Naming Convention Extractor & Analyzer

Advertising teams run campaigns across multiple platforms — Meta, Google Ads, TikTok, LinkedIn, and others. Each campaign, media buy, and creative is given a name that encodes critical metadata such as geography, objective, audience, format, and date. These naming conventions are meant to be structured, but in practice they break down fast. The core problem is threefold: 1. No enforcement at the point of entry Ad platforms like Meta Ads Manager and Google Ads have no built-in validation for campaign names. Anyone on the team can name a campaign anything — and they do. The result is that the same campaign type gets named IN_BAW_NewUsers_2025, india-brand-awareness-nwusr-25, 2025|India|BAW|NU, and Meta_India_BrandAwareness all within the same account. 2. Reporting tools can't handle inconsistency Tools like Datorama, Tableau, Power BI, and Looker all rely on pattern-matching or regex to extract dimensions from campaign names. These rules are rigid — they expect a fixed delimiter, a fixed position, and a fixed format. The moment someone deviates, the rule fails silently and the dimension shows as blank or incorrect. You end up with 11 versions of "India" in your Geo filter and no way to aggregate them reliably. 3. The cost is invisible but significant Bad naming doesn't throw an error. It just quietly corrupts your reports. Performance by audience becomes unreliable. Spend by objective is understated. Creative analysis is impossible. Budget pacing across geos is wrong. Decisions get made on data that looks clean but isn't — and no one knows until someone manually audits row by row, which can take days. What was missing: A tool that can read campaign names as they actually are — messy, inconsistent, abbreviated — and intelligently extract, standardize, and validate the dimensions within them without requiring the data to be clean first

The Naming Convention Extractor is an AI-powered tool that transforms raw, inconsistent campaign names into clean, structured, dashboard-ready data — without requiring any changes at the source. Users upload a CSV or Excel export from any ad platform. The tool then runs a multi-stage AI pipeline: it auto-detects or accepts a user-defined naming convention, intelligently extracts sub-values from each campaign name, expanding abbreviations, resolving inconsistencies, and standardising formats regardless of delimiters or ordering, then evaluates every row and flags exactly what is wrong and why. Unlike Datorama, Tableau, or Power BI which all require consistent naming before they can extract anything, this tool starts with the mess and produces the structure. The output is a fully enriched table with clean extracted dimensions, standardised name variants, and per-row quality evaluations exportable directly into any BI tool. Saved patterns make recurring audits take seconds, not hours.