The lead was good. The data wasn’t.
Enrich. Clean. Rank by fit. Fix lead quality before sales ever sees it.
Before
- Name
- J. Smith
- Job title
- “sr mgr mktg ops”
- Company
- —
- Industry
- —
- Lead score
- —
- Account match
- None
- Assigned to
- Unassigned
Everything in between is Mary.
Trusted by marketing ops, demand gen, and marketing leadership
Lead quality is a data problem, not a scoring problem.
This is what a lead looks like when it arrives — and what it looks like when Mary’s done.
Before
Raw- Name
- J. Smith
- Job title
- “sr mgr mktg ops”
- Company
- —
- Industry
- —
- Lead score
- —
- Account match
- None
- Assigned to
- Unassigned
After
Pipeline-ready- Name
- Jessica Smith
- Job title
- Senior Manager Marketing Operations
- Company
- Acme Corp ($48M ARR · Series C)
- Industry
- B2B SaaS
- Lead score
- 91 · ICP fit: High
- Account match
- Acme Corp — open opportunity
- Assigned to
- Sarah Chen · West Enterprise
Every lead. Every channel. Automatically.
Junk detection
Removes bots, invalid emails, and disqualified records before anything else runs.
Enrichment
Pulls company size, industry, revenue, tech stack, and buying signals from Clearbit, ZoomInfo, LeadIQ, Lusha, Cognism, + dozen other providers — in sequence, until the record is complete.
Data cleaning
Normalises job titles, resolves duplicates, matches leads to accounts. The record is accurate before it's ever scored.
Persona screening
Matches against your ICP and ranks by fit. The leads that matter most surface first.
The Results
12,647 new MQLs. Every month.
Bad data held them below the MQL threshold. Enrichment data pushed them over.
Public SaaS Company
2,432 incremental SALs. Every month.
Bloomreach. Sales stopped wasting time on poor-fit leads — and started working the ones worth calling.
What is lead quality in B2B marketing?
Lead quality is how well an inbound lead matches your ICP — company size, industry, role, intent — and how complete the data about them is. Most teams measure quality with a score, but the score is only as good as the data feeding it. Quality problems almost always start as data problems.
Why is lead data more important than lead scoring?
Scoring is math on top of data. If the data is incomplete, stale, or wrong — which it usually is for inbound — the score is wrong too. Fixing the data first (enrichment, dedup, normalization) makes every downstream decision better. Mary fixes lead data before scoring runs, so quality is a given, not a hope.
How do you fix bad lead data before sales sees it?
You enrich it with a live data source, deduplicate against existing records, match each lead to its account, and score for ICP fit — all before the lead hits sales. You normalize the data to ensure consistency across titles, personas, and industries. Mary does this automatically inside your marketing automation platform, so every lead sales sees is already complete, matched, and scored.
What’s the difference between lead enrichment and lead quality?
Lead enrichment is one input to lead quality. Enrichment adds missing fields to a record (job title, company size, tech stack). Lead quality is the overall fit and completeness of the lead — enrichment plus dedup, matching, normalization, and ICP scoring. High-quality leads require enrichment, but enrichment alone doesn’t make a lead high-quality.
How does Mary score lead quality?
Mary evaluates every inbound lead against your ICP, enriches missing data, deduplicates against existing records, and matches each lead to its account. The quality signal is a reasoning output — not a static score — and it travels with the lead into Marketo, HubSpot, Eloqua, or Salesforce so sales sees full context, not just a number.
Every good lead deserves to be worked. Mary makes sure it is.
See how Sysdig, Zendesk, and PagerDuty use Mary to keep leads moving.
Hire Mary