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CASE STUDY / OUTBOUND RESEARCH
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500 attendees became 10 prospects worth contacting.
The user opened a signed-in Luma roster and asked rtrvr to finish the research. One request found work emails and social profiles, ranked the list against the team’s ICP, and held the best 10 for review before outreach.
AUGUST 2, 2026
Started in the user’s signed-in browser. Finished in a review-ready Google Sheet.
SOURCE ROWS500event attendees
SHORTLIST10prospects to review
PERSONCOMPANYEMAILLINKEDININSTAGRAMXICP FIT
Maya ChenNorthstar LabsVP Salesmaya@northstarlabs.co/in/mayachen@maya.builds@mayachen93
Owen BrooksHarbor SystemsFounderowen@harborsystems.io/in/owenbrooks@owenbrooks@owenbrooks91
Leila KhanTrellis WorksCo-founderleila@trellisworks.co/in/leilakhan@leila.builds@leilak89
IMPACT
The team could review the answer, not 500 profiles.
Each row brought together the person, company, work email, ICP score, research sources, and available LinkedIn, Instagram, and X profiles.
After the shortlist was approved
Find, rank, review, and contact
Work email, LinkedIn, Instagram, and X
COMPANY BACKGROUND
Public data filled the gaps. rtrvr kept the job moving.
Bright Data provides company and contact data through marketplace datasets, live scrapers, and web search. rtrvr chose what to look up, joined it to the private roster, and carried the result through review and outreach.
Bright Data returned public person and company records. rtrvr read the signed-in roster, chose the fields, ranked the candidates, and returned the shortlist.
THE CHALLENGE
The roster had 500 names. The team needed reasons to contact 10.
The list lived behind a login and held little of the context needed for outreach. Each attendee still needed a role, company, work email, social profiles, and a score against the team’s ICP.
“Enrich these guests with professional profiles and work emails, score them against my ICP, and reach out to the top 10.”
THE RUN
rtrvr enriched 500 attendees, qualified the best fits, and reached out to 10.
The user gave the goal once. rtrvr completed the research and qualification, then paused for approval before outreach.
- 01
Read the roster
rtrvr collected all 500 attendees from the Luma page open in the user’s signed-in browser.
- 02
Complete each record
It added roles, companies, work emails, and available LinkedIn, Instagram, and X profiles.
- 03
Rank the list
rtrvr removed duplicates and scored every attendee against the criteria in the request.
- 04
Pause for review
The user approved the 10 strongest matches before rtrvr sent connection requests and emails.
WATCH THE RUN
Watch the roster become a shortlist.
See rtrvr move from the signed-in event page to a sheet the user could review and approve.
THE OUTCOME
The user reviewed one sheet instead of opening 500 profiles.
The sheet kept the useful context together: role, company, work email, LinkedIn, Instagram, X, ICP score, and the sources behind the research.
The complete enriched list
Person, company, and available contact fields landed in a Google Sheet as the records returned.
An ICP score for every attendee
Each score used the buyer criteria from the original request, not a generic lead score.
Research links in every row
The sheet included the links used to research each shortlisted person.
The 10 people to contact
Only the 10 strongest prospects moved into connection requests and emails.
WHAT ELSE THIS UNLOCKS
The next valuable list does not have to start in Luma.
rtrvr can start from the signed-in list or search a team already trusts, then return the people and context needed for the next decision.
01 / CONFERENCES
Turn an attendee roster into a follow-up list.
Add role, company, work email, social profiles, ICP fit, and research links before anyone writes an email.
See lead enrichment 02 / SALES NAVIGATORKeep the search you already shaped.
Use the signed-in filters on the page, complete the people and accounts, and rank the matches against the team’s criteria.
See lead enrichment 03 / STARTUP LISTSFind the people behind the companies.
Start with company names or domains. Return the relevant founders, company facts, work emails, social profiles, and research links.
See lead enrichmentWHY BRIGHT DATA
Marketplace datasets, live scrapers, and web search for current public company and contact data.
Visit Bright DataMatch
Pre-collected datasets for fast, economical bulk matching
Collect
Live scrapers for fresh, URL-specific information
Search
Web search for broad and localized discovery
The costs in this case study describe the recorded run. Paid data lookups are separate from Free Mode.
START WITH THE LIST
Turn your list into people worth contacting.
Start from the signed-in page in your browser. Move larger or repeated runs to Cloud. Bring your team to a demo when it needs to fit how they sell.