Revolutionize Lead Scoring with an Analytics Framework

Revolutionize Lead Scoring with an Analytics Framework

IT Services Company

Enhancing Lead Scoring with Azure Databricks and Azure ML. We developed an automated pipeline to ingest lead data from Multiple Sources into Azure Databricks, built and deployed machine learning models using Azure ML for lead scoring, and enabled visualization of insights through dashboards

A leading provider of cloud-based enterprise service management solutions that automate and streamline IT operations.

USD 200 M+

Global Revenue

35+ Countries

Global Client Base

4 Business Units

Big Data & Infra Services Supporting Data

1,800+ Employees

Teams Across Verticals

Challenges

No existing solution to derive actionable insights from lead scoring data.
Lack of end-to-end infrastructure to integrate data from sources into ML models and visualization tools.
Data is siloed across various systems, hindering comprehensive analysis.
Manual processes lead to delays and potential errors in decision-making.

InfraBeat Solution

Got the data from Multiple Sources to Databricks. Created Feature Stores within Databricks to ease the sharing of features amongst ML Engineers. Also utilized the excellent Spark infrastructure available within Databricks to accelerate the Featurization process.
Machine-learning modelling and predictive models created and implemented using Azure ML for better outcomes.
Visualized the data using Power BI to expedite data understanding by critical shareholders.

Key Achievements

70% reduction in lead processing and scoring time using automated pipelines.
40% improvement in lead conversion rates post-implementation.
Delivered an end-to-end integrated analytics solution—from data ingestion to ML model deployment and dashboarding.
Significantly reduced manual effort and improved time-to-insight with automated, scalable pipelines.

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Success Story

Drives Business Growth with Optimized SAP AMS Excellence