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After 10 years closing deals at companies like Criteo (268% quota), Deel, and Autodesk, I learned something that changed everything: the person across the table doesn't care what you charge per hour.
They care what the result costs them NOT to have.
Here's how I'm structuring revenue on Contra:
- Digital products ($97-$599) = passive income that sells while I sleep
- Services ($1,750-$8K+) = high-ticket consulting for SaaS growth teams
- Subscription product ($299/mo) = recurring revenue from AI sales intelligence
The game isn't picking one. It's building a stack where each tier feeds the next.
What's your revenue architecture look like? Curious how others are stacking income streams here.
The network for creativity
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Connect with clients, get discovered, and run your business 100% commission-free
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In designing this CRM dashboard, our focus was on creating a clear and structured environment for managing sales data and customer relationships. The interface consolidates key metrics such as total deal value, average deal size, win rate, and revenue dynamics into a single workspace, allowing users to quickly understand performance trends and identify changes over time.
this is super clean🙌
Revenue KPI dashboard from a recently delivered project (published version uses modified artificial data).
This dashboard is built around clarity and storytelling. Every KPI is positioned to guide the user through the revenue narrative—performance, variance, and trend—without overwhelming them. Strong readability, clear hierarchy, and focused visuals ensure stakeholders can quickly understand what’s driving growth and where attention is needed. 😉
Project Title: Blinkit Grocery Sales Analysis Dashboard
Project Summary:
This interactive dashboard provides a comprehensive analysis of $1.20M in total sales for Blinkit, transforming raw retail data into actionable business intelligence.
Core Focus
Sales Performance: Tracking key KPIs including Average Sales ($141) and Customer Ratings (3.9).
Inventory Insights: Analyzing performance across 8,500+ items, highlighting top categories like Fruits and Snack Foods.
Outlet Analytics: Evaluating revenue distribution across Outlet Sizes (Small/Medium/High) and Geographic Tiers (Tier 1–3).
Trend Mapping: Visualizing establishment growth and outlet types (Grocery vs. Supermarkets) to identify high efficiency locations
Solid breakdown of KPIs — especially tying outlet size and geographic tier into performance.
I’m curious: did you look at whether customer ratings correlate with revenue per outlet tier? Sometimes high-rating stores in Tier 2/3 outperform purely due to loyalty concentration...
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