Whop AI Reseller Analytics Dashboard
Budget / Salary$30–250
TypeFreelance project
LocationRemote
Posted2 hours ago
I’m building a reseller-facing dashboard for my Whop AI store and I need it to focus solely on analytics and reporting. The interface should surface two core data streams: customer demographics and product usage statistics. For the demographic slice, I care about grouping users by purchase history—first-time buyers, repeat customers, high-value orders, subscription renewals and similar cohorts. Age, gender or location can wait, but please leave room in the data model so they can be plugged in later without rewrites.
I want a clean, responsive UI that loads quickly, presents headline KPIs at a glance and lets me drill down into tables or charts. Real-time or near-real-time data refresh, simple filter controls, and CSV/Excel export are all essentials. Feel free to choose the tech stack—React or Vue on the front end, Node, Python or Go on the back end, Postgres, MySQL or a managed warehouse underneath—so long as the code is modular, documented and easy for another dev to hand off.
Deliverables
• Source code for both client and server, clearly organised
• Live demo or staging link so I can click through before sign-off
• README covering setup, env vars and deployment steps
Acceptance criteria: customer cohorts based on purchase history calculate correctly, product-usage charts match the underlying raw data, and the main dashboard view renders in under two seconds with at least 10 000 rows in the dataset.
I want a clean, responsive UI that loads quickly, presents headline KPIs at a glance and lets me drill down into tables or charts. Real-time or near-real-time data refresh, simple filter controls, and CSV/Excel export are all essentials. Feel free to choose the tech stack—React or Vue on the front end, Node, Python or Go on the back end, Postgres, MySQL or a managed warehouse underneath—so long as the code is modular, documented and easy for another dev to hand off.
Deliverables
• Source code for both client and server, clearly organised
• Live demo or staging link so I can click through before sign-off
• README covering setup, env vars and deployment steps
Acceptance criteria: customer cohorts based on purchase history calculate correctly, product-usage charts match the underlying raw data, and the main dashboard view renders in under two seconds with at least 10 000 rows in the dataset.
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