B2B · Defense, Marine, EMS, IoT, Product Design, Experience · 2025 · INFINIA MÜHENDİSLİK

Running one demand-generation system across six B2B industries with different buyers, budgets, and sales cycles.

A unified paid-media and lead-scoring program spanning public-sector defense procurement, marine dealer networks, EMS, IoT, an experiential franchise business, and product design. Held a 24% lead-to-MQL rate and a 26% MQL-to-SQL rate across all six industries, with a blended pipeline-to-spend ratio above 25x.

The Challenge

INFINIA is a multi-vertical engineering firm: defense systems sold to government and public-sector buyers, marine products sold through dealer networks, EMS and IoT sold to industrial buyers, product design sold as custom engineering work, and an experiential franchise business. Six industries, six buyer types, six sales cycles, one marketing seat. There was no shared lead-scoring model and no shared attribution, so a $310 defense cost-per-lead and a $92 marine cost-per-lead sat side by side with no way to tell if either number was actually good.

The Approach

• 01Built a shared lead-scoring framework, calibrated per industry.Defined what MQL and SQL meant for each buying committee (procurement officer, boat dealer, franchise operator), then tracked conversion at each stage on a consistent basis across all six.
• 02Ran paid media through the channel each industry’s buyer actually used.Google and LinkedIn ABM for defense and industrial buyers, Meta and search for the experience franchise business. A distinct channel mix per industry instead of one blended paid strategy.
• 03Layered email and organic nurture underneath every paid channel.Built lead-nurturing flows and an SEO content engine running in parallel to paid across industries, then compared cost-per-SQL across channels to find where the real efficiency was.
• 04Built one dashboard covering all six industries.GA4, GTM, and CRM data unified with consistent UTM governance, so a defense MQL and a product-design MQL used the same measurement logic even though nothing else about them was the same.
• 05Reallocated budget toward the industries and channels the data actually supported.Once a full data cycle existed, shifted spend toward Product Design, the strongest MQL-to-SQL converter, and away from underperforming IoT paid search.

The Results

Across the six B2B industries, the programme converted leads to MQL at roughly 24% and MQL to SQL at roughly 26%, both above the 13–20% B2B benchmark, with a blended pipeline-to-spend ratio above 25x. Performance varied meaningfully by industry: Product Design converted MQL to SQL at 37%, the highest of any industry, while Defense produced a 56x pipeline-to-spend ratio on the strength of its deal sizes rather than its conversion rate.

On the channel side, email and organic nurture converted MQL to SQL at roughly 45%, at a cost-per-SQL eight to ten times lower than paid social or search, evidence that the highest-value channel wasn’t the one getting the largest budget. Entertainment, INFINIA’s B2C vertical, sits outside this blended B2B figure and converted at a materially different rate (close to 84% MQL-to-SQL) at a fraction of the cost-per-lead, a reminder of how much the funnel math changes once the buyer isn’t a committee.

Cross-Functional Reach

Weekly coordination across engineers, designers, and sales teams in INFINIA’s Ankara, Istanbul, Amsterdam, Manchester, and Doha offices, translating technical product features into industry-specific marketing assets and lead-gen funnels across all six B2B lines, plus entertainment.

Channels & Stack

Internal · Marketing Ops · B2B + D2C + B2C · 2025 · INFINIA MÜHENDİSLİK

The marketing operations infrastructure that lets one specialist run three accounts across three business models.

A unified content production workflow, cross-account attribution architecture, and AI-augmented editorial system supporting the defense account, Marisonia, and XPZONE, compressing content cycles 40% and saving 6+ hours weekly.

The Challenge

One marketing function, three flagship accounts across three different business models: defense (B2B, public-sector buyers), Marisonia (D2C), and XPZONE (B2C/B2B experience). Different audiences, different sales cycles, different stacks, different languages. Without infrastructure, the marketing role would collapse into reactive triage: produce a brief, get it translated, ship the campaign, scramble to assemble reporting, repeat. There would be no time for strategy.

The Approach

• 01Built an AI-augmented content workflow.ChatGPT and Claude for brief drafting, EN/TR translation, and editorial QA, with strict brand-voice guardrails. Defense-sector copy needed human review at three points (technical accuracy, tone, compliance); consumer copy needed lighter editorial.
• 02Codified brand voice per account as reusable prompts.Documented voice (defense formal, marine consumer warm, XPZONE specific) so AI outputs hit the right register before human editing. Reduced rework time per piece by roughly half.
• 03Architected unified attribution across all three accounts.GA4 + GTM + Looker Studio + HubSpot CRM with consistent UTM governance, so the same parameter conventions worked for B2B LinkedIn ABM, D2C Meta retargeting, and internal campaigns.
• 04Built one Looker Studio dashboard collapsing three reporting workflows.Previously three separate spreadsheets, three weekly reports, three stakeholder conversations. After, one dashboard with account-level views and a cross-account summary, one weekly meeting cadence.
• 05Codified the production process.Briefs, asset library, approval workflows, publishing checklist, written down once so it didn’t have to live in my head.

The Results & the Second-Order Effect

~40% content cycle compression (briefs that used to take ten days ship in six, with quality controls intact). 6+ hours per week saved on reporting, reinvested into channel testing and strategy. 3 accounts across 3 business models on one unified system that scales with marginal complexity. The infrastructure is what made the defense ABM programme possible: without it, there was no time for 80-account campaigns.

B2B · SaaS · San Francisco, CA · Apr 2024 to Feb 2025 · Turquoise Choice · PB360.me

Building a full-funnel growth engine for a pickleball SaaS across web, iOS, and Android.

From $0 to $8K/month in paid spend, 2.1K to 28K monthly organic visitors, and a Play Store rating from 4.1 to 4.6, across the fastest-growing racquet sport in North America.

The Challenge

Turquoise Choice operates PB360.me, pickleball events and league-management SaaS for North American clubs and tournament organizers. The sport was scaling fast; the platform’s marketing wasn’t. Paid was at zero, the SEO engine didn’t exist, the apps had no lifecycle email, and Play Store sat at 4.1. I joined as the first dedicated marketing hire and owned the full funnel for web plus iOS and Android.

The Approach

• 01SEO content engine at 15+ articles per quarter.50+ articles in 9 months targeting tournament-organizer and league-manager search intent, with internal linking and topical clustering.
• 02Meta and Google Ads scaled $0 to $8K/month.Systematic creative testing on hooks, formats, and audiences, with retargeting flows that doubled weekly signups and pulled CAC from $14.20 to $11.05.
• 03ASO across iOS and Google Play.Keyword optimization, screenshot iteration, and listing copy refinement drove a 41% organic install lift and moved Play Store rating from 4.1 to 4.6.
• 04Klaviyo lifecycle infrastructure.Welcome, activation, and re-engagement flows that lifted 30-day retention by 27% and strengthened activation-to-paid.
• 05Weekly A/B testing on landing pages and email creative.GA4 + Hotjar insights driving MAU growth of +15% across the 9-month engagement.

The Results

Over nine months, organic visitors grew from 2,100 to 28,000 per month, five high-intent commercial keywords ranked on Google’s first page, paid CAC dropped 20%, weekly signups doubled via retargeting, and 30-day retention lifted 27%. Play Store rating moved 4.1 to 4.6, organic installs lifted 41%, and MAU grew 15%.

CAPABILITIES · 07

Operating stack.
What I actually use day to day.