Use case · project management tools

Predictive trial conversion for project management tools

Project-management trials are won or lost at the team level, not the individual. One admin poking around rarely converts; a team that has invited members, created projects, and started assigning work almost always does. KaQuill models that team-level adoption curve and intervenes when a workspace is stalling short of the critical mass that predicts a paid plan — instead of blasting every trial admin with the same day-7 discount.

3+ active seats

a workspace-level signal worth targeting — KaQuill can focus on teams stalled below it.

Why project management tools trials leak revenue

Single-player trials masquerade as intent

An admin clicking around alone looks active but rarely converts. Seat-based products convert on team adoption, which time-based drips can't measure.

Invitations are the leading indicator

The moment a trial invites its second and third teammate, conversion odds jump. Most tooling never fires on that signal.

Blanket discounts erode seat revenue

Teams that would have bought at full price get the trial discount anyway, quietly shrinking your per-seat margin — textbook Ghost MRR.

Events to send for project management tools

The richer your event schema, the sharper KaQuill's predictions. For project management tools, start with these:

kq.track("member_invited", { user_id, workspace_id, invited_count: 3 })
kq.track("project_created", { user_id, workspace_id })
kq.track("task_assigned", { user_id, assignee_id, workspace_id })
kq.identify(user_id, { plan: "trial", workspace_id, seats: 8, role: "admin" })

Frequently asked

Does KaQuill model team-level or user-level conversion?+

It can weigh workspace-level signals — invitations, active seats, shared projects — so a stalling team gets a nudge even if the admin individually looks active. Send workspace identifiers on your events to enable this.

What's the most important event to send?+

Team-formation events: member invited, project created, task assigned across users. These predict seat-based conversion far better than individual logins.

How does Ghost MRR apply to seat-based pricing?+

It's the seat revenue you give away by discounting teams that would have paid full price. For seat-based products this compounds fast, which is why surfacing it matters.

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