Aiops Observability Marketplace Case Study

Client identity protected under NDA. Details available under mutual sign-off in a discovery call.

Composite company profile

A Series C AIOps and observability platform selling into platform engineering and SRE buyers at mid-market and enterprise accounts. ARR in the $28M range, ACV band $65K–$180K, sales cycle 90 to 140 days. The company had strong product traction inside a partner marketplace channel (a major hyperscaler’s software marketplace) and a solid inbound signup motion, but its organic surface area was thin relative to competitors like Datadog, New Relic, Dynatrace, and a cohort of open-source-first challengers. Marketing team of nine reporting to a VP of Marketing; two content editors and one technical writer in-house.

The problem

The company had spent 14 months investing in a category-education content program built around the phrase “AIOps.” Rankings were mediocre, pipeline attribution was scattered, and their marketplace listing had plateaued at a modest number of transactions per month despite growing partner co-sell activity. Three specific pains were surfaced during the diagnostic:

• High-intent commercial queries around “observability platform,” “log analytics,” “APM alternatives,” and specific competitor comparison terms were dominated by Datadog, New Relic, Better Stack, and Middleware; the client owned no page 1 real estate on any comparison term.
• Marketplace-driven revenue was under-attributed. Sales credited the marketplace as “channel,” while marketing had no visibility into which content assets or campaigns were triggering marketplace searches inside the hyperscaler console.
• Prior efforts had produced 180+ blog posts, most written to educate on AIOps concepts. Traffic came in, converted at under 0.4%, and did not seed the sales-ready comparison and evaluation queries the pipeline needed.

What our team diagnosed

Two root causes that were not visible to the internal team:

1. Category-word obsession displaced buyer-intent coverage. The team had optimized around the vendor-preferred category term (“AIOps”) when the actual buying committee — senior SREs, platform leads, DevOps managers — searched using tool names, comparison verbs, and problem-symptom queries. Ahrefs pulls showed 74% of commercial search volume in the space sat on non-category terms the client had never targeted.

2. Marketplace attribution was a solvable data problem, not an unsolvable channel problem. The hyperscaler marketplace exposed enough referral and UTM signal to trace which self-serve trials came from which upstream campaign, if the client instrumented outbound clicks correctly and stitched marketplace transaction IDs to their CRM. The client had never asked the question in that shape.

Strategy MV3 shipped

We engaged the client on Growth AI ($5,997/mo) plus a one-time GEO Audit and a custom marketplace-attribution build:

1. Commercial-intent content pivot. Rebuilt the content roadmap around three pillars: alternatives pages (12 target competitors), problem-symptom pages (log volume, alert fatigue, high MTTR, dashboard sprawl), and integration pages (top 24 tools their ideal customers already ran).
2. GEO/AIO layer. Ran citation audits against ChatGPT, Perplexity, Gemini, and AI Overviews for 42 target prompts. Rewrote landing pages to answer the first-90-second question the AI answer engines pull, added FAQ + HowTo + Product schema, and shipped an llms.txt reference of canonical comparison pages.
3. Marketplace attribution build. Instrumented outbound marketplace clicks with campaign-tagged deep links, built a nightly job that joined marketplace transaction exports to the CRM via customer email and account domain, and stood up a Looker view giving marketing per-campaign marketplace revenue for the first time.
4. ABM overlay. Layered a 220-account ABM program on top of the top 12 alternatives pages, so accounts researching those competitors received matched LinkedIn ads and a five-touch email cadence signed by a technical AE.

Implementation

Deliverables shipped over the engagement:

• 36 new commercial-intent pages: 12 alternatives, 12 integration, 12 problem-symptom, each 1,600 to 2,400 words with unique diagrams and a comparison matrix.
• Rewrite pass on 41 existing top-traffic-but-non-converting posts, converting each into a hub-and-spoke internal link structure feeding the new comparison pages.
• Full schema deployment: FAQPage, HowTo, Product, BreadcrumbList, Organization, plus author entity pages for two named in-house engineers.
• Marketplace UTM taxonomy, deep-link generator, and Looker attribution dashboard.
• ABM program: 220 accounts, LinkedIn matched audience, five-email cadence, weekly SDR call sheet driven by intent + page-visit scoring in Supabase.
• Monthly GSC + GA4 + marketplace attribution report delivered to VP Marketing and shared to the CRO.

Cadence: weekly Monday content shipping, Tuesday ABM review, Friday GEO citation delta report, monthly executive readout.

Outcomes

Numeric results measured between month 0 baseline and month 9:

• Organic sessions: +214% (from roughly 41K to 129K monthly).
• Ranking coverage: page 1 positions on 9 of 12 target competitor alternatives pages; page 1 on 8 of 12 problem-symptom pages.
• AI citation share: named in ChatGPT and Perplexity responses on 27 of 42 target prompts, up from 4 of 42 at baseline.
• Marketplace-attributed pipeline: $6.4M in newly attributable marketplace-sourced pipeline over the engagement window, previously coded as “unknown channel.”
• Blended CAC on marketing-sourced deals: down 31% vs the prior four-quarter average, driven by higher-intent traffic converting at 1.9% rather than 0.4%.
• ABM meetings booked: 84 discovery calls off the 220-account program in the last two quarters of the engagement.

Timeline

Total elapsed 9 months from kickoff to the results above. First commercial-intent rankings surfaced in month 3. Marketplace attribution dashboard went live in month 2. AI citation lift materialized in month 5 as Perplexity and Gemini refreshed their crawl of the rewritten pages. CAC reduction was measurable by month 6 and stable by month 9.

Composite testimonial

“We had been writing about our category for over a year and the pipeline story was not moving. MV3 walked in, told us we were targeting the wrong 74% of the market, and rebuilt our content and attribution around what our buyers actually search for. Nine months in, we can finally point at marketplace revenue and say where it came from.”
Priya, VP Marketing

Services engaged

Growth AI retainer, GEO Audit (entry offer), custom N8N + Supabase attribution build, ABM program on LinkedIn and email.

NDA framing

Client identity protected under NDA. Composite figures and outcomes above are directionally representative of the engagement and the industry stage. Full case detail, dashboards, and reference conversation available under mutual sign-off in a discovery call.

Ready to see if a similar rebuild would work for your platform?

Book a discovery call — we will walk through your current organic surface area, your marketplace or channel attribution gap, and whether an AIOps-style rebuild fits your motion. See also GEO Audit and ABM Agency.