Mighty Coupons —
Coupon-Driven Local Service Marketplace

Project Overview

Mighty Coupons originated from a structural observation: market volatility has made the earliest phase of local business growth — attracting the first wave of customers — disproportionately difficult. Established businesses have brand recognition and prior engagement signals that feed discovery algorithms. New businesses enter with no prior signal, no review history, and no structural mechanism to earn local trust quickly.

The platform's response is a coupon-driven discovery model. Promotional offers serve as the entry point — they give consumers a concrete, low-risk reason to try an unfamiliar business, while giving merchants a measurable and controllable mechanism for early customer acquisition. The surrounding infrastructure — verification, booking, and AI intelligence — ensures that exchange is reliable and structurally sound for both sides.

Market Context

The problem Mighty Coupons addresses is structural, not cyclical. New local businesses face a compounding set of conditions that make the early customer acquisition phase disproportionately expensive and unreliable regardless of service quality.

Product Vision

The platform is built around three structural mechanisms that define how trust, comparability, and merchant intelligence operate. These are architectural commitments — not features — that determine what kind of exchange the platform can reliably facilitate.

Platform Architecture

The platform is structured around three interdependent systems, each designed for the operating context of its primary user.

The Consumer System is mobile-first — discovery and booking happen close to the point of service, on the move. Consumers search for verified merchants nearby, browse and compare standardized promotional offers, discover deals, and complete service bookings without leaving the app. The interface is structured as a decision flow — proximity, offer comparison, and direct booking as a sequential low-friction path — not a passive directory.

The Merchant System is desktop-first — managing coupon promotions, tracking service demand, evaluating promotion performance, and controlling booking schedules is a deliberate operational task requiring a full-surface interface. A merchant verification system gates platform access: only confirmed, legitimate businesses are permitted to publish listings and appear in consumer-facing discovery. Verification is enforced at the data layer — not as a policy filter, but as an architectural prerequisite.

The AI Intelligence Layer sits on the merchant side, where data asymmetry is highest. Merchants can observe their own bookings; they cannot see platform-wide demand patterns or comparative offer performance. The AI layer closes that gap — surfacing coupon strategy analysis, service demand signals, and operating model recommendations derived from the platform's standardized offer data structure. Scope is explicitly bounded to decisions the data can support.

Hover each node to see how the step functions within the full consumer decision path.

Hover each node to understand the merchant-side workflow and how each step generates platform data.

Hover each cell to understand the platform function and why it is structurally necessary.

MVP Focus: Salon as the Initial Wedge Market

The MVP is scoped to a single service vertical — the Salon category — before expanding to additional business types. This is a deliberate product strategy decision, not a resource constraint. Two-sided marketplaces carry an inherent cold-start risk: without sufficient merchant supply, consumer demand does not materialize; without consumer demand, merchants see no reason to participate. Launching across multiple categories simultaneously amplifies this risk and produces a thin, low-trust experience on both sides. Focusing on one category allows the platform to validate its core mechanics in a controlled environment — building sufficient depth in a single vertical before the expansion model is activated.

• 1
  Single-category launch reduces cold-start risk. Marketplace cold-start is a structural problem: supply without demand and demand without supply are both inert. Concentrating the initial merchant acquisition effort and consumer discovery surface on one vertical builds liquidity in a focused area rather than spreading thin across many categories. A dense, trusted Salon supply is more valuable to early consumers than a sparse multi-category listing.
• 2
  Appointment-based and promotion-sensitive by category default. Salon services run on scheduled appointments, which maps directly to the platform's booking system. Consumers in this category already expect and actively search for promotional offers — first-visit discounts, seasonal deals, and service bundles are category norms. The coupon-driven discovery model fits without requiring a change in consumer behavior.
• 3
  High consumer search frequency for nearby salon deals. Salon services are among the most actively searched categories in local deal and coupon contexts. Consumers regularly search for nearby salon promotions — particularly for first-time visits to an unfamiliar provider. Consumer intent is structurally present; the platform intercepts it with verified, comparable offers and a seamless booking path.
• 4
  High repeat-visit frequency creates compounding platform value. Salon services are not one-time transactions. Consumers return on a predictable cycle — haircuts, coloring, nail care, skincare treatments. Repeat behavior generates longitudinal data on coupon effectiveness and consumer preference patterns, which feeds AI intelligence quality as the platform scales.
• 5
  Validates the full platform loop in one category before expansion. Constraining the MVP to Salons means coupon-driven discovery, verified merchant listings, appointment booking, and AI insight delivery can all be verified as a working integrated system — before adding the structural complexity of additional verticals with different offer schemas, booking cadences, and consumer intent patterns. A single-category MVP produces a clean, unconfounded validation signal.

The platform is currently in merchant testing with early salon businesses. This phase validates the offer creation flow, verification onboarding, and booking system under real merchant operating conditions — before consumer-side scaling begins. The objective at this stage is structural validation: confirming that the systems work as designed in the hands of actual users, establishing that the core mechanics are sound, and identifying friction points before the consumer surface is opened at scale.

The Salon vertical is designed to activate a self-reinforcing loop: as more verified salon merchants join the platform, the deal inventory grows richer, which improves consumer discovery quality, which attracts more consumers, which generates more bookings, which increases merchant revenue and confidence in the platform. This loop — once validated in a single category — becomes the replicable engine for expansion into adjacent verticals. Each new service category inherits the same structural mechanics, with category-specific offer schemas and booking cadences layered on top.

Trust & Verification System

A local marketplace built on promotional offers carries a specific trust risk: consumers cannot distinguish real businesses from illegitimate listings without structural intervention. Verification resolves this at the architecture level — before any offer surfaces in the consumer-facing interface.

My Role

As Head of Design & Product Innovation, this role functions as the platform's primary product intelligence layer — holding full design scope from marketplace system architecture through interaction design, while coordinating across engineering, marketing, and the founder. No dedicated product management layer exists on this team. Product strategy, platform architecture, and cross-functional alignment all run through this function. The scope is structural, not positional — every decision at the product level is owned and operationalized here. I designed the platform mechanics that coordinate value exchange between consumer decision flows and merchant operating workflows — ensuring discovery, booking, verification, analytics, and AI recommendations function as one coherent system.

• 01
  Marketplace System Design

  Defined the structural logic of the two-sided exchange — what merchants offer (verified promotions, standardized schema), what consumers receive (trusted deals, frictionless booking), and the infrastructure that keeps both sides in equilibrium. This modeling determined verification requirements, offer schema design, and the "control variables" standardization approach that enables cross-merchant comparability.
• 02
  Consumer Decision Architecture

  Designed the mobile-first consumer system as a structured decision flow — not a listing directory. The interaction model is built around how consumers make local service choices: proximity, offer comparison, and direct booking as a sequential, low-friction path. Intent-matching drives discovery rather than keyword search or passive browsing.
• 03
  Merchant Dashboard Experience

  Designed the desktop-first merchant system — coupon campaign creation, promotion performance evaluation, demand trend tracking, and booking schedule management with conflict prevention. The dashboard is designed to reduce merchant operational overhead while generating the structured data that feeds AI intelligence quality over time.
• 04
  Coupon Promotion Mechanics

  Designed the offer creation system and standardized promotion schema — the "control variables" approach that structures coupon offers into comparable, measurable units across all merchants. Offer standardization is not a UX constraint; it is the data foundation that makes platform-level AI analysis and consumer-side comparison both possible.
• 05
  AI-Assisted Insights & Onboarding Logic

  Defined the scope and output structure of the AI intelligence layer — coupon performance analysis, demand pattern signals, and operating model recommendations scoped to decisions the data can support. Also designed the AI-assisted merchant onboarding flow at onboarding.mighty.coupons, which guides merchants through qualification and identity verification before platform access is granted. Boundary design is architectural — incorrect scope produces advice the underlying data cannot support and erodes merchant trust in the system.
• 06
  Product Innovation Brainstorming with the Founder

  Work directly with the founder to pressure-test platform assumptions, stress-test category expansion logic, identify structural gaps before engineering handoff, and align product direction with the long-term marketplace model. This collaboration is where MVP scope decisions — including the Salon wedge strategy — are stress-tested against both product integrity and market feasibility.
• 07
  Collaboration with Engineering & Marketing

  Coordinate with engineers on component specifications, data model constraints, and implementation feasibility — translating architecture decisions into buildable requirements. Coordinate with marketing on merchant acquisition messaging, onboarding entry points, and how verification and promotional mechanics are communicated to prospective merchants. Cross-functional alignment is not a separate workstream — it is built into how every product decision is formulated and handed off.

Strategic Contribution

The following reflects the architectural and organizational contribution of this engagement, scoped to what is structurally observable at the current stage of the platform.

The work on Mighty Coupons focuses on structuring value exchange between merchants and consumers — designing the offer schema, verification system, booking infrastructure, and AI intelligence layer as a coherent platform rather than a collection of features. The project's central challenge is marketplace dynamics: how to create sufficient trust and supply-demand equilibrium at launch, and how to validate the platform's core mechanics before introducing the complexity of additional service categories. By executing a controlled category launch in the Salon vertical — a category naturally aligned to appointment scheduling, promotional discovery, and repeat consumer behavior — the platform builds the architectural foundation and operational evidence needed to expand into additional service verticals with confidence and without confounding the learning signal.