CVMBoK

Data analytics/business intelligence (DA/BI) is a vital enabler for CVM, providing the insights and automation required to understand customer behavior and make informed decisions. It ensures the development of high-quality analytical models that validate hypotheses, predict customer actions, and drive the automation of data-driven CVM processes.

Inputs: How Data Analytics Impacts Customer Value Management

DA teams deliver critical inputs that influence CVM’s core activities. They determine the effectiveness of initiatives through robust models and analytics, allowing the CVM team to focus on strategies that produce the best outcomes. Automation facilitated by analytics enhances the CVM teams’ productivity by streamlining processes and ensuring consistent quality and stability in automated operations.

The DA function also supports CVM teams by providing essential insights for key processes such as customer segmentation, behavior analysis, churn prediction, and retention strategies. These inputs are indispensable for tailoring campaigns, tracking impacts, and improving overall CVM effectiveness.

Customer Value Management’s Collaboration with Data Analytics

CVM teams rely heavily on DA teams to meet their objectives. The CVM teams set the business requirements for analytical tasks and work with the DA teams to execute critical functions, such as:

• Analytics and reporting
• Campaign management
• Personalization and recommendations
• Predicting customer behavior
• Customer insights and impact tracking

CVM teams also initiate analytical projects to address business challenges, ensure that feedback is integrated into refining models, and align analytics efforts with organizational goals. This collaboration guarantees that CVM strategies are backed by reliable data and actionable insights.

Outputs: How Customer Value Management Influences Data Analytics

CVM teams determine the scope and direction of analytics activities within their domain. Changes in CVM strategies can lead to updates or overhauls in analytical models, impacting workflows and processes in the analytics team. This interdependency emphasizes the need for ongoing communication to ensure that both teams remain aligned.

The Importance of Effective Collaboration

The collaboration between CVM and DA teams is fundamental to achieving success. Poor alignment between these teams can severely affect CVM’s ability to perform effectively, underscoring the need for seamless integration and cooperation.

We’ve been in the same place where adding new data or a new machine learning model was a challenge for three to six months. Right now, it’s more like several machine learning models, even more than ten, per three-week sprint. It is possible, but it requires a high level of automation and development in MLOps. Cloud technology has been one of the game-changers, giving extra time for data scientists to experiment with new approaches to their models and data. However, the entire value chain must be in place—from machine learning scoring to NBA, campaign systems, and CRM. In many telecoms, these are not fully integrated, creating manual interfaces and making automation difficult.

Marek Wiktor Grabowski

2C Customer Value Management Director at Orange Poland

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