Revolutionizing CRM with Composable Customer Data Architecture

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In the dynamic landscape of consumer businesses, effective management, and leverage of vast amounts of customer data have become imperative for maintaining a competitive edge. As organizations amass tens or even hundreds of millions of customer records, the challenge lies in transforming this extensive data into a valuable asset. One impactful approach to extract business value from customer data involves enhancing both online and offline customer journeys through personalized experiences tailored to individual preferences. In this context, ‘customer journeys’ encompass the steps customers take across various touchpoints leading to purchases. By infusing these steps with personalized elements derived from customer data, organizations can significantly enhance the attractiveness and success rates of these interactions. 

However, the utilization of customer data comes with its own set of challenges. Despite the availability of advanced technology for data collection, privacy laws often require explicit customer consent before leveraging this information for personalization purposes. A strategic customer data model should, therefore, prioritize simplicity, ensuring the secure use of customer data to provide personalized services while respecting privacy regulations. This comprehensive strategy should address different facets of customer data management, facilitating quick and secure access for various digital services such as e-commerce, marketing automation, and AI models. Up-to-date customer data, including consent and preferences, should be readily available, emphasizing the importance of governance, compliance, security, integration, and data accessibility within the organization.

Maximizing Profit with Data-Driven Marketing Strategies

Going beyond journey optimization, various strategies can significantly enhance revenue generation. Targeted marketing activities, for instance, play a pivotal role in elevating product awareness and driving sales. Marketing automation emerges as a powerful tool in orchestrating email campaigns, stimulating both online and in-store shopping. The key to success lies in crafting relevant and personalized campaigns, necessitating detailed customer profiles for precise targeting. Omni-channel messaging stands out as another impactful strategy. Marketers can effectively engage with customers on social media through ads or employ push notifications via SMS or in-app messages. The success of omni-channel messaging relies on delivering the right content to the right audience at the right time. Leveraging high-quality customer data allows for precise targeting based on past behaviors, enabling tailored ads for products of interest displayed on e-commerce platforms. 

The integration of Generative AI introduces yet another avenue for personalization. Traditional content creation for marketing campaigns often demands extensive time and effort from marketers. Generative AI, powered by large language models, automates content generation, enabling the creation of unique messages for each recipient. To achieve this effectively, setting up context and input parameters is crucial, drawing from the customer profile. This approach results in hyper-personalized messages generated based on individual customer’s past purchases or clickstream data. Therefore, the importance of rich and high-quality customer data cannot be overstated when harnessing the potential of generative AI in personalized communication.

Robust Customer Data Foundation in Data Architecture

To drive revenue growth through customer data, establishing a robust data foundation is imperative. This foundation encompasses the essential frameworks and systems for seamless data collection, storage, management, and governance. At the heart of this foundation lies customer master data management, responsible for maintaining a ‘golden record’ for each customer—a unified and authoritative source consolidating their information. Various strategies exist for creating this golden customer record. One common method involves consolidating all relevant customer information within a Customer Relationship Management (CRM) system. Traditionally, businesses have expanded CRM capabilities to store diverse data dimensions about customers, supporting various data management use cases. For instance, CRM systems may house details about past customer purchases and manage business logic or processes for data management, including consolidation, deduplication, quality reporting, and rules for data retention and deletion.

While this centralized approach provides a comprehensive view, it can be cumbersome due to the necessity for agreement on shared data fields and challenges in incorporating complex or unstructured data, such as consent or customer preferences. Managing extensive and diverse datasets within a single system becomes impractical. An alternative strategy adopts a modular approach, keeping master data minimal in the core record and linking to detailed data in specialized systems. This approach allows the golden record to contain only essential information like name, address, and phone number, enabling more flexible data management. Validation and updates can be partially delegated to customers, who can edit their information in user-facing systems linked to the master record. This composable, modular architecture proves ideal for companies handling a large volume of customer identities, managing complex customer data definitions, and requiring scalability and near-real-time data processing capabilities.

Customer Data Architecture for Adaptive and Efficient Data Management

In a composable architecture, the orchestration of customer data is distributed across specialized cloud solutions, all interconnected through a shared data foundation. This foundational layer ensures seamless and consistent data access, maintaining the uniformity of customer records by preventing simultaneous updates. Simultaneously, data replication capabilities empower diverse systems to respond promptly to changes in customer data. Within this architectural framework, customer data seamlessly flows between systems in near real-time, ensuring its uniformity, coherence, and accuracy are preserved.

The subsequent diagram offers a high-level overview of the cloud solutions provided by SAP, illustrating their alignment with a modern and composable customer data architecture. 

SAP CRM

Building an effective customer data strategy is crucial for businesses seeking to enhance customer experiences across various touchpoints. To initiate this process, it is essential to evaluate the current state of customer data by asking specific questions: 

Identifying Essential Customer Information

  • What customer information is critical for our business processes and goals? 
  • What are the key data elements needed to create a comprehensive customer profile?

Assessing Available Data Sets

  • What additional data sets about the customer are already available? 
  • What is the structure and quality of these existing data sets?

Platform Integration

  • Which platforms currently collect or will collect customer data in the future? 
  • How can we integrate data seamlessly across various platforms to maintain consistency?

Legal and Security Compliance

  • How will the organization comply with legal regulations regarding data collection? 
  • What measures are in place to ensure the security and privacy of stored customer data?

Scalability of Data Architecture

  • How scalable is our current data architecture? 
  • Can it accommodate the growing volume of customer information effectively?

Activation and Utilization of Customer Data

  • How can we activate and utilize customer data for marketing, AI models, or service improvement? 
  • Are there mechanisms in place to derive actionable insights from the collected data?

Measurement of Success

  • What key performance indicators (KPIs) will be used to measure the success of the customer data initiatives? 
  • How will enabling access to customer data for marketing automation contribute to sales and customer engagement?

By addressing these questions, organizations can lay the foundation for a comprehensive customer data strategy. It is crucial to establish a Customer Data Foundation layer that orchestrates the core customer master record, ensuring near real-time updates across systems. Additionally, incorporating cloud solutions such as SAP Customer Data Cloud, SAP Customer Data Platform, SAP Emarsys, and SAP Service Cloud can further enhance the organization’s capabilities in managing customer identities, interactions, and service profiles.

Conclusion

In conclusion, crafting a successful customer data strategy is imperative in the ever-evolving consumer landscape. Key elements include identifying essential data, integrating platforms seamlessly, ensuring legal compliance, and prioritizing scalability. To enhance revenue growth and customer experiences, businesses are adopting strategies like personalized customer journeys, targeted marketing, and Generative AI. A composable customer data architecture, exemplified by SAP’s cloud solutions, offers an innovative framework for distributed orchestration, ensuring real-time data uniformity. In embracing this holistic approach, businesses can effectively leverage their customer data to stay competitive, drive revenue growth, and foster lasting customer relationships in the next era of CRM.  

Additionally, with our cutting-edge data management and analytics solutions, we can play a pivotal role in streamlining and optimizing customer data processes. By utilizing VE3’s advanced capabilities and partnership with SAP, you can further enhance our data strategy, ensuring a seamless integration of diverse data sources, heightened compliance, and ultimately unlocking new dimensions of customer insights for sustained success. To know more, explore our innovative digital solutions or contact us directly.

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