Telecoms handle an enormous volume of customer data generated from internal sources. With tens or even hundreds of systems contributing to customer understanding, the data landscape is vast and intricate. This complexity arises not only from the sheer amount of data but also from the multifaceted customer relationships inherent in the telecom industry. Customers may have multiple subscriptions, belong to households, share joint services, or represent entire organizations in B2B accounts. Navigating this web of relationships presents significant challenges in creating a unified and actionable view of the customer.
Despite more than half of telecoms reporting advanced capabilities in data warehousing and customer data management, only 26 percent of CVM teams have achieved a truly unified customer view. This gap highlights the difficulty in integrating diverse data sources and hierarchies to deliver cohesive and personalized experiences. Without a single customer view, tailoring interactions effectively becomes a challenge, leading to missed opportunities in enhancing customer engagement and loyalty.
Understanding Data Complexity in Telecoms
The customer data collected by a typical telecom spans various categories:
- Products and price plans: Details of subscribed services, bundled offers, and pricing structures
- Demographic information: Age, gender, location, and other personal attributes
- Consumption patterns:
- Mobile usage: Voice calls, SMS, data consumption metrics
- Broadband and TV usage: Streaming habits, channel preferences, peak usage times
- Customer interactions:
- Customer service engagements: Reasons for contacting support, issue resolutions, feedback from call centers
- Digital touchpoints: Website and app usage statistics, click-stream data
- Behavioral data:
- Location information: Movement patterns, frequently visited areas
- Browsing history: Web and app browsing activities, insights from DPI analytics
- Device information: Types of devices used, operating systems, device capabilities
- Engagement data:
- Campaign interactions: Responses to marketing campaigns, open rates, click-through rates
- Social media activity: Engagements on social platforms, sentiment analysis
- Financial transactions: Payment histories, account balances, recharge patterns for prepaid customers
- Third-party data: Information from cookies, partnerships, and external databases
Each data point adds depth to the customer profile but also increases the complexity of data integration and management. For example, a customer might use multiple devices, have several service subscriptions, and interact with various channels, both digital and physical. Capturing and unifying this data requires robust systems capable of handling high volumes and velocities of information.
The modern CVM tech stack recognizes this and places customer data at the center of the architecture, in a dedicated platform – the customer data platform.
The Role of the Customer Data Platform
To manage this complexity, telecoms employ customer data platforms (see Figure 4.5). A customer data platform (CDP) serves as the central repository for all customer information necessary for CVM functions. It integrates seamlessly with key systems like campaign management tools, AI/ML models, and business process applications, enabling telecoms to deliver personalized and timely CXs.
The CDP empowers CVM teams by providing fast and easy access to comprehensive customer profiles as well as giving them a self-service capability to do modifications like adding and deriving new customer attributes. This accessibility allows CVM professionals to independently analyze data, design targeted campaigns, respond swiftly to market shifts, and enhance customer engagement without putting extra work on busy BI teams.
Figure 4.5: Customer Data Platform
Customer Data Platform vs. Data Lake and Data Warehouse
Understanding the distinction between a CDP, a data lake, and a data warehouse is crucial for effective data management.
- Customer data platform: A CDP is a system that collects and unifies customer data from multiple sources to create a single, comprehensive customer profile for CVM use. The CDP is specifically designed for CVM, marketing, and CX purposes, enabling real-time personalization and engagement across channels.
- Data lake: This is a storage repository that holds vast amounts of raw data in its native format. It can include structured, semi-structured, and unstructured data. Data lakes are ideal for storing large volumes of data that may or may not be immediately useful but could be valuable for future analysis. It is a multi-purpose tool that is quite low level and technical in nature.
- Data warehouse: This is a centralized repository that stores processed and structured data, optimized for query and analysis. It is designed to support BI activities, providing historical insights based on data from various sources. It is a tool that is designed mainly for reporting purposes.
In the telecom context, data lakes and data warehouses handle the heavy lifting of cleansing and aggregating large volumes of data, such as call detail records (CDRs), location analytics, and DPI data. They process and store this data, making it available for various organizational needs.
The CDP, on the other hand, focuses on customer-centric data relevant to CVM activities. It ingests the processed data from the data lake and data warehouse, along with real-time data streams and low-volume events. The CDP then unifies this information to create actionable customer profiles that drive personalized marketing and engagement efforts.
By recognizing and clarifying separate functions of data lakes, CDPs and data warehouses telecoms optimize their data architecture. The data lake and data warehouse serve as the foundational layers for data storage and processing, while the CDP leverages this processed data to empower CVM teams to leverage customer data easily and in a self-service way.
Core Functions of the Customer Data Platform
The CDP performs several key functions:
- Data ingestion and integration: It loads data from data lakes and, if needed, directly from billing systems, CRM platforms, network data sources, and third-party providers, supporting both batch and real-time data updates. For example, integrating real-time location data enables location-based marketing campaigns, such as sending offers when a customer is near a retail store.
- Data cleansing and normalization: CDP needs to carry out basic data cleansing and standardization, maintaining high data quality and consistency when loading various data sources. This is critical when combining data from different systems, such as aligning customer identifiers across mobile and broadband services.
- Unified customer profiles: It builds consolidated profiles that include behavior, transaction history, preferences, and interactions across all touchpoints. Merging in-store purchase history with online browsing behavior can reveal cross-selling opportunities.
- Customer hierarchy management: It manages multi-level customer hierarchies, linking individual subscriptions to a single customer and grouping customers into households or corporate accounts, and mapping all of this to digital channels, campaigns, and interactions. This is even more vital in B2B contexts, where services are managed at an organizational level but used by individual employees.
- Real-time data processing: It updates customer information in real time to support immediate decisions and personalization. For example, if a customer reaches their data limit, the CDP may be part of the IT chain that instantly triggers a notification with options to purchase additional data.
- Advanced segmentation and analytics: It provides tools for dynamic segmentation, allowing CVM teams to create highly targeted segments based on customer attributes and behaviors. For example, it can identify customers with high ARPU who are heavy data users and have recently experienced service issues for a retention campaign.
- Integration with AI/ML models: It incorporates outputs from ML models, such as churn predictions or propensity models, into customer profiles. This integration enables more precise targeting and AI-driven personalization in campaigns.
- Data accessibility and governance: Secure APIs and access controls ensure that only authorized personnel can access sensitive customer data, maintaining compliance with regulations like EU GDPR. Data governance policies enforce data quality and usage standards.
- Scalability and performance: It is designed to handle high volumes of data and transactions, ensuring consistent performance as the business grows.
Integration with Campaign Management and Telecom Systems
The CDP serves as the foundation for the CVM tech stack that relies on customer data, including:
- Campaign management systems (CMS): By providing real-time data, customer segments, and predictive scores, the CDP enables precise and personalized communications across channels like SMS, email, mobile apps, and social media. For example, if the CDP shows that a customer frequently engages with the mobile app but rarely opens emails, the CMS can prioritize in-app notifications for that individual.
- Assisted channels: Customer service representatives and in-store personnel may view enriched customer profiles to enhance interactions. When a customer contacts support, the agent can see recent issues, service usage patterns, and potential upsell opportunities, leading to more effective and personalized service.
These integrations ensure that all customer-facing systems operate with accurate and consistent data, promoting a cohesive and personalized CX across all touchpoints.
Ownership and Governance
Typically, the CVM team owns the CDP, underscoring their strategic importance in managing customer value. This ownership model allows the CVM team to prioritize enhancements that align with business goals and customer needs. However, strong governance is essential to maintain data quality, security, and compliance with regulatory requirements.
Collaboration with IT, BI, and data governance teams is crucial. IT provides the infrastructure and technical support, BI ensures data accuracy and analytics capabilities, and data governance oversees policies and compliance. Together, they establish protocols for data access, implement security measures to protect sensitive information, and ensure that data usage adheres to legal and ethical standards.
For example, implementing role-based access controls prevents unauthorized access to personal customer data, while audit logs track data usage for accountability. Regular data quality checks and validation processes ensure that the information within the CDP remains reliable and valuable for decision-making.
By placing CDP at the core of the technology stack, telecoms make sure that the architecture is built on a solid ground for the next generation. It is a sizeable undertaking, but well worth it, considering its central future role.
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