On this page
01 · Starting point
02 · Month 1 — actions and results
03 · Month 2 — results and first lead
04 · Month 3 — pipeline impact
05 · What drove 80% of gains
06 · ROI calculation
07 · FAQ
Starting point — May 2026
Client profile: B2B XDR (Extended Detection and Response) vendor, French market, targeting CISOs and CIOs of SMEs and ETIs (50-500 employees). Primary competitive market: NIS2 compliance cybersecurity tools. Engagement started May 2026.
| Metric | Value at start | Context |
| LVI Score | 34/100 | Below competitive threshold (60+) for selection-stage visibility |
| Primary competitor LVI | 58/100 | 24-point gap to close |
| Engine coverage | 1.8/4 | ChatGPT partial coverage only — Perplexity, Gemini, Claude absent |
| AI Share of Voice (NIS2 prompts) | 12% | Appearing in 12% of priority NIS2/XDR queries |
| AI referral traffic (GA4) | 0 sessions/month | No AI referral segment configured at start |
| AI-attributed pipeline | 0 deals | No CRM source field for AI attribution |
Month 1 — actions and early results
Three actions were executed in month 1. All three were selected based on a prompt corpus audit identifying the 8 highest-gap NIS2/XDR queries where the competitor appeared but the client did not.
| Action | Output | Distribution |
| NIS2 compliance guide for industrial SMEs | 1,800-word answer-first article targeting 'XDR PME industrielle NIS2' | MISC Magazine — published week 3 |
| Prompt corpus audit (50 prompts) | Gap analysis: 8 high-priority queries identified, ranked by intent and competition | Internal — informed months 2 and 3 |
| FAQ restructure on client site | 12 answer-first Q&As: 'Quelle difference XDR vs EDR NIS2?', 'Combien coute un XDR pour PME?', etc. | Client site — live week 1 |
LVI at day 30: 41/100 — up 7 points from 34. Perplexity began citing the client on 3 of the 8 target prompts within 12 days of the MISC article publication. AI referral traffic from Perplexity increased 340% vs the pre-engagement baseline (from near-zero to 47 sessions in month 1). ChatGPT coverage remained unchanged — the MISC article had not yet been absorbed into its slower indexation cycle.
Month 2 — expanded distribution and first AI-attributed lead
Month 2 focused on source diversity: adding Reddit contributions (Perplexity's highest-ROI channel) and monitoring the initial prompt corpus to reallocate effort toward the queries with the most traction.
| Action | Output | Impact |
| Reddit expert contributions | 3 substantive answers on r/netsec and r/sysadmin on XDR for NIS2 compliance — no promotion, no brand mention | Perplexity indexed all 3 within 48h. Citation on 2 new prompts within 10 days. |
| ChatGPT citation monitoring | Re-ran prompt corpus on GPT-4o. MISC article now absorbed. 4 new ChatGPT citations identified on NIS2 queries. | LVI engine coverage improved: 2.8/4 from 1.8/4. |
| Competitor gap re-analysis | Gap reduced from 24 to 8 points. Top 3 remaining gaps identified for month 3 content targeting. | Focused month 3 production on high-gap, high-intent prompts. |
LVI at day 60: 55/100 — up 14 additional points. ChatGPT now citing the client on 4 priority NIS2/XDR prompts. Perplexity citation on 6 of the 8 target prompts. Engine coverage at 2.8/4. Competitor gap: 3 points (down from 24).
First AI-attributed lead — Day 47: Inbound contact form submission. CRM source field: "AI citation". Contact note: "I found you on Perplexity searching for XDR for NIS2 compliance." Deal value: EUR 38,000. Time from Perplexity indexation of the MISC article to lead submission: under 72 hours from the date the article was first cited in a Perplexity response to that prompt.
Month 3 — data study and pipeline acceleration
Month 3 added a data-driven content asset (sector study published in L'Usine Digitale) and amplified via LinkedIn. The objective was to move from Perplexity-dominant coverage to balanced multi-engine presence.
| Action | Output | Impact |
| Sector data study | NIS2 compliance readiness among French industrial SMEs — based on anonymized client data. Published in L'Usine Digitale. | High DA co-citation. ChatGPT citation on 3 additional prompts within 30 days of publication. |
| LinkedIn amplification | 4 CEO posts with study data points — average 3,200 views each. No promotional content. | LinkedIn Pulse effect: Perplexity indexed the posts and used them as supporting citations on 2 prompts. |
| GA4 attribution setup | AI referral segment configured (Perplexity, ChatGPT, Claude, Gemini). Source field added to contact form. | Attribution clarity: all subsequent leads correctly tagged. 2 additional AI-attributed leads identified retroactively. |
LVI at day 90: 67/100 — up 33 points total from the 34 at engagement start. Engine coverage: 3.4/4. AI Share of Voice on priority NIS2/XDR prompts: 38% (up from 12% at start). Pipeline: 3 deals attributed to AI citation, estimated combined value EUR 105,000.
What drove 80% of the LVI gains — and what did not
Three actions generated 80% of the total +33 point progression. Everything else contributed the remaining 20%.
| Action | LVI impact | Reason |
| MISC Magazine article (NIS2 for industrial SMEs) | ~+14 pts | High-DA tier-1 cyber media. Perplexity indexed within 12 days. ChatGPT absorbed within 30 days. |
| Reddit contributions (r/netsec, r/sysadmin) | ~+10 pts | Perplexity indexed all 3 within 48h. RAG source diversity — different from MISC DA signal. |
| FAQ restructure (12 NIS2/XDR Q&As on client site) | ~+6 pts | ChatGPT and Claude weight structured FAQ content with exact buyer vocabulary. |
| L'Usine Digitale data study | ~+3 pts (growing) | High co-citation value — ChatGPT still absorbing at day 90. Expect +5-8 more pts by day 120. |
| 4 blog posts on client site | ~0 pts | LLMs do not cite proprietary vendor sites on informational queries. Zero measured impact. |
| LinkedIn CEO posts (4) | ~0 pts direct | Indirect: Perplexity cited LinkedIn posts as supporting citations on 2 prompts. Not counted in LVI directly. |
Critical finding: The 4 blog posts published on the client site during the same 90-day period generated zero measurable LVI impact. Blog-first GEO is not GEO — it is SEO content misallocated to a channel that does not generate LLM citations.
ROI calculation — 90-day engagement
| Item | Value |
| Engine Core engagement (3 months) | EUR 6,000 |
| AI Visibility Audit (one-shot, deducted from first month) | EUR 1,500 |
| Media placements (MISC + L'Usine Digitale) | EUR 0 (editorial, not paid) |
| Total engagement cost | EUR 7,500 |
| Pipeline attributed to AI citation at day 90 | EUR 105,000 (3 deals) |
| Expected revenue at 20% close rate | EUR 21,000 |
| ROI on expected revenue vs cost | 2.8x |
| ROI on pipeline value vs cost | 14x |
Frequently asked questions
How long does GEO take to produce results in B2B cybersecurity?
First measurable AI citation results appeared within 7-14 days of tier-1 media publication. The first AI-attributed lead arrived at day 47. Meaningful pipeline impact (3 deals) was measurable at 90 days. This timeline assumes active distribution in sector media, Reddit contributions, and structured FAQ content.
What LVI score can a cybersecurity vendor expect after 90 days of GEO?
In this case, the vendor went from LVI 34 to LVI 67 in 90 days — a +33 point progression. Brands starting from 0 should expect 45-55 at 90 days with equivalent effort.
What is the ROI of GEO for a B2B cybersecurity company?
In this case: EUR 7,500 engagement cost, EUR 105,000 pipeline at day 90, EUR 21,000 expected revenue at 20% close rate. That is a 2.8x ROI on expected revenue within the first 90 days.
What GEO actions produce the most LVI progression in cybersecurity?
Three actions generated 80% of gains: tier-1 media article (MISC) targeting NIS2 vocabulary, Reddit expert contributions on r/netsec and r/sysadmin indexed by Perplexity in 48h, FAQ restructure with answer-first Q&As on client site. Blog posts generated zero LVI impact.
How do you attribute a sales lead to AI citation?
Three methods: GA4 AI referral segment (sessions from perplexity.ai, chatgpt.com, claude.ai, gemini.google.com), CRM source field with 'AI citation' option in contact form dropdown, and direct self-reporting from buyers who mention the AI response that referred them.
Sources
- LLM Visibility Index — monitoring data May-July 2026 — llm-visibility-index.com
- ZivRank client engagement documentation (anonymized) — July 2026
- GA4 AI referral tracking — client account — July 2026