I sent the email at 6:55pm (Mountain Time), so Day #2 ended up being the peak of the launch.
- Day 1: $9,910
- Day 2: $14,841
- Day 3: $6,558
Two days later, I used @ConvertKit's "Send to Unopens" feature to re-send the email to everyone who didn't open the first one.
Subject line: "Second Brain Notion Template"
This brought in an additional 184 clicks to the sales page.
(IMO: Use this feature sparingly)
Here are the sales results for the full 30 days after this initial launch:
- 1,432 sales
- $88,241 in revenue
On the 31st day, I sent a new launch email to my full list (35,000 at the time), so this period is the best look at the impact of the initial waitlist launch.
The sales page looked very similar to how it still does today.
Here's a breakdown of some of the features and decisions that went into it:
I'll cover this more deeply in a future thread, but the gist is:
I linked the waitlist's landing page in a synced header block on all my free templates.
I also teased it in one video on Thomas Frank Explains (my Notion-focused YouTube channel)
My main marketing funnel is pretty simple:
1. Make great @NotionHQ tutorials 2. Use them to promote free templates 3. Ask people to sign up for newsletter when they're getting a free template 4. Gently promote paid templates at the top of free templates
If you'd like to study my sales page – or if you want the best productivity system for Notion – you'll find it here:
Write and build pages much faster by mastering a handful of Markdown symbols.
Here's a full list:
Heading 1: # + space
Heading 2: ## + space
Heading 3: ### + space
Italic: *text*
Bold: **text**
Strikethrough: ~text~
Pre-formatted text: `text`
Code block: ```
Unordered List: * + space
Numbered list: 1. + space
To-Do: [] + space
Blockquote: ” + space
Horizontal Line: ---
You can create toggle blocks with Markdown as well:
Toggle: > + space
Toggle Heading 1: > + space, then # + space
Toggle Heading 2: > + space, then ## + space
Toggle Heading 3: > + space, then ### + space
In mathematics, there's something known as the Hill-Climbing Problem.
You can write an algorithm that tries solutions to a problem and constantly moves in the direction of the best one it finds.
The problem is that you might not end up at the best possible solution.
You're guaranteed to end up at a "local maximum".
But because your algorithm can only move upwards, and because you don't already know the BEST possible solution, you might end up climbing the wrong hill.
It's very easy to get stuck and never find the global maximum.
This problem is reflected well in real life.
Life is like a mountain range covered in fog.
You can't see all the peaks; you can only tell which direction in your immediate area moves upward.
So you climb upward – and risk getting stuck at a small local maximum.