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Data Intelligence and sales engagement software using machine learning

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Overview

Machine learning based platform with data intelligence and sales engagement features, helping the users with go-to-market strategies.

At A Glance

industry
Industry
Marketing & Advertising
region
Region
UK
duration
Duration
16 Weeks

Technical Stack

Node.js
TensorFlow
Python
Firebase
JavaScript
CSS
PHP
R
SPSS

Client Profile

The client is a leading SaaS company in Europe, with a robust B2B database for sales engagement and GTM strategies.

Challenge

  1. Didn’t have lead prospecting and sales engagement features in a single software.
  2. Prospecting decisions that were made, were not backed by machine learning insights.
  3. The software didn’t support various integrations.
  4. The software earlier didn’t provide reports on customer engagement analytics.
  5. Chrome extension was not available for prospecting on other platforms.
Software interface showcasing data intelligence and machine learning features for sales engagement, empowering effective customer interactions

Solution

  1. Built a 360° solution for the client, from front-end, back-end, and the integration of ML models to scale up the software.
  2. Developed the project's front-end using React JS, JavaScript, and CSS. User data was sent to Firebase and fetched when needed. 
  3. Back-end was developed using three languages: APIs using Node JS, Admin Panel using PHP, and user authentication using Firebase.
  4. Machine learning technologies were incorporated into the software in a customer-centric manner. It was divided into three processes namely:
  • Customer Segmentation: Clustering of different customers using unsupervised ML techniques. Customer data is placed into buckets for segmentation.
  • Customer Churn Prediction: ML algorithms are applied to customer data stored in buckets for detailed segmentation. This process is what we refer to as, Customer churn analytics.
  • Customer Recommendation: The user gets insights backed by data, which can assist their prospecting processes.

Key Benefits

  1. Helped users increase customer engagement by 23%
  2. The client got a 44% increase in users on their platform
  3. More accurate and relevant lead suggestions due to ML integration resulted in better prospecting.

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