Case studies

Enhancing merchandising efficiency with advanced AI systems

index

About the client

Overview

Business Challenge

Solution

Tech Stack

Business Impact

About 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.

Overview

Codvo.ai was engaged to design and implement a series of AI-powered solutions to enhance the client's merchandising and sales operations. The goal was to leverage AI and machine learning to optimize merchandising, streamline field team management, and provide real-time data insights.

Business 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.

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Our Approach and 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

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  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.

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Tech Stack

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

Highlights

Business Impact

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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