The Routing Call: Scoring a Community Export for CQLs

Marketing Academy · Field Work●Growth Marketing

All projects·From: Community-Led Growth Mechanics: Flywheels, Champions, and CQLs

MiniAudit· 25 minutes

Awfis Space Solutions

Objective: Given a real 20-row community engagement export, apply the lesson's three-tier CQL model to decide which members get a direct sales handoff, which get nurture content, and which are just lurkers, without over-notifying sales.

You're the community operations lead at Awfis Space Solutions, India's first listed coworking company (NSE: AWFIS), running a private member Slack for enterprise account admins across its 200+ centres. You've pulled a 20-row engagement export for this week and have one sales team to protect from noise.

Sort every row into high, mid, or low intent using the lesson's signal tiers, flag only the true high-intent rows for a direct sales handoff, and justify why the rest stay in nurture.

Before you start

What you'll need

Free path (everything below is enough to finish)

Google Sheets

FreeImport, filter, and tier the engagement export

Free, no account friction, sortable in minutes

The process

1 step

Step 01 of 01

Scoring community engagement signals into CQL intent tiers

The lesson's CQL model sorts members into high, mid, or low intent tiers based on specific behavioral signals, not raw engagement volume, and only the top tier gets a direct sales outreach.

Of 20 rows (pricing-thread visits, 'what does enterprise include' questions, event RSVPs, and bio-only signups), which rows justify pulling a rep off their queue today?

Google Sheets— Import the Slack engagement export, freeze the header row, filter the `signal_type` and `signal_detail` columns.

Procedure

Import and freeze row 1
Filter signal_type for direct questions ('enterprise plan', 'migration from [competitor]') and mark as high-intent
Filter for repeated pricing-thread visits and event-application activity, mark as mid-intent
Mark bio-only joins and single-visit rows as low-intent
Count each tier and compare against the sales team's weekly capacity

Sample output

Awfis Community Engagement Export, Week 12 (20 rows)

HIGH-INTENT (2 rows)
  1. admin_0142 -- asked 'what's included in the enterprise seat plan for 40+ desks' in #pricing
  2. admin_0289 -- asked 'how do we migrate our WeWork lease mid-term' in #general

MID-INTENT (6 rows, sample)
  3. admin_0311 -- visited #pricing thread 4x this week, no post
  4. admin_0367 -- applied to the quarterly Champion cohort
  ...4 more rows

LOW-INTENT (12 rows, sample)
  9. admin_0402 -- joined, added bio, saved 1 resource
  ...11 more rows

Healthy

2 high-intent rows go to sales today; the 6 mid-intent rows go into a nurture sequence, not a cold call.

Unhealthy

All 20 rows get forwarded to sales because 'they're all engaged.'

What this means

Intent tier, not engagement count, decides who gets a rep's time; a member who visited a thread 10 times but never asked a buying question still isn't high-intent.

So what do I do about it?

Symptom	Action	Effort
Sales complains the community team is flooding their queue with cold leads	Re-score the export by direct-question signals only before forwarding anything	5 min

YouYou can do this yourself, no engineering access required.

Final deliverable

A 20-row export re-sorted into high/mid/low intent tiers with only the high-intent rows flagged for a sales handoff.

See a reference example

Sample output

dbt Labs Community Slack, Week 9 export (excerpt)

HIGH-INTENT (1 row)
  1. admin_2201 -- asked 'does the enterprise tier support SSO for 200+ seats' in #general

MID-INTENT (4 rows, sample)
  2. admin_2255 -- visited #pricing 3x, applied to the ambassador cohort

LOW-INTENT (9 rows, sample)
  6. admin_2299 -- joined, added bio, no further activity

Success criteria

You're done when you can:

Correctly separates direct-question signals (high) from repeated-visit signals (mid)
Flags only the genuinely high-intent rows for sales, not the whole engaged segment