First Query

Campaign Operations Lead at an influencer marketing agency. Built the scraping infrastructure, qualified 12,000+ creators, and ran end-to-end campaigns that improved ROAS for clients.

First Query

Campaign Operations Lead at an influencer marketing agency. Built the scraping infrastructure, qualified 12,000+ creators, and ran end-to-end campaigns that improved ROAS for clients.

Project Image
Project Image

Overview

First Query (formerly Haven Influence) is an influencer marketing agency I co-founded and ran Campaign Operations. We were a lean team, which meant I owned a wide range of responsibilities: from building the technical infrastructure to running campaigns end-to-end. The core problem we solved was that brands were wasting hours on manual creator vetting with no reliable signal on audience quality or real engagement. I built the infrastructure to fix that, and then ran the actual campaigns on top of it.

What I built and owned:

  • Built custom scraping software to qualify 12,000+ creators by engagement rate, audience quality, and growth trajectory

  • Owned the full campaign lifecycle: creator outreach, negotiation, contracts, creative briefs, and final approvals

  • Built internal dashboards on GitHub and Vercel with Supabase as the data layer, using SQL to keep performance data structured and current

  • Used Cursor to iterate on and fix bugs in the dashboards when things broke

  • Set up outreach infrastructure in Apollo, Clay, Smartleads, and Infraforge, and used AI to clean data, draft outreach, and summarise client meetings into clear next steps

  • Monitored campaign KPIs (engagement, CTR, conversions) and adjusted the creator mix to improve ROAS

  • Replaced manual sourcing processes with a scalable, reusable creator database

Growth Process

Influencer marketing has an AARRR problem. Most teams optimise for Acquisition (reach, impressions) without closing the loop on Revenue (did it convert?). At First Query, I built the ops layer to connect those two ends, starting with smarter creator selection, then tracking the full funnel through to ROAS.

The approach:

  • Engagement as a filter, not a vanity metric: I set minimum thresholds on engagement rate, audience authenticity, and niche fit before any creator entered the pipeline

  • Test small, then scale: ran initial pilots before committing to full budgets, escalating only when performance data justified it

  • ROAS as the acceptance test: every campaign had a clear revenue-per-spend target, and creator selection was adjusted mid-flight when numbers weren't tracking

  • Toolstack: Apollo, Clay, Infraforge, custom scraping scripts, GitHub/Vercel/Supabase dashboards, Smartleads for outreach sequencing, Cursor for dashboard fixes, AI for data cleaning and meeting summaries

Project Image
Project Image

The Challenge

Influencer marketing is a data problem that most teams treat as a relationship problem. Creator vetting was entirely manual: no standardised scoring, no quality controls, no way to move fast without missing signals that matter (fake followers, low-intent audiences, engagement that doesn't convert).

The side-effect: campaigns were expensive to start and hard to stop. There was no test-then-scale structure, no mid-campaign adjustment logic, and no clear ROAS target that tied the creator fee to a real business outcome.

Results

  • Numbers I can point to: 12,000+ influencers qualified and scored through the scraping system, replacing a process that previously took days per creator

  • What this proved: when you treat creator selection as a data problem and ROAS as the acceptance test, influencer marketing becomes a lot less expensive to get right.

  • 3x+ faster creator sourcing vs. manual benchmarks

  • Measurably improved ROAS for OptiNourish (DTC supplement brand) through tighter creator selection and mid-campaign KPI adjustments

  • Reusable creator database built as a durable asset and not just a one-campaign list