User-level vs account-level analytics
User-level analytics tracks what one individual does in your product: logins, clicks, feature use. Account-level analytics rolls that activity up to the company or team that individual belongs to. In B2B, the buying decision is usually made at the account level, but the behavior that predicts it happens at the user level. You need both, connected, not one instead of the other.
Why the distinction matters in B2B
A single user churning out of your product might mean nothing. A whole account going quiet, three seats stopped logging in, the admin hasn't touched a key feature in weeks, is a different signal entirely. If your dashboard only shows user-level numbers, you'll miss that pattern until the renewal conversation is already going badly.
The reverse problem also happens. An account-only view can hide the fact that adoption is uneven inside the account: one power user carrying the whole usage number while four other seats never logged in. That account looks healthy in aggregate and isn't.
How the two levels work together
Most B2B teams start by tracking individual actions: sign-ins, feature adoption, activation events. That's the user level. The useful step after that is grouping those same events by the account or company each user belongs to, so a marketing or growth team can see both views without switching tools or re-exporting data.
This is the same principle behind Funnelsight's shared funnel view: product usage data and CRM data live together, across activation, retention, and expansion, so an account's usage pattern and its sales status show up in the same place. You're not maintaining two separate spreadsheets and reconciling them by hand.
If you're setting this up for the first time, start simple. Pick one segment, maybe your highest-tier accounts, and get the user-to-account rollup working there before trying to cover every plan type. A batch refresh once a day is enough to start; you don't need a live stream to get useful signal.
What to actually look at
A practical starting point: for each account, how many distinct users were active in the last 7 or 14 days, and did that number go up or down. That single rollup catches the "one power user propping up a quiet account" problem without requiring a complex model. From there, teams doing PQL scoring typically feed account-level activity into the account's score, not just one user's, since the buying decision rarely rests on a single seat.
See user and account activity in the same dashboard, without stitching data together yourself.
Start free trialIs account-level analytics only relevant for larger deals?
No. Even a small self-serve account usually has more than one user once it's past the trial stage. Tracking at the account level just means grouping those users together, which matters whether the account has 2 seats or 200.
Do I need a data team to set up account-level rollups?
No. Defining which users belong to which account is a configuration step a marketing or growth team can do directly, without a data team or ongoing engineering support.
What about trial users who haven't invited a team yet?
You don't need a shared company email domain to group users into an account. If your trial works on an invite basis, one person signs up and invites teammates into the same workspace, that invite relationship groups their activity from day one, regardless of what email domain each person used. The real gap is a solo trial user who never invites anyone: there's no "account" to roll up yet, because there's only one person using it. That's not a tracking problem, it's just an accurate reflection of where that trial actually is.