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SERP Simulation Data Contributor

SERP Simulation Data Contributor

Pending
💰 CAD 750–1500 👤 Unknown 🕒 23d ago status: new
Internet Marketing SEO Link Building Marketing Market Research Content Strategy Data Analysis Data Collection
We’re working on a system that helps SEOs move from explaining results → to showing outcomes. Instead of long SEO proposals, we’re focused on one idea: “What does the Google result look like after the work is done?” What this is about? Most SEO proposals today explain actions: - backlinks - content strategy - technical fixes But clients usually respond better when they can see the outcome, not the process. We’ve been testing a different approach: simulating what a future SERP could look like before changes happen. Example of what we mean Instead of saying: “We will improve rankings” We show: - what the SERP could look like in the future - how brand narratives shift across results - how visibility of positive/negative content changes Some teams now pitch it like: “This is your future Google page.” What we’ve observed - Traditional SEO proposals focus on activities - Clients react more strongly to visible outcomes - “Before vs projected SERP” closes decisions faster than explanations The challenge is not ranking. It’s showing the outcome before it happens. What we need: We are collecting structured data to improve this system. Your task is simple: help us map real SERPs and brand situations into structured interpretation data. Data format (simple) You will work with lists like: - Brand / company name - Associated SERP perception (positive / neutral / negative mix) - Key visible signals (news, reviews, forums, articles) Example: - Brand A → mixed reputation (news + reviews + forum complaints) - Brand B → strong positive visibility - Brand C → unstable narrative (conflicting sources) We are not collecting keyword rankings. We are collecting: how brands and SERPs feel in real search results. Core task For each brand list: - identify overall SERP sentiment structure - highlight dominant narrative (positive / negative / mixed) - note what is shaping perception (news, reviews, authority pages, etc.) Data sourcing You are expected to: - find or propose your own SERP examples - ensure they are real and verifiable - structure them in a consistent format There is no fixed dataset provided. We rely on contributors to build it from real-world SERPs. Ideal contributor This is not pure SEO execution work. We are looking for people who: - understand how SERPs influence perception - can think beyond rankings - can structure messy information into clear patterns
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