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# Advertisement Decision-Intelligence Platform

A decision-intelligence platform that helps businesses make confident ad spending decisions across online and offline channels.

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## Problem Statement

### Primary Problem
**Businesses struggle to make confident ad spending decisions due to fragmented data and poor offline attribution.**

### Supporting Problems
Businesses invest in advertising across multiple online and offline channels, but:

- Ad data is fragmented across platforms and formats, making it hard to form a unified view
- Offline advertising lacks reliable feedback loops, leading to gut-driven decisions
- Existing tools are primarily built for digital ads and assume technical expertise
- Insights are difficult to translate into concrete next actions
- There is no single system acting as a “back office” for ad decision-making
- Experimentation with ad spend (what to increase, pause, or stop) is difficult and slow

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## Goals

### Core Goals
These goals define **what must become true** for the problem to be meaningfully solved.

- Enable advertisers to make **more confident ad spending decisions**
- Reduce reliance on **gut feeling**, especially for offline advertising
- Provide **unified visibility** across online and offline ad efforts
- Reduce uncertainty and ambiguity in ad performance interpretation
- Translate data and insights into **clear, actionable decisions**
- Enable **location-aware** ad decision-making
- Support a wide variety of business models and product types
- Build **trust and credibility** through transparent and explainable insights
- Remain useful in **real-world, messy environments** with incomplete data
- Minimize friction for businesses adopting and using the system

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### Supporting Goals
These goals focus on **accessibility and usability**, not implementation.

- Make ad insights understandable for **non-technical users**
- Allow users to reason about their data using **plain, natural language**
- Reduce cognitive load when interpreting analytics and performance trends
- Minimize effort required to bring existing operational and sales data into the system

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### Long-Term Vision (Out of Scope for Initial Validation)
These represent **future expansion**, not requirements for initial success.

- Enable an ecosystem connecting advertisers with service providers such as:
	- Billboard and offline media vendors
	- Digital ad specialists and agencies
	- Designers, video editors, and creative professionals
- Act as a collaboration layer around ad planning and execution, not just analysis

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## Explicit Non-Goals

- This product does **not** replace ad platforms such as Google Ads or Meta Ads
- The system does **not** promise perfect or 100% accurate offline attribution
- The product does **not** automate or replace human judgment
- The platform augments decision-making; it does not make decisions on behalf of users

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## Target Audience

- Businesses or individuals spending approximately **₹50,000 to ₹10,00,000 per year** on advertising
- Users willing to pay **₹500–₹2,000 per month** for better ad decision support
- Founders, business owners, and operators who:
	- Advertise across multiple channels
	- Feel existing tools are fragmented or too technical
	- Want clarity and confidence rather than complex dashboards
- Both technical and non-technical users who prefer:
	- Simple explanations
	- Minimal dependence on heavy spreadsheet workflows
	- On-demand visual summaries when needed

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## Success Expectations

- Achieve **500 active users** within the first year after launch
- Reach a sustainable subscription price of approximately **₹1,500 per month**
- Validate usability and value with **50 active users** in the first 6 months

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## Risks & Constraints

- Scope creep due to the breadth of advertising channels and use cases
- Over-engineering before sufficient user validation
- Risk of over-promising attribution accuracy if expectations are not managed carefully

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## Notes

This document intentionally focuses on **problems and goals**, not features or implementation.
Specific features, MVP scope, and technical design should be derived **only after validation** of these goals with real users.

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