thing you know you are whiteboarding solutions. Because teams are small, prioritizing becomes key. You have to leave good things on the table to focus on the essentials.

Big Tech

Big Tech is organized. There is a hierarchy and structure like any corporation. The more senior you are, the more cross-team alignment you need to do. This means more meetings, more sign-offs from leaders, and more politics. You will work with more functions (privacy, legal, content writers, etc.) than just your normal product people (product managers, engineers, designers).

You will have to create roadmaps on top of roadmaps and will have long planning cycles. If you need more structure to succeed, Big Tech will treat you better than a startup. If you want more flexibility and less hierarchy, Big Tech can be a challenge to get used to.

Talent Density

Startups

Startups can have lots of great talent, especially ones that are well-funded and can compete on compensation. But others are a bit and miss when it comes to talent density. It usually will come back to the founder. Can they hire well? Do they have the experience, vision, or right people around them to lead a company?

Ultimately, it comes down to success. Talented people want to work for talented founders and companies that will succeed in a competitive market.

Big Tech

The talent density in Big Tech is unmatched. These companies have a high bar and hundreds or thousands of applicants per job opening. Most employees are well accomplished before they get to a Big Tech company, even if they are an entry-level hire right out of school. During my time in Big Tech, I can count on one hand the number of people I’ve worked with who I felt weren’t up to par.

Choose Your Winner

There you have it. It’s difficult to say which of these two options is better. It’s more a matter of preference. Both have pros and cons, but ultimately you have to decide what is more appealing to you and what excites you. Did I miss anything? Disagree? Let me know your thoughts.

The Evolving Superpowers of the Tech Sector

In Silicon Valley’s early days, the ability to build something revolutionary was the ultimate superpower. The engineers behind the personal computer revolution, the pioneers of the early internet, and the creators of mobile-first products all had one thing in common: they had technical expertise at scale.

But times are changing. AI and no-code tools are leveling the playing field, making it easier than ever to bring ideas to life. What once required teams of specialists and months of development can now be prototyped by a single individual in a matter of days or hours. As a result, the real advantage today isn’t in what you build—it’s in how you bring it to the world.

Distribution is now the decisive factor between success and failure. The companies and individuals who understand how to connect with the right audiences, craft compelling narratives, and build lasting relationships will stand apart. That’s why the thesis of this post is how technology companies' edge has shifted from pure technical expertise to the ability to distribute. The question isn’t just who builds the best product—it’s who gets people to care.

This post will give an overview of how superpowers have changed from one tech era to the next and what companies can do in today’s AI era to get ahead.

Winning Skills by Tech Generations

Every major wave of technology has been defined by a dominant skill set that separates the winners from the rest. Let’s examine how this played out across different eras.

The PC Era (1980s-1990s)

• Focus: Hardware engineering and operating systems.

• Superpower: Technical expertise to assemble hardware and build operating systems (e.g., Apple, Microsoft).

• Impact: Only those with deep technical knowledge and resources could participate.

This era marked the birth of personal computing. Companies competed on their ability to create reliable, high-performing hardware and accompanying software. Success required an intimate knowledge of electronics, manufacturing, and system architecture—skills that were scarce at the time.

Engineers capable of solving complex design problems delivered innovations that were simply unattainable to competitors. Their breakthroughs transformed computers from industrial machines into tools for businesses and eventually personal use.

The Dot-Com Era (Late 1990s-early 2000s)

• Focus: Building web platforms and connecting a global audience.

• Superpower: Backend engineering and scalable infrastructure (e.g., Amazon, eBay).

• Impact: Early movers had enormous advantages due to the novelty effects as long as scalable infrastructure was in their DNA.

The rise of the internet unlocked an entirely new arena for tech. The skills that dominated this era were the ability to design scalable platforms that leveraged the web to connect users, provide information, and facilitate transactions.

Engineers and entrepreneurs skilled in backend architecture flourished, creating the foundational infrastructure for e-commerce, search engines, and social networks. These skill sets allowed companies to build platforms capable of handling millions of users.

Subscribe now

The novelty of the internet meant there was untapped potential for innovation. Users were flocking online, and the companies that moved quickly captured these new markets first.

The Mobile Era (2010s)

• Focus: Mobile-first products and app ecosystems (e.g. Uber and Airbnb).

• Superpower: UX/UI design and cross-platform development.

• Impact: Enabled smaller teams to compete globally and the growth of the importance of design.

The smartphone revolution demanded a new approach to product design and development. Success hinged on creating intuitive, beautifully designed apps that leveraged the strengths of mobile—portability, GPS, cameras, and touchscreens.

Companies that thrived in this era prioritized user experience. They understood that mobile products required simplicity, speed, and intuitive navigation. Furthermore, integrating and seamlessly navigating the ecosystem requirements of Apple’s App Store and the Google Play store created competitive advantages.

The AI Era (2020s)

• Focus: Widespread access to AI-powered tools, no-code platforms, and hyper-automation (winners currently unknown).

• Superpower: Distribution and community building.

• Impact: More non-technical talent can start to build companies with an advantage for those with existing audiences. Additionally, prices will be driven down, giving more options to consumers.

The current era is unique. Tools like generative AI, no-code platforms, and APIs have lowered the barriers to creating products, making building accessible to almost anyone. Unlike any era before it, competitive advantages lie in distribution as opposed to engineering and product building.

In a saturated market, those who can stand out by building trust, fostering engaged communities, and telling compelling stories will win. Distribution is no longer just a “nice-to-have.” It’s the skill that will separate great companies from great products.

Social platforms have turned into content machines, and those who can win over eyeballs and engage users will have competitive advantages when building businesses off their distribution.

How Companies Can Build Distribution Machines

Shifting to the new superpower of distribution requires companies to embrace new skills. Here are five ways companies can adapt to build a distribution superpower.

1. Master the Art of Storytelling

Products alone don’t sell—stories do. Companies must move beyond simply showcasing features and instead frame their offerings around the idea of, “Here’s how we improve your life.”

• How to Do It:

  • Craft brand messaging that makes people feel something, not just understand something.

  • Use storytelling across marketing channels—websites, emails, and social media.

  • Highlight customer stories to make the brand more relatable and credible.

2. Build and Engage Communities

Communities create trust, loyalty, and organic growth—core components of successful distribution. As companies shift from viewing customers as transactional to seeing them as participants in their ecosystem, communities can start to be built. However, this is easier said than done. Creating a group or forum isn’t going to bring engagement. There has to be value for users to consistently come back and engage.

• How to Do It:

  • Generate content that helps users feel they are gaining value and have a desire to contribute to the conversation.

  • Foster user-generated content that makes the community feel valued.

  • Foster authentic interactions—respond, interact, and engage consistently.

Subscribe now

3. Leverage Existing Platforms

Success in distribution comes from knowing where your audience already spends their time and meeting them there. Instead of trying to pull audiences to you, meet them where they already are and build a consistent presence. For example, Duolingo has successfully grown its brand by creating viral, humorous content tailored to TikTok’s unique culture.

• How to Do It:

  • Embrace platform-specific strategies (e.g., TikTok for younger audiences, LinkedIn for professional content).

  • Partner with influencers or existing communities.

  • Stay ahead of platform trends.

4. Prioritize Personalization at Scale

People are more likely to engage when content feels tailored for them specifically. Evolve from one-size-fits-all marketing to personalized, data-driven communication. Spotify’s annual “Wrapped” campaign makes users feel special by turning their listening data into shareable, personalized stories.

• How to Do It:

  • Analyze user preferences and behaviors and segment your users.

  • Deliver hyper-personalized content, ads, and recommendations.

  • Send segmented communications that are relevant at the right time (e.g., onboarding emails tailored to specific user behaviors).

5. Combine Product Excellence with Audience Centricity

Distribution strength amplifies great products but can’t fix bad ones. Companies still need to start with a foundation of product excellence. However, distribution should be viewed as an extension of product development. A great example of this is Figma, which integrated community-building into launches, earning massive word-of-mouth distribution.

• How to Do It:

  • Involve users early in the development process.

  • Treat product and distribution as intertwined functions.

  • Create built-in network effects that encourage organic spread.

Conclusion

The AI era has fundamentally changed the playing field. The dominant skills of each tech generation have evolved in response to market needs.

In a world where anyone can build, distribution is the last remaining edge. Companies that master storytelling and community-building will thrive. Those who assume good products alone will sell themselves will be left behind.

Thanks for reading! If you are interested in deep-diving into any one of the topics touched upon in this post, leave a comment or reply to this email. I will take requests!

Clubhouse’s Quick Ascent

In the spring of 2020, as the world grappled with pandemic lockdowns, a new social media platform called Clubhouse emerged. It promised an innovative approach to digital connection through audio-only conversations. Clubhouse's launch timing couldn't have been better. Founded by Paul Davison and Rohan Seth in March 2020, the app debuted when people were desperately seeking new ways to connect.

The platform's unique selling proposition was simple yet compelling: live, audio-only “rooms” where users could host or join conversations on any topic, replicating the spontaneity of real-world interactions. Users could join rooms for live discussions on various topics and either listen in or add their own thoughts to the conversation by asking to hop on the “stage.” Early adopters included tech luminaries and celebrities like Elon Musk, Mark Zuckerberg, and Oprah Winfrey, whose presence on the platform generated significant buzz.

By the spring of 2021, Clubhouse had secured around $100 million from investors such as Andreessen Horowitz and Tiger Global, bringing the total valuation to around $4 billion. In less than a year, Clubhouse was considered one of the hottest startups in the world and had over 17 million downloads.

Clubhouse’s success came down to a few key factors:

1. The pandemic created favorable market conditions. Almost all social apps and internet companies saw a boom in business during the pandemic. As people were locked inside their homes, they sought social interaction in new ways.

2. Invite-only, celebrity hype. Clubhouse began as an invitation-only app popular among Silicon Valley tech workers. Conversations held on Clubhouse would leak onto other platforms such as X (Twitter), causing outsiders to experience FOMO. It was about who you knew and securing an invite was viewed as aspirational. Joining the app was so desirable that invites were being sold for as much as $125 on eBay.

3. An innovative approach to social interaction. Live audio-only communication with the ability to have thousands of people tune in had never been done. It was like a cross between a podcast and a phone call and added a more human approach to communication than messaging. The format also lent itself to easily and casually talking with people you looked up to. Clubhouse was the only place you could go to hear Elon Musk answer questions from a random guy on the internet.

Growing Pains

During the peak of Clubhouse, Shaan Puri, a popular voice in the tech community, Tweeted why Clubhouse would fail. His analysis resonated with many and made others upset. Clubhouse investors blocked him. However, in the end, his analysis was bang-on.

By mid-2021, Clubhouse's growth had significantly slowed. Monthly downloads dropped from 9.6 million in February 2021 to just 900,000 in April 2021. Clubhouse was having issues retaining users and problems started to become apparent.

Subscribe now

Content Quality

As the platform opened up to more users, the quality of conversations became diluted. Many rooms devolved into promotional pitches or low-value discussions, diminishing the platform's initial appeal. The Clubhouse experience was more like a museum than it was an internet company. The removal of being an invite-only app and the increase in the number of users on the platform made for a worse experience, which is the opposite of how network effects work.

Content Discovery

Clubhouse struggled to effectively match users with relevant content. As the platform grew, it became cluttered with rooms that weren't interesting. The lack of sophisticated content recommendation algorithms meant users often had to wade through irrelevant conversations to find engaging content.

Unlike other social platforms where users could quickly scroll through content and find something to keep their attention, Clubhouse required a significant time investment. Users needed to actively listen and participate, oftentimes joining mid-conversation and having no context as to what was being discussed. It was like attending a networking event and joining a table mid-conversation. It was just an awkward experience.

Technical Limitations

When users did find a room worth listening to (usually a scheduled show or panel), there were often technical issues. The app's infrastructure struggled to scale effectively, leading to frequent crashes and audio quality issues. These technical problems frustrated both hosts and listeners. Imagine inviting Mark Zuckerberg to join your room only for the app to crash multiple times as he was trying to talk.

Notification Fatigue

One of the main ways to learn about active rooms and interesting conversations was through notifications. However, you also found out about live rooms and people you didn’t know or care about. The app's aggressive notifications, while initially effective at driving engagement, became overwhelming for many users. Constant alerts about new rooms and active speakers led many to either mute notifications or uninstall the app entirely.

The Competition

There were many apps that launched competitive features to Clubhouse. X introduced Spaces and Facebook developed Live Audio Rooms. Facebook later killed off its Audio Rooms, but X found some success because of existing social graphs.

However, the biggest competition to Clubhouse was users’ time constraints. As mentioned earlier, tuning into a conversation on Clubhouse took a lot of time. Most people didn’t have an hour or two to sit and listen to a room in the middle of the day, especially as people started to go outside and go back to the office as the pandemic restrictions were lifted. If listening to long-form content, users preferred something that was a little more polished and coming from someone with subject matter expertise. Most find that in podcasts or audiobooks.

When people do get on their phones and open social apps, they do so in short spurts and opt for quicker entertainment from TikTok and Meta’s Reels, which was becoming popular at the time.

The New Clubhouse

In April 2023, Clubhouse announced that it was laying off 50% of its staff to “reset the company.” In the note to the team, the Clubhouse founders said it had become difficult for people to find their friends on the app.

Share

Over the next several months, Clubhouse pivoted to become more focused on helping friends connect with friends to try and improve retention and engagement. They did this in several ways:

Shifting Focus to Social and Casual Interactions

• Clubhouse shifted its focus from large, celebrity-driven rooms to smaller, more intimate communities. Clubhouse now emphasizes more personal, smaller-scale, casual communication, rather than large-scale, live events (though live rooms still exist).

• The design aimed to recreate the intimacy of friends chatting, reducing the pressure to speak or host in front of large audiences.

On-Demand Formats

• Clubhouse introduced asynchronous voice-only group chats, allowing users to participate in conversations at their convenience instead of in real-time. It added direct messaging capabilities to facilitate connection-building.

• Clubhouse created "Houses,” which are persistent groups where people can send voice chats and plan rooms together.

Clubhouse today looks very different from what it did a few short years ago. The once viral live audio app is now just another (audio) messaging app competing with dozens of other messaging apps.

While Clubhouse revolutionized social media by popularizing audio-only interaction, its inability to address core user experience challenges ultimately led to its decline. Its story shows the importance of building sustainable engagement mechanisms and solving fundamental content discovery problems before scaling.

Key Learnings

Clubhouse's rise and fall offers several key lessons for social platforms:

1. Timing and exclusivity can create initial buzz, but sustainable growth requires delivering consistent value over and over again—which preferably increases when more users onboard, not the opposite.

2. Social apps are won or lost with content discovery. The first few seconds after opening a social app are the most important. If something entertaining or interesting doesn’t present itself from a creator or friend, users will quickly move on to the next app.

3. User experience must evolve with scale—what works for 10,000 users may fail for 10 million.

4. Notifications are a great growth hack, but if you are consistently dependent on them to get users to engage, you have already lost.

In the age of AI, everyone has their predictions of how industries and job functions will change. I’d like to give my two cents on how AI will change UX research, or more broadly, how it will change how product teams understand customer data and react to it.

Subscribe now

On one end of the spectrum, there is the doom and gloom scenario. For many professions, AI is a scary prospect. It threatens to upend livelihoods and force workers to learn new skills from scratch.

In this scenario, AI takes off and can replace the operations and execution of UX research. Companies will only need one research expert on staff to give a prompt to an AI. The AI will search internal documents and external sources to find the answer to the question.

AI will leverage facial data as users navigate through an app to predict future behavior without ever asking a user for their opinion. AI will identify and fill gaps in understanding based on its own learnings, conduct its own interviews and surveys, and fill in with synthetic data where feasible.

It will reduce the decision-making process when it comes to concept testing multiple UI designs. Instead of running research or A/B testing, it will analyze designs and select which one will perform better based on millions of data points it is already trained on.

All the researcher will need to do is ask the right questions. The AI will do the rest. While most of these use cases sound more sci-fi than reality, some may come to fruition eventually. Still, it is likely years down the road.

In reality, AI will likely displace a portion of the UX research workforce, but not all of it. It will shorten workflows and make researchers more efficient. Researchers will be able to have more impact on decision-making, provide more data points for product teams, and drive better outcomes for the business and users.

My bold prediction is that qualitative research will become more important in a world filled with AI. Product teams will be itching for more emotional intelligence, user insight-driven innovation, and ethical oversight in product development, which will be more difficult to replicate with existing datasets that LLMs and AI models are built on.

I’ll get into why these three things will still be important, but first, how will AI actually change the operations of data gathering and data analysis for UX researchers?

Replacing Mundane and Operationally Intensive Tasks

Data Gathering

AI is already able to help with data gathering in some ways. The most obvious way is to prompt an LLM to help with creating an interview guide and survey design. LLMs can give researchers a great starting point—something like 75% of the way there in most cases—to reduce the amount of time and brain power needed. But the prompt is important. Researchers need to have the key research questions in mind to get relevant content back that isn’t just 90% of the same questions redesigned for a specific product.

Part of the data-gathering process is also ensuring you are gathering data from the right user segments. AI can pull customer lists from company databases. Soon, there will be a way to prompt, “I want a list of users with A characteristics, B behaviors over the last month, and C demographics.” Instead of having to write SQL code, the AI will be able to easily pull the list based on plain English instructions.

When it comes to recruiting participants for a study, AI will be able to craft emails or in-app messaging and adjust compensation amounts based on real-time feedback and response rates. Once participants are found, the AI will be able to schedule participants for moderated qualitative studies based on who most closely aligns with the study requirements, without the intervention of a human.

Eventually, once humans are more familiar with naturally interacting with AI, AI avatars will be able to conduct interviews directly with participants. In this future, the most important piece of qualitative research will be the creation of the interview guide.

Data Analysis

AI will be a powerful tool for data analysis. Researchers will use AI to run power analyses and get recommendations on which statistical methods to use based on the desired insights. While it can do this now, it can get so much better.

Subscribe now

In analyzing qualitative data, AI will be able to find patterns and put probabilities on when someone is telling the whole truth versus a half-truth.

For quant datasets, AI will not only be able to respond to prompts to perform different statistical analyses and models, but it will also be able to identify trends and unique insights in the data without specific prompts to do so.

What AI Won’t Replace (At Least in the Short-Term)

Emotional Intelligence

AI doesn’t currently have emotional intelligence. Identifying different types of emotions, being empathetic, and building rapport are things AI can’t currently do. Skilled qualitative researchers still need highly refined soft skills and processing capabilities to translate users’ emotions into beneficial insights for product teams.

In a world of AI, researchers who are educated and trained in behavioral and social sciences will become more important than researchers trained in statistical modeling. Understanding and knowing how and when to use statistical methods will be important, but reasoning and math are far easier for AI to replicate than generating qualitative insights.

User Insight-Driven Innovation

AI bots and LLMs are built from existing data. We will still need human minds to take what we learn from research and existing data and find ways to innovate for future products. AI doesn’t know what it doesn’t know, but humans can use their imagination and ideas to bring new concepts to life that AI can’t.

Ethical Oversight

AI will mainly be leveraged to optimize for shareholder value and product success. It won't be incentivized to think of the user first and err on the side of caution when ethical or moral concerns arise. Humans can step in and use common sense and a user perspective to drive product changes and innovations while still implementing ethical oversight when an AI’s job may not do so.

Key Takeaways

If you are a UX Researcher or working on the voice of the user in your organization, don’t fret. I don’t think you will be losing your job over night because of AI. First, AI will make you more efficient. Then, it will make your skillset more valued. My advice would be to embrace these new technologies. Play around with them. Find ways to implement AI into your workflows. Become comfortable with it and find ways to gain an edge over the competition.

Then, up-level in the areas of research you feel you may be lacking. If you are a purely quantitative researcher, take on qualitative projects. Learn how to conduct an interview. Understand the nuances of analyzing qualitative data. If you are a qualitative researcher, learn the language of statistics. As AI becomes more prevalent, more researchers will likely be required to use mixed methods and be more efficient. It’s a great time to be a researcher and dive into customer data. Embrace the AI revolution.

As you open the dashboard to your onboarding flow, you see that conversions continue to trend downward as they have been for the last month. You review the numbers, see the potential impact on revenue, and see you won’t meet your goals for the year. A shot of adrenaline shoots through your body. A small sweat trickles down your brow…

Ok, too dramatic? Yeah, probably. But this is the reality for product teams every day. Teams will create war rooms over a few percentage points to see what they can do to revive a business.

As teams explore potential culprits for subpar numbers, the question is always—and rightly so—“How can we improve the business metric?” But what if we turned that question around and asked, “Where are we falling short in solving our users’ problems?”

When looking to improve business outcomes, we usually just focus on the needs of the business. However, business metrics aren't what make a business successful. It’s the users and customers that enable us to have metrics that are up and to the right.

Subscribe now

The X → Y → Z Framework

To achieve a desired business outcome, we must understand how it impacts a user. Enter what I like to call the X → Y → Z framework to align user and business needs.

If we solve X user problem, it will lead to Y user behavior, resulting in Z business outcome. This framework can be worked backwards as well. If we want to achieve Z business outcome, we need to change Y behavior, and we can do that by solving X user problem.

The Framework in Practice

Let me walk you through a very real scenario. I’ll use a similar funnel example from the beginning of this post. Let’s imagine you work for a credit card company and see that your applications aren’t where you want them to be. The Z business outcome you want is a 5% increase in completed applications.

You look at the application funnel and see that out of every 100 people who enter the application process, 50 drop off on the information page. Another 20 drop off when asked for their employment and income information. Another 25 drop off when asked for their social security number. And another 3 drop off before clicking the submit button.

Let’s imagine that a couple months back your user researcher conducted a study and found that trust (or lack thereof) is a key component in users deciding to apply for a credit card with one company vs another. With that information in mind, let’s walk through a few scenarios I’m sure play out across teams in similar situations.

Scenario 1: Prioritizing Funnel Length

The team decided that removing the informational first page is the quickest way to address the drop-off. The rationale?

• The page isn’t collecting user data—it’s just informational

• The first page is just too clunky. No one reads all the text anyway

• Shortening the flow will reduce perceived effort, thus increasing completions

Armed with this hypothesis, the team invested two weeks into reworking the funnel, removing the informational page, and running an A/B test. When the results come back, the numbers are disappointing: drop-off actually increased by 5%, leading to a net loss in completed applications.

Why Did This Fail?

By removing the informational page, the team unintentionally undermined a key user need: reassurance. Without understanding what to expect in the application process, users felt uneasy about proceeding. Questions about security, credit impact, and the type of information required went unanswered, eroding trust.

The assumption that shortening the funnel would increase completion rates overlooked the deeper role the informational page played in addressing user concerns.

Scenario 2: Prioritizing Trust

In this approach, the team revisited the original research to understand the nuanced role of trust in the user journey. They recognized that:

• Users enter the funnel with questions and doubts

• It is the job of the team to ensure users are comfortable every time they click the “Next” button

• The informational page was acting as a trust builder but could be better optimized

Instead of removing the page entirely, the team worked to improve its clarity and usefulness:

1. Simplified the Content: Rewrote the copy to be concise and user-friendly, ensuring key points (e.g. “Your credit won’t be affected,” “Your information is secure”) were prominently displayed.

2. Trust Signals: Included prominent badges for security (e.g., “SSL Secured”), endorsements from trusted organizations, and a quick FAQ section addressing common concerns.

After implementing these changes, the team ran an A/B test. This time, the drop-off on the information page decreased from 50% to 30%, and overall funnel completion rates increased by 10%.

What Made the Difference?

Instead of seeing the information page as a bottleneck, the team viewed it as an opportunity to solve a user problem: the need for trust and reassurance. By addressing this problem while maintaining a focus on efficiency, they achieved both user and business goals.

Subscribe now

Engagement-Driven Business Models

Let’s shift gears to an engagement-based business model, where the relationship between user needs and business outcomes isn’t always straightforward. Engagement-based business models are when companies generate more revenue the more a user engages with the app. Companies like Google, Meta, and Amazon generate significant revenue from ads, yet the user problems they solve often don’t align directly with the business need to show more ads.

For example, a product manager at a company such as TikTok might ask, “How can we increase ad impressions to boost revenue?” On the surface, this question centers entirely on the business problem. But let’s apply the X → Y → Z framework to explore the intersection of user and business needs:

1. X (User Problem): TikTok users come to the platform to be entertained and discover interesting content—not to see ads.

2. Y (User Behavior): If users encounter too many irrelevant or intrusive ads, their engagement drops—they scroll less, leave the app sooner, or even stop using the platform altogether.

3. Z (Business Outcome): Lower engagement leads to fewer ad impressions and reduced revenue, directly undermining the business goal.

The challenge for an engagement-driven business lies in balancing these elements. Users may not have an explicit problem that ads solve, but businesses can design ad experiences that feel relevant, unobtrusive, and beneficial to preserve engagement while driving revenue.

Here’s how a platform like YouTube might address this balance:

Scenario A: Business-Only Focus

Imagine YouTube increases the frequency of ads before and during videos to maximize revenue. While this may temporarily boost ad impressions (Z), it comes at a cost. Users grow frustrated with the interruptions and reduce their overall time on the platform (Y). Over time, this disengagement results in fewer opportunities to show ads, ultimately harming user entertainment (X) and revenue (Z).

Scenario B: User-Centric Approach

Alternatively, YouTube could leverage user behavior data to optimize the ad experience. For instance:

1. Show more personalized ads that align with user interests.

2. Offer non-intrusive formats like skippable ads or smaller banner ads.

3. Introduce value exchanges, such as allowing users to watch one long ad in exchange for uninterrupted viewing.

By respecting user intent (X) and enhancing the ad experience, YouTube retains engagement (Y), ensuring a steady stream of ad impressions and revenue (Z).

The Engagement-Revenue Sweet Spot

The trick is to find the sweet spot where engagement and revenue can cross to optimize both. This principle is true for any business where engagement is the driver of revenue.

Even in engagement-driven business models, user needs should anchor decisions. While ads may not directly solve users' intent of using an app, they can be delivered in ways that minimize disruption and, in some cases, enhance the user experience.

Broader Implications

Whether your business relies on subscriptions, transactions, or ads, the X → Y → Z framework remains a powerful way to align user and business needs. Focusing on user problems and tying them to business problems helps to improve business outcomes and removes the guesswork of experimentation.Neglecting the user in favor of short-term metrics might provide temporary gains, but it often leads to long-term losses in users and revenue.

Every newsletter publisher (writer) whose product is their writing has two main challenges on their mind every day: Growth and Monetization. Growth and monetization are the two biggest challenges that, if solved, make writing as a side hustle or profession an unlock for success.

Subscribe now

Substack has chosen to focus on helping publishers grow a newsletter. Substack's built-in features help publishers improve their reach, unlike alternative platforms that leave publishers to their own growth efforts. This is especially helpful if a publisher doesn’t have an existing following on social media or doesn’t already have a product or service they sell elsewhere. 

If Substack wanted to focus on monetization, it would have more robust solutions aside from paid subscriptions. This not only would help with a publisher's profitability, but also its own profitability. But I regress…Let’s stay focused on the problem they ARE solving: Growth. 

Substack’s Growth Unlock

Growing any type of product or service is difficult. Substack knows this, and over the last couple of years has decided to focus its efforts on making it easier for its publishers to grow. Substack’s focus on growth has been the biggest factor in my using and staying on the platform. 

Not only is growth great for its publishers, but it’s also good business. I came across this note from Taylor Lorenz the other day:

Her first comment about follower vs subscriber growth has been a hot topic among publishers. I’ll address this a little later. But her second comment about locking in readers (and publishers) is exactly what Substack wants. 

Substack provides value for readers and publishers, and in return, it’s building a moat. What Lorenz is complaining about is actually a feature of Substack. 

It’s the whole point—to build a product that is more like Gmail than Taco Bell. Substack wants to build a product where switching costs feel cumbersome and painful because of the value it provides. It’s good business. 

So, let’s explore how Substack is building growth mechanisms for publishers and locking in publishers and readers at the same time.

Content Feed

In March 2022, Substack launched its app. It was a transformational moment for a company going from a way to send emails to becoming a content platform. What ensued was a feed-like experience where users can read all the Substacks they were subscribed to in addition to discovering new Substacks via search.  

Readers can also block Substack newsletters from appearing in their inbox and instead can be notified in the app when a new newsletter drops. This solved the reader's problem of having a cluttered inbox filled with newsletters. It enabled readers to be more willing to subscribe to Substacks they may have otherwise passed over because they didn’t want their inboxes to be inundated. 

Subscribe now

With content now being presented in a feed format, readers have a destination to consume on, which drives time spent reading on Substack. Substack built an app that can compete in the attention economy. 

Notes

The next big product release to improve user growth was Notes. Notes is Substack’s X-like (Twitter) feed which allows users to micro-blog. The Notes feature caused Elon Musk to block and suppress Substack links on X. 

Notes allow publishers (and readers) to broadcast to their audience directly in the Substack app. Now, Substack also has tabs based on content verticals, which allows users to deep dive into topics and publishers they may have not interacted with in the past.

The combination of Notes with Substack’s algorithm (albeit not super robust yet) doubles down on the Substack app being a destination to discover new content, which enables growth opportunities for publishers as long as they are posting Notes. 

Again, this also increases time spent on the app as users scroll to find and consume new content related to their interests.

Followers

Follow is the last big feature I will touch on that locks in publishers to the Substack platform.

Circling back to Lorenz’s comment, publishers are frustrated with the follow button being prominent. However, the alternative to a follow is likely a scroll past the content. 

You see, most users will only subscribe to a newsletter that they are really interested in. The option to follow is a signal that a reader is interested in following along with a writer’s content, but wants more exposure before fully committing to subscribing and handing over their email address. 

Follow allows publishers to broaden their audience to readers who may choose to pass them by if a follow option isn’t available. The way breaks down the value of a follower sums up my comments nicely:

Takeaways

All these growth features—a content feed, Notes, and followers—make Substack look more like a social media platform. However, they lock in publishers more securely because it helps with growth while still allowing publishers to own their audience. 

Just as important as these three features is the Substack dashboard, which reports how many subscribers are from the Substack network (the above features are useless if publishers can’t measure their growth). With one easy glance, publishers can see what percentage of their subscribers come from the Substack network. If that number is above 15 percent-ish, it’s a great value proposition to stay on the platform. 

Compared to other newsletter solutions, Substack is arguably the easiest to grow. And we haven’t even talked about other legacy features, such as the ability to recommend and be recommended by other Substacks during the subscription flow. 

The next time you criticize a feature or strategy, think about why the feature is there in the first place. In this case, it not only drives time spent on the platform but also drives the discovery of new Substacks, which benefits growing publishers.

After graduating from university, a friend and I tried starting our own startup. I did lots of desk research. I understood the market, the economics, and the market inefficiencies that “needed” to be solved. In my mind, I just needed to talk to potential customers to validate the pain points and then offer my solution. 

After talking with potential customers I learned that I correctly understood the market inefficiencies. I updated my financial projections based on additional data and confirmed that the assumed pain points were, in fact, pain points. 

Subscribe now

I threw up a website, built a very unscalable MVP, and started to market my idea. I went into businesses and explained the solution to them. Everyone was very kind and genuinely interested, but no one paid up. 

After a while, I figured it out: Though I was solving a pain point and market inefficiency, it wasn’t big enough for customers to care to change their workflows to solve it. 

What I learned was that finding pain points wasn’t enough, and finding the right pain points is essential.

There are four key things to know when finding the right pain point:

1. The pain point type

   1. Emotional: Does it cause significant frustration, stress, or inconvenience?

   2. Financial: Does it result in significant financial costs?

   3. Time: Does it waste a lot of time?

   4. Social: Does it make customers look bad in front of their peers?

2. The frequency of the pain point

   1. Does it happen frequently enough that it’s worth solving for?

3. The severity of the pain point

   1. Is it big enough that customers have tried to solve it in the past?

4. Alternatives to solve the pain point

   1. Why do the current solutions not work?

There are several ways you can go about finding answers to these four key aspects of a customer pain point. For a mature product with lots of people to inquire, you could conduct a survey and run statistical analysis to find a stat sig answer. 

But in most cases, a much more economical and quicker way is to talk to 5-10 (potential) customers to get a directional understanding to pivot or double down.

Subscribe now

Once you have your target customers ready to talk to, there are only four key questions you need to ask them (+ some follow-up “why?” questions to get more details). Here they are:

1. [Pain point type] What is the hardest thing about {doing the thing you are solving for?}

   1. Nail down what the actual pain point is. If the pain point is different from what you thought, it could be a good opportunity to pivot your solution.

   2. Understand why it is a pain point so you can use it in your marketing and sales.

2. [Frequency of pain point] Tell me about the last time you encountered this problem?

   1. The more frequently a potential customer encounters the pain point, the better. The less frequent the pain point, the less they will need to rely on your solution. The answer to this question will be a good gauge to help with a go/no-go decision.

3. [Severity of pain point] What, if anything, have you done to try to solve the problem?

   1. Have they hacked together solutions or used competitor products? If so, that’s usually a good sign. Have they not tried to solve the pain point? Probably a sign that the pain point isn’t very severe and it may be time to look for a different pain point to solve.

4. [Alternatives] What other solutions have you tried? Why haven’t you stuck with them?

   1. Again, if they haven’t tried other solutions, this could be a sign that the pain point isn’t severe enough.

   2. If there are many reasons why they haven’t stuck with another solution, this is a good sign. It means that competitors haven’t nailed the solution and you now have answers on how to nail it. 

   3. If they can’t think of any reasons why they haven’t stuck with a competitor, it could mean one of two things. 1) they don’t have the money to pay for it, which isn’t a good sign, or 2) The competitor's solution is good enough, which may mean there isn’t much room in the market.

Lastly, after talking to a number of (potential) customers, you need to ask yourself if you are hearing similar pain points across your conversations. If the answer is yes, you are onto something. If the answer is no, it may mean that the market to solve the pain point isn’t big enough to grow a profitable business. Be careful though to not produce a false negative—this can happen if the people you are talking to are from completely different segments of (potential) customers.

There you have it. Asking these four simple questions can save you months or even years of work searching for a solution to a problem that isn’t worth solving. What I have come to realize is that I identified a market inefficiency, but it wasn’t considered a big user problem among those who experience the inefficiency. Hopefully, the same thing doesn’t happen to you. Happy building, everyone.

Thanks for reading. If you enjoy this piece, consider subscribing and sharing with your friends.

Subscribe now

Have you ever looked at your metrics dashboard and seen something concerning? Yeah, me too. Let’s imagine you’ve narrowed down the possible culprits and one hypothesis you have is that your product or feature is confusing or designed in a way that causes friction for users to accomplish a task. 

For example, if you observe people entering an onboarding flow, but not completing it and want to know why. Or, if you own an e-commerce site and see that your conversion rates are low during the checkout process despite having free shipping. 

An easy and quick way to identify such frictions is to run a simple usability test. You don’t need to be a UX Researcher to do it. This quick guide should give anyone the tools to start practicing and perfecting usability testing to quickly get your product back on track.

What is Usability Testing?

Usability testing is a way to quickly test a product to understand if the product functions as expected in the minds of its users. Have you ever seen the comical video of the girl watching as all the different shaped blocks are put into the square hole? Yeah, usability testing is essentially that.

It is a way to evaluate the usability or ease of use of a product. The researcher will usually give tasks to the user and then observe them performing the task, often asking the user to express their thoughts throughout. After a few usability tests, the researcher will be able to find patterns in the data and provide suggestions to the team to improve the usability of a product, thus improving metrics such as conversion.

When Should I Use a Usability Test?

When performing user research, there are many different methodologies to choose from. Knowing when to run a usability test is as important as knowing how to run one.

In most cases, it should not be used to test if you understand the users’ problem correctly. I don’t believe it even should be used to test if the solution to the users’ problem is correct. Instead, usability testing should be employed to understand if the solution is functional and easily usable. 

If you are about to run a usability test, but don’t truly understand the user problems you are trying to solve, stop! A different research method should be used, but that’s a post for another time.

Preparing for a Usability Test

Finding People to Test

It is best practice to find people closely aligned with your users or customers. For example, if you are building an app for car insurance brokers, you don’t want to ask an accountant to participate. In this case, only car insurance brokers can give you the most relevant feedback. 

To find the right people to test your product, you can create a quick 4–5 question screener or survey asking people about their behaviors, attitudes,  or demographics. If you are testing something that will benefit your current customers/users, you can recruit from your customers/users list and email them or ask them if they want to participate directly on your app or website. 

However, if you need to test something like a new onboarding flow, you may want to recruit people who don’t currently use your product. You can post the screener to Craigslist, Facebook groups, Subreddits, or other platforms you think your users will hang out on. 

In writing a screener, you want to write the questions in a way that targets the segment of users you need to better understand. For example, if you need to talk to power users, you should recruit for the behaviors they exhibit instead of a user who only uses your platform once in a blue moon. Once you collect responses, you can sort through the submissions and pick the people that best align with your users.

Providing Compensation

You will have the most success finding people to test if you provide compensation. I generally give a $50-$100 gift card for consumers and a bit more for B2B customers. If you don’t have a budget for that, you can get creative. I have used t-shirts in the past and have even gotten some people to do it for free. Just remember that the way you incentivize will attract different types of people.

A Few Tools Needed

You will need a device for the participant to test your prototype. You may want to also screen record, record the face of the participant, and record audio. Having these recordings not only helps when you are analyzing the data but also to sell members of your team on your recommendations when presenting the data. You can pretty easily hack some of these things together by using free tools. You don’t have to have a professional usability lab to do this.

Conducting a Usability Test

Discussion Guide

You will want a discussion guide before you start testing. This helps set the stage for a successful usability test. You can then have the guide as a cheat sheet as you run the research. Be consistent in asking the questions from one participant to the next. 

An Overall Research Question

This will guide you on what you should be trying to answer. Don't talk about this with the participant, but keep it at the top of your guide to remind you throughout the usability test. An example of an overall research question could be, “How can we improve our onboarding process to ensure users complete the signup flow?”

Subscribe now

Introductions to Put the Participant at Ease

Once the participant is in the (virtual) room with you, put them at ease. Engage in small talk for the first couple of minutes. Ask them how their day is going, what they do for work, etc. Even small talk will give you insights into how they think and behave. 

Next, explain that you are testing the product, not the participant. You don’t want them to feel embarrassed if they “don’t get it.” Tell them you are looking for honest feedback and that they can’t hurt your feelings. Lastly, explain to them that you will be having them navigate through a product and that you will be giving them tasks to complete. As they do so, tell them to think out loud so you know what is going through their mind. You will probably have to remind people to do this throughout.

Consent Form

This is the boring stuff, but it will protect you from the minuscule chance someone wants to sue you. A consent form will usually state that you have permission to use the data they provide, will keep their feedback anonymous, and that any ideas generated during the study will be the company’s to keep. You can Google templates for consent forms, but if you are concerned, you can have your company lawyer write one up.

Tasks

You will need tasks for the participants to complete so you can test the usability of each action they take. If you are testing the user flow of an e-commerce solution, give them a task by saying, “Ok, now I want you to purchase a pair of shoes and go all the way through checkout.” 

Then shut up and watch them do it! Watch for where they get hung up, make mistakes, click in the wrong place, get confused, etc. Create tasks for each part of the design you want to test. Just be careful not to be too specific in your task. You don’t want to give away the answer on how to complete it. As the user makes mistakes or ponders their next click, ask them what they are thinking or why they did a certain thing.

Summarize

After the participant finishes all the tasks, talk through their experience with them. You will usually uncover more insights here. Make sure to keep recording until they leave the room. Sometimes people are more honest when they feel the “test”  is over. As they leave, make sure to give them their promised incentive.

Analyze the data

You will need to do usability tests with multiple participants. Once you start to hear the same thing consistently, you can probably stop. This is called “saturation.” Most people see patterns after 5–10 participants. Once all the data is collected, you can pick out the patterns to present to the team.

If you want to add a bit of quantitative findings into the mix, you can report back the number of mistakes people make or how long it takes. It’s not uncommon for companies to have a running usability program so you can track your progress over time.

One thing to note is the 5-10 participant rule is per user segment. If you are testing multiple segments (e.g. SMBs vs Enterprises), you may need to double your participants. Similarly, after each significant design iteration, you may want to run additional usability tests to ensure the product is still intuitive.

Get Started

There you have it. You are now all set to run your first usability test. Pretty soon you will be uncovering user insights to solve some of those pesky issues that have been annoying your users for months. Before you execute your study, below are a few research do's and don'ts to ensure you get the most reliable data possible.

Do:

1. Listen and allow for silence. If the participant isn’t talking 80% of the time, you are talking too much. You are learning from them, not the other way around.

2. Observe. You can’t always trust what people tell you. Their actions can give you the answers that their words can’t or won’t.

3. Ask Good Questions. Questions such as, “Why did you do that?” or “What do you expect to happen when you click there?” or “Tell me more about what you are thinking right now.” are questions that will uncover good insights.

4. Simplify. Ask your questions in a way that a child can understand. It is human nature to act smarter than you are and your participants will do the same, often resulting in bad data. Avoid jargon at all costs!

5. Clarify. Clarify what the participant is saying or doing to ensure you understand correctly. They will often expand on their original comments or actions.

Don’t:

1. Ask leading questions. “Do you think this is a good design?” “Why do you like this feature?” I can tell you the response to those questions before the participant answers. You are the researcher and should be able to infer the answer to these questions based on how the usability test goes.

2. Ask them to predict future behavior. This is impossible to answer with 100% accuracy. Instead, ask about their past behavior, which helps you to predict future behavior.

3. Sell or persuade. You want pure, unbiased data. As soon as they feel that you have a preference or agenda, they will tell you what they think you want to hear.

4. Ask multiple questions at once. “What are your first thoughts when seeing this screen and have you seen a screen like this before?” These types of questions are confusing.

5. Don’t draw conclusions immediately. You don’t want to draw conclusions after one usability test. Try your best to not let one participant influence another test. View the data holistically to not bias your findings.

Thanks for tapping in and reading. If you enjoy this piece, consider subscribing and sharing with your friends.

Subscribe now

Today's article focuses on the most critical factor Audible needs to master to retain users—helping them discover new audiobooks to listen to.

Most people who purchase a subscription to Audible do so to continually listen and find new content. They aren’t one-off buyers of an audiobook. However, finding a new audiobook to listen to can be frustrating.

The Pain of Deciding on a New Audiobook to Buy

For all Audible users reading this, think about when you are about to finish a wonderful audiobook. You see that a new credit will hit your account in five days. You quietly ponder which new audiobook you want to listen to. 

You start scrolling through your wish list but aren’t sure what to select. You listen to a sample of an audiobook that looks interesting, but the narrator sounds a little monotone. You find another audiobook, but a well-written review mentions the slow buildup.

Four days pass and instead of deciding on an audiobook to buy, you cancel your monthly subscription until you figure out what you want to listen to next. 

As a regular Audible user myself, I have experienced this pain point. Canceling my subscription is something that I’ve regularly done when I can’t make up my mind on what to listen to next. It’s my biggest pain point and, I suspect, a major cause of churn. 

If this is unfamiliar to you, let me give a quick overview of how Audible’s business model works. Most users purchase a monthly subscription plan, which delivers one credit per month. With that credit, you can select an audiobook to listen to. Every month, you receive another credit. 

If you fall behind in your listening, you'll accumulate credits. However, if you cancel your subscription before using your credits, you lose them. Users are incentivized to use their credits before canceling, or in my case, cancel before receiving a new credit. 

Business Goal

Now that we’ve identified the user pain point, let’s focus on the business goal. Audible makes money by keeping people subscribed and earning credits. For a user who is already converted into a customer, the goal is to reduce churn. 

To measure success, I would track two key metrics:

• New audiobook Starts: This indicates users are finding new audiobooks to start

• Daily Listens: This reflects users getting value from the audiobook and are more likely to stick around

At first glance, New Audiobook Starts may seem counterintuitive. It indicates users are using their credits, which makes it easier to cancel a subscription if they can’t find something they want. But hold tight, I have a potential solution to fix the leak in the incentive structure—stay tuned!

On the other hand, Daily Listens are intuitive. It means that a user is enjoying their time on the platform, which is a strong indicator that they will stick around. However, daily listening only lasts as long as there are pages left in the audiobook. Loyalty is only shown to the audiobook; not to Audible. That’s why New Audiobook Starts are so important. It’s the metric that helps keep the cycle going. 

Audible’s current experience is built to improve new audiobook starts and promote daily listening by minimizing pain points when selecting a new audiobook (spoiler: I think they can be doing better though!). Let’s look at what Audible is doing to drive these metrics and solve users’ pain points. I’ll share some of my personal thoughts along the way.

Recommendation System

Audible’s recommendation system suggests audiobooks similar to what users have listened to, helping them discover other titles they might enjoy. This is likely an effective way to get readers to continue to use their credits on genres they are interested in. However, in my experience, the recommendations can be repetitive and fail to spark new discoveries, becoming too one-dimensional.

Subscribe now

Consider TikTok’s algorithm. It serves users content they dwell on and engage with, but it will also serve new content to see if users have other interests they want to consume. Audible would likely benefit from employing a similar strategy. 

Audible could use signals such as previous audiobooks the user has completed (especially if it was a title read daily). They could also use the data of similar listeners who are listening to the same audiobook or the same genre to recommend titles from their listening history.

Samples & Reviews

Surely, one of the best selling points for users before purchasing a new title is to listen to a couple minutes of an audiobook's sample. In audiobooks, the narrator is just as important as the content of the audiobook itself. If you read through the reviews of any given audiobook, you are almost always going to see some critique of the narrator. 

There is no easy solution to this problem, but with the development of Generative AI, there could be opportunities to create more engaging narrations. Perhaps it’s an extra offering that others can pay for (it would probably be cheaper than paying a human narrator), or maybe it’s something Audible can eat the costs for to improve engagement with their wide selection of audiobooks. 

Similarly, it would be easy to sort reviews by star ratings and bury the poor reviews unless a user seeks them out. This is a bit of a dark pattern though, so I wouldn't necessarily recommend it.

Wish List Building

One of the best ways Audible helps users find and remember interesting audiobooks is by allowing users to build a wish list. It is generally the first place I go when selecting a new audiobook. However, I imagine many users are like me and end up having a wish list that has 50+ audiobooks on it. By the time a user has over 10 audiobooks on their wish list, my guess is that choosing an audiobook becomes more difficult and takes longer, increasing the risk of a user deciding to cancel their subscription. 

If I were Audible, I’d make the wish list dynamic, reorganizing titles based on a user’s recent searches, engagement, and listening history. Similar to the recommendation system, I'd also prioritize titles that other similar users have enjoyed. These changes would help reduce decision paralysis and the endless loop of listening to samples and reading reviews.

All Out Audiobook Attack

After a user completes an audiobook, the only thing Audible should do is go on a full-out attack to get users to select their next audiobook. The long, drawn-out process of selecting a new audiobook needs to be simplified and personalized as I've outlined above.

Moreover, Audible should aim to reduce the perceived commitment when choosing a new audiobook. One experiment Audible could try is allowing users to reverse a credit within the first 30 minutes of listening to a new audiobook.

The last thing Audible wants is for a user to buy a new audiobook and quickly find out they are disinterested. This is the worst-case scenario and is likely a high driver of churn. 

Similarly, reversing a credit and building up a bank of credits isn’t a bad thing because users are less likely to cancel a subscription due to a loss of credits while doing so. An idea like this helps people find audiobooks they enjoy so Audible can turn users into Daily Listeners.

Learnings

• Unblock Bottlenecks: When users feel friction, their first instinct is to churn. Focus efforts on unblocking bottlenecks in the user journey to patch up a leaky bucket.

• First Impressions Matter: Build personalized recommendation systems for individual wants and needs. Audible’s ability to keep users engaged and reduce churn depends on it.

• Quality Over Equity: Recommend high-quality content over poor-quality content. Don’t be equitable in your recommendations. In the case of Audible, recommending audiobooks with great narrators will prevail. 

• Provide Flexibility: Allow users to test and try your product to get them hooked. Fighting against flexibility when it’s in the interest of the user and the long-term interest of the business is death by a thousand paper cuts. 

Did you like this breakdown of how Audible tailors their experience to drive core metrics forward? If so, you can support me by subscribing and sharing this with your friends.

Ever wonder how Robinhood changed a traditional industry dominated by incumbents? Learn how they appeal to user needs and how you can adopt their strategy for your work.

Subscribe now

Many have said that to find a successful product, look no further than the Seven Deadly Sins. If a product addresses one of those “needs,” the team is probably on the right track. 

Robinhood plays into the Deadly Sin of greed perfectly. I’m being a little facetious, but people invest their money to build wealth and it is the promise of future wealth that drives investing behavior today. 

The Nuances of Greed

If you had to categorize all the tools and platforms to invest your money, the Fidelity’s and Charles Schwab’s of the world attract the old school thinkers—people who slowly build a treasure chest over time.

But there is another segment of users that don’t believe they should have to wait decades to build wealth. If they are smart enough and can beat the market, they can get rich quickly. To them, it’s a game and this is exactly where Robinhood has been able to carve out their piece of the market. 

Robinhood is building a product for the segment of (the Deadly Sin of greed) users who experience FOMO when it comes to the next stock that is run up by retail investors. 

Yes, it probably isn’t the best financial decision to chase meme stocks, but there is a legitimate market for this type of user. They are often younger, have their first introduction to investing, and want a platform that is easy to understand and use. Robinhood has leaned into all of these things to get this segment of investors in the game, even if it does mean embracing the FOMO culture.

As you can see from the graph below, the number of Robinhood users peaked when NFTs, crypto, and the Gamestop stock peaked. Robinhood was viewed as the easiest way to buy and sell Gamestop and other meme stocks among the FOMO crowd. 

With the decline in users using Robinhood as their primary investment app since then, there is likely a lesson in retention to be had, but that’s for another time. 

Business Goal

Investopedia states, “Robinhood makes money in many ways, notably through a system known as payment for order flow. That is, Robinhood routes its users' orders through a market maker that actually makes the trades and compensates Robinhood for the business at a rate of a fraction of a cent per share.”

In essence, Robinhood makes money on every trade. An increase in trading volume is an increase in revenue. 

The more time they can get users to log in and be on the platform every day, the more they will increase their chances of users making a trade. Daily Active Users (DAU) = increased trading volume = $$$. So, how does Robinhood do this through a seamless user experience?

Share

Embracing its Value Proposition

Robinhood speaks to its users’ problems and needs the moment they land on the website. 

“Put in a buck or a billion. Earn extra every time,” is written in gold, appealing to the Deadly Sin of greed. 

“Join a new generation of investors” appeals to a new way of investing money. It’s young and hip. This isn’t your grandfather’s app. Robinhood knows its audience and speaks the language that appeals to them. 

Gamification

Robinhood’s app is like an online casino, so much so that they were fined by the state of Massachusetts for gamifying online trading. With confetti glittering across every user's phone screen as they complete actions that lead to a trade, regulators finally said Robinhood went too far. 

Robinhood uses rewards to help users feel they are progressing toward their wealth goals. The app sets milestones for users as they complete educational modules, diversify their investments, and reach saving goals. 

These rewards progress with the user as they become more educated and sophisticated in their trading behavior. You can’t talk about Robinhood’s gamification without mentioning the chance to win free stock to some of the world’s best companies as milestones are met (even if the chance of winning an Apple stock is quite low). 

To ensure users continually come back to meet their milestones, Robinhood provides real-time notifications when stock prices change, encouraging users to quickly buy and sell during these fluctuations. It gives users a dopamine hit every time they receive a notification or open the app.

Share

 Simplifying the Complex

Historically, stock trading has been viewed as something only the financially literate and wealthy do. Robinhood has helped change that perception by making trading extremely accessible and easy to understand for new generations of investors. 

They offer education to understand the ins and outs of stocks, giving users confidence in their investments. Robinhood calls it democratizing access to financial literacy, but I call it breaking down barriers to increasing trading frequency.

Key Takeaways

What can we learn from Robinhood to, in turn, make products people love?

1. Focus on what users deeply want: In this case, it’s about making money and building wealth. Don’t shy away from asking “why” a few times to get to the true reason people engage with a product.

2. Make a boring thing fun: No 20-year-old had ever told their friends about “their awesomely cool investment app” until Robinhood came along. By speaking to the “kid” in us and making investing a game (and maybe slightly addicting), Robinhood became as fun to use as Flappy Birds.

3. Break down complex processes: Simplicity is king when trying to re-engage users day after day. By addressing barriers to entry and tackling education, Robinhood allowed new investors to have confidence depositing their hard-earned cash into the app.

Did you like this breakdown of how the Robinhood user experience meets the needs of its users to accomplish business goals? If so, you can support me by subscribing and sharing this with your friends.