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Card class: Non-HeroCategory: Email Marketing
Count of marketing-qualified leads that hit the MQL stage over 60 days ago and never progressed. Cold leads that are re-engagement candidates or pipeline-removal candidates.

At a glance

MQLs Not Advanced After 60d counts contacts that reached the marketing-qualified-lead stage more than 60 days ago and have not moved forward to SQL, opportunity, or customer since. These are the leads marketing handed to sales that went cold. A large or growing number means one of three things: MQL scoring is too generous and is qualifying contacts who were never ready, sales is not actioning the handoff, or the offer simply does not convert this cohort. The card turns a silent accumulation into a decision: re-engage with a dedicated play, or remove from active pipeline so the funnel metrics stay honest.
What it countsThe count of contacts whose lifecycle stage is currently MQL and whose MQL-entry date is more than 60 days in the past, with no subsequent advance to a later stage. The drill-down lists each contact with its MQL-entry date, owner, and last-activity date.
Stage definitionUses HubSpot’s lifecycle-stage concept. “Advanced” means the contact moved to any stage after MQL (SQL, opportunity, customer, evangelist). A contact that slid backward or was reset is treated per the profile’s lifecycle rules.
The 60-day clockMeasured from MQL-entry, not from contact-creation. A contact created long ago but newly promoted to MQL has a fresh clock; only time spent sitting at MQL counts.
Re-MQL handlingA contact demoted and later re-promoted to MQL restarts the clock from the most-recent MQL entry, so a recent re-qualification does not show as 60-days cold.
Activity awarenessThe card focuses on stage progression, not engagement. A contact can be opening emails and still be cold by this definition if the stage never moved; pair with engagement cards to tell genuinely-dead leads from warm-but-unworked ones.
Currencyn/a (count).
Time windowRT (real time). The count refreshes as lifecycle stages change.
Alert triggerMore than 10 MQLs sitting cold beyond 60 days.
Rolesowner, marketing. Marketing owns MQL definition and re-engagement; the owner watches it as a lead-quality and pipeline-hygiene signal.

Calculation

Calculated automatically from your HubSpot data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.

Worked example

A B2B SaaS-plus-hardware company on Marketing Hub Professional with a Sales Hub pipeline. Reading on 12 Apr 26. The card reads 142 cold MQLs. The team segments the drill-down:
CohortCountLast activityRead
Webinar-sourced MQLs, no sales touch6445-70 days agoSales never actioned the handoff
Content-download MQLs, still opening emails38Within 14 daysWarm but unworked; re-engage
Trade-show scans, no activity since3160+ days, noneLow-intent; likely badge-scan noise
Genuinely engaged, stalled on budget9Within 7 daysReal, just slow; keep in pipeline
What the numbers reveal:
  1. The 64 webinar MQLs are a sales-process problem, not a lead-quality problem. They were qualified correctly but never picked up. The fix is a routing or capacity issue on the sales side, plus a nurture sequence to keep them warm until sales catches up.
  2. The 31 trade-show scans expose MQL scoring being too generous. Badge scans were auto-promoted to MQL on the strength of a single touch. They have shown no activity since. This is the cohort that inflates MQL counts and depresses MQL-to-SQL conversion. Tightening the score so a badge scan alone does not reach MQL would fix the source.
  3. The 38 still-opening MQLs are the re-engagement gold. Cold by stage but warm by behaviour. A targeted re-engagement play (case study, offer, direct sales outreach) is most likely to pay off here.
  4. The 9 budget-stalled leads should stay in pipeline. They are recently active and genuinely interested; they are slow, not dead. Removing them would understate real pipeline.
  5. The headline 142 is only useful once segmented. The raw count crossing the alert threshold of 10 is the trigger to investigate; the value comes from splitting it into the four actions above. Two cohorts get re-engagement, one gets a scoring fix, one stays put.
Illustrative numbers only.

Sibling cards merchants should reference together

This is a lead-hygiene and pipeline-quality card. Pair it with these to find the cause and the action:
CardWhy pair it with MQLs Not Advanced After 60d
MQL → SQL Conversion Rate (30d)The flow-rate companion. A growing cold-MQL pile and a falling MQL-to-SQL rate together confirm either over-generous scoring or a stalled handoff.
MQLs (Marketing Qualified Leads)The total stock. Cold MQLs as a share of all MQLs is the staleness ratio worth tracking.
Contact Lifecycle Stage DistributionThe funnel-shape context. A bulge at MQL is exactly what a large cold-MQL count produces.
Lead → MQL Conversion %The upstream gate. If too many leads become MQLs too easily, the cold pile grows; this card shows whether the gate is too loose.
SQL → Customer Conversion Rate (30d)The downstream close-rate, for sizing what a re-engaged MQL is worth if it does advance.
Email Open RateThe behavioural overlay that separates warm-but-unworked cold MQLs from genuinely dead ones.

Reconciling against HubSpot

Where to look in HubSpot: HubSpot can build this view with an active list, but it is not a standing report out of the box. The closest native approaches:
HubSpot → CRM → Contacts filtered to Lifecycle stage is Marketing qualified lead and the MQL-date property is more than 60 days ago. HubSpot → CRM → Lists to save that filter as an active list and watch its size over time. HubSpot → Reports → Analytics tools → Lifecycle for stage time-in-stage analysis where available on the plan.
The merchant traditionally maintains this as a hand-built active list and checks it periodically; this card keeps the count live and segments it. Why our count may differ from a HubSpot list:
ReasonDirectionWhy
MQL-date property choiceEitherHubSpot exposes more than one date that can represent “became an MQL”. Choosing a different anchor date shifts which contacts cross the 60-day line.
Re-MQL clock resetOur count lowerWe reset the clock on the most-recent MQL entry; a naive list using the first-ever MQL date counts re-qualified contacts that we treat as fresh.
Backward-slide handlingEitherA contact that slid back to MQL from a later stage is handled per the profile’s lifecycle rules, which a simple HubSpot filter may not replicate.
Time zoneBoundary effectThe 60-day boundary is anchored to the portal’s reporting time zone; contacts right on the line move in or out by a day depending on read time.
Filter scopeVariableProfile-level filters (region, brand, test contacts) can narrow our view relative to an all-contacts HubSpot list.
Cross-connector reconciliation:
CardExpected relationshipWhat causes legitimate divergence
Lifecycle Stage vs Ecom RevenueSome cold MQLs may actually have placed commerce orders and should be customers; that card surfaces the mis-classification this card cannot see.A self-serve purchase that never updated lifecycle stage leaves a contact counted as a cold MQL here while showing real revenue there.
Shopify Total RevenueNo direct numeric tie, but a cold MQL who quietly bought is revenue the CRM funnel did not credit.Lifecycle updates that lag behind store activity cause this drift.

Known limitations / merchant FAQs

Does a cold MQL mean a dead lead? Not necessarily. Cold here means the stage has not advanced in 60 days, which is a stage signal, not an engagement signal. Some cold MQLs are still opening and clicking emails; those are warm-but-unworked and are the best re-engagement candidates. Cross-reference an engagement card before writing any cohort off. The count keeps climbing. What does that tell me? A steadily rising cold-MQL pile usually means one of two upstream issues: MQL scoring is qualifying contacts too easily so unready leads accumulate, or sales is not actioning the handoff so qualified leads stall. Segment the pile by source and last-activity to tell which. Should I just delete cold MQLs to clean up the funnel? Removing them from active pipeline keeps your funnel metrics honest, but deleting contacts loses history and any latent value. The usual play is to demote stage or move to a re-engagement segment rather than delete. Reserve deletion for clear junk (mistyped emails, bot submissions). Why does the alert fire at more than 10? Ten is a generic default. A small B2B operation with a handful of high-value deals wants to know about even a few cold MQLs; a high-volume consumer funnel naturally carries more and needs a higher threshold. Tune it to your scale in the Sensitivity tab. A contact shows as a cold MQL but they bought from our store. Why isn’t the stage updated? Because lifecycle stage only advances when something updates it, and a self-serve store purchase may not have written back to HubSpot. That is a sync or automation gap. The Lifecycle Stage vs Ecom Revenue card is built to catch exactly these mis-classified contacts. Does this count change anything in HubSpot? No. It is read-only. Re-engaging, demoting, or removing a cold MQL is a manual action you take in HubSpot or via a workflow you build. Action playbook:
  1. Segment the drill-down by source and by last-activity date.
  2. Route still-engaged cold MQLs into a re-engagement sequence with a strong offer.
  3. For no-activity, low-intent cohorts (badge scans, single-touch downloads), tighten the MQL score so they never qualify in the first place.
  4. If a large cohort is qualified but un-actioned, treat it as a sales-routing or capacity issue, not a marketing one.
  5. Re-baseline the count after 30 days and confirm the pile is shrinking, not just churning.

Tracked live in Vortex IQ Nerve Centre

MQLs Not Advanced After 60d is one of hundreds of KPI pulses Vortex IQ tracks across HubSpot and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English. Start for free or book a demo to see this metric running on your own data.