What You'll Learn
What you’ll need
When Fabien “Fab” David joined Notion, it was already a rapidly-growing startup. His job? Build on that organic momentum, meaningfully scaling the company’s paid advertising efforts and building market share.
Fab started out by launching segmented campaigns across paid search, Facebook, LinkedIn, and display. But he quickly realized a big problem: not only was the company’s main landing page for their campaigns not originally designed for paid traffic, but he also had no ability to personalize page copy based on the ads users saw.
Notion is an amazing product that has a ton of different use cases, from project management to wikis and document sharing. And while Fab had campaigns expertly segmented across these use cases by persona, his landing page was generic. This meant that conversion rate wasn’t as high as the team knew it could be, and Fab was determined to fix that.
The original, non-optimized landing page Fab was using in campaigns. The language was generic compared to his campaign assets, which were customized by industry and use case.
Fab needed a landing page that would quickly demonstrate value and show relevancy to the ad that brought a user to Notion’s site in the first place. And shrinking attention spans meant he needed it to make an impression fast.
“Most traffic is on mobile. So you're not working with five seconds of someone’s attention span, you're working with three seconds max. That’s all they’ll take to make the call on whether something is relevant or not. So you have to throw everything at your landing page experience to make it convert as much as possible. Otherwise you’re just wasting money.”
Fab believed that Notion could increase conversion rates for app sign ups by:
Creating a new landing page template, specifically built with paid traffic in mind
Dynamically personalizing that new page based on the specific ad that a user saw
He wanted to build a strategy around the people behind the click. He wanted landing pages that spoke directly to the visitor's needs.
First up: Fab wanted to get a better sense of how other companies were designing their paid landing pages to inform how the team built their new template.
“I looked around at competitors in the space. I also refreshed myself on what I knew in terms of best practices for a paid landing page. Then I sketched out what I wanted my new template to look like.”
A single CTA. Simple language and minimal site navigation. A headline that elicits an emotional response and speaks to a specific pain point. These were all things Fab saw and liked—and what he wanted to reproduce for Notion’s own paid landing page template.
To create a template, the team first simplified one of their existing page designs, removing most of the screenshots and gifs they’d been using and adding the key elements he liked from his research.
This is what they ended up with for their new landing page:
Notion's new paid landing page, featuring simplified content and navigation, and a single CTA. The template page copy was still generic and didn't speak to all use cases.
Fab knew that he would eventually want to scale landing page personalization to all channels and ads, so he decided to use Mutiny to build out and measure the impact of his experiments.
“I wanted to move faster. There was a real sense of urgency to get to a better place where we could really scale the program. And I knew that using something that was out of the box would be a lot quicker than going through all of the iterations of wireframing with our engineering team for every new page.”
Fab was running a lot of ads across several channels, so he needed to prioritize which ads and audiences to create personalized experiences for first.
As any good performance marketer would, he turned to his data. In Mutiny, he saw that display, search, and Facebook presented the largest opportunities for impact, as these channels drove the most traffic to the site:
Example of report Fab used in Mutiny, with sample (dummy) data
For each of these top channels, Fab looked at what ads or keywords were driving the most volume of traffic to the site.
For example, for paid search, he examined top keywords and saw that wikis and project management were two popular non-branded search terms - a perfect place to start.
The next step: create personalized versions of the page based on the ad a visitor clicked on. Fab achieved this by dynamically personalizing the headline and hero image in Mutiny based on a user’s UTM parameters.
For example, let's say a user went to Google and searched for a "wiki" related search term, and clicked through on Notion's paid search ad at the top:
For these visitors, Fab personalized his paid landing page to show content that was relevant to Notion's wiki use case, like this:
Once Fab started to see strong results with his personalized experiments, he started to test out new formats for the page itself, as well. For example, with so much of their traffic coming from mobile, Fab wanted to test a shortened version of the paid landing page for all mobile visitors, knowing that the “above the fold” portion of the page was most important.
Using Mutiny’s banners, he added a pinned CTA once a visitor scrolled to the 50% mark of the page, to make signing up easier on a long-scroll page.
Fab was able to drive lifts in product signups on his paid landing page experiments by up to 60% in under 3 weeks with ~1 hour dedicated to launching experiments per week.
“By focusing on improving conversion on our paid landing pages, we’ve been able to make all of our paid programs more efficient, reinvest those savings back into our top performing channels and expand to new ones. So the results we’ve seen are not only making a direct impact on our campaign’s ROAS, but they’re also allowing us to be more effective with our entire paid advertising strategy.”
This strategy has also helped him create excitement and buy-in across both the marketing and data science teams at Notion. Whenever he shares this strategy’s impact on Slack, teammates across the org — including Notion’s founders — take notice.
“I’ve gotten really good feedback from leadership, marketing and data science on what we’re doing. My data science colleagues in particular have applauded the speed at which I’ve been able to gain learnings and results. Being able to test so rapidly has informed our content strategy by better understanding the pain points and use cases that are resonating most with our audience.”