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Continuum - Models and Applications
Continuum - Models and Applications
  • Continuum Labs - Applied AI
  • Overview
    • What we do
    • Our Features
    • Secure and Private GPU Infrastructure
    • Generative AI Implementation Risks
  • Model Range
    • ⏹️Model Range
    • Investment Management
    • Employment Law
    • Psychology and Mental Health
    • Home Insurance
    • Consumer Surveying
    • Government Grants
    • Aged Care
    • Pharmaceuticals Benefit Scheme
  • Discussion and Use Cases
    • Three ideas for autonomous agent applications
    • Financial Statement analysis with large language models
    • The Evolution of AI Agents and Their Potential for Augmenting Human Agency
    • Better Call Saul - SaulLM-7B - a legal large language model
    • MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models
    • Anomaly detection in logging data
    • ChatDoctor: Artificial Intelligence powered doctors
    • Navigating the Jagged Technological Frontier: Effects of AI on Knowledge Workers
    • Effect of AI on the US labour market
    • Data Interpreter: An LLM Agent For Data Science
    • The impact of AI on the customer support industry
    • Can Large Language Models Reason and Plan?
    • KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents
    • The flaws of 'product-market fit' in an emerging industry
    • Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence
    • The Disruption of the Administrative Class: How Generative AI is Reshaping Organisational Operations
    • How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries
    • Embracing AI: A Strategic Imperative for Modern Leadership
    • Artificial Intelligence and Management: The Automation-Augmentation Paradox
    • Network effects in AI models
    • AI impact on the publishing industry
    • Power asymmetry
    • Information Asymmetry
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Continuum - Accelerated Artificial Intelligence

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  • Axolotl Platform

Copyright Continuum Labs - 2023

On this page
  • Consumer Profiling
  • Problems Solved

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  1. Model Range

Consumer Surveying

A deeper and more nuanced interaction with consumers

Consumer Profiling

The Aleph-1 Home Consumer Profiling Model has been developed to assist organisations and marketing agencies engage with their customers to ask natural language questions to discover more nuanced views and uncover perceptions around their brand.

These interactions are supplemented by access to social media platforms.

These models can be fine tuned to act within survey design parameters and take on the persona of a partical brand and to engage empathatically and conversationally with consumers.


Factor

Domain

Marketing

Name

Aleph-1 Consumer Profiling

Expertise

Brand Perception and Marketing

Personality

Friendly and conversational

Training Data

Focus groups and Historical Consumer Interactions

Base Model

Llama3-13b

Training Platform

Continuum-Axolotl

Inference

Cloud L40S GPU

Data Privacy

Nemo Guard

Knowledge Base

Interview Techniques

Foundations of Research Methodology

Brand Equity and Brand Management

Facilitation Techniques

Problems Solved

Streamlining Survey Collection

  • Efficiency: The model automates and expedites the survey collection process, allowing businesses to gather consumer feedback quickly. This rapid collection enables companies to stay agile, responding to consumer needs and market changes in a timely fashion.

Personalising Surveys

  • Customisation: By personalising surveys based on individual consumer histories or preferences, models increase engagement rates. Personalised surveys are more likely to elicit honest and detailed feedback, providing richer insights into brand perception.

Sentiment Analysis

  • Targeted Improvements: The model has the ability to perform sentiment analysis, particularly on open-ended responses, offers precise insights into consumers' feelings about a brand. Identifying areas of satisfaction or concern allows for targeted actions to enhance the brand experience.

Predictive Analysis

  • Future Insights: Analysing trends in consumer feedback helps predict future behaviours and preferences. These predictions are crucial for guiding product development, tailoring marketing strategies, and making informed business decisions.

Enhancing Product Innovation

  • Customer-Driven Innovation: By analysing what consumers are saying about products or services, models can identify desired features or innovations. This customer-driven approach ensures that new or improved offerings align closely with consumer expectations.

Streamlining Market Research

  • Dynamic Surveys: your model can create surveys that adapt questions based on respondent answers, leading to deeper insights. This dynamic approach uncovers nuanced understandings of consumer behaviour and market trends.

Customer Sentiment Analysis

  • Deeper Consumer Insights: A model can offer a sophisticated analysis of qualitative feedback, revealing nuanced sentiments. Understanding these deeper consumer sentiments helps address not just the symptoms of dissatisfaction but its root causes.

Automating Follow-ups and Real-time Monitoring

  • Prompt Action: Automated follow-ups and real-time monitoring of feedback ensure that businesses can react promptly to consumer needs. This level of responsiveness is key to maintaining customer satisfaction and loyalty.

The Continuum Consumer Profiling model transforms how brands gather, analyse, and act on consumer perceptions.

This model not only makes the process more efficient and personalised but also ensures that insights are actionable, directly influencing product innovation, marketing strategy, and overall customer experience.

By leveraging the capabilities of our model, businesses can achieve a deeper, more nuanced understanding of their brand's perception in the marketplace.

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Last updated 1 year ago

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