Loom has been making waves since it was founded in 2017 (and later acquired in 2023 by Atlassian). So, what makes this tool stand out in the crowded world of workplace communication?  

Let’s break it down:

• Its mission is empowering effective communication at work through video messaging.
• It serves a global workforce with 25+ million users in over 120 countries.
• It’s trusted by 350,000 companies worldwide.

One of the essential components that led to that success was the focus on user experience starting from the very first interaction with the product. As we know, this is all connected to user onboarding and activation.

What is activation at Loom?

At Loom, the activation metric is defined by a key user behavior: the Video First View (VFV). 

A user is considered “activated” when they create and share their first video, and that video receives at least one view within the first week. 

This metric focuses on ensuring that creators not only record a video but also successfully share it in a way that leads to meaningful engagement, signified by someone watching the video.

1 VFV (Video First View) = One recording with at least 1 view in Week 1

If you’re still trying to define the activation metric for your product — here’s a quick reminder for you: Define your Habit moment, Aha moment, and Setup moment (yes, in that order). It will help you connect your activation metric to longer-term retention and monetization from the beginning.

How Loom added value to the first experience with AI

Imagine spending time recording a video, hitting send, and then… crickets. No one watches it. It’s discouraging, right? 

This was the reality for many new Loom creators. While they embraced the ease of recording, they often skipped adding titles or any context, leading to a big problem: viewers received a video link with no idea what it was about. Unsurprisingly, this lack of context resulted in a significant drop-off between creators sharing their videos and those videos being viewed.

For the team at Loom, this was a major hurdle in driving activation rates — new creators weren’t seeing their videos engaged with, making it harder to onboard them successfully. 

The challenge? How do you ensure videos get viewed without adding extra friction to the creator’s workflow?

🔴 Before → Missing context leads to lost engagement

To summarise, Loom experienced 2 problems:

1. For creators: New creators were sharing videos without adding titles or any context. While they preferred the ease and simplicity of recording and sending videos, the lack of clear information meant their videos often went unwatched. This drop in engagement left creators discouraged, as their hard work failed to generate meaningful interactions.
   
2. For viewers: Viewers received videos with no explanation of what the content was about, making it hard to determine if the video was worth their time. Without titles or summaries, videos felt unclear and irrelevant, leading viewers to skip them entirely, further contributing to the drop in activation rates.

🟢 After → Adding valuable context with AI

The team at Loom knew they needed a solution that wouldn’t burden creators with more tasks. Instead of asking users to manually add titles, chapters, or summaries, they turned to AI. 

Using existing video transcripts, Loom started generating titles, chapter markers, and summaries automatically. This allowed viewers to get an immediate sense of what the video was about, making it easier for them to engage with the content.

The team uncovered 3 use cases where AI played a valuable role:

AI-generated titles

As soon as a creator finished recording, AI would generate a clear, concise title based on the video content. 

This eliminated the need for creators to manually add a title while still ensuring viewers immediately understood the purpose of the video. 

This simple addition gave viewers a one-second glance at what to expect, drastically increasing the likelihood they would watch.

Chapters

For longer videos, AI automatically inserted chapter markers, breaking the content into digestible sections. 

Viewers could quickly jump to the parts that mattered most to them, reducing the friction of watching a long, uninterrupted video. 

This was a game-changer for time-strapped viewers, giving them more control over their viewing experience.

Summaries

AI also provided brief summaries of the video content, offering an extra layer of context. These summaries gave viewers a snapshot of the video, helping them decide if it was relevant or worth their time.

By handling these tasks automatically, AI relieved creators of the burden of polishing their videos manually. 

This was crucial because many new users were hesitant to spend extra time perfecting their recordings. 

Meanwhile, viewers benefited from having more context upfront, making videos more engaging and easier to navigate.

🎯 Results → Driving higher “view rates” and better engagement

Once Loom implemented AI-generated titles, chapters, and summaries, the impact on both creators and viewers was clear. By making videos more polished and easier to navigate, the team saw improvements across the board.

Let’s look at the outcomes — both for business and users.

Quantitative📈: Increased engagement

• Higher view rates: AI-generated context significantly increased the likelihood of videos being viewed. The drop-off between sharing and viewing decreased, while the number of “Video First Views” (VFV) increased significantly.
• Improved engagement: Features like chapters allowed viewers to focus on the most relevant parts, increasing overall interaction time with videos. The number of recordings with comments increased as well.
• Successful A/B testing: Each AI feature was A/B tested, showing consistent improvements in viewership and engagement. The combined rollout further enhanced the positive impact.

Qualitative 🤝: Enhanced UX for both creators and viewers

• For creators: The AI features reduced the burden of polishing videos. Creators could focus on recording without worrying about adding context, as AI handled it automatically.
• For viewers: Clear titles, summaries, and chapters made videos easier to understand and navigate. Viewers could quickly assess the relevance, boosting their willingness to watch and engage.
• Validated hypothesis: The core assumption that polished videos lead to higher engagement was proven true — videos became more accessible, and interactions improved without adding complexity for creators.

While the AI features were part of a paid plan, the overall impact was significant enough to consider rolling them out globally. Despite some users not having immediate access to these premium features, the overall customer experience saw a major uplift.

What can we take away from here? The AI-driven enhancements not only improved the technical metrics of view rates and engagement but also provided a smoother, more enjoyable experience for both creators and viewers, reinforcing the importance of adding context to video communication.

Summary

We’ve explored how Loom successfully enhanced user engagement and activation by tackling the practical barriers faced by creators and viewers. 

What can you apply from these learnings to your product? Remove unnecessary friction for users by leveraging AI. Think outside the box to find solutions that solve user problems without burdening them with more work to do. 

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