DealNest
Executive Summary
Vision Statement
To become the go-to platform for romance readers seeking the best ebook deals, saving users time and money while supporting a diverse and vibrant reading community.
Problem Summary
Romance book enthusiasts face a fragmented landscape when searching for ebook sales and deals. Platforms like Amazon, Kobo, and others regularly host discounts, but deals are often regional and time-limited. Community-driven posts attempt to aggregate these, but there is no centralized, automated, and customizable solution. This leads to missed opportunities, inefficient manual tracking, and frustration among avid readers.
Proposed Solution
DealNest is a web and browser-based service that aggregates romance ebook deals from major platforms, allowing users to filter by genre, price, region, and platform. Users can set alerts for specific titles or authors, track historical pricing, and receive real-time notifications about new deals that match their preferences.
Market Analysis
Target Audience
The primary users are avid romance readers, typically women aged 18-55, who consume multiple ebooks per month and are highly price-sensitive. Many are active in online communities (e.g., Reddit, Goodreads) and value both convenience and the ability to discover new authors affordably. A secondary audience includes deal hunters and book bloggers who share curated recommendations.
Niche Validation
The source Reddit post demonstrates clear, repeated demand for centralized deal aggregation—users actively share and request sale information, specifying needs for platform and regional filtering. The upvote ratio and comment engagement indicate genuine pain and community value in such threads. Similar demand is echoed in other book communities and forums. This niche is validated by recurring user frustration and the absence of a comprehensive, user-friendly solution.
Google Trends Keywords
Market Size Estimation
Focusing on English-speaking markets (US, UK, Canada, Australia), which represent about 60% of romance ebook sales, the SAM is approximately $2.4 billion. Filtering further to digital-first readers and deal-seekers (estimated 20% of the market), the SAM is $480 million.
Assuming an initial penetration of 0.1% of the SAM, the SOM is $480,000 in annual gross merchandise value influenced, with a realistic target of 10,000 active users in the first 2 years.
The global ebook market was valued at over $16 billion in 2024, with romance as the top-selling genre, accounting for an estimated 25% of all fiction ebook sales (source). This suggests a TAM of roughly $4 billion annually for romance ebooks alone.
Competitive Landscape
Competitors include BookBub (bookbub.com), which offers curated deals and alerts, and Fussy Librarian (thefussylibrarian.com), both of which aggregate ebook sales but lack deep genre filtering and regional specificity. eReaderIQ (ereaderiq.com) provides price tracking, but its UI is dated and genre focus is broad. No solution currently combines real-time alerts, robust romance filtering, and region/platform specificity in a modern, community-driven UX.
Product Requirements
User Stories
As a romance reader, I want to receive notifications when books by my favorite authors go on sale, so I never miss a deal.
As a user, I want to filter deals by region and platform, so I only see offers relevant to me.
As a deal hunter, I want to track historical prices for specific titles, so I can buy when the price drops.
As a community member, I want to submit and upvote deals, so the best bargains are surfaced to everyone.
MVP Feature Set
Automated aggregation of romance ebook deals from major platforms (Amazon, Kobo, etc.)
User registration and profile management
Genre, author, platform, and region filtering
Customizable deal alerts via email and browser notifications
Deal submission and voting system
Non-Functional Requirements
Responsive and accessible UI across devices
GDPR-compliant data handling
99.5% uptime SLA for deal notifications
Scalable backend to support 10,000+ concurrent users
Key Performance Indicators
Number of active users (weekly/monthly)
Deal alert open and click-through rates
User retention and churn rate
Affiliate revenue and paid subscription conversion rate
User-submitted deal volume and community engagement
Data Visualizations
Visual Analysis Summary
The following chart illustrates the relative search interest in 'romance ebook deals' versus broader ebook deal terms, highlighting strong niche demand and seasonal spikes (e.g., holidays, summer).
Loading Chart...
Go-to-Market Strategy
Core Marketing Message
Stop missing out on romance ebook deals—DealNest tracks, filters, and alerts you to the best bargains, tailored to your favorite genres, authors, and platforms.
Initial Launch Channels
- Targeted posts and AMAs in Reddit communities (r/RomanceBooks, r/FreeEbooks, r/BookDeals)
- Launch on Product Hunt and Indie Hackers
- Partnerships with romance book bloggers and BookTok influencers for early reviews and feature demos
Strategic Metrics
Problem Urgency
High
Solution Complexity
Medium
Defensibility Moat
The business is defensible through aggregated deal data, personalized user profiles, and community features (e.g., user-submitted deals, reviews). Partnerships with publishers and affiliate programs strengthen the moat. Building a trusted brand in the romance niche and offering superior filtering/customization increases switching costs.
Source Post Metrics
Business Strategy
Monetization Strategy
A freemium model: core aggregation and alerts are free; premium features (advanced filters, historical price charts, early access to deals, ad-free experience) are available via a $3.99/month or $39/year subscription. Affiliate revenue from partner platforms (Amazon, Kobo, etc.) supplements subscription income.
Financial Projections
With 10,000 active users and a conservative 5% conversion to paid ($3.99/mo), MRR would be $1,995. Affiliate commissions could add another $1,000-$2,000/month depending on deal volume and user engagement.
Tech Stack
Node.js with Express for scalable API endpoints and real-time notifications; Python microservices for scraping and deal aggregation if needed.
PostgreSQL for structured user, deal, and price history data; Redis for caching real-time alerts.
Next.js for fast, SEO-optimized web app and browser extension base, supporting responsive design and quick iteration.
Stripe for payments, SendGrid for transactional email alerts, AWS S3 for static assets, affiliate APIs (Amazon, Kobo, etc.) for deal ingestion.
Risk Assessment
Identified Risks
- Affiliate platform policy changes could reduce revenue or restrict deal aggregation.
- Scraping or API limitations may impact the breadth of deals collected.
Mitigation Strategy
- Diversify affiliate partners and prioritize direct publisher relationships. 2. Build flexible ingestion pipelines and prioritize official APIs where available; maintain manual submission as a community feature.