Stop Building for “Everyone”: The Simple Persona Playbook That Prints $$$

Most companies don’t plateau at $2–10M ARR because of product or marketing.

They plateau because they’re building for “everyone who might buy,” instead of a crystal-clear someone.

This typically involves developing “personas”, “ideal customer profiles (ICPS)”, “jobs to be done” or something similar.

The tricky part here is that most of the traditionally processes here are a waste of time.

I’ve seen teams waste months here with very little to show for it.

This is the process that I’ve found to be much faster and impactful.

The single biggest inflection point in most companies happens when they know these groups, collect the data, user the data correctly and then change their strategy.

Let's say you sell a product that digs holes. What kind of hole does your customer want?

Well that depends if your customer a is:

All of these people want a hole. However, why they want a hole dug and what that hole has to look like is very different.

Because of that, the value that they put on a hole is also very different.

A 5 year old at the beach might only want a single hold dug and doesn’t have any money to pay you. An oil prospector might pay $100k per hold and might need hundreds of them.

Granted these holes need to look different, but that’s kind of the whole point.

There are lots of different methodologies to define your users personas.

In my experience, the most useful one by far is grouping users by the underlying problem they are coming to your product to solve.

This is the best indicator of both their retention potential and willingness to pay.

At Codecademy, we had four main personas:

After we started collecting this data and using it, we saw some unsurprising trends.

They typically didn’t buy the paid product. If they did, they didn’t stick around.

However, “Career Upskillers” and “Career Switchers” had a real problem we could solve.

If they gain new skills, they can make more money relatively quickly, so not surprisingly they had the highest LTV.

When we used this segmentation information in our data, we saw something like the below. Our best personas had a meaningfully different retention rate and LTV.

Like all good consultants, I came up with a catchy & complex framework for you to solve this:

In my experience most of the long expensive research frameworks here are a waste of time.

Founders should have been in your problem space long enough and spoken with enough users that they can probably segment people with about 30 minutes of thinking here.

If they don’t, you have other problems.

You’re better off forming some rough categories and filling in details as you speak with users in the course of routine user research.

As with most things in the startup game, velocity is everything.

For this data to be useful, you need to do the following things:

The easiest way to do this is with onboarding surveys.

I've never seen any material drop-off in early-stage engagement from onboarding and if you use this data to customize the early-stage experience, which you should, you will likely increase activation.

Let's take a look at a few examples.

Duolingo does this. they asked for the core motivation up front as they can't infer this from the course data.

Notion asks for your organizational information, likely as they have materially different PLG motions in selling to these users

Airtable collects the role of the users, likely as they both customize the early stage experience and their product gets used on the team level primarily.

As stated above, the basic playbook looks like this:

The hardest part of this by far is having the discipline in to focus on the best persona in your marketing and product development.

This cannot be over stated.

The larger your team and/or the longer you’ve been doing this, the more resistance there will be to stop doing things that kind of work and start trying to find thing that really work.

Good luck out there.