CRM & Sales Forecasting Using Machine Learning for Healthcare Providers
Project Overview
CRM & Sales Forecasting Using Machine Learning
The project involved developing a cloud-based CRM application for a Japanese client, with an integrated machine learning model to predict future sales. The solution aimed to improve forecasting accuracy, enabling better decision-making for inventory planning and marketing strategies, while also modernizing legacy systems.
The Challenge
The organization faced several hurdles in delivering efficient support:
Legacy Application Complexity
Migrating data from an outdated system while maintaining security and continuity posed significant hurdles.
Data Quality Issues
CRM data contained missing values and inconsistencies, threatening prediction reliability.
Feature Engineering Difficulties
Identifying impactful features from historical data was critical to building a reliable ML model.
Model Selection
Needed an algorithm that balanced interpretability, simplicity, and accurate forecasting.
Our Solution
We implemented a two-part solution combining CRM modernization with predictive analytics:
Data Quality Handling
Applied mean/mode imputation to clean data and ensure dataset consistency for accurate predictions.
Feature Engineering
Incorporated historical sales trends and time-based features (month, year) to enhance model performance.
Model Development
Chose a Random Forest model for its balance of accuracy, simplicity, and interpretability.
Cloud-Based CRM Development
Built a scalable CRM using React, Node.js, PostgreSQL, and GraphQL, combining modern architecture with data security.
Implementation Process
Requirement Analysis
Reviewed legacy workflows and identified gaps in CRM data and processes for modernization planning.
Data Preparation
Cleaned historical CRM data, addressed missing values, and structured it for ML model training.
Model & Feature Development
Engineered relevant features and trained the Random Forest model for accurate sales forecasts.
CRM Development & Integration
Built a modular, cloud-native CRM and integrated the ML model for real-time forecasting.
Results
Improved Forecast Accuracy
Achieved a 12% increase in sales prediction accuracy, enabling more reliable business decisions.
Optimized Resources
Improved inventory management and resource allocation, minimizing waste and costs.
Data-Driven Insights
Sales and marketing teams gained predictive insights for strategic planning and better outcomes.
Legacy System Modernization
Seamlessly transitioned to a secure, cloud-based platform, improving performance and scalability.
Technology Used
CRM : React, Node, PostgreSQL, Prisma ORM, Next.js, Nest.js, GraphQL, ML : Python (Pandas, NumPy, Scikit-learn, Matplotlib), Random Forest Algorithm, Flask Framework.
