Case studies

Overview

Blinq had shipped a full event lead capture product (scanner + AI enrichment + CRM sync + campaigns) but had no go-to-market motion behind it. I pulled the usage data across all 1,715 workspaces and mapped the actual distribution. Then ran 10+ stakeholder interviews across sales, customer success, and growth.

My Role

Before writing a single asset, I ran the diagnostic:

• Pulled and analysed full usage data: 81% of workspaces had zero credit activity, zero workspaces hit the paid tier
• Interviewed 10+ stakeholders across sales, CS, and growth to validate what the data showed
• Discovered the real competitor wasn't Popl or Mobly (SaaS tools). It was the badge scanner included with every conference. 95% of event floors use them
• Identified that every closed deal was an add-on to an existing account. No standalone demand existed
• Reframed the ICP from "event managers" to teams running 10+ events/year without a dedicated solution

Built an 8-initiative GTM plan sequenced by what would unblock the most downstream:

• Structured onboarding flow to close the sale-to-activation gap
• "One consistent tool" value prop to counter the "we already have Cvent" objection
• Training materials so sales could actually demo the product (they couldn't before)
• New competitive battlecard with talk tracks built from Gong transcripts, G2, Capterra, and Reddit reviews

Challenges and Solutions

• The "Event" framing was limiting. Customers connected it to conferences only. Repositioned around the 100-200 person regional event as the entry wedge (no badge provider, no hardware, Blinq wins on simplicity)
• Zero standalone demand. Shifted entire GTM from demand gen to activation-first. If solved, 1,715 workspaces become 1,715 trials with 10% conversion = 140+ pipeline deals

Results

The diagnostic became the company's top priority. CEO adopted the GTM plan as the roadmap for Event Lead Capture, shifting from demand gen to activation-first. Shipped all 8 initiatives within the first quarter. By the time I left, 8 ELC deals were in trial including 2 large enterprise accounts, and a single customer campaign tracked through the product showed $157K in campaign ROI from 16 of 30 captured leads converting.

Delivered

Evidence

Independent review naming Blinq the best event lead capture app in 2026, published after the GTM work shipped.

Overview

Blinq built a native HubSpot integration so every contact your team meets in person lands in HubSpot the same day, enriched with details, AI conversation notes, and event tags. Ready for follow-up before the team leaves the room. The launch needed to land the marketplace listing, drive installs from existing HubSpot customers, and open a long-term co-marketing relationship with HubSpot.

What I Did

• Built the marketplace listing from scratch. Benchmarked 6 reference listings (PandaDoc, Calendly, Aircall, Surfe, QuotaPath, Popl) and reverse-engineered the pattern: 5-slide Calendly-style carousel with branded backgrounds, "inside HubSpot" anchored across copy, review velocity prioritised over total count for search ranking
• Led the storytelling pivot. Three days before launch the plan was a tactical feature announcement. After a strategy session with my manager we reframed the launch around the 20-year CRM gap. Cards and badges became the proof point, HubSpot the first-move example, HubSpot Ventures backing the credibility close
• Wrote the full launch suite off one narrative spine. Customer email to all business admins, founder LinkedIn post, Blinq company post, team posts for sales and growth leads, internal announcement, sales one-liner, newsletter blurb
• Directed the founder video reshoot. First cut was feature-led. Refilmed with the CEO the day before launch around the storytelling arc, with hook variants tested on set
• Owned the Co-Catalyst partnership. Joined HubSpot's Co-Catalyst program, ran the partner-manager relationship, locked a $5K-$15K co-marketing budget commitment with a 3x ROI target, newsletter feature, and self-referral pipeline for existing HubSpot customers
• Locked launch-day mechanics. Send-time targeted for US business hours, four segmented email campaigns, button routing tied to admin workspace

Challenges and Solutions

• "Just another integration" framing. Anchored the story on the in-person-to-CRM gap instead of the integration itself. HubSpot became the proof point, not the headline
• Mixed audience across the launch. Customer email stayed anchored on event lead capture as the most concrete use case. Founder video and social went broader on the relationship-intelligence story so the launch read as company narrative, not a single-feature push
• Late-stage narrative pivot with the video already filmed. Refilmed with the CEO the day before launch instead of shipping the feature-led cut. The narrative-led version became the founder LinkedIn post and the partner repost asset

Results

• Customer launch email landed at 3-9x the standard click rate across segments, the highest-converting email Blinq had ever shipped: 100 unique clickers from 5,823 sends, 21 demo flows started and 7 meetings booked
• Listing live on the HubSpot App Marketplace, with certification running against HubSpot's 60-install six-month benchmark
• Co-Catalyst partnership with HubSpot live: co-marketing budget commitment ($5K to $15K with a 3x ROI target), newsletter feature, self-referral pipeline opened

Delivered

Evidence

The Relationship Intelligence section on the live pricing page (current brand). The new AI features as one usage-based category across the plans, not a separate add-on.

Overview

During the rebrand, Blinq's product had moved from sharing and scanning cards to capturing and understanding conversations: a universal scanner, automatic AI contact enrichment, an AI Notetaker, and native CRM sync. The commercial packaging had not caught up. Lead Capture sat on the pricing page as a separate add-on with a $199/month minimum, which gated the new AI features off from the free and individual users who should have been trying them, and made Blinq read like a business-card utility with a feature bolted on. I owned the product-marketing rebuild of the page and the packaging.

What I Did

• Studied how the best SaaS pricing pages do it. Pulled apart Notion, Canva, Airtable, Clay and Slack. The pattern: group features under a single named headline, and present AI as a distinct value box inside the plan card (Notion's "Notion AI" box was the main reference). Used AI tooling, including Manus, to run competitor pricing-page analysis at speed
• Made the core packaging call. Pulled the new AI features into one category, Relationship Intelligence, with Lead Capture as one pillar inside it rather than the headline, and moved it from a $199 add-on to a usage-based credit model folded into the Business and Enterprise plans. That is what let Blinq run a product-led motion on features competitors made you buy outright
• Defined the logic for which feature sits where. Differentiator first (contact enrichment, free for us while competitors charge), then new capability (AI Notetaker), then table-stakes (Lead Capture), then infrastructure (CRM sync)
• Built a working mockup in Claude Code so the team could react to a real page, not a doc. Used it to align growth, brand and GTM and to drive the page to the CEO for sign-off. The mockup approach spread, and I ended up running internal Claude Code sessions for the team off the back of it
• Pushed for customer validation before locking copy, because usage-based credits were new territory for our buyers and a confusing model loses deals quietly
• Resolved the details that had been stuck for months. Settled the long-running "per card" vs "per user" billing-line debate, briefed the copy, and built the feature comparison table

Challenges and Solutions

• A pricing model with no prior pattern at Blinq. Usage-based credits were new for the buyers, so I surfaced "usage-based pricing" once at the bottom of the Relationship Intelligence box rather than tagging every feature with a price, so it read as confidence, not nickel-and-diming
• "Lead capture" undersold the platform but buyers searched for it. Kept Lead Capture as a searchable pillar inside the Relationship Intelligence category, so the page led with the platform story without losing the term prospects compare on
• A compressed window and a sign-off bottleneck. Used the live Claude Code mockup to align stakeholders and reach sign-off faster than a written brief could

Results

• The rebuilt page went live with the Relationship Intelligence launch in February 2026, and the structure I shipped (the Relationship Intelligence box, the usage-based credit line, the plan layout) is the pricing page Blinq still runs today
• Reframed Blinq from a business-card utility into a relationship intelligence platform, and removed the add-on gate so the AI features could run a product-led motion
• The packaging became the spine for the launch comms, the help-centre explainer of how credits work, and the sales pricing collateral that followed

Delivered

Evidence

The rebuilt series, Capture-First. It branches on day 4 based on whether the user has shared their card yet, so sharers and non-sharers get different next steps.

Overview

Lifecycle was the highest-leverage channel in the business and the least owned. Emails had been written feature by feature over years, with no one accountable for the journey a new user actually experienced. The welcome series experiment gave us the proof that email moved the activation metric the company cared about, which turned a copy cleanup into a growth project.

What I Did

• Audited all 25 lifecycle automations end to end. Mapped every trigger, segment, and message against the user journey, and flagged where emails contradicted each other or fired at the wrong moment
• Used the experiment as the strategy. The welcome series test lifted activation 16% and referrals 115% against control. That result set the priority: fix the journeys closest to first value before touching anything else
• Rebuilt the welcome series. Rewrote the sequence around the first action that predicts retention, not the feature list, with subject lines and sends tested by segment
• Wrote the in-app nudges. Copy that meets dormant users at the moment they return and routes them to the action that activates them
• Cleaned up the hidden surface. The audit surfaced 47 transactional emails running outside marketing oversight, brought into one view so the lifecycle could be managed as a system

Results

Where it was heading

In my last weeks I worked with the incoming lifecycle marketer on the next version. We agreed the binary "shared their card or not" signal was too blunt as the way to branch the journey, and started moving toward richer engagement signals (card updated, last login, profile completeness, features explored) and a "what to do next" nudge for everyone, rather than treating not-sharing as a negative. On my last day I mapped out the future-state journeys to align that direction before handing it over.

Delivered

Evidence

The web-page attribution dashboard I spec'd in Amplitude and built with our data analyst (recreated view). Per-page bounce and exit rate, conversion paths, and trial starts by landing page, captured as a baseline before the rebrand went live.

Overview

Blinq was about to rebrand the entire website, with new positioning and copy across the enterprise, pricing, integrations, AI Notetaker and lead-capture pages. The trap with a rebrand is that it ships and nobody can say whether it improved anything. There was no clean, page-level view of how the site converted, and no baseline to compare against once the new content went live. I set out to fix that before a single page changed.

What I Did

• Spec'd the dashboard. Defined the five pages and the metrics that mattered per page (page views, time on page, demo-request conversion, trial starts), with the explicit goal of a before-and-after comparison around the rebrand. Built it in Amplitude with our data analyst
• Drove the metric decisions. When bounce rates came in around 50 percent, time-on-page was unreliable for half our visitors, so I killed it as a key metric and pivoted the dashboard to bounce rate by page, conversion paths, and trial starts by landing page, adding exit rate as the cleaner attention signal
• Captured the baseline before launch. The whole point was to lock the baseline before any new content went live, so the uplift could be attributed to the work and not to luck
• Used it as a diagnostic. The baseline immediately flagged the integrations page leaking nearly 8 in 10 visitors, which targeted the copy rewrite, while enterprise had the lowest bounce on the smallest traffic
• Shared it as a team resource. Rolled the dashboard out to marketing with a walkthrough so the whole team could track page performance through the rebrand

Challenges and Solutions

• Half of visitors bounced, making time-on-page unreliable. I chose not to sink engineering time into fixing it and switched the key signal to bounce, exit, and conversion paths instead
• I do not own the data stack. The job was to spec precisely and make the trade-offs, then enable the data analyst to build and query it fast
• Timing risk. A baseline only counts if it is captured before the change, so I made that the priority over a more elaborate build

Results

• A web-page attribution dashboard live in Amplitude, with the baseline captured before the rebrand, used as the team's shared way to track page performance
• It turned the rebrand from a creative project into a measurable one. On launch day I could say we had been baselining the pages for months and could now read the uplift from the new content
• Honest scope: I left soon after launch, so I do not claim a final uplift number. What I owned was making the work measurable in the first place

Delivered

Visit promptcowboy.ai →

Overview

Prompt Cowboy went from 2,000 weekly users to 55,000 in two months with zero marketing spend. The founders had a product power users loved, customer love so strong that an M&A consultant said it got him "80% of the way there in 10 seconds instead of two to three hours." But they couldn't articulate the why behind the love, couldn't separate free from Pro, and couldn't tell the story to investors. I came in as the marketing strategist to diagnose what was actually working, build the positioning, and shape the narrative that turned customer love into a fundable thesis.

What I Did

• Diagnosed the real product insight from 1.5M+ prompts. Ran a Top 100 analysis of the highest-iterated prompts. Found that power users weren't just writing better prompts, they were engineering context. Framed the insight as "Context Engineering" and pushed it into the product strategy as the through-line for everything Pro
• Built the Free vs Pro tier positioning. Free for users who want to write better prompts. Pro for users who want to build intelligent AI systems powered by their own context. Clear value-based distinction, compelling upgrade reason, mapped to real user behaviour
• Ran the customer research that informed product direction. Interviewed power users across real estate, M&A, strategy consulting, agency, head of product roles. Surfaced the insight that 34% of repeat prompts wanted to become reusable assistants. That insight became the foundation for the Sidekicks feature direction
• Wrote the fundraise narrative. Built the 15-minute investor presentation script the founders used to pitch the round. Anchored on the 2K to 55K traction hook, threaded customer love throughout, mapped the strategic vision from prompt engineering to captured expertise to platform of expert frameworks
• Authored the Pro tier go-to-market memo. July 2025 strategy memo defining how Pro should be positioned, what features should anchor it, and how a parallel consulting motion ("Context Engineering Audit") could generate both qualified leads and product feedback
• Surfaced competitive moat language. Worked with the founders to articulate the iteration-pattern data as Prompt Cowboy's actual moat. Not the model, not the UI, the proprietary dataset of what makes prompts work across millions of iterations. Language the founders carried into investor conversations

Customer Evidence

• Real estate operator: "You are the GOAT, it changed my workflow with AI overnight"
• Strategy consultant: "Like having a personal AI coach guiding you to better results every time"
• M&A professional: "80% of the way there in 10 seconds rather than spending two to three hours"
• Risk management professional: Compliance reports went from a week to 2-3 hours. "I can use it almost as is"
• Agency owner: Featured Prompt Cowboy in his presentation to 500+ retail executives

Strategic Insight That Drove the Work

Power users spent 20 minutes per prompt, wrote detailed context, used frameworks. Most people wrote 42 words and wondered why AI sucked. The gap wasn't the technology, it was that most people don't know how to be good AI directors. Prompt Cowboy's job wasn't to make AI smarter, it was to make humans feel smart when using it. That insight reframed everything from positioning to onboarding to the Pro tier roadmap.

Results During the Engagement

Where the Strategy Compounded

The positioning carried. Today Prompt Cowboy has 350,000+ users, 2,000 paying customers, and roughly $260K ARR on a live Pro tier positioned around "for professionals who refuse to compromise on AI quality." That's the direct compounding of the Context Engineering thesis, separating users who want better prompts from users who want a system that thinks with them. The frame I built in 2025 is the frame the product is sold on today.

Delivered

Evidence

Overview

I led the launch of Promoted Listings Advanced, a new advertising product targeting larger eBay sellers and brands. I launched four new ad products from zero across APAC, US and EU, attracting 2,500+ active brands and helping grow eBay's ads business from $600M toward $1B (it has since doubled to ~$2B).

Process

• Research: Conducted 20+ in-depth seller interviews and analysed competitor offerings to understand the market landscape
• Positioning: Developed "Get more sales" messaging, resulting in an 87% increase in click-through rates
• Product Collaboration: Worked with the product team to prioritise features based on seller feedback
• Beta Testing: Recruited key brands like Dyson and Nespresso, incorporating their feedback into the final product
• Sales Enablement: Created an education platform, webinars, and sales materials for the GTM team
• Global Rollout: Supported teams worldwide in adapting the launch strategy to local markets across APAC, US, and EU

Results

Overview

I led a collaborative webinar with ChannelAdvisor to drive adoption of our Promoted Listings product through their new API integration. Despite having a robust API integration, adoption and revenue among sellers was unexpectedly low.

Key Challenges

• Low awareness of the API integration among 2,000+ sellers on ChannelAdvisor's platform
• Complexity and early stages of the Promoted Listings Advanced product
• Engaging a diverse audience across various industries
• Balancing technical details with practical insights

Development Process

• Collaborated with ChannelAdvisor to understand their platform and user base
• Worked with product managers to ensure accurate feature representation
• Spoke to sales team to ensure content addressed common seller objections
• Analysed API usage data with SQL queries and seller pain points to inform content
• Worked with global legal and Investor Relations to get script and content approved
• Set clear goals for API adoption, budget increases, and revenue growth

Results

Strengthened partnership leading to more joint initiatives including a newsletter.

Evidence

Listen to the Intelligent Growth podcast →

Overview

Most marketers use AI to write the same posts faster. I started Intelligent Growth to show a different version: AI as an operating system, not a writing tool. Every episode is a workflow demo, not a thinkpiece. The newsletter teaches the systems behind them. Everything runs on beehiiv as the single home for the audience.

What I Did

• Picked the wedge before picking the format. "AI as operating system" defined the show's reason for existing and the type of guest worth booking. Every episode shows a real workflow with the troubleshooting kept on camera
• Chose beehiiv as the single platform. Newsletter, podcast, and lead magnet forms all live there, so the audience meets every format from one record. Wired the beehiiv API into my publishing pipeline so automations run directly against it
• Designed the show format around live demos. Episode 1: Charlotte Norman, ex-Head of PMM at Canva, building Claude-powered brand voice systems. Episode 2: ElevenLabs Head of Growth, in production. The format sells itself to guests because they get to show real work, not deliver a pitch
• Built the AI content engine end to end. Five Claude Code skills do specific jobs: idea mining, angle extraction, drafting, a pre-publish voice gate, and publishing. Pipeline: a real work session becomes a newsletter, three LinkedIn posts, and a reel in roughly two hours
• Wrote my own newsletter HTML email builder. So I can publish plain text first and layer in HTML when needed, without leaving the system or fighting an email tool

Why beehiiv

One platform for newsletter, podcast, and lead-magnet forms, with API access so the Claude Code pipeline publishes straight into it. No stitched stack of separate tools.

Challenges and Solutions

• "Why another AI marketing newsletter?" The category is crowded with tool reviews. Anchored every episode on a workflow demo, not a thinkpiece. The show is named for what it teaches, not for the host
• One person, three formats. Built the workflow so the same raw material (a Claude Code session, a podcast moment, a real client launch) becomes the newsletter, LinkedIn posts, and YouTube clips through a shared content engine
• Booking high-signal guests with no audience. Led with the format, not the reach. Guests get to record a workflow they're proud of and that the audience hasn't seen before. Charlotte Norman said yes via cold outreach. ElevenLabs Head of Growth followed

Results

• Episode 1 with Charlotte Norman live on beehiiv
• Episode 2 with ElevenLabs Head of Growth in production
• AI content engine live: roughly two hour turnaround from raw work to published post across newsletter, LinkedIn, and a reel
• beehiiv platform fully wired: newsletter, podcast feed, lead magnet, and API automation all in one record

Delivered

Evidence

Overview

AI was talked about in every meeting but nobody had a way to share what was working. Sales was rebuilding the same competitive answer every week. Product was reading customer calls a month after sales had heard them. I treated this as a PMM problem before it was a tooling problem: the context wasn't moving fast enough across the company.

What I Did

• Founded the AI Working Group. Biweekly forum across product, sales, growth, and engineering. Four sessions across three months. Each one centred on a single live demo from someone shipping with AI internally, not vendor pitches or theory
• Built the GTM Knowledge Base. Aggregated customer call transcripts, competitor intel, product language, and ICP notes into one searchable source. Made it accessible through the AI tools the team was already using, so the friction to query was zero
• Got the sales team using it. Partnered with RevOps to physically set up local AI access on every rep's machine, paired the launch with three concrete use cases (deal queries, competitive answers, email drafting), and ran enablement against the use cases not the tool
• Taught the practice, not the product. Sessions focused on how to write a brief for an AI, how to share a working prompt, how to think about context as an asset. The bar I set: every working AI workflow needs to ship as a doc someone else can pick up
• Connected the work to GTM outputs. Used the same Knowledge Base to power customer interviews, competitive battlecard updates, and launch messaging. PMM moved at the velocity of a larger team without adding headcount

Challenges and Solutions

• "Why is this PMM's job?" Treated AI adoption as a context problem and PMM owns context. Framed every session around a GTM output (a better sales answer, a sharper customer story, a faster launch) rather than the tool itself
• Adoption past the early enthusiasts. Built the Knowledge Base around the use cases sales already had pain on (competitive, deal context, email crafting) instead of asking them to invent a new workflow. The first wins came from sales reps answering competitor questions in seconds, not from anyone "learning AI"
• Demo culture vs. operating culture. Pushed every session toward a shippable artifact (a brief template, a working prompt, a doc with examples) so the group built operating capability, not a wow reel

Results

• GTM Knowledge Base in production use across the sales team for pricing, proposals, competitive queries, and outbound copy by the end of the quarter
• Cross-functional adoption: product, growth, and engineering all running demos in the same forum within three months
• Positioned internally as the context architect for non-technical teams, with PMM treated as the system that moves the company's customer context

Delivered

The course trailer. Built with Build Club, made with Manus, free to start.

Overview

Most marketers use AI to brainstorm. I wanted to show what happens when you give a fully autonomous agent a real marketing job and walk away. Manus runs computer use, persistent logins, and parallel agents in the background. So I built four workflows I actually use as a product marketer, recorded them as a course, and shipped it free on Build Club with the Manus team as co-marketing partner.

What I Did

• Picked the four use cases that matter most to a PMM. Competitive messaging, positioning, influencer discovery, pricing intelligence. Each is a workflow I use in real work, not a synthetic demo
• Built every workflow on real product data. Module 2 mines Otter and Fireflies reviews to extract differentiating language (the "feels like malware" framing came from G2 comments, not a workshop). Module 3 maps competitive territories beyond the homepage. Module 4 finds 15 creators already in your problem space ranked by relevance, not follower count. Module 5 reverse-engineers SaaS pricing structures across 10 companies
• Wrote the prompts so the domain expertise is baked into the brief. Every module includes the exact prompt and a breakdown of why each line matters. Marketers swap in their own competitors, ICPs, and platforms and the same workflow runs unchanged
• Got the Manus team on the collaboration. Co-branded launch on Build Club, in their distribution, with a certification track on top of the free course
• Filmed and shipped end to end myself. On-camera host, RØDE setup, editing in Descript, YouTube plus Build Club distribution. Six modules, roughly 15 minutes of total runtime
• Designed the certification track on Build Club so completing the course produces a portfolio piece the marketer can use back at work

Challenges and Solutions

• "How do you teach an agentic workflow without an hour-long screencast?" Kept each module under four minutes. Showed the prompt, showed the output, narrated the why. The viewer can copy the prompt and ship the workflow themselves in their next work session
• "How do you write a prompt a generalist marketer can adapt?" Built every prompt around the structure of the job rather than the specifics of the brand. The script narrates the spec, not the prompt verbatim, so the audience learns the underlying pattern
• "How do you make a free course feel like a real product, not a video dump?" Built the certification track. Marketers who complete the course submit their own Manus-driven workflow for review. Turns the course into productised accountability rather than passive content

Results

• Six modules published on YouTube and Build Club, roughly 15 minutes of total runtime
• Co-marketing with Manus including newsletter feature and Build Club platform placement
• Hosted on Build Club's certification platform with an open enrolment course path
• First Intelligent Growth free product shipped
• Became the credibility opener for AI-marketing speaking and podcast bookings

Delivered

Evidence

Open the full deck (PDF) →

Overview

A strategic GTM deck I built for Gamma: how to take it from 40,000 teams of 3 users to 1,000 teams of 10+ users, across four personas. The deck covers positioning, persona reframing, pricing research methodology, and the GTM motion. The Bridge Sponsor is the framing I built for it.

What I Did

• Ran original primary research before touching the personas. Interviewed Kevin Bogdanov (ex-Indeed and Salesforce sales enablement leader) on how enablement teams actually buy AI content tools. Validated the pattern through conversations with sales enablement and commercial leaders at Mastercard, UpGuard, Deel, and Rithum. Cross-referenced with Gartner buying-committee research and McKinsey adoption data
• Pushed past the four personas in the brief. The real enterprise buyer wasn't the VP Gamma had named, it was the enablement leader who feels the pain, translates product into content, runs pilots, and proves ROI before the VP signs the PO. Reframed the persona model around that insight
• Built a two-persona dual GTM motion. Marcus (sales rep) lands individual users through PLG, Kevin (Enablement Director) converts scattered usage into team adoption. Same product, two stories, no mixed messaging between marketing and sales
• Named the buying-committee dynamic. "Target Kevin to reach James, that's the enterprise motion." Gartner says the average B2B buying committee has 14 people. James (VP) signs the PO. Kevin (enablement) runs the pilot, trains the field, proves the value. Transformation is weaponising enablement
• Designed two parallel pricing-research tracks. Van Westendorp price sensitivity research with two competing hypotheses: is complexity killing conversion (simplify 6 plans to 4), or is credit confusion the blocker (transparency calculator showing ROI upfront). Test both, measure what works, no more guessing
• Anchored positioning on a category claim. "Gamma is the AI creation layer for sales enablement." Used April Dunford's framework on how market category shapes buyer perception. Seismic stores and distributes, Gong analyses, Gamma creates. Reduces surface area by integrating with what exists rather than competing for new platform real estate

The Strategic Reframe

The brief named the VP as the decision-maker. The research said otherwise. Enterprise tools get hired by the enablement leader and approved by the VP after the leader builds the case. Targeting James directly bypasses the person who feels the pain and runs the pilot. Targeting Kevin reaches both.

The Dual Motion

Marcus (PLG entry) and Kevin (enterprise conversion) are two heads of the same monster. Same product, different stories. Marcus message: "Turn client research into winning decks in 30 minutes." Kevin message: "Turn 4-week content cycles into 1-week launches." Marketing and sales stay aligned because the messaging is engineered for the layer they own.

Delivered

Evidence

Overview

The AI Notetaker needed a launch that was honest about what it does and who it's for, without killing the message. The story needed to come from the person who built it. The consent narrative wasn't part of the initial plan, but it needed to be front and centre.

What I Did

• Ran a full analysis of 10,677 recordings across 3,794 users and found only 2.9% were sales conversations, against 12.8% planning and strategy. Shifted the campaign messaging off "sales meetings" and onto how people actually used it
• Ran a briefing session with the CEO to capture his real perspectives: why we built it, the problem with networking today, the relationship context gap
• Distilled those into talking points he could own (not a script he'd read) for all launch comms
• Wrote the launch blog post independently: long-form, explains the product in plain language, addresses consent head-on, frames it as relationship intelligence rather than a recording tool
• Wrote and shipped segmented email campaigns across free users and premium users with A/B variants
• Sat in on five user interviews alongside the product manager and product designer to hear the consent and value questions first-hand and feed them straight into the messaging

Results

• Founder LinkedIn launch post drove strong organic reach: 202 reactions, 73 comments, and 14 reposts (an estimated 12,000+ impressions on the video)
• Premium engaged campaign delivered a 56% unique open rate and $7,880 in attributed revenue from a single send to 16,022 recipients

Delivered

Evidence

Update: Activation Experience (Apr 2026)

Notetaker had strong retention once people used it, but too many users never made it to first recording. We rebuilt the empty state to pull people into the feature on day one: a redesigned activation screen with a brand-team motion video explaining what Notetaker does and how to use it, plus a persistent red dot on the Notetaker tab that stays until the first recording completes.

My role: in-app activation copy and the internal launch announcement to the Blinq team.

Watching: conversion rate to first recording, new users as a percentage of overall monthly active users.

Overview

Campaigns shipped to public beta but the content team was blocked on the video script and sales needed a one-pager. The real problem: no one could clearly explain what the product did. Feedback from a competitor: "I never understood what campaigns is." This was a messaging problem before it was a content problem.

What I Did

• Built the messaging house before touching any asset: WHO (champion + company type), WHAT (product category + use case), WHY (problem with current way), HOW (capability + feature), SO THAT (benefit)
• Created three value prop canvases for three distinct use cases
• Wrote punch-up copy for headlines and CTAs
• Briefed the video editor with a 5-beat structure matching the app's actual flow
• Wrote email and LinkedIn copy off the same messaging spine
• Built the sales one-pager for the enterprise team

Challenges and Solutions

• Internal confusion about what the product even does: Started with messaging discipline (WHO/WHAT/WHY/HOW/SO THAT) before any creative work
• Key objection: "We already have campaigns in Salesforce": Developed response frame: "Blinq does the data cleanup upfront so your Salesforce campaigns actually work"

Delivered

Evidence

Visit nextdocs.io →

My Role & Approach

Co-founder driving positioning, growth, and enterprise sales.

• Repositioning That Drove the Growth: Repositioned the product from "another AI writing tool" to one tool that replaces the ChatGPT, Google Docs, and DocuSign juggle. That single frame carried the 0 to 15,000+ user run in the first 4 months
• Market Discovery: Identified the enterprise pain point through direct consulting with Malaysian enterprises struggling with new ESG reporting mandates
• High-Friction Sales Environment: Navigated multi-stakeholder approval processes across finance teams (data ownership concerns), legal departments (AI liability questions), and sustainability officers (regulatory accuracy requirements)
• Partnership Strategy: Built consultancy partnership model to access corporate decision-makers
• Enterprise Contract Negotiation: Secured $30K development contract with Alliance Bank through 6-month sales cycle involving multiple stakeholder committees, legal reviews, and proof-of-concept demonstrations

Results

NextDocs has since grown to 50,000+ users and 1,000+ paying customers across 200 countries.

Evidence

Overview

Tasked with figuring out positioning and driving growth for Rephonic, a podcast analytics platform. Complex product with numerous features, diverse target audience, unclear messaging, limited budget, and a 5% trial-to-paid conversion rate.

Development Process

• Research & Positioning: Ran user interviews across the existing customer base to understand workflows, pain points, and value perception. Identified that technical, feature-focused messaging wasn't resonating
• Messaging Pivot: Simplified the core message to: "Find the right podcasts to be featured on." A/B tested messaging, achieving a 37% lift in landing page CTR
• Activation (PLG Tactics): Shifted from limited freemium to free trial. Implemented 7-email onboarding sequence with 45% open rates. Created in-app tooltips reducing time-to-first-value from 12 days to 18 hours
• Retention & Engagement: Launched weekly email digest with personalised podcast recommendations (increased WAU 40%). Built knowledge base to reduce support load

Results

Improved 3-month retention from 60% to 75%. Grew from 500 to 2,000 active users.

Overview

I developed a modular deck to help our GTM team communicate our advertising products to diverse sellers. This was crucial after our sales team was reduced from 18 to 6 members, including 4 new hires unfamiliar with our products.

Development Process

• Collaborated with product team on weekly syncs to track 15+ feature updates per quarter
• Worked with marketing to craft messaging, A/B testing key phrases with 200+ sellers before finalising positioning
• Shadowed 25+ sales calls to identify top 5 objection patterns and co-create response strategies
• Partnered with analytics to develop key stats and performance benchmarks
• Engaged customer success to identify and develop industry-specific case studies

Results

• Sparked more in-depth conversations with sellers at events like RetailFest
• Enabled sales team to quickly prepare for client meetings and presentations
• Supported new team members in understanding and communicating product value
• Served as the foundation for a global sales enablement program

(Slides unavailable due to eBay's privacy restrictions)

The eBay Ads Academy I ran (the AU homepage): a self-paced, free, on-demand learning platform for sellers, with full learning paths for Promoted Listings Standard and Advanced.

Sample marketing videos

Two marketing videos I produced for eBay Ads, made with the eBay content team and localised for the Australian market. (The Academy itself also had an education-focused how-to series.)

Overview

Promoted Listings Advanced was a powerful product, but a complicated one. If a seller did not know how to optimise their keywords or set their budgets, they got poor results and concluded the product was the problem. The post-launch reality was that the initial adoption spike drops off if people cannot get value. Education was the activation lever, so I launched the eBay Ads Academy: a self-serve platform that gave sellers a path to running campaigns like an expert.

What I Did

• Built the Promoted Listings Advanced learning path. Created the educational module on Promoted Listings Advanced, step by step, covering keyword optimisation, budgets and bidding, reading reports, and seller success stories by vertical
• Made it self-serve. Webinars, step-by-step seller guides, and sales enablement materials, so sellers and the sales team could learn the product without a rep walking them through it every time
• Ran the Australian Academy and produced the content. Localised the global Ads Academy content for the AU market and produced a three-part how-to video series on setting up a Promoted Listings Advanced campaign (manual, quick setup, and suggested), working with the e-learning team
• Designed it into the launch, not after it. The Academy existed to close the gap between buying the product and getting value from it, so adoption and activation were built in rather than bolted on
• Took load off sales. A self-serve path to expertise meant reps stopped repeating the same product education and could focus on selling

Challenges and Solutions

• Complexity was driving churn. Sellers who could not optimise blamed the product, not their setup. The Academy turned that complexity into a learnable skill, so trained sellers got results and stayed
• Education is usually an afterthought. I built it as part of the launch so activation was designed in, the same lesson I now apply to any complex product launch

Results

• Course completers saw around 30% better ad performance
• Around 26% lift in product adoption after launch
• Reduced churn, because trained sellers got results and stuck with the product
• Lower support and sales load, with a self-serve path replacing repeated one-to-one product education

Delivered

Evidence

Overview

I created and managed a monthly APAC Ads Feedback Newsletter to bridge the gap between APAC sellers and global product teams, ensuring valuable insights were effectively communicated and acted upon.

Impact

• Directly influenced the product roadmap, leading to 20+ new features including Quick Setup for automating campaign creation and a Campaign Details Page Dashboard
• Received comments from product and senior leadership including the VP Global Advertising at eBay
• Increased APAC's involvement in global product meetings and decision-making
• Improved cross-regional collaboration between product marketing and product development

Overview

At Ogilvy for KFC Australia, the mobile app had 20% retention. Users downloaded for discounts then deleted immediately. Zero marketing budget to fix it. I discovered frontline staff creating unofficial menu items internally (like "Beese Churger," "Nug-a-Lot"). The creative team proposed hiding these behind an 11-second logo hold tied to "11 herbs and spices."

What I Did

• Coordinated the campaign execution and wrote the discovery seeding strategy
• Wrote cryptic hints buried in app T&Cs: "Secret Menu available for those who know where to look"
• Created the breadcrumb trail across channels: edited 6-month-old YouTube comments with vague clues, coordinated hiding hints in email footers and split-second digital billboard frames
• Worked with product team on the unlock flow and interface copy
• Briefed photographers on menu item styling to look exclusive and shareworthy
• Designed the measurement approach tracking social mentions and app session patterns

Challenges and Solutions

• Staff weren't app advocates: Made them co-creators by featuring their menu hacks. Staff became natural promoters showing customers their creations
• Zero budget for paid discovery: Treasure hunt mechanics made early users feel like insiders who wanted to teach friends, creating organic tutorial content

Results

Users created 1,000+ organic tutorials teaching each other how to unlock it.