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Client Success Story

AI Solution for Efficient Qualitative Survey Response Mapping

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PROJECT OVERVIEW

Simplify. Analyze. Decide: Empowering Decision-Making with AI-Driven Survey Response Coding

Objective

Automate the coding of qualitative survey responses, accurately mapping them to predefined topics and stakeholders.

Client Need

Process 700+ unstructured survey responses weekly with speed and precision, reducing manual effort while ensuring high accuracy in data classification.

Project Image

Scope of Projects

Existing Setup
  • Data extraction and cleaning are currently performed manually, supported by an in-house automation tool, with outputs stored in Excel.
  • The tagging process is manual, leading to inefficiencies, inconsistencies, and a higher risk of errors.
The Challenges
  • Highly Unstructured Data: The open-ended survey responses varied significantly in language, tone, and context, making accurate classification a complex task.
  • Manual Inconsistencies: The existing manual process was time-intensive and often led to inconsistent tagging across datasets.
  • Predefined Frameworks: The model needed to classify responses into over 24+ predefined topics and assign them to relevant stakeholders accurately.
  • Recurring Workload: The process had to be automated for weekly survey data inputs, requiring a reliable end-to-end solution.
Project Requirements
  • Streamlining High-Volume Data Processing
Data Volume
  • Handle 700+ responses every week.
  • Process open-ended responses and stakeholder tagging efficiently.
Recurring Task
  • Implement automated data coding for seamless, weekly processing.
  • Ensure accuracy, speed, and consistency in data management.

Proposed Solution

AI-Driven Intelligence

  • An AI-powered model trained on client-provided labeled data to automatically classify survey responses, mapping them to predefined topics and relevant stakeholders with precision.

High Accuracy Predictions

  • Expected Accuracy: 95%-98%
  • Continuous Improvement: Model performance enhancement over time with additional training data, ensuring adaptability to new patterns and nuances.

End-to-End Automated Workflow

  • Model Training: Initial training phase spanning 3-4 weeks using labeled datasets.
  • Ongoing Automation: Weekly processing for seamless coding and stakeholder assignment, reducing manual intervention.

Customization & Ongoing Support

  • Tailored support for fine-tuning the model as business needs evolve, ensuring optimal performance and alignment with changing data dynamics.

Workflow of Solution

Input

Raw survey responses are provided in Excel format for processing.

Processing

The AI model analyzes and classifies responses based on predefined topics.

Output

Labeled data mapped to relevant stakeholders for actionable insights.

Integration

Easily integrates with the client's existing survey platform and workflows for smooth operations.

How We Did it

The AI model is designed to classify survey responses across 24+ diverse topics, organized into key categories for comprehensive analysis:

  • Browsing Interface: Navigation, Site Performance, Filters
  • Conversion Support: Pricing, Reviews, Marketing
  • Product Information: Dimensions, Images, Assembly Details
  • Delivery Options: Shipment Process, Shipping Area Coverage
  • Customer Experience: Post-Purchase Communication, Overall Customer Experience (CX)
AI Diagram
Training Data & Tools

Training Data & Tools

  • AI-Assisted Tagging: Utilizes advanced AI-driven tagging methods to streamline data labeling.
  • Cross-Verification: Ensures enhanced accuracy through rigorous validation and continuous refinement of training data.
How it Worked

How it Worked

Sample Question
  • Was there anything missing from your checkout experience that would have made it easier?
Example Responses & Classification

01 - A cleaner interface and not unnecessary info.

  • Topic: Site Performance
  • Stakeholder: A

02 - A clear indication of the restocking fee.

  • Topic: Returns Policy
  • Stakeholder: B
Processing Workflow
  • The AI model analyzes the context and intent behind each response.
  • It then automatically classifies responses into predefined topics and assigns them to the relevant stakeholders for action.

Continuous Improvement & Support

Ongoing Model Updates

Enhances accuracy with additional labeled data.

Expanded Classification

Adapts to new topics and evolving client needs.

Seamless Reporting

Potential integration with visualization tools for better insights.

Fine-Tuning & Optimization

Continuous adjustments to improve model performance.

Quick Issue Resolution

Fast support for workflow or integration challenges.

Client-Centric Approach

Ensures high satisfaction with consistent performance improvements.

Technology Stack

Backend Technologies

  • Python: Core programming language used for building the AI model and data pipelines.
  • NLP Libraries: TensorFlow, Keras, and spaCy for intent classification and natural language understanding.
  • Data Processing: Pandas and NumPy for efficient handling of unstructured survey responses.

Frontend Technologies

  • Output Management: Labeled data outputs in Excel format for seamless integration with the client’s workflow.

Workflow Automation

  • Integrated with the client's manual data extraction tool for a fully automated end-to-end process.

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