From lead overload to intelligent prioritization

A Middle East automotive company partnered with Nagarro to unify fragmented lead data and embed AI-powered lead scoring and dynamic prioritization into its CRM, helping sales teams focus on higher-potential opportunities while improving visibility across the sales funnel.

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Industry

Automotive

Region

Middle East

Solution

AI-powered lead management & prioritization

About the client

An automotive sales operation built across multiple channels

Operating in the Middle East, the automotive company manages leads from multiple online and offline channels across distinct customer journeys. Sales teams navigate different follow-up requirements, while business and IT stakeholders need visibility into lead performance across the funnel. Together, these moving parts create a complex, high-volume lead management environment.

Real-time

Sales-funnel visibility

Power BI dashboards give business and IT stakeholders visibility into lead status, lost-sale reasons, time-to-close metrics, and other sales KPIs.

Two models

Lead scoring and dynamic prioritization

One ML model predicts conversion probability at lead creation, while another updates priority as customer interactions evolve.

In CRM

Lead intelligence at the point of action

Lead scores and priority rankings are surfaced directly within existing CRM screens through API integration and custom fields.

Unified

Data and analytics foundation

An Azure-based architecture brings lead data, reporting, analytics, and machine learning together on a shared foundation.

The business challenge

When lead volume outpaces the process

As opportunities arrived from multiple online and offline sources, deciding where sales attention should go became increasingly complex. The organization wanted to move beyond fragmented lead management and give sales representatives a more data-driven way to identify priority opportunities, while giving business and IT stakeholders a clearer view of what was happening across the sales funnel.

Prioritizing leads effectively

Sales representatives needed a more data-driven way to identify which opportunities should receive attention first.

Supporting tailored follow-up

Different customer groups required different engagement approaches throughout the sales journey.

Improving sales-funnel visibility

Business and IT stakeholders needed greater transparency into lead status, sales outcomes, and operational KPIs.

Where friction was building

The information needed to make better lead decisions existed, but it was spread across systems, customer journeys, and reporting processes—making consistent prioritization and funnel visibility difficult.

Lead data across multiple systems

Lead information was distributed across several systems and needed to be consolidated and cleansed before it could support reporting and machine learning.

Limited prioritization intelligence

Sales representatives needed better insight into which opportunities had stronger predicted conversion potential.

Different follow-up requirements

Distinct customer groups followed different journeys and required tailored engagement strategies

Limited funnel visibility

Stakeholders needed better access to information such as lead status, reasons for lost sales, and time-to-close metrics.

Disconnected analytics foundation

Reporting and predictive decision-making required a shared data foundation capable of supporting both business intelligence and machine learning.

The core problem

The organization needed a way to combine fragmented data, predict conversion probability, and continuously reprioritize opportunities as new interactions occurred. Those insights also needed to be available within the CRM environment already used by sales representatives.

Unified lead data platform

Lead data stored across multiple systems was consolidated, cleansed, and integrated into a central data platform. Azure Data Factory supports ingestion from SAP HANA into Azure Data Lake Storage Gen2, creating a shared foundation for analytics, reporting, and machine learning.

Bronze–Silver–Gold data architecture

A Delta Lake-based lakehouse architecture organizes data into Bronze, Silver, and Gold layers: Bronze: Raw ingested data and validated source files; Silver: Cleansed data with business logic applied; Gold: Curated, business-ready datasets for reporting and machine learning.

This layered architecture supports both analytics and predictive modeling use cases.

Lead scoring model

When a new lead is created, the lead scoring model evaluates it and predicts its conversion probability. The resulting score helps sales representatives identify higher-priority opportunities for follow-up at the beginning of the lead journey.

Dynamic lead prioritization

As sales representatives engage with leads, a second machine learning model updates lead rankings based on new interaction data.

This dynamic approach helps align sales attention with opportunities identified as more ready for conversion as the sales journey evolves.

CRM integration via API

Machine learning outputs are pushed directly into the existing CRM through API integration. Two custom CRM fields are mapped to the model outputs, allowing sales representatives to view lead intelligence within the CRM screens they already use.

Real-time Power BI dashboards

Power BI dashboards provide visibility into: lead status, reasons for lost sales, time-to-close metrics, additional business KPIs.

The dashboards give business and IT stakeholders a clearer view of performance across the sales funnel.

Automated orchestration and retraining

Azure Data Factory and Databricks Workflows support orchestration across data ingestion, transformation, machine learning, and output delivery.

Following deployment, the engagement included a dedicated model-retraining phase and ongoing support to help keep the machine learning models aligned with evolving lead patterns.

One lead view.
Smarter prioritization.
More focused selling.

What it enabled

More focused sales effort

Machine learning insights help sales representatives prioritize opportunities based on predicted conversion probability and evolving customer interactions.

CRM-integrated insights

Machine learning outputs are surfaced directly within existing CRM screens, placing decision-support information inside established sales workflows.

Better sales visibility

Real-time dashboards give business and IT stakeholders greater visibility into lead performance, sales outcomes, and operational KPIs.

Unified analytics foundation

A shared Azure-based architecture supports business intelligence and machine learning workloads from the same data foundation.

Data platform & engineering

Microsoft Azure
Azure Data Factory
Azure Data Lake Storage Gen2 (ADLS Gen2)
Delta Lake
SAP HANA

Machine learning &
MLOps

Azure Databricks, MLflow

CRM integration

Custom API / Push API

Business intelligence & reporting

Microsoft Power BI

From fragmented data to a unified lead foundation

Lead data from multiple systems is consolidated and cleansed to create a shared foundation for reporting, analytics, and machine learning.

From manual judgment to predictive prioritization

Lead scores and conversion probabilities give sales representatives additional intelligence for deciding where to focus attention.

From static priority to dynamic ranking

Lead priorities update as new customer interactions occur, allowing opportunity rankings to evolve throughout the sales journey.

From separate analytics to embedded intelligence

Machine learning outputs appear directly within existing CRM workflows, placing lead intelligence where sales representatives already work.

From limited visibility to connected funnel insight

Power BI dashboards give business and IT stakeholders visibility into lead status, reasons for lost sales, time-to-close metrics, and other key sales indicators.

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