The First Cut: Auditing a Usage Export for PQL Signals

Marketing Academy · Field Work●Growth Marketing

All projects·From: Product-Led Sales: When PLG Meets Enterprise

MiniAudit· 25 minutes

MapmyIndia (CE Info Systems)

Objective: Given a real 15-row product usage export, identify which free-trial accounts show genuine PQL trigger signals versus which are just active free users who aren't sales-ready.

You're the growth analyst at MapmyIndia (CE Info Systems), India's listed digital mapping and geospatial API provider (NSE: MAPMYINDIA), reviewing this week's self-serve API trial signups before your one weekly sync with sales.

Apply the lesson's PQL trigger criteria to a 15-row export of trial accounts and separate real PQLs from accounts that are just exploring the free tier.

Before you start

What you'll need

Free path (everything below is enough to finish)

Google Sheets

FreeFilter and flag the trial usage export

Free, no account friction, handles a weekly 15-row export easily

The process

1 step

Step 01 of 01

Identifying PQL trigger signals from raw product usage data

The lesson defines a PQL by specific behavioral thresholds, three or more teammates invited, a power feature used five or more times in two weeks, a seat count near the plan limit, or API usage suggesting an integration is already underway, not just general activity.

Of 15 trial accounts, which ones cross an actual PQL trigger threshold, and which are just a single developer poking at the API sandbox?

Google Sheets— Import the trial usage export, freeze the header row, filter on `teammates_invited`, `api_calls_14d`, and `seats_used`.

Procedure

Import and freeze row 1
Filter for accounts with 3+ teammates invited
Filter for accounts with sustained API calls across multiple days (not a single burst-test)
Filter for accounts nearing their free-tier seat or call-volume limit
Flag rows matching two or more criteria as PQL-ready

Sample output

MapmyIndia Trial Export, Week 6 (15 rows)

PQL-READY (3 rows)
  1. acct_0091 -- 4 teammates invited, API calls on 11 of 14 days, 88% of free call quota used
  2. acct_0114 -- 6 teammates invited, geocoding endpoint called daily for 2 weeks
  3. acct_0138 -- 5 teammates invited, 95% of free quota used in week 2

NOT YET (12 rows, sample)
  4. acct_0102 -- 1 teammate, 40 API calls total, single burst on day 1
  ...11 more rows

Healthy

3 of 15 accounts flagged as PQL-ready based on two or more matching criteria.

Unhealthy

Flagging any account with high total API call count, including a single-day load test from one developer.

What this means

Sustained, multi-day usage with team growth is a PQL signal; a one-off spike from a single tester is not.

So what do I do about it?

Symptom	Action	Effort
Sales is calling trial accounts that ghost immediately	Re-filter for sustained multi-day usage plus team growth before forwarding any account	5 min

YouYou can do this yourself, no engineering access required.

Final deliverable

A 15-row trial export re-sorted with PQL-ready accounts flagged and the matching trigger criteria noted for each.

See a reference example

Sample output

Calendly Trial Export, Week 3 (excerpt)

PQL-READY (2 rows)
  1. acct_5521 -- scheduled 11 meetings via booking link in 9 days, 3 teammates invited

NOT YET (8 rows, sample)
  2. acct_5544 -- 1 meeting scheduled, no teammates invited

Success criteria

You're done when you can:

Correctly distinguishes sustained multi-signal usage from a single burst of activity
Flags only accounts matching two or more real trigger criteria, not just high raw activity