Why good analytics data matters and what you can do with it

If you worked at Loom, the data below would allow you to do the following:

• Rescue slipping accounts before the QBR surprise

• Nudge power users into champions (and case-study stars)

• Time upsell offers when engagement is peaking

• Score trials so Sales stops guessing who’s ready to buy

• Kill one-off support tickets with data that shows the full story

Most importantly, you can answer this question:

“Is this customer actually getting value from Loom?”

That list isn’t theory. It’s what Loom’s CS, sales, marketing, and support teams could do tomorrow with the right events feeding Accoil.

Step 1: Define Loom’s core value

Loom isn’t just a video recorder. Loom sells async clarity for teams.

So the big question is simple:

“Is this account using Loom as a regular part of their workflow?”

Everything else from stickiness to expansion and referrals flows from that answer. Keep the question taped to your desk; it keeps the data plan lean and focused.

Step 2: What healthy Loom usage looks like

For a tool like Loom, a healthy account feels like:

• Many voices recording (not one hero user)

• Frequent sharing across roles and departments

• High view-through – people actually watch the videos

• Replies and reactions that prove async dialogue, not one-way broadcasts

• Weekly cadence – Loom pops up in rituals (Friday founder updates, demo loops, stand-ups)

That kind of activity leads to retention and expansion. For example, if engagement scores dip two weeks in a row, you could trigger a “save account” play.

Can you see that it’s worth tracking more than just “number of videos recorded”?

Step 3: What should Loom track?

Loom doesn’t need a 100-event firehose. Eighteen well-named events can tell the whole story.

What is product event schema?

A product event schema is the labels that are consistently applied to the actions your users take in your products.

Here’s our take on ~20 analytics events or signals that would show the Loom team what’s really happening in their app:

Activation (first week)

• comment_left

• video_shared

• video_created

• video_first_replied_to

• video_viewed_by_team_member

Metrics (ongoing)

• videos_created_per_user_per_week

• videos_viewed_per_user

• view_completion_rate

• internal_share_count

• time_to_first_view

• comment_count

• video_emoji_reaction_added

Account Signals

• engagement_dropoff (vs. prior 4 weeks)

• account_growth_rate (new users added who also engage)

• power_user_dependency (e.g. 80% of videos from 1 person)

• num_active_creators_per_week

• new team members who activate within X days

• % of invited users who’ve created/viewed a video

Step 4: How we would use this data

Armed with those events, the Loom team could build an account and user health model that rolls up these metrics:

It gives you clear, quantified account health signals. No guesswork. And these are just a sample of the metrics and next steps Loom could track and action.

Final Thought

Product analytics isn’t about counting clicks; it’s about proving value. When your data:

• Flags customers who are getting value, you can celebrate and expand them

• Highlights customers who aren’t getting value, you can intervene while there’s still time

For Loom, that means zooming in on collaboration, engagement, momentum, and spread; the exact signals Accoil was built to surface.

Plug in the events above, let Accoil crunch the score, and give your CS team the super-power they’ve been promised.