Competition Spy Automation

Key results

• 300/day — Videos curated and sent to team Slack
• 6M/year — Revenue unlocked through smarter creative scaling
• 5x — Larger candidate pool reviewed with less effort

TL;DR

• Creative and strategy teams were manually checking 30-40 competitor ads per day across multiple platforms, costing 5+ hours daily and slowing market response.
• We built a Node.js competition intelligence stack that scrapes Facebook Ads Library, Spy Hero, and upload-signal platforms by keyword and geography.
• A winning-video algorithm scores and ranks creative quality based on uploads, impression velocity, and trend momentum so teams only review the best opportunities.
• The system now sends around 300 curated videos per day into Slack with topic-based bundles, saving teams massive creative bandwidth and scaling growth.

Project Overview

A competition-intelligence automation built for media buyers, scriptwriters, and video editors.

The team had a painful daily ritual: media buyers, scriptwriters, and editors spent hours scanning competitor ads, manually opening pages, and guessing what was actually performing. The process was repetitive, inconsistent, and mentally expensive.

We replaced that with a custom Node.js architecture that continuously ingests and normalizes ad performance clues across multiple data sources, then applies a winner-detection model that flags videos with the highest probability of scaling.

• Scrape competitor ad feeds by keyword and selected geographies
• Score each creative with a custom winning-video algorithm
• Distribute only curated, high-signal videos to Slack channels
• Support team-level categorization by keyword, market, and campaign format

The output is now a daily flood of high-quality references—without forcing the teams to dig through low-performing and low-signal content.

Business Challenge / Problem Statement

Manual competitor monitoring was collapsing creative energy.

Scriptwriters, editors, and media buyers were spending at least five hours daily on content scouting.

Teams reviewed mostly average-quality videos because they never had the capacity to evaluate everything.

Different people used different methods, so results were inconsistent and hard to scale.

Important creative directions were discovered too late, creating a real opportunity cost in spend optimization.

The process consumed cognitive bandwidth that should have been spent on strategy and execution.

• Massive Time Sink — 3 to 5 people repeatedly reviewed 30-40 videos each day, consuming 5+ hours in total.
• Low Signal Ratio — Most reviewed content was low-performing noise; winning ideas were buried.
• No Reliable Ranking — The team had no repeatable scorecard for what was actually winning.
• Geographic Blind Spots — Market-specific ad trends were missed without targeted location filters.
• Scaling Barrier — As budget and campaigns grew, manual spying stopped being sustainable.
• Execution Delays — Late insight handoffs reduced the ability to move fast on winning ad themes.

Objectives & Goals

What the automation had to solve from day one.

Reduce manual scouting time by automating competitor ad discovery and ranking.

Standardize creative intelligence so every person receives the same signal framework.

Identify winning videos from objective metrics including volume and trend indicators.

Deliver categorized daily bundles for immediate creative production decisions.

Support scalable operation as market coverage increases across keywords and geographies.

• Automated Source Collection — Continuously ingest competitor content from multiple sources, including Facebook Ads Library and Spy Hero.
• Winning-Video Scoring — Score videos using upload activity, impressions trend, and momentum signals to reduce false positives.
• Signal Filtering — Keep the pipeline output lean and action-focused by dropping low-confidence candidates.
• Topic + Market Routing — Group outputs by keyword and geography to match actual buying decisions.
• Team-Ready Distribution — Push hand-picked, review-ready sets to Slack channels that media buyers already use daily.
• Revenue Expansion Through Scale — Enable adding more people, campaigns, and markets without increasing manual scouting workload.

Key Features & Innovation

A practical intelligence engine for scaling paid growth teams.

Multi-Source Scraping Engine

A custom Node.js data layer scrapes multiple competitor-ad ecosystems to remove dependency on single-platform windows or manual checks.

Geo + Keyword Controls

• Keyword targeting: Teams define exact search terms and markets for focused competitive monitoring.
• Geographic filtering: Each bundle is segmented so teams can compare regional trends quickly.
• Category grouping: Videos are grouped by campaign context for easier script and creative planning.

Winning-Videos Scoring Model

• Upload volume: Measures how many creative versions or updates indicate market momentum.
• Impression trend: Tracks growth velocity and momentum rather than static snapshot counts.
• Trend correlation: Filters duplicate or stale videos and keeps the stream aligned with current winner patterns.

Delivery Intelligence

The output is formatted for team speed, not for dashboards: daily Slack drops with the most relevant winning videos and enough context to act immediately.

Category-Driven Slack Routing

The daily output is not random. Teams receive a fixed number of videos per keyword so review bandwidth is always protected.

Daily Scaling Loop

• Steady volume: About 300 curated videos sent every day.
• Balanced delivery: Each keyword can receive 5, 10, or 20 videos depending on market demand.
• Brainpower preserved: People focus on ideation, scriptwriting, and iteration instead of hunting.

Scale-Ready Architecture

• Modular adapters: Each data source is isolated so new platforms can be added without breaking the core ranking logic.
• Normalization layer: Inconsistent source payloads are standardized before scoring and distribution.
• Team extensibility: More buyers or markets can be added without retraining the whole process.

Operational Reliability

Scheduler controls and parsing checks keep the flow running day-to-day with resilience against source fluctuation.

Results & Impact

The manual hunting loop was replaced with a structured daily intelligence stream, eliminating repeated low-quality scans.

Teams can now focus on high-performing ideas and rapidly test script and creative combinations that align with live competitor momentum.

This build was a compound advantage: one-time architecture, repeatable operations, and continuously increasing ROI as more people and markets were introduced.

What our clients say

We stopped spending half our day digging through losing content. The automated spy feed gave our media buyers and creators a daily, ranked shortlist of winning videos, and that changed our speed across every campaign.

Campaign Intelligence Lead — Marketing Team

Technologies & Tools Used

Competition intelligence and delivery stack

We built this case on a Node.js automation core with multi-source ad scraping, scoring logic, and Slack delivery workflows designed for non-stop operations.

The stack prioritizes practical signal quality: scrape, normalize, score, bundle, and ship so media teams can move faster without overload.

Technology stack

• Node.js — Custom orchestration and transformation layer that handles scraping, scoring, and dispatch logic.
• Facebook Ads Library Scraping — Automated retrieval of competitor ad data with consistent refresh and parsing controls.
• Spy Hero — Secondary intelligence source for deeper competitor creative trend detection.
• Upload Signal Intelligence — Additional source signals used to detect trending content velocity.
• Scoring Framework — Custom model combining upload frequency, impression trend, and freshness signals to identify winners.
• Slack API — Automated delivery channel for team-ready daily bundles by keyword and geography.