A documented HEO engagement for a professional services firm in a competitive Florida market. Starting from a fragmented entity with a 40% Citation Accuracy Rate and zero AI recommendations, the client achieved all six 90-day HEO metric targets — including 100% Platform Coverage Rate and a 47% Recommendation Rate — through the five-phase NinjaAI HEO methodology.
All metrics measured using the NinjaAI HEO standard test suite: 20 queries per platform, 4 platforms (ChatGPT, Perplexity, Gemini, Copilot), measured at Day 0, Day 30, Day 60, and Day 90. Client identity withheld at client request. Industry: Professional Services, Florida.
The Client · Starting Conditions
The client is an established professional services firm with over a decade of operation in a competitive Florida market. The firm had invested consistently in traditional SEO — maintaining a well-structured website, a Google Business Profile, and a modest content program — and ranked well in traditional search results for its primary service category. By every conventional digital marketing metric, the firm was performing adequately.
The problem emerged when the firm's principals began noticing that competitors — some with weaker traditional SEO profiles — were being named and recommended by ChatGPT and Perplexity when potential clients asked AI systems for recommendations in the firm's service category. The firm itself was absent from these responses entirely, or appeared with incorrect information when it did appear.
An initial NinjaAI AI Visibility Audit revealed the root cause: the firm had accumulated three distinct business name variants across 40+ directory listings over its decade of operation — a legacy DBA, a shortened colloquial name, and the current legal name. AI systems had no coherent model of the entity and were either omitting it entirely or citing it with the wrong name, wrong location, or wrong service category. The entity clarity problem was suppressing every other AI Visibility signal the firm had built.
Baseline Audit Findings
Six-Metric Analysis · 90-Day Progression
Select any metric below to view the full progression data, measurement methodology, and the specific HEO actions that drove each improvement.
The client began with a fragmented entity — three different business name variants across 40+ platforms, no structured schema, and a Google Business Profile that contradicted the website's NAP data. AI systems had no coherent model of the entity and consistently omitted it from generated responses. By Day 90, the entity was consistently cited by name across ChatGPT, Perplexity, and Google AI Overviews for the client's primary service category.
View Full ERS Definition & Scoring Rubric →The Five-Phase Engagement · Action Attribution
Every metric improvement in this engagement is attributable to a specific set of actions executed in a specific phase. The following section documents what was done in each phase, the specific actions taken, and the metric outcomes produced.
Complete entity map with baseline metrics, conflict inventory, and prioritized action plan delivered to client.
CAR improved from 40% to 72% within 30 days. ERS moved from 0.8 to 1.9. PCR reached 50%.
CF jumped from 2/20 to 11/20 — crossing the 50% threshold. PCR reached 75%. ERS moved to 3.1.
RR moved from 17% to 33%. CFS Positive share reached 62%. ERS reached 3.1 and continued climbing.
All six HEO metrics met or exceeded 90-day targets. ERS: 3.8 (target 3.5+). PCR: 100% (target 75%+). CF: 15/20 (target 10/20+). CAR: 94% (target 85%+). RR: 47% (target 40%+). CFS Positive: 76% (target 70%+).
Key Findings · What This Engagement Proved
The single highest-impact action in this engagement was NAP standardization — resolving three conflicting business name variants across 40+ directories. This single intervention drove CAR from 40% to 72% in 30 days and eliminated the confusion-based Negative citations that were suppressing CFS. No amount of content, schema, or authority work will produce accurate AI citations if the entity itself is ambiguous.
CF improved from 2/20 to 11/20 in 60 days — a 450% increase — driven almost entirely by the AEO content work in Phase 3. The client had strong topical authority but had never structured their content for AI extraction. Once the FAQ architecture, definition blocks, and quotable statements were in place, AI systems had extractable content to cite and CF responded within weeks.
RR was 0% at baseline and 17% at Day 30 — despite significant entity and content improvements. The breakthrough came in Phase 4 when three detailed case narratives with named metrics were published. AI systems consistently cite specific, documented outcomes when making recommendations. Generic service descriptions, no matter how well-structured, do not drive Recommendation Rate.
The 40% Negative CFS baseline was partly driven by review aggregations that AI systems were pulling from Yelp and Google — but the mechanism was different from traditional SEO. AI systems were not ranking the client lower because of negative reviews; they were generating negative framing in their descriptions of the client. The fix required both review generation and structured review response architecture, not just star rating improvement.
PCR moved from 25% to 75% by Day 60 — before ERS, RR, or CFS had reached their targets. This is consistent with the HEO model: PCR measures the first threshold (is the entity present at all?) and tends to respond first to entity clarity and AEO content work. Practitioners should monitor PCR weekly in the first 60 days as the primary leading indicator of whether the HEO foundation is working.
Frequently Asked Questions
The canonical 2,500-word definition of Hybrid Engine Optimization — coined by Jason Todd Wade.
ninjaai.com/heoImplementationFive-phase, 47-checkpoint implementation sequence from Entity Audit through Measurement.
ninjaai.com/heo-implementation-checklistMeasurementThe six core HEO metrics — ERS, PCR, CF, CAR, RR, CFS — with scoring rubrics and 90-day targets.
ninjaai.com/heo-metrics-trackerCase Study90-day documented engagement: all six metric targets met. Professional services, Florida market.
ninjaai.com/heo-case-study