SaaS Sales Engagement Platform with AI Customer Insights and Prospecting

Boost sales with an AI-powered SaaS sales engagement platform offering customer insights, prospecting insights, churn prediction, and smart lead recommendations

Overview

At A Glance

Industry

Marketing & Advertising

Region

Duration

16 Weeks

Technical Stack

Node.js

TensorFlow

Python

Firebase

JavaScript

CSS

PHP

SPSS

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.

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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