Numbers backed by methodology

Context

A catalog of ready-to-use AI agents for marketing, sales, HR, and analytics. At launch, there was no catalog structure, product logic, or clear view of which use cases customers would pay for.

What I did

• —Defined the product logic, catalog structure, backlog, and roadmap
• —Partnered with engineering to launch 10 agents, from use-case and MVP hypothesis to production catalog
• —Launched Jay Vibe, an agent that builds web apps and prototypes from text, and an AI data analyst
• —Segmented users by value and role, then redesigned onboarding around those roles
• —Established recurring product analytics across cohorts, roles, and use cases, and reconciled unit-economics reporting
• —Built dedicated Acquisition and Activation measurement and reporting
• —Developed and defended the 2026 strategy and roadmap with the CPO, CMO, and technical leads

Context

Jay Copilot needed stronger post-onboarding activation, a reliable sample-size standard for decisions, and role-specific value instead of a generic scenario catalog.

What I did

• —Calculated sample size in Python to evaluate onboarding and experiment effects without sampling noise
• —Redesigned onboarding and delivered a measurable activation lift
• —Built a role-based recommendation feed for user scenarios

Context

The product needed more pipeline, but marketing could not connect acquisition channels to revenue or see which campaigns paid back and which segments activated.

What I did

• —Rebuilt the GTM strategy and marketing mix for the B2B product
• —Implemented end-to-end Metabase analytics from lead and channel through revenue and ROMI
• —Introduced segment and cohort reporting to evaluate acquisition quality
• —Automated research, SEO, and campaign preparation with n8n, Python, and GenAI APIs
• —Validated hypotheses with hands-on prototypes, including ROI calculators and landing pages

Context

The business needed step-change acquisition growth without losing control of lead costs. Decisions were partly data-blind because there was no end-to-end BI view of the funnel.

What I did

• —Owned 360° marketing across strategy, offers, PPC, CRM, events, and partnerships
• —Tested 72 offers in discovery; most failed, while the winners drove step-change growth
• —Instrumented AARRR funnel stages in BI and supported decisions with ad hoc analysis in Python, Power BI, and DataLens
• —Optimized campaigns, creatives, and funnels while owning budgets and offer unit economics
• —Led cross-functional delivery across content, sales, design, engineering, data, and legal

Context

A Microsoft 365 enterprise portfolio covering document management, corporate portals, and procurement automation faced high acquisition costs, long B2B sales cycles, and expansion plans for the US and Middle East.

What I did

• —Rebuilt campaigns, funnels, and product-level segmentation
• —Improved packaging and the 4Ps while expanding paid acquisition and SEO
• —Built end-to-end Power BI analytics from MQL to the SQL opportunity stage
• —Launched and operated GTM in the US and Middle East through Google Ads and LinkedIn Ads
• —Mapped customer journeys, managed the idea backlog with engineering, and built KPI and budget cases