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Mostly Used By Retail Industry

OpenAI Application for Cohort Analysis

Cohort analysis is a technique used to analyze the behavior of a group of individuals (or "cohorts") over a specific period of time. We leverage OpenAI's NLP and ML capabilities to understand the data associated with your cohorts and build predictive cohort analysis models.

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How It Works?

We are leveraging Generative AI to fill in gaps in the data, identify patterns and trends, test new features, and create synthetic data for training cohort analysis models.

Data Collection & Preparation

In this phase, we leverage tools like Azure Data Factory, Azure Blob Storage, Azure Databricks or Azure Synapse Analytics to store, manage and transform the data collected. The data collection typically includes information about the individuals or customers, such as their acquisition or signup dates, demographic details, and relevant metrics like their purchases, engagement levels, or churn status. Additional data can also be collected depending on the specific analysis goals, such as product usage data, marketing campaign information, or customer feedback.

Model Training Using OpenAI

For training the model for cohort analysis we have used OpenAI platform. OpenAI's advanced language model provided the foundation for training and fine-tuning the model, enabling it to understand and generate human-like responses. The extensive capabilities of OpenAI's technology were harnessed to enhance the accuracy and effectiveness of the cohort analysis model.

Model Deployment

We prefer Azure Services like Azure Functions, Azure Kubernetes Services (AKS), or Azure App Services to deploy the Contact Center Analytics model, but the same model will also be able to deploy on other cloud platforms like AWS Services e.g; Amazon SageMaker, Amazon Elastic Compute Cloud (EC2), Amazon S3 (Simple Storage Service) AWS Lambda, for GCP Services like Google Compute Engine, Google Cloud Functions, Google Cloud AI Platform can be used, HuggingFace,etc

What We Offer

ThirdEye Data builds advanced AI-powered systems for enterprises to execute time-based, size-based, and segment-based cohort analysis. 

OpneAI Integration

Leveraging OpenAI's machine learning models and NLP capabilities to to identify patterns and trends in user behavior, and segment users.

Data Transformation

Taking care of the data collection, cleaning, transforming, and preparing the customer behavioral data at scale to train the ML model.

Model Development

Develop custom machine learning model including feature selection, algorithm and techniques to execute seamless cohort analysis.

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We have prepared advanced Cohort Analysis AI models which are ready to be deployed.

Customer Feedbacks

All the reviews are independently verified by Clutch.

ThirdEye Data brought in their best to support us.

Kumar Mankala Advisor of AI/ML Program & Products, SCE

They went above and beyond to actualize the our ambitions.

Matt Singer CEO, EcoTrak

Their project management and communication were top-notch.

Andriy Romanyuk Head of R&D, Glas Trösch Group

Frequently Asked Questions

Before leveraging the cohort analysis models, businesses may have some common asks. We are trying to answer them here.

Generative AI powered cohort analysis systems can be used with a variety of data, including behavioral data, demographic data, and transactional data. The specific type of data that you can use will depend on the specific system that you are using.

The accuracy of generative AI powered cohort analysis systems depends on a number of factors, including the quality of the data that is used, the complexity of the model, and the specific use case. In general, generative AI powered cohort analysis systems are more accurate than traditional cohort analysis methods.

When using generative AI powered cohort analysis systems, it is important to ensure the security of your data. This includes taking steps to protect your data from unauthorized access, use, or disclosure. We consider using a system that offers encryption and all other important security features.

The scalability limitations of these cohort analysis systems depend on the specific system that you are using. In general, generative AI powered cohort analysis systems are more scalable than traditional cohort analysis methods. However, there are some systems that have scalability limitations.

There are a few risks associated with this kind of system:

  • Data Security
  • Model Bias
  • Cost
These systems can be very useful to increase your revenue by helping you to:
  • Identify high-value customers
  • Target your marketing campaigns more effectively
  • Develop new products and services that meet the needs of your customers
  • Improve your customer service

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