Faiz Khan
B2B Lead Generation | Prospect Research | Data Enrichment
- 5.0
- Rating
- 70
- Followers
I built an investor research database for an international technology company that needed qualified, evidence-based investor profiles in AI, robotics, SaaS and IoT.
What I delivered:
25 investor profiles across 5 world regions
18 data fields per profile: investor type, current title, LinkedIn, website, investment evidence, two independent sources, email status, validation notes and a fit score
A scoring breakdown with High / Medium / Low follow-up priority
A clear split between firm-level investments and personally attributable ones
An honest email status (Verified / Unverified) with source or contact form
Process:
The client reviewed the work in several rounds. I fixed titles and missing profiles, replaced duplicated sources, corrected mismatched comments and re-checked every email status.
Result:
The client accepted the final file as a research base and described it as “considerably more rigorous and professional.”
Tools: Excel, CSV, LinkedIn, Crunchbase, public company sources
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A 60-second walkthrough of how I research multifamily and single-family real estate companies and map decision-makers to the right location.
The challenge:
A client's property list covered several target locations per company. The goal was a spreadsheet where every contact sits at the most relevant location, not simply at headquarters.
My process:
→ Classify each property as multifamily or single-family from evidence, and flag anything unclear
→ Research the operating companies, offices and leadership on official websites
→ Identify relevant decision-makers on LinkedIn across executive, sales, marketing and property-operations roles
→ Match each person to a location using their title, profile, team page and office directory
→ Check business emails and label them honestly: Verified, Unverified, Invalid or Not Found
→ Run quality control, then deliver a clean, organized spreadsheet
The part I care about most is location matching. When the evidence supports one office, I assign it and note the source. When it doesn't, I flag the record for review instead of guessing.
What I delivered:
• Property type, name and address
• Company, website and target location
• Decision-maker name, title and department
• LinkedIn URL and business email with a status
• Location match evidence, source URLs and verification notes
The video is a motion-graphics walkthrough built with fictional sample data. No real client information is shown.
Need research like this? Let's talk.
Faiz Khan
B2B Lead Generation | Prospect Research | Sales Intelligence
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A 45-second explainer for my B2B research service: Decision-Maker & Account Research.
The idea: know which companies to target, who to contact, and why they are relevant, before the first message is sent.
What the video covers:
→ The problem with unresearched lists
→ 10 deliverables, from target account research to CRM-ready output
→ My 5-step process: Target Accounts, Company Research, Decision-Maker Mapping, Contact Research, Sales Intelligence
→ Where the research helps: outbound, ABM, sales intelligence, partnerships, business development
→ A sample of the final spreadsheet (fictional data)
Designed and animated as a motion piece: custom icons, mini-visuals for each step, smooth scene transitions.
If you need clean, structured account and decision-maker research for your sales team, let's talk.
Faiz Khan
B2B Lead Generation | Prospect Research | Sales Intelligence
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