Build US Paid Ads ICP Database

# Build a US Paid Ads / Paid Acquisition ICP & Advertising Intelligence Database We are looking for an experienced **data engineer / web scraping / marketing intelligence specialist** to build a high-quality database of US companies that match our ideal customer profile. This is **not a simple lead list-building project**. The goal is to identify US B2B companies that already invest meaningfully in paid customer acquisition, sell high-value products or services, and use a lead-to-sales funnel where conversion leaks can materially impact revenue. We want to combine: * firmographic data * paid advertising intelligence * website and funnel analysis * marketing technology signals * sales process signals * public advertising libraries * third-party advertising intelligence data into one structured and scored database. ## 1. Ideal Customer Profile We are primarily looking for companies that match the following profile: ### Geography * United States * US headquarters preferred ### Company Size Preferred: * **25–300 employees** This should not be treated as an absolute hard limit if a company otherwise strongly matches the ICP. Our strongest preference is approximately: * 50–300 employees ### Business Model The company should primarily generate customers through a **lead-to-sales process**, for example: * Book a Demo * Schedule a Call * Request a Consultation * Request a Quote * Contact Sales * Free Assessment * Application * Strategy Call * Sales Inquiry We are **not looking for traditional ecommerce / DTC companies** where the primary conversion happens directly through an online checkout. ### Customer / Deal Value Preferred: * Estimated value of a new customer / first-year contract / project value of at least **$10,000** * Ideally **$20,000 or more** The exact CLV does not need to be known. A reasonable estimate based on: * industry * pricing * ACV * contract value * project value * typical customer economics is acceptable. ### Paid Acquisition Spend Minimum indication: * approximately **$5,000 per month paid advertising** Preferred: * **$10,000 or more per month** Strong preference: * **$25,000 or more per month** We understand exact competitor ad spend is generally not publicly available. We therefore want to estimate advertising intensity using multiple proxy signals rather than claiming exact spend figures. ### Paid Advertising Channels Priority channels: 1. Google Search Ads 2. LinkedIn Ads 3. Meta Ads Companies using multiple paid channels should generally receive a higher score. ## 2. Priority Industries For the initial project, we want to focus primarily on **B2B companies with high customer value and meaningful sales funnels**. Priority industries may include: * B2B SaaS * Cybersecurity * IT Services * Managed Service Providers / MSPs * Cloud Services * ERP Consulting * CRM Consulting * Salesforce Consultancies * HubSpot Consultancies * Microsoft / Dynamics Consultancies * Data / Analytics Services * AI / Automation Services * Recruiting * Executive Search * Staffing * Professional Services * Commercial Insurance * Business Financing * Accounting / CFO Services * Compliance Services * B2B Legal Services * Industrial / Commercial Services * Other high-value B2B lead-generation businesses We are open to recommendations for additional industries that strongly match the economics described above. For the first phase, **B2B should be prioritized over high-ticket B2C/local businesses**. ## 3. Company Discovery Build an initial universe of companies likely to match the ICP. Potential data sources may include: * LinkedIn / Sales Navigator * Apollo * Clay * Crunchbase * People Data Labs * BuiltWith * Similarweb * Semrush * SpyFu * DataForSEO * other relevant databases or APIs Please recommend the best combination of data sources rather than limiting yourself to this list. For each company, collect where possible: * Company name * Domain * LinkedIn company URL * HQ location * Industry * Employee count * Estimated revenue if available * Company description * Primary product/service * Estimated customer / contract / project value * Business model classification * Lead generation vs ecommerce classification ## 4. Paid Advertising Intelligence For every company, determine whether it appears to be meaningfully investing in paid customer acquisition. We are particularly interested in determining whether the advertiser most likely belongs to one of the following tiers: ### Tier A Estimated paid media spend: * **$25,000 or more per month** ### Tier B Estimated paid media spend: * **$10,000–$25,000 per month** ### Tier C Estimated paid media spend: * **$5,000–$10,000 per month** ### Below Threshold * Less than approximately $5,000 per month * No meaningful advertising activity detected The spend tier should be an **estimate based on multiple signals**, not presented as verified exact spend. ## 5. Google Ads Intelligence Google Search Ads are especially important for this project. For each company, collect where technically possible: * Google Ads detected: Yes / No * Google Search Ads detected: Yes / No * Estimated monthly PPC spend * Estimated paid search traffic * Number of paid keywords * Number of observed ads * Advertising history * Estimated duration of paid search activity * Paid landing page URLs * Primary advertised products/services * Branded vs non-branded paid search activity where possible Possible sources may include: * Google Ads Transparency Center * Semrush * SpyFu * Similarweb * DataForSEO * other competitive intelligence providers We understand that the normal Google Ads API does **not** provide competitor account spend. Please explain your proposed methodology for estimating advertising intensity. ## 6. Meta Ads Intelligence For each company, determine where possible: * Meta Ads detected: Yes / No * Currently active ads * Approximate number of active ads * Ads observed during the last 30 days * Ads observed during the last 90 days * Ads observed during the last 180 days * Oldest currently active ad * Advertising longevity * Main advertised offers * Landing page URLs * Ad formats Potential sources: * Meta Ad Library * compliant third-party providers * scalable public-data methods Please do **not** simply propose using the official Meta Ad Library API without explaining its limitations for commercial US advertisers. We want applicants to explain how they would realistically solve this. ## 7. LinkedIn Ads Intelligence For each company, determine where possible: * LinkedIn advertising detected: Yes / No * Number of ads observed * Ads found during the last 30 days * Ads found during the last 90 days * Ads found during the last 180 days * Advertising longevity * Ad format * Main offer * Landing page URL * Primary CTA Potential sources: * LinkedIn Ad Library * third-party data sources * other scalable approaches Please explain your proposed technical method. ## 8. Website & Funnel Analysis Advertising activity alone is not sufficient. We want to understand what happens **after the ad click**. For every qualified advertiser, analyze the website and relevant landing pages. Identify the primary conversion flow, for example: Ad → Landing Page → Demo Request → Sales Call or: Ad → Landing Page → Consultation → Sales Process Collect where possible: * Paid landing page URL(s) * Primary CTA * Secondary CTA * Demo request present * Consultation request present * Quote form present * Contact sales present * Lead magnet present * Calendar booking present * Phone call conversion present * Form length * Dedicated landing page detected * Generic website page vs dedicated campaign landing page * Ecommerce checkout detected * Lead-generation funnel detected ## 9. Sales & Marketing Maturity Signals We would also like to collect signals indicating whether the company has enough marketing and sales maturity to be a strong customer. Examples: ### CRM / Sales Technology Detect where possible: * HubSpot * Salesforce * Microsoft Dynamics * Pipedrive * Zoho * Marketo * Pardot * other major CRM / marketing automation systems ### Advertising / Tracking Technology Detect where possible: * Google Ads tracking * Meta Pixel * LinkedIn Insight Tag * Google Tag Manager * conversion tracking * retargeting technology ### Marketing Team Identify whether the company appears to employ roles such as: * CMO * VP Marketing * VP Demand Generation * Head of Growth * Head of Marketing * Director of Marketing * Director of Demand Generation * Performance Marketing Manager * Growth Marketing Manager * Marketing Operations Where possible, estimate: * size of marketing team * whether there is a dedicated demand generation / paid acquisition function ### Sales Team Where possible, identify whether the company has: * SDRs * BDRs * Account Executives * Sales Directors * VP Sales * CRO Companies with an established marketing-to-sales handoff should generally score higher. ## 10. Scoring Model We do not want a simple Yes / No lead list. Every company should receive an **ICP / Opportunity Score**. The scoring model should reflect factors such as: ### Paid Media Intensity Examples: * Estimated $25k or more per month paid media * Estimated $10k–25k per month * Estimated $5k–10k per month * Paid keyword volume * Number of active creatives * Advertising longevity * Number of paid channels ### Customer Economics Examples: * Estimated customer/project value above $10k * Above $20k * Above $50k ### Funnel Complexity Higher score for companies with: Ad → Lead → Meeting → Opportunity → Proposal → Closed Won rather than: Ad → Direct Purchase ### Marketing Maturity Examples: * dedicated marketing team * demand generation roles * marketing operations * paid acquisition roles ### Sales Maturity Examples: * SDR / BDR team * account executives * defined sales process ### Technology Examples: * Salesforce * HubSpot * Dynamics * marketing automation * conversion tracking * retargeting pixels ## 11. Suggested Final Classification We would like companies classified approximately as: ### Dream ICP Strong fit across most criteria, for example: * 50–300 employees * $25k or more estimated monthly paid media * $20k or more customer / contract value * B2B * Google Search and/or LinkedIn advertising * established marketing team * CRM * sales team * demo / consultation funnel * meaningful advertising history ### Strong ICP For example: * 25–300 employees * $10k or more estimated paid media * $10k or more customer value * strong lead-generation model * sufficient marketing maturity ### Possible ICP For example: * $5k–10k estimated paid media * high customer value * lead-generation model * weaker or incomplete advertising signals ### Not ICP Examples: * ecommerce * very low customer value * little meaningful traffic * no real sales process * little/no paid advertising * very small businesses without marketing infrastructure ## 12. Required Output The final database should ideally contain: ### Company Data * Company Name * Domain * LinkedIn URL * Location * Industry * Employee Count * Revenue estimate if available * Company Description * Primary Product / Service * Estimated Customer / Contract Value ### Business Model * B2B / B2C * Lead Gen: Yes / No * Ecommerce: Yes / No * Primary Conversion Type * Sales Process Complexity ### Google Ads * Google Ads Detected * Google Search Ads Detected * Estimated Monthly Spend * Paid Keywords * Estimated Paid Traffic * Advertising History * Landing Pages ### Meta * Meta Ads Detected * Active Ads * Ads in Last 30 / 90 / 180 Days * Oldest Active Ad * Landing Pages * Primary Offer ### LinkedIn * LinkedIn Ads Detected * Ads Observed * Ads in Last 30 / 90 / 180 Days * Ad Formats * Landing Pages * Primary Offer ### Technology * CRM * Marketing Automation * Google Tracking * Meta Pixel * LinkedIn Insight Tag * Retargeting Technology ### Team * Marketing Team Detected * Relevant Marketing Roles * Sales Team Detected * Relevant Sales Roles ### Scoring * Estimated Paid Media Spend Tier * Paid Media Confidence Score * Customer Value Score * Funnel Complexity Score * Marketing Maturity Score * ICP Fit Score * Final Classification: * Dream ICP * Strong ICP * Possible ICP * Not ICP ## 13. Initial Pilot We want to start with a pilot of approximately: **300–500 companies** The purpose of the pilot is to validate: * data quality * accuracy of advertiser detection * accuracy of spend estimates * ability to distinguish lead-generation companies from ecommerce * ability to estimate customer value * quality of landing-page detection * reliability of Meta and LinkedIn ad detection * automation rate * cost per analyzed company If the pilot is successful, we expect to scale the system to: **5,000–20,000 or more companies** We are interested in building a repeatable system, not only receiving a one-time spreadsheet. ## 14. Automation A major goal is to automate as much of this process as reasonably possible. Please explain: * which parts can be fully automated * which parts require human review * which APIs/data providers you would use * expected API/data costs * expected processing cost per 1,000 companies * expected refresh cost * whether the database can be updated periodically Bonus if you have experience with: * Clay * Python * Apify * n8n * Make * Airtable * DataForSEO * Semrush APIs * Similarweb * BuiltWith * LinkedIn data * ad intelligence platforms * LLM-based website classification ## 15. When Applying Please answer the following questions clearly. 1. Which data sources would you use to build the initial company universe? 2. How would you determine whether a company is primarily a lead-generation business rather than ecommerce? 3. How would you estimate whether a company spends approximately: * $5k–10k per month * $10k–25k per month * $25k or more per month on paid advertising? 4. How would you detect Google Search advertising activity? 5. Which APIs or competitive intelligence tools would you use for Google Ads data? 6. How would you programmatically identify commercial US companies running Meta Ads? 7. What are the limitations of the official Meta Ad Library API for this use case? 8. How would you retrieve or detect LinkedIn advertising activity? 9. How would you identify paid landing pages? 10. How would you estimate customer / contract value? 11. How would you detect CRM, tracking and marketing technology? 12. How would you determine whether the company has a dedicated marketing and sales organization? 13. What percentage of this workflow could you automate? 14. Have you built a similar advertising intelligence, competitive intelligence or lead-scoring system before? 15. Please provide examples of similar projects if available. 16. What would you estimate the ongoing cost per 1,000 analyzed companies to be after the system is built? 17. How would you design the system so that advertising activity could be refreshed monthly or quarterly? ## Important Applicant Filter Please specifically answer this question: **Explain exactly how you would identify US companies running commercial Meta Ads programmatically. Do not simply answer “Meta Ad Library API.”** Also: **Explain how you would estimate competitor Google Ads spend. Do not answer “Google Ads API,” since we are analyzing companies whose Google Ads accounts we do not control.** Generic applications that do not address these technical questions will not be considered. ## What We Are Looking For We are primarily looking for someone with experience in: * data engineering * web scraping * competitive intelligence * advertising intelligence * APIs * automation * data enrichment * lead scoring We are **not** primarily looking for a virtual assistant who manually builds lead lists. The successful candidate should be able to help us develop a scalable methodology for identifying the companies where paid traffic, high customer value, and a complex lead-to-sales funnel create the highest potential economic opportunity.