The untold story of startup success: building a company starts as a sidegig

It’s a common misconception that successful startups are born out of a single-minded focus and an all-consuming passion. We tell the story of founders who “make the leap” and give up everything – sell their house, move into a small apartment, work out of a garage – and end up believing that it’s 100% in or else there’s no hope for building a successful company. In reality, many of the most successful startups were started as side projects by founders who were full-time students or had day jobs.

Take Airbnb, for example – it was founded by three friends who were struggling to pay rent in San Francisco. They started renting out air mattresses in their apartment to local conference attendees, and the idea eventually grew into a billion-dollar company.

Even take Slack as another example, started as an internal tool for a gaming company called Tiny Speck. The founders realized that their tool could be useful for other companies, and Slack was born.

You might be surprised that Google falls into this pattern as well. Google, as one of the largest technology companies in the world, was started as a research project by Larry Page and Sergey Brin while they were PhD students at Stanford.

The point is that so many of the successful startups you know and love weren’t created as full-time endeavors. They were born out of a need or a passion that founders pursued in their evenings and weekends, often while working full-time jobs.

This contrarian view is important for traditional venture capitalists to consider, because it challenges the conventional wisdom that founders are only good if they have full commitment. That becomes true later, but if we didn’t have the explorers who were willing to build in their evenings, then we wouldn’t have a lot of the innovative companies that we have today. We tend to look for startup founders with single-minded focus and a team that is 100% committed to building a product. But the reality is that many successful startups start out as side gigs.

I’m really excited to look for startups that don’t fit the mold of a traditional full-time company. With trends like “The Great Betrayal” and full-time work looking less attractive, I’m willing to bet that we’ll see a huge wave of part-time entrepreneurs, some of whom will merely dabble, others becoming solopreneurs, but also some will build the the next biggest companies. You never know where the next billion-dollar idea may come from.

30 software legends that started part-time

There’s a much, much longer list that isn’t captured on the Internet, but for starters, here’s a list of 30 software companies that you’ve probably heard of that were started as side projects:

  1. Microsoft: Bill Gates and Paul Allen started Microsoft while they were still in high school, and continued to work on the company as a part-time venture while attending college.
  2. Amazon: Jeff Bezos started Amazon as an online bookseller while working as a senior vice president at a hedge fund.
  3. Google: Larry Page and Sergey Brin started working on the search engine that would become Google while they were Ph.D. students at Stanford University.
  4. Slack: Stewart Butterfield and his team started working on the team communication tool while they were still working on a different project, and continued to work on Slack as a side project until it became a full-time venture.
  5. Dell: Michael Dell started building personal computers in his college dorm room as a part-time venture before eventually quitting school to start Dell Inc. full-time.
  6. Apple – Steve Jobs and Steve Wozniak created the first Apple computer in Jobs’ parents’ garage while working full-time jobs.
  7. Airbnb – Founders Brian Chesky, Nathan Blecharczyk, and Joe Gebbia started renting out air mattresses in their apartment to conference attendees as a way to make extra money.
  8. WhatsApp – Co-founders Jan Koum and Brian Acton created WhatsApp while working as engineers at Yahoo.
  9. Dropbox – Drew Houston started developing the first version of Dropbox while working full-time at a startup called Accolade.
  10. Evernote – Phil Libin, Stepan Pachikov, and Dave Engberg started Evernote as a part-time project while working at other companies.
  11. Hootsuite – Ryan Holmes started Hootsuite as a side project while running a digital agency.
  12. Wunderlist – Christian Reber started developing Wunderlist while working full-time as a designer.
  13. Twitter – Jack Dorsey, Biz Stone, and Evan Williams created Twitter while working on another startup called Odeo.
  14. Atlassian – Mike Cannon-Brookes and Scott Farquhar started Atlassian while studying at the University of New South Wales.
  15. WordPress – Matt Mullenweg started developing WordPress as a side project while working as a consultant.
  16. Trello – Joel Spolsky and Michael Pryor created Trello as a way to manage their own projects more efficiently.
  17. MailChimp – Ben Chestnut started MailChimp as a side project while running a web design company.
  18. Salesforce: Marc Benioff started Salesforce as a part-time venture while he was still working as an executive at Oracle.
  19. Hubspot: Brian Halligan and Dharmesh Shah started Hubspot as a part-time venture while they were still professors at MIT.
  20. Asana: Justin Rosenstein and Dustin Moskovitz started Asana as a part-time project while they were still working at Facebook.
  21. Freshdesk: Girish Mathrubootham started Freshdesk as a part-time project while he was still working as a product manager at Zoho.
  22. Basecamp: Jason Fried and David Heinemeier Hansson started Basecamp as a part-time venture while they were still working as consultants.
  23. Airtable: Howie Liu, Andrew Ofstad, and Emmett Nicholas started Airtable as a part-time project while they were still working at various tech companies.
  24. Canva: Melanie Perkins, Cliff Obrecht, and Cameron Adams started Canva as a part-time project while they were still students.
  25. Pipedrive: Timo Rein and Davide De Guzman started Pipedrive as a part-time venture while they were still working as consultants.
  26. Heroku – James Lindenbaum, Adam Wiggins, and Orion Henry started Heroku as a part-time project while working at different companies.
  27. Typeform: Robert Finn and David Okuniev started Typeform as a part-time project while they were still working as designers.
  28. Adobe – John Warnock and Chuck Geschke started Adobe as a part-time project while working at Xerox.
  29. Red Hat – Bob Young and Marc Ewing started Red Hat as a part-time project while working at Cornell University.
  30. Grammarly – Alex Shevchenko and Max Lytvyn started Grammarly as a part-time project while studying at UC Berkeley.

What the future of performance marketing holds

There is a lot of buzz around AI-driven marketing today. Tools like Jasper and ChatGPT have caused many go-to-market professionals to question whether their jobs will be replaced by robots in the near future. It can be unsettling to think about, but I’m also excited about the potential of these technologies to drastically improve our work and productivity. In my daily work, I’ve been able to use Jasper to quickly generate strategy statements and ad taglines with minimal input. It’s even helped me proofread parts of this write-up.

While this is all novel and exciting, AI-driven marketing is already making an impact on performance marketing channels. In fact, I’ve been using an ML-based performance ad channel over the last year in my role running growth at Descript, and these campaigns have broken our performance ceilings month after month. Let’s explore what’s happening now and how it affects our digital marketing and go-to-market decisions.

ML-based performance marketing is already here

  • Channels are all converging toward ML-based conversion optimization for targeting. Example: Google’s Performance Max campaigns; Facebook/Instagram’s broad targeting with conversion optimization campaigns
    • For those who aren’t familiar: Google Performance Max is a paid search campaign type that uses machine learning to optimize ad delivery and targeting in real-time, which results in achieving better performance and ROI outcomes. Instead of setting up targeting, you set up ‘”anti-targeting’” by telling the system what’s not considered a valid conversion.
  • ML-based targeting is ideal from the lens of ROI – the machine-learned system will most certainly be more precise in targeting and use each ad dollar in more effective way than human allocation could. But, it’s not ideal from the lens of gaining customer insights.
  • Perhaps in 6-12 months time, all paid search and paid social will utilize blackbox targeting where we don’t define or know who the platforms are targeting, except understanding broad filters geography, demographics, or platform, to name a few examples.
  • This creates a reliance on ads, where you can get a ton of results but you don’t know what drove its success and you can only count on the blackbox targeting system to continue working well.
    • A good parallel might be to understand what Amazon does with brands/sellers today. You pay Amazon to sell on your behalf, and the units sell or they don’t, but you don’t know who the customer is at the end of the day.
    • Overall, the Amazon-esque system can work incredibly well, but it rules out a certain type of seller that isn’t savvy enough to learn and exploit the system.

Skills for the future are technical and strategic

Performance marketing becomes a technical sport

  • Less focus on hiring those with ads operating experience, which tends to be the background of most junior performance marketers or agency hires. The things that performance marketers spend time on today, such as building the post, creating targeting parameters like frequency caps and daily budgets, setting up an a/b experiment, or optimizing based on results, will all become automated. Instead, people will spend more time defining clear optimization signals and structuring campaigns across these signals. Think of it almost as prompt creation.
  • Performance marketing as a whole will become a technically performant function where the operators behind the scenes will need to be data-minded in order to understand how utilize targeting and ranking mechanisms.
  • Instrumenting marketing data systems isn’t a common skillset for data engineers. That’s why the performance marketer will need to become an acting product manager to help ensure accurate instrumentation, and define a data taxonomy that becomes useful for marketing teams.

Content marketing and product marketing work requires more precise customer segmentation

  • It will be imperative to develop strengths across multiple niches in order to gain scale. Otherwise, we’ll hit the ceiling within the constraints of our economics – i.e. willingness to pay for a conversion – and time horizon.
  • More micro-targeted content positioned to the customer that will be likely to convert. This could mean developing more landing pages with focused content that gives greater chance of fitting into the blackbox targeting match.
  • Having a multi-faceted product that spans customer segments should play to our advantage in that it allows us to compete in multiple audience segments at one time, which increases our chances of exposure and conversion.
  • This increases our need to have a clear data-driven signal customer segments that can be exposed externally somehow.

The chasm between digital performance and brand marketing tactics will widen, yet the function of these are intertwined

  • Today, performance marketers look toward increasing reach as a mechanism to improve the likelihood of finding a converting audience. Tactics include bidding on a CPM basis to achieve a broader set of conversion outcomes. However, in the world of ML targeting, the system will reach as few users as possible to reach conversion objectives, which minimizes total reach and frequency of reach.
  • Brand and performance assets are thought of as separate entities right now, but these are becoming inseparable parts of the customer’s experience. For example, a user that is abruptly shown a use case landing page will most certainly want to explore the brand’s larger offering on the homepage or other branded pages (in my experience, this happens 20-30% of the time). Both types of pages will be relevant to cold prospects who are looking to learn about the brand while trying to get more information about their use cases. It’s about identifying the ideal navigation path and understanding what happens after a user interacts with a brand for the first time, or vice versa.
  • The bridge from brand marketing to performance marketing can be built via a multi-touch attribution model that companies either develop themselves by centralizing all data within a CDP like Segment, or they can be outsourced and imported to a 3rd party tool that does this for you, like Branch.

Preparing for the future means hiring for technical skills & building for channel-market fit

In a world of ML-driven customer acquisition, digital marketing teams must create clear signals and high-quality user experiences to be successful. This means that the better you perform today, the better you’ll perform tomorrow – it’s a performance flywheel. This wasn’t the case in the past, when performance marketing was limited by target audience ceilings and manual optimization decisions. The more signals you have, the better these platforms will be able to acquire new customers for you.

To accomplish this, you should hire ad managers from diverse backgrounds, with an emphasis on those with technical expertise. Content quality is still paramount for delivering persuasive messages that convert, but product marketing, content marketing, growth marketing, and product teams must work together to create a well-managed journey from first touch to product entry.

Why monetization is one of the most important yet underutilized growth levers

What is monetization strategy?

First, let’s understand: what is monetization? Monetization strategy refers to how a company decides to make money by offering its product or services to customers. This is a company deciding what they’re going to start charging users for them to use their products or services. Figuring out how you make money is tied in with what you’re naming it, when you charge, how often you charge, and how much it costs. For most software companies, revenue is the ultimate metric, and monetization is the strategy that touches revenue the deepest. The key reasons for a company to prioritize monetization strategy are:

  • Monetization allows companies to make money, which enables the team to reinvest into growth.
  • Monetization completes your product-market fit: it’s a reflection of your product’s positioning and packaging that ultimately allows you to build a sustainable and profitable business.
  • A focus on monetization strategy forces companies to focus on growing the set of users with the highest ARPC
  • Rather than cost cutting, monetization strategy is a lever for accelerating revenue through its compounding effects on acquisition and activation.

To grow a company, product and marketing teams will think about several strategies, including but not limited to common ones such as acquiring paid users, reducing product friction, and improving product retention. When it comes to revenue, it seems like sales alone is somehow responsible for numerical dollar amounts, and it can feel as if product and marketing will dust their hands and work on user and retention problems (often times as proxy or leading indicator) rather than directly on revenue ones. 

For product teams, the first default tactic to impact the bottom line is often to “cut costs”. This is also true for marketing teams that track metrics like LTV:CAC, where after a 4-5 years, LTV rarely grows and instead we look to decrease the cost basis of the users we acquire. Product teams often look at the traction of their time investments and determine whether products and features are additive or detrimental – if the latter, we shut down the feature, or roll it back. If the product or feature is neither good or bad, then usually nothing happens and is handed over to sales and marketing to grow adoption. These are problematic perspectives that are miss the mark on distributing a product to an end customer. Ultimately, a user will purchase a product that they need if the price and packaging are right in their moment of need. Companies can inspire users to get to that moment, but for the most part, a product’s core job is to get users to that point. 

Pricing strategies today – SaaS & enterprise

Pricing is a very relevant topic today, where companies need to adjust prices due to the rising costs of doing business as a result of decades-high inflation rates. Companies like Netflix or Beamer for example, have announced their price increases. Some of these companies are taking extra care to word their messages by addressing value-added features to justify the increases (Netflix explained that they are providing a lot more value through an extensive new collection of movies and TV shows). Yet others simply saying that their costs have risen and they need to increase prices as a result (Beamer announced that COGS have increased). Regardless of the circumstances, price changes will impact users in different ways, such as affecting the satisfaction of existing users or changing the perceived value of new potential users. Prices also affect how users perceive a company’s competitive positioning. In the examples above, companies expressed that their prices were forced due to broader economic conditions, and that they had no choice but to begrudgingly change. But while these example are driven by an increased cost basis, there are many benefits to thinking about pricing and packaging proactively relative to positioning.

In a world where user growth is typically seen as a proxy to revenue growth, there are many missed opportunities when it comes to driving growth through the components of monetization. Let’s break those down into the raw pricing and packaging components that a monetization strategy represents:

If we only rely on growing the # and composition of users to grow revenue, we missing out on a big part of our conversion engine. For example, what if your business only offered two plans today – one for consumers and the other for businesses, and you found out that the prosumers using your product were looking more for business-type features, but were paying for the consumer plan? That’s both money and potential users left on the table, so there’s a ton of value in exploring how you could create a separate package for your prosumer users. The company Webflow, a low-code website builder platform, does a great job of identifying several distinct segments of users that would want to pay to create a website. Though the monthly prices are each within a low range of prices, they individually speak to the specific type of customer who is looking to create a website through the tool – a blogger would look at the CMS plan, whereas a small business would start with the business plan, and someone who is just getting started and doesn’t have the need to collaborate with others or utilize scalable page types would go for the starter or Basic plan. 

Source: Webflow’s pricing page

Why don’t companies use pricing as a growth lever?

While it makes sense that we would want to package appropriately for our target customer segments, there are still reasons why companies don’t prioritize monetization as a growth lever. Based on my experience working at large companies like Pinterest and Facebook, to smaller startups like Indiegogo and Descript, these are the key reasons why companies don’t prioritize pricing changes as a growth lever:

  1. It seems customary to focus on getting customers first, then getting monetization right later. For many consumer companies, we’re trained to think of the goal as a hockey-stick shaped user growth curve. SaaS companies track ARR, but even then, revenue growth is driven by assuming changes in the number of paid seats rather than increasing revenue per user.
  2. Pricing and packaging isn’t a typical skill set for anyone on product or growth teams – it feels like an abstract decision that is driven by experienced people.
  3. Pricing changes aren’t easy to make. Not only are these changes deeply embedded within existing product flows, such as checkout experiences, feature gates, or pricing pages, they are also decisions that no single team can make alone. Changing prices also risks the satisfaction of your core customers, if you’re not doing it right.

Given the potential hurdles of getting buy-in from internal stakeholders such as the CEO, product and marketing leaders, to your own customers, updating your company’s pricing can be hard to get started. But the changes of a company having gotten pricing and packaging right on the first try is low, and particularly as a company is scaling its growth, there are many reasons that make pricing changes incredibly advantageous.

How do you build a monetization strategy for your product?

There’s an art and science to landing a monetization strategy. There’s no one-size-fits all strategy, even as a baseline, that will apply to the specific context of the product and market that you’re building for. The best thing to do is to start with the intuition you have for your customer set, and begin to validate it through experiments. Start with your own baseline and go from there. There’s no one else’s pricing strategy that you can copy and instantly become successful with (in fact, it can often be detrimental to just follow what your peers are doing without considering how it impacts your customers). That said, here are a few of the most important considerations for creating your baselines and experimenting from that point:

  • Monetization isn’t just price changes, it’s about positioning relative to your understanding of customer value and how your product delivers it. Think about how packages are separated, and make the decision points clear for customers. Your goal isn’t to confuse them on what they want, but rather to offer them a clear way to make a decision on how pricing scales value for their usage of your product.
  • Don’t bet all of your eggs in one basket, test changes where you can. Geographical or audience roll outs are a great way to test without disrupting the entire user base.
  • Don’t reengineer all of your product SKUs right out of the gate. Painted door tests are a way to get real reactions that mimic true conversion rates.
  • If unit economics / profitability don’t work yet, don’t rely solely on cost-cutting. Look toward pricing and packaging as a lever proactively, as it could change your COGS considerations.

Implementing a monetization strategy at your company

So then how do you know if you even have the right monetization strategy before you’re a prime candidate for pricing and packaging changes? The reality is that pricing and packaging is never going o be a decision made in a vacuum. Being at the bottom of the funnel, monetization strategies will see the compounded effects of acquisition and activation for better or for worse. You might be charging the wrong price or on the wrong value metric for the right customer, even if it gates access to the right thing.

Monetization is an incredibly interdisciplinary sport. In this topic alone, you’ll end up running through at least these functions:

  • Product marketing view of different segments.
  • Qualitative and quantitative research to run pricing surveys such as Max diff or conjoint analysis. 
  • Data science-led pricing tests, with test vs. control audiences, a/b variants, or other tests of statistical significance.
  • Customer support management to manage new and existing user questions and sentiment.
  • Sales & marketing will need to update pricing and packaging messages across the website, emails, and in external publications.
  • Finance to work through implications of how it impacts bottom line and other company numbers.
  • Product and design to identify the right flows to introduce pricing and packaging.

Not only does pricing require a strong general manager skillset, it also requires driving alignment amongst many stakeholders. Monetization isn’t going to be as simple as one person’s decision – because it ties to revenue and product, it will likely require working with the CEO in earlier stage and growth stage companies. This will involve bringing data, customer insights, strategic insights, risk management, and more to the table just in the process of writing a proposal alone. Given the complexity of pushing through pricing changes, testing every change isn’t always possible. Sometimes, you just have to roll it out and see how it works. If it doesn’t work well, you can roll back as long as you’re transparent and keep your customers updated each step of the way.

Use Python to study foreign language vocabulary on your Desktop (aka “digital flashcards”) – Python code included

Are you studying a foreign language, such as Chinese, Japanese, Korean, French, etc., and want an easier way to quiz yourself? Look no further, I’ve written a Python flashcards program that cuts through all of the complex designs and ads so you can study locally on your computer in peace.

Flash Cards Python Program Demo

Download Python and CSV files

I’ve included the Python script here. You’ll be directed to Google Drive, where you’ll be able to safely down the .py file. I’ve also included a CSV template sample so you can ensure that you’re using the right headings to run the code.

If you have any questions on how to use the code or feedback on how I can improve it, shoot me a note as I’d love to hear from you!

How to Backup WeChat Files, Chat History, and Contacts List (Screenshots Included)

For whatever reason, you may be looking to backup, download, or export your WeChat (微信)files, chat logs, or contacts. After scouring the Internet for hours, I wasn’t able to find comprehensive tutorials for my needs, so I decided to pull together these step-by-step instructions to help those who are facing similar problems. Without further ado, here are the best ways I found to backup my WeChat profile data:

3 Ways to Backup WeChat Data
  1. For chat history and files: Use WeChat’s PC App or Mac Desktop App to store logs on computer (for device to device transfer only)
  2. For chat history, files, contacts, or moments: Request personal data backup files (EU, US only)
  3. For chat history and files: Use third party desktop apps

For chat history and files: Use Wechat’s PC/Mac Desktop App to store logs on computer (for device to device transfer only)

Time: 15 minutes+, depending on how much chat history and file data you have
Preparation: Laptop (WeChat PC or Mac Desktop App), Phone (iOS or Android WeChat App)
Cost: Free

  1. Go to Wechat’s official site to download the Mac or Windows version of the Desktop app.
  2. Open the Desktop App and scan with your WeChat phone App to log in.

4. Once you’ve logged in, find the menu icon at the bottom left corner and click ‘Backup and Restore’.

5. Choose ‘Back up on Mac’ to store the files within the Desktop app on your computer.

6. Take out your Phone and follow on-screen guidance to ‘Backup All’, or select specific chat records that you want to save. This process will take you anywhere from a few minutes to hours, depending on how much data your chat logs have (i.e. image files, large attachments, etc). Keep both the computer and phone Apps open the entire time, or else the backup process will pause.

7. That’s it! The data is now stored on your Desktop WeChat. When you need to restore this data to a new phone or mobile device, just log in to the Desktop App, go into ‘Backup and Restore’ and choose ‘Restore on Phone’.

For chat history, files, contacts, or moments: Request personal data backup files (EU, US only)

Time: 10-15 minutes
Preparation: Phone only (iOS or Android WeChat App)
Cost: Free

  1. Open your iOS or Android WeChat App
  2. Go to ‘Me’ → ‘Settings’ → ‘More Settings’ → ‘Export Personal Data’

3. You’ll meet one of two scenarios:

A) If you haven’t linked your Wechat to an email address, you’ll need to verify your email first before you can request a personal data export:

B) If you’ve already linked and verified an email address, click ‘OK’ to begin the data request process. IMPORTANT: Do not close this Wechat window – minimize the App to check your email, and then come back to this screen. The email verification must happen while this window is running. If you click out of this screen, you’ll have to start the process over again. You’ll receive the verification email in your inbox about 2-5 minutes later.

After you click ‘OK’ in the email and see the success screen, go back to the App, where you’ll see that your email was successfully verified. Click ‘Next’.

4. Select which data types you want to export. Hit ‘Next’ to begin the data request process.

5. You’re done! Depending on how large the data file is, it can take up to 72 hours to receive an email with the data download link. You’ll need to open the email on your Desktop, and the download link will expire in 72 hours from the time it was generated. Note that the downloaded data is all stored in (.js) extension files, so you’ll need a way to parse through that.

Wechat data page must be opened in a Desktop browser to download.

For chat history and files: Use third party desktop Apps for backup

Time: 30 minutes or more
Preparation: Laptop, Phone
Cost: Depends on App (Free, $10-$50 is the typical range I’ve seen)

Of the three featured backup methods, this is the least consistently effective one. There are dozens of third-party software providers out there that claim to backup Wechat files, but depending on whether the original developers have updated their software, version compatibility, and many other reasons, the experience will be less reliable. Do your research prior to purchasing and make sure that other users can vouch for a positive experience (you can do by searching for user reviews on Google, Reddit, etc). I do not endorse any of the following options, but am laying out some options that I came across in my research:

  • MobileTrans – Backup Wechat history and account contacts to a PC before transferring to another mobile device. The same developers also created dr.fone, which offers similar functionality on their website.
  • iCloud – Backup your iPhone (for iOS users) including WeChat data, and you’ll be able to reinstate all of your chat history and contacts after you log into iCloud on a different mobile device.

Anything else?

That’s it! I hope one of the methods above worked for you. If not, do not fret – there are alternative (but less straight forward) ways to backup your data, including several that require reading the Chinese language. If none of the above worked for you, send me a message and I’ll help recommend an alternative solution.