The problem with job titles has always been context. We just solved it.
Job title classification is one of those problems that seems simple until you actually try to do it. Most B2B data providers are still using the same basic approaches from years ago: keyword matching, lookup tables, maybe some regex patterns. It works fine until you realize a CMO in healthcare is a Chief Medical Officer has nothing to do with marketing.
Most job title classification systems operate on a simple assumption: titles are universal. They build massive lookup tables, apply some pattern matching, and call it a day.
Here’s what they miss:
Example 1: The CMO Paradox
Traditional systems default to marketing every time. That’s a massive data quality issue for anyone selling into healthcare.
Example 2: The Geography Gap
Most systems either ignore non-English titles entirely or run them through Google Translate and hope for the best.
A “Principal” at a tech company is likely a senior engineer. At a school? That’s the head administrator. At an investment firm? Senior investor. Yet most classification systems pick one interpretation and run with it globally.
Instead of building bigger lookup tables, we built a system that actually reasons about job titles. Here’s the actual prompt architecture powering our classification:
Input Context:
- Job title
- LinkedIn job description
- LinkedIn industry (weighted heavily)
- Country of employment
Output Dimensions:
1. JOB_FUNCTION (21 categories)
2. JOB_LEVEL (6 tiers)
3. PERSONA (25 buyer archetypes)The key innovation? We don’t just classify. We triangulate. By considering industry context and geographic norms, we can correctly identify what a role actually does versus what the title might suggest.
Let’s look at how this plays out with actual titles:
Title: “Chef de Projet Digital” (France)
Title: “リードエンジニア” (Japan, Lead Engineer)
Title: “CDO”
Our system makes these distinctions automatically by weighing the LinkedIn industry context. No manual rules needed.
Title: “Managing Director”
The combination of country and industry tells us what the title actually means in context.
Traditional classification approaches fail in predictable ways:
Keyword Matching: Sees “Chief” and assumes C-suite. Misses that “Chief of Staff” isn’t an executive role.
Lookup Tables: Work great for common English titles. Fall apart with “Leiter Vertrieb” (German for Head of Sales) or emerging titles like “Head of Revenue Operations.”
Rule-Based Systems: Hundreds of if/then statements that break the moment someone creates a new title. Remember when “Growth Hacker” became a thing?
Our approach uses contextual reasoning. Every classification considers:
If you’re building on RevenueBase data, this impacts everything:
Better ICPs: You and your users can actually differentiate between technical and business buyers now, even when their titles overlap.
Global Expansion: Launch in Germany without spending months figuring out that “Prokurist” is basically a VP-level role with signing authority.
Reduced Waste: Your SDRs stop calling Chief Medical Officers about marketing automation software.
Smarter Routing: Route based on actual buyer personas, not guesswork about what a title means.
Here’s what actually happens when we classify a title:
This isn’t revolutionary ML architecture. It’s just doing the obvious thing that nobody else bothered to do: considering context.
LinkedIn industry classification becomes incredibly powerful when you use it right. Consider how the same title shifts meaning:
“VP Operations”
Without industry context, you’re just guessing. With it, you know exactly who you’re targeting.
Job titles are getting weirder. “Head of Remote” is a real title now. So is “Chief Meme Officer” (seriously, look it up). The old approaches of pattern matching and lookup tables were always going to hit a wall.
By moving to contextual classification that considers industry and geography, we can handle whatever creative titles companies dream up next. A “Wizard of Light Bulb Moments” at a creative agency? We’ll figure out they’re in creative services. A “Digital Overlord” at a tech startup? Probably IT leadership.
The point isn’t that we’ve solved every edge case. It’s that we’ve built a system that can actually reason about titles instead of just matching patterns.
For GTM teams using RevenueBase data, this means cleaner data, better targeting, and fewer embarrassing mistargeted outreach campaigns.
Because at the end of the day, knowing who you’re actually talking to is kind of important.
Want to explore how our classification system handles your specific use cases? Your RevenueBase account team can walk you through the classification logic for your target industries.
