G2M Customer Story

Enabling Data-Driven Sales Targeting with Propensity Scoring

Challenge

A leading cable operator sought to improve the effectiveness of its sales and marketing efforts for specialized network services. The organization faced challenges in identifying high-propensity prospects due to fragmented data sources and limited analytics capabilities. Existing tools and methods failed to provide actionable insights, resulting in suboptimal targeting and inefficiencies.

Impact

G2M Insights partnered with the client to develop a comprehensive propensity model, leveraging advanced machine learning techniques to predict the likelihood of prospects purchasing specialized network services.

Solution

Data Infrastructure and Preparation

Designed and implemented a data architecture to consolidate and cleanse disparate data sources, including CRM opportunities, third-party data, and internal data. Staged a robust data environment with automated pipelines for ongoing analytics.

Predictive Model Development

Built a state-of-the-art propensity scoring model to analyze 70+ attributes and drivers, such as telco spend, multi-site attributes, firmographics, and geographic factors. Scored full prospect universe, identifying the top locations with the highest propensity to buy. Achieved high model accuracy outperforming the benchmark model.

Integration and Scalability

Developed a roadmap for integrating model outputs into enterprise systems, such as CRM and marketing lead identification platforms, to support ongoing targeting efforts. Proposed enhancements, including a telecom spend model and up-sell/cross-sell propensity models, to maximize revenue opportunities.

Key Outcomes

Improved Sales Targeting: Empowered sales teams to prioritize high-propensity prospects, increasing efficiency and effectiveness.

Actionable Market Insights: Enabled data-driven decision-making by identifying key drivers of purchase propensity.

Revenue Growth Opportunities: The top high-propensity prospects represented an estimated $200M+ in potential revenue capture.

Scalable Analytics Framework: Established a foundation for ongoing predictive analytics, supporting future use cases such as upselling and cross-selling.