What behaviors predict an account will upgrade?

Why this matters

Every PLG team wants the inverse of activation: “what behaviors predict an account will UPGRADE?” The activation-moment recipe asks “what makes them stick;” this asks “what makes them spend more.”

The mechanics are identical. Build two cohorts (upgraders vs flat-stayers from last quarter), compute behavior in the pre-upgrade window, find the events with the largest lift in the upgrader group. The output is a leaderboard of “if a user does X, they’re 4x more likely to upgrade.”

PostHog can do this — it’s called “correlation analysis” — but the UI requires you know which event you suspect. The agent doesn’t need a hypothesis; it scans every event.

What you get

• A list of events ranked by upgrade-lift score (P(event | upgraded) / P(event | flat))
• The top 5 predictive events, each with a confidence band
• A suggested in-app nudge: “users who do X are 4x more likely to upgrade — surface X in onboarding”

Walk through it

agentry_create_cohort {
  project_id: "default",
  name: "upgraders_q1",
  description: "Accounts that upgraded plan_rank during the last 90 days",
  filters: [
    { event: "plan_changed", to_plan_rank_gt: "from_plan_rank", since: "90d" }
  ]
}

{ "cohort_id": "coh_up_1", "member_count": 64 }

agentry_create_cohort {
  project_id: "default",
  name: "flat_stayers_q1",
  description: "Accounts that were paid throughout last 90d and did not upgrade",
  filters: [
    { event: "subscription_started", before: "90d ago" },
    { event: "plan_changed", since: "90d", absent: true }
  ]
}

{ "cohort_id": "coh_flat_1", "member_count": 318 }

agentry_analytics_query {
  project_id: "default",
  hogql: "WITH upgraders AS (SELECT properties.account_id AS aid, min(timestamp) AS event_ts FROM events WHERE event = 'plan_changed' AND properties.to_plan_rank > properties.from_plan_rank AND timestamp >= now() - INTERVAL 90 DAY GROUP BY aid), up_events AS (SELECT e.event AS event_name, count(DISTINCT e.distinct_id) AS upgrader_users FROM events e JOIN upgraders u ON e.properties.account_id = u.aid WHERE e.timestamp >= u.event_ts - INTERVAL 30 DAY AND e.timestamp < u.event_ts AND e.event != '$exception' GROUP BY event_name), flat_events AS (SELECT event AS event_name, count(DISTINCT distinct_id) AS flat_users FROM events WHERE properties.account_id IN (SELECT properties.account_id FROM events WHERE event = 'subscription_started' AND timestamp < now() - INTERVAL 90 DAY) AND timestamp >= now() - INTERVAL 60 DAY AND timestamp < now() - INTERVAL 30 DAY AND event != '$exception' GROUP BY event_name) SELECT u.event_name AS event, u.upgrader_users AS upgrader_n, f.flat_users AS flat_n, round(u.upgrader_users * 1.0 / 64 / nullIf(f.flat_users * 1.0 / 318, 0), 2) AS upgrade_lift FROM up_events u LEFT JOIN flat_events f ON u.event_name = f.event_name WHERE u.upgrader_users >= 10 ORDER BY upgrade_lift DESC LIMIT 20"
}

{
  "rows": [
    { "event": "api_key_rotated",            "upgrader_n": 28, "flat_n": 18,  "upgrade_lift": 7.7 },
    { "event": "webhook_created",            "upgrader_n": 41, "flat_n": 42,  "upgrade_lift": 4.8 },
    { "event": "team_member_invited",        "upgrader_n": 52, "flat_n": 78,  "upgrade_lift": 3.3 },
    { "event": "report_exported",            "upgrader_n": 38, "flat_n": 88,  "upgrade_lift": 2.1 },
    { "event": "integration_slack_connected","upgrader_n": 32, "flat_n": 88,  "upgrade_lift": 1.8 },
    { "event": "dashboard_view",             "upgrader_n": 64, "flat_n": 318, "upgrade_lift": 1.0 }
  ]
}

The output

Upgrade Lift Analysis (last 90d)

EVENT                          UPGRADER  FLAT   LIFT
api_key_rotated                     28    18   7.7x   ★ surprising winner
webhook_created                     41    42   4.8x   ★ operationalizing
team_member_invited                 52    78   3.3x
report_exported                     38    88   2.1x
integration_slack_connected         32    88   1.8x
dashboard_view (baseline)           64   318   1.0x

INTERPRETATION:
  Rotating an API key = treating you as production infra. These users are
  the warmest upgrade leads you have. Trigger an in-app nudge or sales
  outreach on this event.

SUGGESTED EXPERIMENTS:
  1. Show "production checklist" + upgrade CTA after first api_key_rotated
  2. After webhook_created → email "your webhook is live, here's how teams
     scale this"
  3. Make team-invite a first-class onboarding step (currently buried)

CAVEATS:
  - n=64 upgraders is small. Re-run quarterly as the cohort grows.
  - Lift != causation. Could be that mature teams do BOTH "rotate keys"
    AND "upgrade" naturally. Treat as signal, not proof.

Setting it up

If you’ve already set up the cohorts and account-id-on-everything pattern from account-health-score / tier-downgrade, you have what you need. The recipe asks the agent to join plan_changed events back against the per-account event history.

If you don’t yet have plan_changed with rank metadata:

// In your Stripe webhook for `customer.subscription.updated`:
const PLAN_RANK: Record<string, number> = {
  free: 0, starter: 1, pro: 2, scale: 3, enterprise: 4,
};

await fetch(`https://api.agentry.sh/v1/analytics/${process.env.AGENTRY_PROJECT_ID}/`, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.AGENTRY_DSN}`,
    "Content-Type": "application/json",
    "User-Agent": "myapp-stripe/1.0",  // REQUIRED — Cloudflare 403s default UAs
  },
  body: JSON.stringify({
    event: "plan_changed",
    distinct_id: subscription.metadata.account_id,
    properties: {
      account_id: subscription.metadata.account_id,
      from_plan: oldPlanName,
      to_plan: newPlanName,
      from_plan_rank: PLAN_RANK[oldPlanName],
      to_plan_rank: PLAN_RANK[newPlanName],
    },
  }),
});

The 90-day default window is reasonable for SaaS; if your sales cycle is shorter (PLG with weekly upgrades), drop it to 30d for faster signal.

Variations

• “Same analysis but for SELF-SERVE upgrades only. Sales-led deals confound the signal.”
• “For the top predictive event (api_key_rotated), show the distribution: when do users first do it relative to signup?”
• “Build the inverse — what behaviors predict DOWNGRADE? Should I worry about cohorts doing X?”
• “Compare upgrade-lift across plan tiers — different signal for free→starter vs pro→scale?”