Enhancing Merchandising Efficiency with Advanced AI Systems
The Client

A leading beauty brand management company focused on enhancing merchandising and sales processes across retail partners. They provide a comprehensive SaaS platform designed to support beauty brands in managing fractional field staff and training activities, ensuring exceptional customer experiences.

The Challenge

The client faced challenges in managing merchandising operations, training field staff effectively, and gaining real-time insights into field activities. They needed an integrated solution to address these issues and improve overall efficiency.

Key Issues

  • Inefficient management of merchandising operations and field teams.
  • Lack of real-time data insights to inform decision-making.
  • Need for personalized training and communication for field staff.

The Solution

Codvo.ai adopted a comprehensive approach, starting with requirement gathering and regular feedback sessions with the client to ensure alignment with their needs.

Implemented Solutions:

  1. AI-Powered Merchandising:
  • Intelligent Matchmaking: Efficiently matching customer requests with suitable vendors based on skills, specializations, performance metrics, location, and availability.
  • Recommendation Engine: Developing an ML-based system to match requests to vendors.
  • Scoring System: Creating a scoring system to evaluate and rank vendors.
  • Continuous Learning: Implementing a feedback loop for continuous improvement.
  1. Gen-AI Powered Service Booking Assistant:
  • NeIO Booking Assistant: Utilizing advanced natural language processing to understand customer inquiries and engage them in dynamic dialogues.
  • Integration with Recommendation Engine: Analyzing customer data to offer tailored service options and improve recommendation accuracy.
  1. Rich Notification & Contextual Data Insights:
  • NeIO Pulse: Providing critical business alerts with detailed insights, avoiding information overload.
  • Contextual Knowledge Search: Streamlining information search across enterprise knowledge bases and IT platforms.
Tech stack

The tech stack includes Machine Learning Models, Natural Language Processing, NeIO Booking Assistant, NeIO Pulse, Integrated Contextual Knowledge Search.

The Outcomes

-Increased Customer Satisfaction: Matching customers with the best-fit contractors, enhancing service delivery and positive customer experiences.

-Enhanced Contractor Utilization: Assigning jobs that align with contractors' skills and availability, leading to better resource utilization.

-Operational Efficiency: Automating matchmaking processes and routine inquiries, reducing time and effort required by in-house teams.

-Improved Customer Experience: Providing a user-friendly platform for customers to communicate their needs and book services efficiently.

-Focused Information: Ensuring critical alerts and insights are delivered without information overload, improving decision-making.

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