A marketing-qualified lead and a sales-qualified lead are operating definitions, not universal facts. In a practical model, an MQL is a record marketing believes is ready for sales review under shared criteria; an SQL is a lead sales has accepted and qualified for active pursuit. The exact thresholds should match the business’s sales motion, not a software default.
THE HANDOFF
Qualify, accept, learn
THE LEAD ATLAS METHOD
Lead Atlas Data can supply a focused business-contact list for the customer’s selected campaign, categories, locations, and market, while the handoff process defines how researched contacts become accepted sales work.See how custom list research works ↗Write definitions in business language
Define the minimum account fit, contact relevance, need or use case, serviceable location, engagement or request, timing evidence, and disqualifiers for an MQL. Then define what sales must confirm for an SQL, such as a real problem, credible next step, buying process, feasibility, or active opportunity.
A content download, email click, or form fill may be a signal without being sufficient on its own. Use the smallest set of observable criteria that the team can apply consistently.
- Account and contact fit
- Reason for interest
- Serviceability
- Disqualifiers
- Required sales confirmation
- Next committed action
Add sales acceptance between the labels
Use a sales-accepted status when the team needs to distinguish routed leads from reviewed leads. The owner should accept, reject with a coded reason, or return the record for more information within an agreed time.
This separates marketing’s readiness decision from sales confirmation and prevents an untouched queue from being reported as SQL pipeline. Keep the number of statuses small enough that people will use them.
Route the context, not only the contact
Send the source, campaign, category, location, page or offer, message history, consent or outreach basis where applicable, fit evidence, score components, owner, and recommended next step. A salesperson should understand why the record arrived without reconstructing the entire journey.
For a Lead Atlas Data cohort, keep the original research brief and list batch attached. The fact that a business matches a selected category and location supports context; it does not by itself make the contact an MQL or SQL.
- Source and campaign
- Fit evidence
- Relevant activity or conversation
- Known exclusions
- Recommended action
- Required response time
Create rejection and recycling rules
Use a short controlled list of reasons: wrong account, wrong contact, no current need, unsupported location, duplicate, existing customer, bad data, unreachable, not ready, or sales capacity. Allow notes, but do not rely on free text alone.
Define what happens next. Some records should be corrected, some nurtured until a real trigger, some routed to another owner, and some permanently suppressed. Add review or expiry dates where circumstances can change.
Measure and improve the handoff
Track MQL volume, sales acceptance, response time, contact rate, MQL-to-SQL movement, opportunity creation, win rate, stage time, and rejection reasons by source and segment. Volume without acceptance or pipeline quality can reward the wrong behavior.
Review a sample together each month. Adjust definitions, routing, content, category choices, and location briefs based on real outcomes, then version the rule so historical reports are not silently reinterpreted.
THE TAKEAWAY
Define MQL and SQL with observable criteria, add a sales-acceptance step, route complete context quickly, record rejection reasons, and judge the handoff by pipeline quality rather than lead volume alone.OFFICIAL REFERENCES