Improving EF-Map Visibility: Analytics, SEO, and AI Integration

When you build a tool for a niche game like EVE Frontier, discoverability becomes a real challenge. Players searching for "EVE Frontier route planner" or "how to find SSU in EVE Frontier" need to actually find your tool. This post documents the infrastructure we built to make EF-Map easier to discover—through both traditional search engines and the emerging world of AI assistants.

Why Visibility Matters for EVE Frontier Tools

EVE Frontier is a complex game with a dedicated player base that actively seeks out third-party tools. But the search landscape has changed. Players don't just type queries into Google anymore—they ask ChatGPT "what tools exist for EVE Frontier" or query Claude about route planning options. To serve our users, we need to be discoverable in both paradigms.

Our approach involves three pillars: SEO fundamentals (landing pages, structured data, sitemaps), analytics instrumentation (measuring what works), and AI Engine Optimization (making our content machine-readable for LLMs).

SEO Enhancements: Dedicated Landing Pages

We created dedicated landing pages for each major feature, giving search engines (and users) clear entry points:

Killboard — Combat statistics and player activity tracking
Blueprint Calculator — Manufacturing cost analysis for EVE Frontier industry
Log Parser — Combat log analysis and session statistics
EF Helper — Desktop companion app with in-game overlay

Each landing page includes comprehensive metadata:

                Metadata Stack Per Page
                Open Graph tags — Rich previews when shared on social media
Twitter Card tags — Optimized display in tweets
JSON-LD structured data — Machine-readable context for search engines
Canonical URLs — Preventing duplicate content issues

            

We also integrated Google Search Console to monitor indexing status and identify crawl issues. The updated sitemap.xml now includes all landing pages, blog posts, and the main application entry point.

Analytics Infrastructure: GA4 + Search Console APIs

Understanding how users find and interact with EF-Map required proper instrumentation. We implemented programmatic access to both Google Analytics 4 and Search Console data. This isn't just for dashboards—it enables our LLM-driven development workflow to query analytics directly.

Custom Dimensions (9 Total)

We created custom dimensions to track EVE Frontier-specific interactions:

efm_panel_name — Which UI panel triggered an event (Routing, Logs, SSU Finder)
efm_algorithm — Pathfinding algorithm used (A*, Dijkstra, Scout)
efm_log_type — Type of combat log parsed (kills, damage, mining)
efm_route_type — Single destination vs multi-waypoint routes
efm_search_type — System search vs assembly search vs SSU search
Plus 4 additional dimensions for feature-specific tracking

Custom Metrics (5 Total)

efm_waypoint_count — Number of waypoints in optimized routes
efm_jump_count — Total jumps in calculated routes
efm_entry_count — Combat log entries parsed
efm_system_count — Systems included in searches
efm_result_count — SSU/assembly search results returned

Conversion Events (5 Total)

We configured these as conversion events to track meaningful user actions:

efm_route_planned — User calculated a route
efm_log_parsed — User analyzed combat logs
efm_route_shared — User shared a route via link
efm_helper_download — User downloaded the desktop app
efm_ssu_search — User searched for Smart Storage Units

Instrumented Components

The following React components now emit analytics events:

App.tsx — Core application events (cinematic mode, panel toggles)
LogsPanel.tsx — Combat log parsing metrics
HelperBridgePanel.tsx — Desktop app connection events
RouteSystemsPanel.tsx — Route calculation and sharing
SSUFinderPanel.tsx — SSU Finder search interactions

Real Search Performance Data

Here's what Search Console shows for our brand queries over the last 30 days:

Query	Clicks	CTR	Position
"eve frontier map"	45	60%	1.1
"ef map"	36	35.6%	1.2

Brand Query Performance

Our brand queries perform excellently—position 1.1-1.2 with strong CTRs. Users searching specifically for EF-Map find us immediately.

Opportunity: Generic Queries

The query "eve route planner" shows 299 impressions but only 4.4% CTR. This suggests users see us in results but don't click through. The landing page for routing features may need stronger value proposition copy.

AI Engine Optimization (AEO): Preparing for the AI Search Era

Here's where things get interesting. Traditional SEO optimizes for Google's crawler. But increasingly, users ask AI assistants questions like:

"What tools exist for EVE Frontier?"

"How can I plan routes in EVE Frontier?"

"Is there a killboard for EVE Frontier?"

AI systems like ChatGPT, Claude, and Perplexity consume web content to answer these questions. But they don't see pages the way Google does—they need structured, machine-readable context that clearly describes what a tool does.

Our AEO Strategy

JSON-LD everywhere — Every landing page includes structured data describing the software application, its features, and its relationship to EVE Frontier
Clear feature descriptions — Plain language explaining what each tool does, avoiding jargon
Semantic HTML — Proper heading hierarchy and semantic markup that LLMs can parse
FAQ schema — Structured Q&A content that AI systems can directly quote

Early Results

We're already seeing early ChatGPT referral traffic—2 sessions in the last 30 days where the referrer indicated ChatGPT. While small, this validates the approach. As AI assistants become primary search interfaces for technical queries, having machine-readable landing pages positions EF-Map to be recommended.

The SSU Finder feature documentation was specifically written with AEO in mind—clear step-by-step instructions that an AI could summarize accurately when users ask "how do I find SSU in EVE Frontier?"

Developer Productivity: Programmatic Analytics Access

One benefit of this infrastructure is that our vibe coding workflow can now query analytics directly. LLM agents can:

Fetch conversion rates without navigating the GA4 UI
Generate snapshot reports for specific date ranges
Compare performance before/after feature launches
Query Search Console for ranking changes

The Python helpers (ga4_reports.py, search_console_reports.py) enable this programmatic access. When we ship a new feature, we can immediately ask the LLM to "compare SSU Finder usage this week vs last week" and get actual data.

What's Next

This infrastructure is foundational. Future improvements include:

A/B testing landing page copy — Improving CTR for generic queries like "eve route planner"
Expanded structured data — Adding HowTo schema for tutorial content
AI citation tracking — Monitoring when AI assistants reference EF-Map
Conversion funnel analysis — Understanding the path from search to active user

Building tools for EVE Frontier players means meeting them where they search—whether that's Google, ChatGPT, or asking in Discord "what's the best route planner?" The goal is simple: when someone needs an EVE Frontier mapping tool, they find EF-Map.

Vibe Coding: Building a 124,000-Line Project Without Writing Code — The development methodology behind EF-Map and how analytics integration fits into LLM-driven workflows
Hetzner VPS Migration: Local to Cloud — The infrastructure migration that powers EF-Map's backend services
Cloudflare KV Optimization: 93% Reduction — How we optimized the storage layer serving these landing pages
Database Architecture: Blockchain Indexing — The data infrastructure behind EF-Map's real-time features

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EF-Map is an interactive map and toolsuite for EVE Frontier. Try it at ef-map.com.