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SaaS Sales Engagement Platform with AI Customer Insights and Prospecting

Overview

At A Glance

Industry

Industry

Marketing & Advertising

Region

Region

UK

Duration

Duration

16 Weeks

Technical Stack

Node.jsNode.js
TensorFlowTensorFlow
PythonPython
FirebaseFirebase
JavaScriptJavaScript
CSSCSS
PHPPHP
RR
SPSSSPSS

Client Profile

The client is a leading European SaaS provider offering a comprehensive B2B sales engagement platform and B2B database designed for sales engagement and B2B GTM strategy execution.

Challenge

  • Lack of Unified Platform
    The system did not provide lead prospecting and sales engagement features within a single solution, limiting its ability to function as a complete customer insights platform.
     
  • Absence of Machine Learning Insights
    Prospecting decisions were not supported by data intelligence or ML-driven insights.
     
  • Limited Integration Capabilities
    The software lacked support for multiple third-party integrations.
     
  • No Customer Engagement Analytics
    The platform did not offer analytics or reporting features for tracking customer behavior, preventing the application of customer churn prediction insights.
     
  • Missing Chrome Extension
    Prospecting across external platforms was not possible due to the absence of a Chrome extension.
     
  • Underutilized Data Intelligence
    The interface failed to demonstrate ML-driven data intelligence for impactful sales engagement.
Data intelligence and ML-based sales software, optimizing sales strategies and customer engagement.

Solution

  • Comprehensive 360° Platform Development
    Delivered a holistic solution covering the front-end, back-end, and ML model integration to scale software performance, laying the foundation for a fully AI-powered sales engagement platform and advanced lead intelligence platform.
     
  • Modern Front-End Architecture
    Developed using React JS, JavaScript, and CSS, with Firebase for user data storage and retrieval.
     
  • Multi-Language Back-End Development
    1. Node.js for APIs
    2. PHP for the admin panel
    3. Firebase for user authentication
       
  • AI and ML Integration
    Implemented machine learning in three core modules
     
  • Customer Segmentation
    Used unsupervised ML techniques to cluster customers into defined segments, functioning as an intelligent customer segmentation tool for prospect prioritization.
     
  • Customer Churn Prediction
    Applied ML algorithms to segmented customer data to analyze churn probability.
     
  • Customer Recommendation Engine
    Provided actionable insights and lead recommendations to improve prospecting accuracy, enhancing the value of the sales prospecting software and lead prospecting tool capabilities.

Key Benefits

  • 23% Increase in Customer Engagement
    Advanced AI-driven insights helped users interact more effectively with prospects. Enhanced customer engagement analytics enabled smarter decision-making and campaign optimization powered by sales engagement analytics.
     
  • 44% Growth in Platform Users
    Improved functionalities and integrations attracted more users to the B2B sales engagement platform, added features such as ML insights and a Chrome extension, and enhanced product value and adoption.
     
  • Better Prospecting Accuracy
    Machine learning recommendations guided users to prioritize higher-quality leads. Resulted in more accurate, relevant, conversion-driven lead suggestions powered by AI-driven prospecting insights.

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