Are MVPs outdated and have no place in modern product culture?

Frank Robinson coined "MVP" in 2001. In the same year as the Agile Manifesto, the first Scrum experiment began. MVPs are old.

The concept was created to solve the waterfall problem at the time.

The market was still sour from the .com bubble. The tech industry needed a new approach. Product and Agile gained popularity because they weren't waterfall.

More than 20 years later, waterfall is dead as dead can be, but we are still talking about MVPs. Does that make sense?

What is an MVP?

Minimum viable product. You probably know that, so I'll be brief:

[…] The MVP fits your company and customer. It's big enough to cause adoption, satisfaction, and sales, but not bloated and risky. It's the product with the highest ROI/risk. […] — Frank Robinson, SyncDev

MVP is a complete product. It's not a prototype. It's your product's first iteration, which you'll improve. It must drive sales and be user-friendly.

At the MVP stage, you should know your product's core value, audience, and price. We are way deep into early adoption territory.

What about all the things that come before?

Modern product discovery

Eric Ries popularized the term with The Lean Startup in 2011. (Ries would work with the concept since 2008, but wide adoption came after the book was released).

Ries' definition of MVP was similar to Robinson's: "Test the market" before releasing anything. Ries never mentioned money, unlike Jobs. His MVP's goal was learning.

“Remove any feature, process, or effort that doesn't directly contribute to learning” — Eric Ries, The Lean Startup

Product has since become more about "what" to build than building it. What started as a learning tool is now a discovery discipline: fake doors, prototyping, lean inception, value proposition canvas, continuous interview, opportunity tree... These are cheap, effective learning tools.

Over time, companies realized that "maximum ROI divided by risk" started with discovery, not the MVP. MVPs are still considered discovery tools. What is the problem with that?

Time to Market vs Product Market Fit

Waterfall's Time to Market is its biggest flaw. Since projects are sliced horizontally rather than vertically, when there is nothing else to be done, it’s not because the product is ready, it’s because no one cares to buy it anymore.

MVPs were originally conceived as a way to cut corners and speed Time to Market by delivering more customer requests after they paid.

Original product development was waterfall-like.

Time to Market defines an optimal, specific window in which value should be delivered. It's impossible to predict how long or how often this window will be open.

Product Market Fit makes this window a "state." You don’t achieve Product Market Fit, you have it… and you may lose it.

Take, for example, Snapchat. They had a great time to market, but lost product-market fit later. They regained product-market fit in 2018 and have grown since.

An MVP couldn't handle this. What should Snapchat do? Launch Snapchat 2 and see what the market was expecting differently from the last time? MVPs are a snapshot in time that may be wrong in two weeks.

MVPs are mini-projects. Instead of spending a lot of time and money on waterfall, you spend less but are still unsure of the results.

MVPs aren't always wrong. When releasing your first product version, consider an MVP.

Minimum viable product became less of a thing on its own and more interchangeable with Alpha Release or V.1 release over time.

Modern discovery technics are more assertive and predictable than the MVP, but clarity comes only when you reach the market.

MVPs aren't the starting point, but they're the best way to validate your product concept.

Discover How We Used Facebook Ads to Grow a New Mobile App from $0 to $15K MRR in Just 6 Months and Our Strategy to Hit $100K a Month.

Our client introduced a mobile app for Poshmark resellers in December and wanted as many to experience it and subscribe to the monthly plan.

An Error We Committed

We initiated a Facebook ad campaign with a "awareness" goal, not "installs." This sent them to a landing page that linked to the iPhone App Store and Android Play Store. Smart, right?

We got some installs, but we couldn't tell how many came from the ad versus organic/other channels because the objective we chose only reported landing page clicks, not app installs.

We didn't know which interest groups/audiences had the best cost per install (CPI) to optimize and scale our budget.

After spending $700 without adequate data (installs and trials report), we stopped the campaign and worked with our client's app developer to set up app events tracking.

This allowed us to create an installs campaign and track installs, trials, and purchases (in some cases).

Finding a Successful Audience

Once we knew what ad sets brought in what installs at what cost, we began optimizing and testing other interest groups and audiences, growing the profitable low CPI ones and eliminating the high CPI ones.

We did all our audience testing using an ABO campaign (Ad Set Budget Optimization), spending $10 to $30 on each ad set for three days and optimizing afterward. All ad sets under $30 were moved to a CBO campaign (Campaign Budget Optimization).

We let Facebook's AI decide how much to spend on each ad set, usually the one most likely to convert at the lowest cost.

If the CBO campaign maintains a nice CPI, we keep increasing the budget by $50 every few days or duplicating it sometimes in order to double the budget. This is how we've scaled to $400/day profitably.

Finding Successful Creatives

Per campaign, we tested 2-6 images/videos. Same ad copy and CTA. There was no clear winner because some images did better with some interest groups.

The image above with mail packages, for example, got us a cheap CPI of $9.71 from our Goodwill Stores interest group but, a high $48 CPI from our lookalike audience. Once we had statistically significant data, we turned off the high-cost ad.

New marketers who are just discovering A/B testing may assume it's black and white — winner and loser. However, Facebook ads' machine learning and reporting has gotten so sophisticated that it's hard to call a creative a flat-out loser, but rather a 'bad fit' for some audiences, and perfect for others.

You can see how each creative performs across age groups and optimize.

How Many Installs Did It Take Us to Earn $15K Per Month?

Six months after paying $25K, we got 1,940 app installs, 681 free trials, and 522 $30 monthly subscriptions. 522 * $30 gives us $15,660 in monthly recurring revenue (MRR).

Next, what? $100K per month

The conversation above is with the app's owner. We got on a 30-minute call where I shared how I plan to get the app to be making $100K a month like I’ve done for other businesses.

Reverse Engineering $100K

Formula:

For $100K/month, we need 3,334 people to pay $30/month. 522 people pay that. We need 2,812 more paid users.

522 paid users from 1,940 installs is a 27% conversion rate. To hit $100K/month, we need 10,415 more installs. Assuming...

With a $400 daily ad spend, we average 40 installs per day. This means that if everything stays the same, it would take us 260 days (around 9 months) to get to $100K a month (MRR).

Conclusion

You must market your goods to reach your income objective (without waiting forever). Paid ads is the way to go if you hate knocking on doors or irritating friends and family (who aren’t scalable anyways).

You must also test and optimize different angles, audiences, interest groups, and creatives.

Could this happen?

Medium isn't like other social media giants. It wasn't a dot-com startup that became a multi-trillion-dollar social media firm. It launched in 2012 but didn't gain popularity until later. Now, it's one of the largest sites by web traffic, but it's still little compared to most. Most of Medium's traffic is external, but they don't run advertisements, so it's all about memberships.

Medium isn't profitable, but they don't disclose how terrible the problem is. Most of the $163 million they raised has been spent or used for acquisitions. If the money turns off, Medium can't stop paying its writers since the site dies. Writers must be paid, but they can't substantially slash payment without hurting the platform. The existing model needs scale to be viable and has a low ceiling. Facebook and other free social media platforms are struggling to retain users. Here, you must pay to appreciate it, and it's bad for writers AND readers. If I had the same Medium stats on YouTube, I'd make thousands of dollars a month.

Then what? Medium has tried to monetize by offering writers a cut of new members, but that's unsustainable. People-based growth is limited. Imagine recruiting non-Facebook users and getting them to pay to join. Some may, but I'd rather write.

Alternatives:

• Donation buttons

• Tiered subscriptions ($5, $10, $25, etc.)

• Expanding content

and these may be short-term fixes, but they're not as profitable as allowing ads. Advertisements can pay several dollars per click and cents every view. If you get 40,000 views a month like me, that's several thousand instead of a few hundred. Also, Medium would have enough money to split ad revenue with writers, who would make more. I'm among the top 6% of Medium writers. Only 6% of Medium writers make more than $100, and I made $500 with 35,000 views last month. Compared to YouTube, the top 1% of Medium authors make a lot. Mr. Beast and PewDiePie make MILLIONS a month, yet top Medium writers make tens of thousands. Sure, paying 3 or 4 people a few grand, or perhaps tens of thousands, will keep them around. What if great authors leveraged their following to go huge on YouTube and abandoned Medium? If people use Medium to get successful on other platforms, Medium will be continuously cycling through authors and paying them to stay.

Ads might make writing on Medium more profitable than making videos on YouTube because they could preserve the present freemium model and pay users based on internal views. The $5 might be ad-free.

Consider: Would you accept Medium ads? A $5 ad-free version + pay-as-you-go, etc. What are your thoughts on this?

Original post available here

Learn or earn

In 2020, I started software engineering. My base wage has progressed as follows:

Amazon (2020): $112,000

Microsoft (2021): $123,000

Google (2022): $169,000

I didn't major in math, but those jumps appear more than a 7% wage increase. Here's a deeper look at the three.

The Three Categories of Compensation

Most software engineering compensation packages at IT organizations follow this format.

Minimum Salary

Base salary is pre-tax income. Most organizations give a base pay. This is paid biweekly, twice monthly, or monthly.

Recruiting Bonus

Sign-On incentives are one-time rewards to new hires. Companies need an incentive to switch. If you leave early, you must pay back the whole cost or a pro-rated amount.

Equity

Equity is complex and requires its own post. A company will promise to give you a certain amount of company stock but when you get it depends on your offer. 25% per year for 4 years, then it's gone.

If a company gives you $100,000 and distributes 25% every year for 4 years, expect $25,000 worth of company stock in your stock brokerage on your 1 year work anniversary.

Performance Bonus

Tech offers may include yearly performance bonuses. Depends on performance and funding. I've only seen 0-20%.

Engineers' overall compensation usually includes:

Base Salary + Sign-On + (Total Equity)/4 + Average Performance Bonus

Amazon: (TC: 150k)

Base Pay System

Amazon pays Seattle employees monthly on the first work day. I'd rather have my money sooner than later, even if it saves processing and pay statements.

The company upped its base pay cap from $160,000 to $350,000 to compete with other tech companies.

Performance Bonus

Amazon has no performance bonus, so you can work as little or as much as you like and get paid the same. Amazon is savvy to avoid promising benefits it can't deliver.

Sign-On Bonus

Amazon gives two two-year sign-up bonuses. First-year workers could receive $20,000 and second-year workers $15,000. It's probably to make up for the company's strange equity structure.

If you leave during the first year, you'll owe the entire money and a prorated amount for the second year bonus.

Equity

Most organizations prefer a 25%, 25%, 25%, 25% equity structure. Amazon takes a different approach with end-heavy equity:

• the first year, 5%

• 15% after one year.

• 20% then every six months

We thought it was constructed this way to keep staff longer.

Microsoft (TC: 185k)

Base Pay System

Microsoft paid biweekly.

Gainful Performance

My offer letter suggested a 0%-20% performance bonus. Everyone will be satisfied with a 10% raise at year's end.

But misleading press where the budget for the bonus is doubled can upset some employees because they won't earn double their expected bonus. Still barely 10% for 2022 average.

Sign-On Bonus

Microsoft's sign-on bonus is a one-time payout. The contract can require 2-year employment. You must negotiate 1 year. It's pro-rated, so that's fair.

Equity

Microsoft is one of those companies that has standard 25% equity structure. Except if you’re a new graduate.

In that case it’ll be

• 25% six months later

• 25% each year following that

New grads will acquire equity in 3.5 years, not 4. I'm guessing it's to keep new grads around longer.

Google (TC: 300k)

Base Pay Structure

Google pays biweekly.

Performance Bonus

Google's offer letter specifies a 15% bonus. It's wonderful there's no cap, but I might still get 0%. A little more than Microsoft’s 10% and a lot more than Amazon’s 0%.

Sign-On Bonus

Google gave a 1-year sign-up incentive. If the contract is only 1 year, I can move without any extra obligations.

Not as fantastic as Amazon's sign-up bonuses, but the remainder of the package might compensate.

Equity

We covered Amazon's tail-heavy compensation structure, so Google's front-heavy equity structure may surprise you.

Annual structure breakdown

• 33% Year 1

• 33% Year 2

• 22% Year 3

• 12% Year 4

The goal is to get them to Google and keep them there.

Final Thoughts

This post hopefully helped you understand the 3 firms' compensation arrangements.

There's always more to discuss, such as refreshers, 401k benefits, and business discounts, but I hope this shows a distinction between these 3 firms.

I've summarized each.

Anne Lamott said books are important. Books help us understand ourselves and our behavior. They teach us about community, friendship, and death.

I read. One of my few life-changing habits. 100+ books a year improve my life. I'll list life-changing books you can read in a day. I hope you like them too.

Let's get started!

1) Seneca's Letters from a Stoic

One of my favorite philosophy books. Ryan Holiday, Naval Ravikant, and other prolific readers recommend it.

Seneca wrote 124 letters at the end of his life after working for Nero. Death, friendship, and virtue are discussed.

It's worth rereading. When I'm in trouble, I consult Seneca.

It's brief. The book could be read in one day. However, use it for guidance during difficult times.

My favorite book quotes:

• Many men find that becoming wealthy only alters their problems rather than solving them.

• You will never be poor if you live in harmony with nature; you will never be wealthy if you live according to what other people think.

• We suffer more frequently in our imagination than in reality; there are more things that are likely to frighten us than to crush us.

2) Steven Pressfield's book The War of Art

I’ve read this book twice. I'll likely reread it before 2022 is over.

The War Of Art is the best productivity book. Steven offers procrastination-fighting tips.

Writers, musicians, and creative types will love The War of Art. Workplace procrastinators should also read this book.

My favorite book quotes:

• The act of creation is what matters most in art. Other than sitting down and making an effort every day, nothing else matters.

• Working creatively is not a selfish endeavor or an attempt by the actor to gain attention. It serves as a gift for all living things in the world. Don't steal your contribution from us. Give us everything you have.

• Fear is healthy. Fear is a signal, just like self-doubt. Fear instructs us on what to do. The more terrified we are of a task or calling, the more certain we can be that we must complete it.

3) Darren Hardy's The Compound Effect

The Compound Effect offers practical tips to boost productivity by 10x.

The author believes each choice shapes your future. Pizza may seem harmless. However, daily use increases heart disease risk.

Positive outcomes too. Daily gym visits improve fitness. Reading an hour each night can help you learn. Writing 1,000 words per day would allow you to write a novel in under a year.

Your daily choices affect compound interest and your future. Thus, better habits can improve your life.

My favorite book quotes:

• Until you alter a daily habit, you cannot change your life. The key to your success can be found in the actions you take each day.

• The hundreds, thousands, or millions of little things are what distinguish the ordinary from the extraordinary; it is not the big things that add up in the end.

• Don't worry about willpower. Time to use why-power. Only when you relate your decisions to your aspirations and dreams will they have any real meaning. The decisions that are in line with what you define as your purpose, your core self, and your highest values are the wisest and most inspiring ones. To avoid giving up too easily, you must want something and understand why you want it.

Only two of the following three options can be achieved: consistency, availability, and partition tolerance

Someone told me that growing from 30 to 60 is the biggest adjustment for a team or business.

I remember thinking, That's random. Each company is unique. I've seen teams of all types confront the same issues during development periods. With new enterprises starting every year, we should be better at navigating growing difficulties.

As a team grows, its processes and systems break down, requiring reorganization or declining results. Why always? Why isn't there a perfect scaling model? Why hasn't that been found?

The Three Things Productive Organizations Must Have

Any company should be efficient and productive. Three items are needed:

First, it must verify that no two team members have conflicting information about the roadmap, strategy, or any input that could affect execution. Teamwork is required.

Second, it must ensure that everyone can receive the information they need from everyone else quickly, especially as teams become more specialized (an inevitability in a developing organization). It requires everyone's accessibility.

Third, it must ensure that the organization can operate efficiently even if a piece is unavailable. It's partition-tolerant.

From my experience with the many teams I've been on, invested in, or advised, achieving all three is nearly impossible. Why a perfect organization model cannot exist is clear after analysis.

The CAP Theorem: What is it?

Eric Brewer of Berkeley discovered the CAP Theorem, which argues that a distributed data storage should have three benefits. One can only have two at once.

The three benefits are consistency, availability, and partition tolerance, which implies that even if part of the system is offline, the remainder continues to work.

This notion is usually applied to computer science, but I've realized it's also true for human organizations. In a post-COVID world, many organizations are hiring non-co-located staff as they grow. CAP Theorem is more important than ever. Growing teams sometimes think they can develop ways to bypass this law, dooming themselves to a less-than-optimal team dynamic. They should adopt CAP to maximize productivity.

Path 1: Consistency and availability equal no tolerance for partitions

Let's imagine you want your team to always be in sync (i.e., for someone to be the source of truth for the latest information) and to be able to share information with each other. Only division into domains will do.

Numerous developing organizations do this, especially after the early stage (say, 30 people) when everyone may wear many hats and be aware of all the moving elements. After a certain point, it's tougher to keep generalists aligned than to divide them into specialized tasks.

In a specialized, segmented team, leaders optimize consistency and availability (i.e. every function is up-to-speed on the latest strategy, no one is out of sync, and everyone is able to unblock and inform everyone else).

Partition tolerance suffers. If any component of the organization breaks down (someone goes on vacation, quits, underperforms, or Gmail or Slack goes down), productivity stops. There's no way to give the team stability, availability, and smooth operation during a hiccup.

Path 2: Partition Tolerance and Availability = No Consistency

Some businesses avoid relying too heavily on any one person or sub-team by maximizing availability and partition tolerance (the organization continues to function as a whole even if particular components fail). Only redundancy can do that. Instead of specializing each member, the team spreads expertise so people can work in parallel. I switched from Path 1 to Path 2 because I realized too much reliance on one person is risky.

What happens after redundancy? Unreliable. The more people may run independently and in parallel, the less anyone can be the truth. Lack of alignment or updated information can lead to people executing slightly different strategies. So, resources are squandered on the wrong work.

Path 3: Partition and Consistency "Tolerance" equates to "absence"

The third, least-used path stresses partition tolerance and consistency (meaning answers are always correct and up-to-date). In this organizational style, it's most critical to maintain the system operating and keep everyone aligned. No one is allowed to read anything without an assurance that it's up-to-date (i.e. there’s no availability).

Always short-lived. In my experience, a business that prioritizes quality and scalability over speedy information transmission can get bogged down in heavy processes that hinder production. Large-scale, this is unsustainable.

Accepting CAP

When two puzzle pieces fit, the third won't. I've watched developing teams try to tackle these difficulties, only to find, as their ancestors did, that they can never be entirely solved. Idealized solutions fail in reality, causing lost effort, confusion, and lower production.

As teams develop and change, they should embrace CAP, acknowledge there is a limit to productivity in a scaling business, and choose the best two-out-of-three path.