🌱 5-Bit Fridays: Minimizing Time To Value, how AI affects our sense of self, the causal loop mental model, Ants & Alien thinking, and a mental model for managing PMs
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Time to Value (TTV) and the Bowling Alley Framework
How AI affects our sense of self
Ants & Aliens: Long-term product vision & strategy
The causal loop mental model
A powerful mental model for managing product managers
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(#1) Time to Value (TTV) and the Bowling Alley Framework
The faster you find what you’re looking for, in any situation, the happier you’re going to be.
That’s why, especially for product-led companies where users are helping themselves, maximizing the Time To Value (TTV) is so important. TTV is basically how quickly a customer goes from sign up to your aha moment—the best predictor of free→paid conversion, and retention.
And what’s the best way to speed up that path to value?
Have a really good bridge between what you promise people your product does, and the actual promised land within your product. AKA—focus on excellent onboarding by eliminating, delaying, and even adding steps.
Just jogging my memory on a few of the deep dives we’ve done, and Canva, Notion, Duolingo, and Headspace are all really excellent examples of this. (Click into any of those for specific case studies on onboarding done right). With all those products, you can almost immediately use them and get value. And that’s a result of ruthless testing across activation and engagement.
Take Duolingo (one of my favorite onboarding examples). Before even handing over your email you get sucked in, have learned some new words and phrases, and before you know it, you've just become their customer.
, has a great way of thinking about minimizing TTV. He calls it the Bowling Alley Framework.
I remember the first time I went bowling with my son. He was frustrated because the balls kept falling off the track. It was hard for him to experience the joy of hitting a target.
Then we discovered the bumpers, which made the game much more accessible and allowed him to enjoy the game before acquiring more skills.
The Bowling Alley Framework is a powerful onboarding strategy. It's like using "bumpers" to guide users to the outcome your product promises.
And to use “bumpers”, you first need to understand your customers. Where are they coming from (different acquisition channels may behave differently)? What do they care about? What frictions might be holding them back from using your product more? What are their problems, needs, and motivations?
After you know who you’re trying to minimize TTV for, you can build a magical first mile to get them to their goal. Pro tip: You’ll have a few different personas, and a one-size onboarding glove is likely not the best option.
Anyway, continuing the thread of the Bowling Alley Framework which I think is a great mental model to think about TTV, Pawel highlights two types of bumpers you to use:
1. Product bumpers
These are all the critical things you do inside your product—the in-app user experience.
Here are a few of these bumpers you can consider adding. Just some ideas, definitely not exhaustive.
Welcome messages: Greet people in a friendly and conversational way. Relate things to your value proposition, and have continuity by weaving in things people have told during sign-up into your messages to them.
Product tours: You know how these go. A modal pops up and asks if you can show the user around. Either the tour points out key areas, or the good ones even get users to action something along the way. This is like the product bumper, except lots of people ignore these.
Assistive AI mascots: A different approach to a traditional product tour is getting a little more fancy with an AI-based mascot that talks to the user, and helps give them advice on the next best action. The mascot should have context about who the user is and what they’re doing, and from there, suggest pro tips to help them discover value. This can work well for more complicated SaaS.
Checklists: Embedded checklists that focus on getting users to complete 3/4 key actions can work well. They could even be used to limit the number of actions someone can take at the beginning of their journey.
Delayed hard walls: The best example here again is Duolingo. You can use the product before being asked for any info. This should be tested for sure, but showing core product value as early in the funnel as possible can work really well.
Onboarding tooltips: Helpful, non-intrusive, messages that you show folks while they are interacting with different elements (e.g., mouse hover).
Templates: Canva and Notion are prime examples here of products with a ton of value (and bigger learning curves) guide users to that aha moment with suggested templates to help people get started.
Educational empty states: After a user gets inside your product, don’t greet them with white pages. Leverage smart, beautiful, and education empty states.
The Reverse Trial: We’re getting a little into monetization here but that’s ok. But instead of the free trial or the freemium, think of using a Reverse Trial, where users get the full product on a trial, and after it ends, they fall back to a freemium. The key is that value is never taken away. Learn more here.
Simplified/focused UI during onboarding: Especially for SaaS with lots of features and products within the platform, many of those are irrelevant until a user has used the first one. But lots of SaaS still reveal everything to the user. One way to avoid overload and drive people to that first essential thing is to think about onboarding that more directly enables the most popular JTBD. Maybe you gray out other stuff, or have a training-wheels UI for first-timers.
Use persona-based/personalized flows: To crush onboarding, think about personalization for specific use cases, Jobs-To-Be-Done, or levels of intent.
Leverage gamification: People think using game mechanics is just for B2C. Wrong. All products are used by people, and gamification is a proven way to get people to engage with important things. Think about how earning points etc. can be used to move people along more complex onboarding experiences.
And outside of “bumper product features”, here’s some other advice on shortening your TTV
Simplify your flow, and reduce the number of steps: Only ask essential questions that help you learn about your user's goals, allowing you to drive more contextual onboarding and communication.
Make smart recommendations/defaults: Remove friction and cognitive load by pre-populating user journeys with relevant content based on job titles, and adding search suggestions in your fields.
Use various authentication providers (Apple, Google, FB, etc.). Always have legacy email, but single sign-on can work very well for moving people through the top of the funnel quicker.
Think about top-of-funnel targeting better: Qualify people early on—not all traffic is equal—and consider moving some folks to a human-touch onboarding experience. Or, hot take, even disqualifying some people from using your product. 👀
2. Conversational bumpers
Conversational bumpers work to educate your users outside of the product. These touch points help set expectations, nudge them to come back, and aim to educate. Two big ones are:
Onboarding emails: From welcome messages to automated campaigns that introduce things like features to try, tips, social proof, demo requests, etc. The majority of people who sign up won’t activate, meaning conversational bumpers are your key to bringing them back into the product for those product bumpers to get to work.
Push notifications: If you have an app, and users have opted in, these can be handy ways to nudge folks.
Explainer Videos: Videos can generate even 1200% more engagement than text and images. For complex products with lots to show, these can work well. Although, my preference is always Action > Information.
🤔 Question: What other bumpers have you tried? I’d love to hear in the comments, whether they worked, and also, things that didn’t.
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(#2) How AI affects our sense of self
In the 1950s, General Mills launched an incredibly innovative product. The Betty Crocker cake mix. All you had to do was add water, give it a mix, and throw it in the oven to bake.
It was magical. Cake-making had never been easier.
But sales were flat.
Confused because the product was clearly exceptionally innovative, General Mills did some user research to find out what was going on. What they found was fascinating: The mix made baking too easy, and buyers felt they were somehow cheating when they used it.
So, they removed the egg powder and added a 4th step: throw in an egg yourself.
The rest is history, with Betty Crocker mixes still on shelves across the country, and still using the same formula.
In the world of AI, that brings us a particularly relevant lesson: As products and services get more automated, builders need to understand how taking work away from a user and making it too easy affects how those things make customers feel about themselves—what emotions it evokes.
Why? Because how people feel about themselves affects everything— from sales, customer loyalty, word-of-mouth, employee satisfaction, and employee performance.
A great post in the Harvard Business Review (paid) gets into this in detail. Gizem Yalcin and Stefano Puntoni have been studying people’s reactions to automation for more than seven years, and in their article, they focus on psychological responses to AI and automation that they’ve observed in (1) service and business-process design and (2) product design. They also offer practical advice to help leaders and managers figure out how best to use these new technologies to serve customers, support employees, and grow their businesses.
Here’s a high-level rundown for you:
(1) Services and business-process design
AI and automation are now integrated into various services and business processes affecting the customer, and employees.
Examples: Upstart for lending decisions, Monster and Unilever for job candidate assessment, GEICO for car insurance premiums, and IBM and Lattice for performance feedback.
Key questions:
How do people react to decisions from AI and automated technologies?
How can businesses incorporate AI to maximize satisfaction?
What their research found:
People react differently to acceptances from AI vs. humans.
Acceptance from humans brings more joy than from AI.
Rejections are perceived similarly, whether from AI or humans.
People's self-perception varies based on the evaluator (AI or human).
Practical implications:
Positive decisions from humans generate better reactions than those from AI.
Negative decisions are perceived the same, regardless of the decision-maker.
Many leaders are unaware of these effects.
Recommendations for businesses:
Maintain active human involvement in evaluation processes.
Use humans to deliver good news and AI for negative news.
Understand the symbolic value of products and consider human involvement in their production.
(2) Product design
AI and automation are prevalent in various products, changing how people perform tasks at home, and at work.
Examples: iRobot’s Roomba, Tesla’s Autopilot, Jura’s coffee machine, IBM’s Watson, Adobe’s AI in Photoshop, Toyota's automated tools, and OpenAI’s DALL-E and ChatGPT.
Key questions:
How do interactions with automated technologies affect our sense of identity and accomplishment?
How will this influence product demand?
What their research found:
People's reactions to automated products relate to identity-based consumption.
Those identifying with specific activities may see automation as a threat, leading to reduced product adoption.
Example: People who love cycling and identify with the culture are reluctant to use electric bikes that help them cycle faster.
Digitalization and Identity:
Symbolic products are less adopted in digital form due to weaker identity expression.
Example: Having an actual bookshelf with real books validates a literary identity far more than digital collections living on a Kindle.
Practical implications:
People resist technological enhancements of products that tie to their identity.
Professional identity can be threatened by AI and automation.
Recommendations for businesses:
Avoid targeting identity-driven consumers with fully automated products.
Focus on features that allow users to feel involved and proud.
Conduct market research to gauge potential identity threats from automation.
🤔 Question: Is there any product you use that you think is on a path to having so much automation that you’d actually no longer enjoy using?
That’s quite the time horizon. I can honestly say I’ve never thought about strategy and vision so far out.
Because it’s a contrarian view, here’s Ken explaining. It’s certainly an interesting perspective.
How fast can an ant travel? It’s an easy question to answer with the help of the internet: about 300 meters per hour, which translates to 0.19 miles per hour. But wait, there’s more than one right answer. Ants also move at 1,000 miles per hour—the speed at which ants, humans, and everything else on Earth are rotating. Or how about 67,000 miles per hour? That’s how fast the Earth travels around the sun. (Now 1,000 mph seems puny.) There are even more right answers because our solar system is rotating around the galaxy at 483,000 mph. And our galaxy—you guessed it—itself moves at over 1,300,000 mph relative to cosmic background radiation
Phew. The lesson here is that there are a number of correct answers to the ostensibly simple question “how fast is an ant?” depending on what it’s relative to—the reference frame. (That’s how physicists explain why a passenger on a train might feel like they’re sitting still whereas an observer standing on the platform sees them moving by at 50 mph.) Relative to the grass, the ant is moving at one speed, but the hypothetical alien—with a galactic frame of reference—would give a different answer.
The same goes for technological progress. In our industry, product managers are the tiniest of ants. Our reference frame is day-by-day, sprint-by-sprint, feature-by-feature. What’s happening today, tomorrow, next month? Like the ant, we’re deep in the weeds, intimately familiar with the dirt but unaware of just how fast the world is moving under our feet. We can easily become myopic to the pace of technology and to the larger forces that will ultimately have more influence whether our product succeeds or fails more than which feature comes next.
So, when Ken says it’s important to have a thirty-year vision, he’s basically asking, “If you were an alien far away in the galaxy, how could you take their frame of reference?”
One company that’s doing this is Meta:
There is no point in having a 5-year plan in this industry. With each step forward, the landscape you’re walking on changes. So we have a pretty good idea of where we want to be in six months, and where we want to be in thirty years. And every six months we take another look at where we want to be in thirty years to plan out the next six months.
In Ken’s article, he gives two examples where failing to take the right frame of reference has left massive companies with their pants down:
When we read about failed companies such as Blockbuster, Kodak, or RIM, we’re often told that they were “caught by surprise” or “didn’t see it coming.” The truth is much more complicated. In each of these examples, the companies were acutely aware of the threat posed by the new technology. In fact, they were often the first to see it—Kodak invented digital photography, and RIM brought the first smartphones to market. What they were wrong about, however, was just how fast the technology was moving. They had the wrong frame of reference. Blockbuster looked at how many DVDs they rented today compared with yesterday and felt placated. But you’ll never notice how fast a glacier is melting if you’re only comparing measurements minute by minute.
But what if you go through the mental exercise of forcing yourself to imagine your product and the landscape it operates within thirty years in the future? Ask, where might the world be? Ken notes that when you think on this so far into the future, the little details always fall away. “Who the hell knows what device we’ll be using to communicate in 2046?! It’s impossible to predict. Yet it’s easy to anticipate that we’ll be using something that will be even easier, faster, more powerful, and more ubiquitous than the smartphones of today.” Zooming out as far brings things into focus: technology is progressing much faster than we think, and will only continue to do so.
Peter Schwartz (”The Futurist”) has a useful framework for leading the discussion around thinking about the types of forces that will shape your product's future. He uses the acronym STEEP: Social, Technological, Economic, Environmental, and Political.
Each is a good starting point to help compensate for the blind spots we have in tech. AKA, when was the last time you considered how global boiling or a shrinking population might shape your product’s future?
From here, once you’ve considered what these possible futures look like, you can form an opinion about where your product should go, which long-term trends you can’t ignore, and against which trends you might need to hedge. Your periodic thirty-year plan feeds into your six-month plan.
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Mental models are frameworks for making decisions.
We have lots of them that operate on autopilot as we navigate the world—helping us make sense of things. Go to the grocery store, and you have a mental model that drives the stuff you put in your cart.
But when you're working on a product, you need to carefully select your mental models to make sure you're incorporating the most important factors into your decisions. The earlier you are in your career, the more it’s helpful to actively think about mental models and frameworks to guide your decision-making. The more senior you become though, and the sharper your product sense, all it means is those same, albeit better-tuned, mental models are now operating without you directly thinking about them.
Exactly like a pro tennis player. They started by thinking about their shots and technique, and later, those models became ingrained.
To get there though, we need to build up our mental model toolbox, and as often as possible, pull out the right tool and apply it to our critical thinking. 🧰
On that note, here’s a new one for you. 🪛
Imagine this likely scenario: Your designers want to simplify a product dashboard, thinking it’s getting overcrowded. But, your sales team is pushing to keep everything because the robust dashboard is a huge selling point.
To understand the multi-spoked impact of your decisions, build causal loops. These are visual maps that identify positive and negative correlations.
It’s a useful exercise in systems thinking, as looking at the positive and negative correlations between events (the obvious, and less obvious) can prevent you from being blindsided by unintended consequences. The causal loop mental model can also help you explain and justify your decisions to different teams who might not directly see the pros and cons of certain decisions.
Any artifact that helps justify your decision, especially is a more junior PM, is a great thing to create.
For example, imagine that you're prioritizing tasks for your team: adding features, improving UX design, improving product speed, and fixing bugs. Just to illustrate it in action, let’s say you evaluate each on whether the time you spend on it will directly add or detract from getting new users (acquisition) and retaining existing ones (happiness).
Here’s what that could look like in a causal loop. 👇
New features can be a great selling point for new customers, but at first, they might frustrate current customers who have to relearn parts of the product.
Improving speed and fixing bugs will make current customers happy, but it's not a selling point for acquiring new users.
Improving product design is great for winning new users and improving the experience of current users. This adds to both those goals.
Most decisions that you'll make as a product manager are going to affect pretty much all the teams at your company. Of course, in different ways—so it's your job to navigate which changes are worth making.
To close us out for this week, here’s a different type of mental model—one for managers of product managers. 👇
(#5) A powerful mental model for managing product managers
Most successful PMs crave responsibility and ownership and have strong traits of being achievers and mission-driven. A mix of traits that explains why a lot of PMs either break into the function by being a founder or eventually go on to start companies.
Being a PM is the best job in the world to “practice startup”.
A couple of years ago, Brandon Chu (ex VP of Product and GM of Platform at Shopify) wrote a popular piece that touches on this, and how managers need to think about, and leverage, this entrepreneurial spirit many PMs have.
He introduces this awesome mental model for managers: “They’re entrepreneurs, you’re the investor”.
This is my favourite mental model for thinking about managing PMs. It guides you on the working relationship, and sends a powerful signal to your PMs as to their responsibilities.
Investor <> Entrepreneur Model for managing PMs
It’s their vision, you just buy into it.
To paraphrase a Steve Jobs quote, “it makes no sense to hire smart people and then tell them what to do”.
It’s your job as the investor to identify great PMs that want to build something in an area you think is a great opportunity. After you’ve hired them though, you should aspire to truly let them run with it.
The takeaway from this is to be very transparent during hiring about what product area you’re looking for someone to lead. You should be looking for candidates who are genuinely excited to lead the area, just like a founder would be of their own startup. This reinforces the investor <> entrepreneur model.
Investors don’t meddle in a portfolio company’s operations:
PMs should solve their own problems. Don’t make decisions for them, and you should own up to your promise of autonomy and ownership for the PMs you bring onto your team.
Coach them and give them constant feedback, but just like an investor would you’re holding them to the end result, not how hard they work or the way they do it.
Focus on helping to support their blind spots
Your job is to work with PMs to find out where they are weak, where you are strong, and to help them by supporting their blind spots. Just like how VCs often help portfolio companies that don’t have experience in hiring, operations, etc.
Look for opportunities to learn from them as well. You’re hiring people from diverse backgrounds—leverage that.
They should have more to gain in success than you do
“When you have a superstar PM, your responsibility is to accelerate their career, even if that means it surpasses yours, and do it quickly. PMs are super ambitious and they will not wait for you to provide them opportunities they’ve earned. If you’re too slow, they will leave — I promise you that.” — Brandon Chu
They should have more to lose in failure
“If you feel like you’ve supported your PM, coached them through a few ‘pivots’ to get them winning, and yet they still aren’t performing — you need to let them go. Remember, it’s not just about them, it’s actually more about everyone else. We have a saying at Shopify that ’it’s better to have no PM than a bad, or even a mediocre one.’” — Brandon Chu
🌱 And now, byte on this if you have time 🧠
You might have heard that Netflix is getting into cloud video gaming.
A huge market that a company with massive distribution advantages wants to, and is about to, tap into. But instead of talking about the news, today’s extra byte is a piece that Matthew Ball wrote back in 2021, where he answers questions like: Why does Netflix want to enter gaming (beyond the money)? Why now? Can it succeed? What makes the category so uniquely challenging for the streaming giant?
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Until next time. — Jaryd✌️
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A specific tip for good onboarding: use ChatGPT API for prepopulating.
Example from DemoTime: before we can create highlights of your meeting we need to know what to look for. The user tells us with scenes.
One part of this is the user selects an icon for each feature. We can give the headline to GPT and smart pre populate the icon.
Think smart prepopulation rather than AI mascot wizard.
As an engineer led company we implemented onboarding very simply for DemoTime. Just flags on the project for each completed step.
It's a checklist but it starts easy (upload a logo etc.) and finishes hard (get highlights from a demo meeting).
Would be great to see a future topic on onboarding specifically for high complexity software products.