AI-Based Pricing & Deal Optimization for Inpact

Overview
Determining the right sponsorship price is a challenge in influencer marketing. This feature aims to introduce an AI-powered pricing engine that suggests optimal sponsorship rates based on engagement data, market trends, and past deals.

What This Feature Will Do
Fair & Data-Driven Pricing – AI will predict a reasonable sponsorship price based on follower count, engagement rate, and industry trends.
Market Trend Analysis – Pricing will adjust dynamically based on seasonality, trending topics, and competitive benchmarks.
AI-Powered Deal Negotiation – Brands and creators will receive pricing suggestions to streamline negotiations.
How We’ll Build It
Collect & Process Data

Gather creator insights, including engagement rate, audience demographics, and content type.
Track market trends such as seasonality, industry benchmarks, and trending sponsorships.
Analyze past sponsorship deals, including pricing, contract details, and performance.
Store data in Supabase/PostgreSQL for real-time access.
Train an AI Model

Develop a model to predict fair sponsorship pricing using regression algorithms such as Linear Regression, XGBoost, or LightGBM.
Key input factors:
Engagement rate
Follower count
Industry category
Past sponsorship prices
Market demand score
Deploy as an API

Convert the trained model into a FastAPI microservice that returns pricing recommendations.
Store AI-generated price suggestions in Supabase/PostgreSQL.
Integrate with the UI

Display AI-suggested pricing when a creator applies for a sponsorship.
Allow brands to adjust pricing based on budget constraints.
Implement an AI-powered negotiation tool for refining deal terms.
Why This Matters
Creators receive fairer sponsorship deals based on real data.
Brands save time and gain transparency in influencer pricing.
The process becomes more efficient and data-driven.
Tech Stack
Frontend: React.js
Backend: FastAPI, Supabase/PostgreSQL
AI Model: Scikit-learn, TensorFlow, XGBoost
Data Processing: Pandas, NumPy