# Advertisement Decision-Intelligence Platform A decision-intelligence platform that helps businesses make confident ad spending decisions across online and offline channels. --- ## 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 --- ## 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 --- ### 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 --- ### 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 --- ## 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 --- ## 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 --- ## 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 --- ## 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 --- ## 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.