A full-stack CRM and marketing analytics dashboard engineered to centralize inbound lead capture pipelines, monitor cross-channel marketing efficiency, and systematically prioritize high-intent prospects through algorithmic automated lead scoring architectures.
Technologies Used
React • FastAPI • Python • PostgreSQL • Supabase • Render • Vercel
Key Engine Capabilities:
Lead Management & Source Analytics: Dynamic capture interfaces mapped to monitor paid media performance distribution.
Predictive Lead Scoring: Algorithmic prioritization logic to isolate high-intent sales pipelines automatically.
Enterprise Data Operations: Client-side asynchronous search routing, multi-variable filtering, responsive pagination, and structural CSV data exportation streams.
Cloud Architecture: Decoupled database state micro-services deployed across Supabase, PostgreSQL, Render, and Vercel.
The Challenge: Give stakeholders a single, live view of how a product’s website and app ecosystem was actually performing, traffic, engagement, geography, and conversion signals without needing to dig through GA4’s native interface for every question.
The Results:
• 7,148 Total Users
28,417 total views tracked across a 5-month reporting window.
• 49.17%Engagement Rate
Nearly half of all sessions showed meaningful engagement, well above typical benchmarks.
• 1,112 Sign-Up Events Tracked
Key conversion event isolated and tracked directly on the dashboard, not buried in raw GA4 data.
The Build:
The Challenge: Drive qualified traffic across two very different use cases under one AI agent platform, a high-intent Search audience (students actively searching for study help) and a broader Display awareness push (introducing an enterprise security AI agent to a colder audience) while keeping cost-per-click efficient across both.
The Results:
• 19.4k Total Clicks
Combined Search + Display clicks across a 5-month flight (16 Mar – 3 Aug 2026).
• R810k Total Impressions
734k from Search, 76.5k from Display, full-funnel reach across intent and awareness stages.
• R0.69 Blended Avg. CPC
R0.70 on Search, R0.61 on Display, efficient across both high-intent and awareness-stage traffic
• R13.4k Total Ad Spend
R12.4k Search / R1k Display, lean budget managed across two distinct campaign objectives.
The Build:
The Challenge: Bus and group transport is a high-intent, time-sensitive purchase, people need a quote fast, or they book with someone else. The goal: capture that intent the moment it happens, without losing people to a slow, clunky form.
The Results:
• 149 Leads Generated
Real quote requests, ready for the sales team to close
• R8.72 Per Lead
Lean, efficient acquisition cost sustained across the full campaign.•
• R1,299.70 Total Spend
Full 2-month flight proof that smart targeting beats big budgets.
The Strategy:
The Challenge: Grow a real, engaged audience organically for a leading online auction platform, in a content landscape where algorithms increasingly favor short-form video, without relying on ad spend to manufacture reach.
The Results:
• 115,647 Total Followers
+28.4% growth in 90 days , 24,616 net new follows
• 193,812 Total Engagement
+167.8% engagement growth in the same period, driven primarily by Reels.
• 1.7M Content Views
+147% views growth, 3-second and 1-minute view rates both climbing faster than reach.
The Strategy:
The Challenge: Determine where ad spend actually earns its keep and where it doesn’t. Rather than assuming paid amplification always wins, this required testing organic content against a boosted version of similar content to see what the data actually said.
The Finding: Across a 90-day window, organic content drove 96.2% of all page views, with ads contributing just 3.8%, despite active spend. A head-to-head comparison of a top organic post against a boosted post confirmed the pattern.
The Results:
• +176% Higher Engagement (Organic)
12,609 organic engagements vs. 4,564 on the paid post despite active ad spend behind the boosted version.
• ~2x Higher Engagement Rate
30.8% organic engagement rate vs. 15.1% paid, measured against viewers reached.
• 11x More Follower Growth
341 net new followers from the organic post vs. just 30 from the boosted post.
The Takeaway:
The Challenge: Getting indexed by Google is table stakes. Getting cited by Google’s AI Overview where the algorithm trusts a site enough to synthesize its content into a direct answer requires structured data Google can parse with confidence, not just good copy.
The Build:
The Results:
• 59 Days From Launch to AI Overview
Site went from a standard organic listing to being featured and directly cited in Google’s AI Overview.
• 0 Errors Schema Validation
Structured data confirmed clean and fully machine-readable via Schema.org’s official validator
• Cited, Not Just Ranked
Google’s AI Overview synthesized site content directly into its answer, the highest-trust signal in modern generative search.
The Takeaway:
The Challenge: Transitioning a niche storefront into a highly streamlined, conversion-optimized digital ecosystem while maintaining data integrity and keeping Cost-Per-Acquisition (CPA) highly efficient.
The Architecture:
Shopify Catalog Integration: Synchronized dynamic inventory catalogs, product variants, and promotional matrix structures directly into Meta’s server architecture to fuel zero-latency tracking.
Full-Funnel Demand Gen: Deployed precise Facebook and Instagram conversion campaigns utilizing granular audience scaling, lookalike targeting arrays, and dynamic product retargeting (DPAs).
Omnichannel Expansion: Managed cross-platform execution across TikTok, Instagram, and core social funnels to ensure total brand continuity and predictable conversion pathways.
Traffic Velocity Growth >
Sustained scaling of qualified storefront users across lookalike and core demographic test pools.
Checkout Pipeline CRO
>Engineered frictionless checkout rules and real-time pricing updates to protect average order values (AOV).
Meta Catalog Sync
>Automated database catalog mapping for live, dynamic ad asset delivery to high-intent buyers.
>Used the synced catalog to serve personalized dynamic retargeting ads to past store visitors, dramatically lowering CPA.