How to Navigate Rapid Growth

🔗 Houck’s Picks

My favorite finds of the week.

 Opportunity: Letterboxd for Personal Spending

Benchmarks help people make decisions:

• What should a startup’s burn multiple be at the seed stage?

• What salary should a startup pay its first designer?

• What share of my earnings should I put into savings each month?

• What stock trades has Nancy Pelosi made recently?

You get the point. Despite the obvious benefit of having information about what other people like you have done, there aren’t great resources for understanding how people are making common every-day budgeting decisions.

And in a world with inflation that’s rising once again, crowdsourced info can be a big help.

To build this, I’d do a round of user research interviews to identify a single specific use case that many people cite as what they’d want to use something like this for the most, and build a product specifically around that (ignoring all other use cases).

This makes it easier to target a specific ICP, and also to market to them.

Only after getting significant user design would I consider expanding to other use cases (the more tangential the better).

You may find that it becomes a bigger opportunity to build a vertical product experience around whatever that initial use case is, rather than adding new verticals and expanding that way instead. As usual, my advice here is to follow the momentum, see what your users are trying to use it for on their own.

🧠 Framework: The Psych Framework

How do you measure a user’s motivation to continue caring about your product?

The Psych Framework tries to help you map it throughout a user journey by framing moments in the journey as either increasing or decreasing motivation, and assigning points to each moment.

For example, asking a user to enter information manually will decrease future motivation, you’re “spending” your site’s mental capital with the user to get them to perform that action.

While the actual point values that an action increases or decreases motivation by are likely to vary from user to user, using the framework is a good reminder that you can’t ask for too much from users, you have to give as well, to keep them excited.

📈 Trend: ATS Checker

Applicant tracking systems typically have the ability to recommend certain candidates based on keywords in their resume. They scan each resume that’s been submitted for a job and surface the ones that seem like the best fit.

We can argue about whether existing options actually do a good job of this or not, but a recent trend is actually seeing candidates reverse engineer this by creating their resume for a machine to read and assess it, not a human.

As a result, “ATS checkers” like this one are having a moment:

As these resume “checkers” continue to rise in popularity, it follows to wonder why humans need to be in the loop for the creation of resumes at all?

It’s a little dystopian, but imagine the following:

1. You record a voice note into an app on your phone describing your background after also linking to your social media profiles and the types of roles you want to apply for.

2. The app crawls job boards and company careers pages, and creates an AI-optimized resume for you that’s customized for each individual job, based on what keywords it thinks that company’s ATS will want to see for the role.

That would increase not only the volume of jobs you can reasonably apply to, but also your interview request hit rate. If it works well it could shift the power in the hiring world to the candidate entirely, due to the increased volume of interview requests.

You could take it one step further by building in job recommendations for candidates, skills training, interview prep, and more to monetize it.

I’m sure there are other ideas to capture this trend as well, but this one stood out to me.

💬 Quote: Navigating Rapid Growth

Not a lot of founders ever experience true hypergrowth. But, in case you do, most good advice on how to handle it can be boiled down to “take it slow(er than you think you should).”

Oftentimes your (over-)reaction will be worse than the base case, even if things are already starting to break.

A valuable thing I took away from the hypergrowth days of working on Uber Eats was that less is different from the 0 → 1 stage than I expected. In a weird way, hypergrowth is an extension of the 0 → 1 stage:

• You spend time learning & shipping, not optimizing

• A lot is going to go wrong

• Urgency wins

Most startups end up plateauing or dying too early to feel this. If you’re aiming to build a unicorn but find that your startup starts to feel like a more mature company before you’re at $100 million in annual revenue, consider that you may be letting this happen too soon, and that you need to be more aggressive with your resources.