Subconscious AI specializes in causal market research, utilizing synthetic respondents to enhance data realism and improve decision-making capabilities. The company employs a cyclical approach that combines quantitative analysis, qualitative insights, and human validation. It offers capabilities such as causal modeling and user journey simulation.

subconscious.ai

Subconscious AI | 1,284 followers on LinkedIn. 🧠 Behavior Change as a Service 🌞 | Conduct Research faster, with higher quality and more ethically.

Our technology allows users to conduct causal experiments for any human behavior at a fraction of the cost and time of traditional methods.

Get started today: https://docs.subconscious.ai/

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Building Products That Make an Impact | Writer, Researcher, Advisor | Ex-Microsoft, Twilio, BetterUp, Deloitte

Connor Joyce

Frontend | Full Stack | React | Next | Typescript | Node | UI/UX | Python | Data Analytics | Project Management

Mohak Sunil Waghchaure

Software Engineer @ Valley

Mehul Gawde

PhD | Data Scientist | Economic choice behaviour | Mathematical Psychology | Applied Econometrics & AI

Subodh Dubey

Subconscious AI

Today, humans fill out the surveys and computers fill in the gaps. In the future, it will be the opposite: AI will fill out the survey and, when the AI isn’t sure what box to check, humans will fill the gaps. Read More here: https://lnkd.in/efztYTkf

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If you had all the data and a data scientist at your disposal, what would you want to know? 🤔 Vote below and let us know in the comments if your question isn’t listed!

Why is Subconscious.ai different? - We combine economic models with Large Language Models (LLMs) to run behavioral experiments with unmatched accuracy. 🎯 - We have cracked the code on efficiency, delivering insights fast and affordable. ⚡️ - Our results are causal and actionable. 🌎

We read this paper so you don't have to! 📝 "Scaling Synthetic Data Creation with 1,000,000,000 Personas" Here's the gist: 1. A collection of 1 billion diverse personas created from web data, representing 13% of the world’s population. These personas serve as distributed carriers of world knowledge, allowing for the creation of synthetic data from various perspectives. 2. The methodology leverages a large language model (LLM) to create high-quality synthetic data across domains, including math problems, logical reasoning, game NPCs, and more. 3. This process is adaptable to different data synthesis scenarios, potentially influencing how LLMs are developed and researched. 4. This approach in data creation can simulate diverse user behaviors, predict reactions to new products or policies, and support the development of virtual societies in the metaverse. 5. The paper emphasizes the importance of ethical and responsible application to avoid misuse and ensure the technology benefits society. Link to the full paper: https://lnkd.in/dneZe9Dt

The 'why' behind decisions shouldn't take forever. Traditional market research often feels like a slow, costly process. But with Synthetic Respondents and Causal AI, there’s a better way: Conduct experiments in minutes, not months. Cut research costs by up to 100x. Gain reliable, human-like data. Use Cases: Market Research: Quickly understand consumer behavior. Policy Design: Simulate policy outcomes effectively. Product Development: Test new features efficiently. www.subconscious.ai

🔍 Another successful replication! Check out our latest replication of the Bechtel, Scheve, and van Lieshout study on International Carbon Tax Policy for Environmental Mitigation, with a Spearman Correlation of 0.6711. The study assesses the preferences of residents in the U.S., U.K., Germany, and France for various aspects of carbon tax packages using a discrete choice experiment. Our replication confirms key insights, such as the importance of mitigation efforts, cost implications, and regional preferences. Interact with our data here: https://lnkd.in/d4X3QJfB Conduct causal market research: www.subconscious.ai

Unpacking consumer preferences for bioplastic fabrics! 🌱 Our latest experiment delves into the factors influencing consumer choices in sustainable materials. The data reveals that a higher percentage of fabrics' bioplastic significantly enhances consumer preference. Biogenic resources such as sugar cane and corn also play a vital role, while factors like origin and certifications (e.g., climate protection, fair production) further shape consumer decisions. Interestingly, price remains a key determinant, with lower costs strongly preferred. This insight emphasizes the importance of affordability in driving sustainable consumer behavior. Check out the graph for a detailed breakdown of the findings. Let’s continue exploring how consumer preferences can guide sustainable innovation! https://lnkd.in/d3TxcMAu

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