The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Meta Ads Campaign for an E-commerce Business.
I managed a Facebook Ads campaign for an e-commerce business with the objective of generating messaging conversations from potential customers.
Campaign Details
Platform: Facebook Ads (Meta Ads Manager)
Industry: E-commerce
Campaign Objective: Messaging Conversations
Ad Spend: $1.90
Impressions: 3,925
Messaging Conversations Started: 27
Cost per Conversation: $0.07
My Approach
I focused on audience targeting, ad delivery monitoring, and budget management to generate customer inquiries within a limited advertising budget. I monitored campaign performance and reviewed audience demographics to understand how the ads were performing.
Key Result
The campaign generated 27 messaging conversations with just $1.90 in ad spend, at an average cost of approximately $0.07 per conversation.
This project demonstrates my practical experience with Meta Ads campaign setup, audience targeting, budget monitoring, and performance analysis.
Services: Meta Ads Management, Facebook Advertising, Audience Targeting, Campaign Optimization, Performance Reporting.
Facebook Ads ManagementFacebook AdsDigital Marketing Social Media ManagementClaudeFacebook Business Manager
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
Night Quota – a Halloween game about my hometown
Monsters work one night a year. In Kharkiv, that night ends at 11 p.m. Not because of monsters. Because of the curfew.
Pick one of four monsters, walk the streets, scare people, knock on doors, and hit your quota before dawn. After eleven the streets empty and the job gets harder.
I’m from Kharkiv and I miss it, so I rebuilt it as a game. 18 districts, each started from my own photo of a real street and drawn as a constructivist poster, the style the city is known for. Every district has a local with a story about Kharkiv. The passers-by are people I love: my friends, my daughter as a little witch, and my cat Lisa, who hides in every district.
Features used: Rive CLI + GPU Canvas (both), Scripting, Data Binding, State Machines.
How it’s made
• Rive CLI + Claude Code: I’m a designer, not a game developer. I described the game, the agent wrote it, I watched the live preview and corrected it.
• RML for the scenes, Luau scripting for the logic: the night clock, the districts, the scares, the dialogues.
• GPU Canvas: one WGSL shader over the whole game. Curfew frame, searchlights, air-raid flash, a camera shutter for cat photos, the dawn report developing out of a halftone raster.
• Data Binding drives the HUD, State Machines run the buttons.
• Rive Editor to play it and check every screen.
• Higgsfield for character art, from my own references and photos.
Play it on desktop, with a keyboard and mouse.
Give it about 20 seconds to load, the whole city is in there.
The combination of clawed hands and the friendly priest is awesome! 😂🔥
What if fintech didn’t just explain numbers, but made people feel the story behind them? 🎬
Meet Kestrel a concept exploring the intersection of AI filmmaking, visual storytelling, and financial technology.
Less complexity. More clarity. Stories worth watching.
Built with a cinematic visual direction in mind. ✦
What do you think of this direction?
Fintech often fails because users process data as cold numbers rather than personal narratives. Using AI to generate cinematic visuals can bridge that gap by making abstract financial concepts emotionally tangible and significantly easier to grasp.
Your own secure AI workforce, built on your own data.
Most SMEs pay for one expensive, generalist AI that knows nothing about their business. We're flipping that: a small, focused AI assistant for each department (HR, Accounts, Marketing, Sales, and Operations), each trained on your own business data.
How we build it:
The result is lower cost, higher accuracy, and an AI that actually understands how your business runs.
We're piloting this internally at Indigloo first. If you run an SME and want to see a department assistant in action, DM me. 🚀
For the internal pilot, I'd keep a held-out question set per department and compare RAG alone with RAG plus fine-tuning. Which department do you expect to benefit most from tuning, and what errors would you want it to reduce?