Cloud and GenAI Transformations in Customer Experience Enhancement
Big organizations like Financial Services Institutions (FSIs) and telcos often struggle to see the expected return on investment (ROI) from projects aimed at delivering data-driven customer experiences. Here's why:
- Legacy Systems: Outdated IT infrastructure can be difficult to integrate with modern data analytics tools, making it challenging to transform customer data into personalized experiences.
- Data Silos: Data is often scattered across departments, making it difficult to create a comprehensive understanding of customer interactions.
- Data Quality: Low-quality data can lead to faulty insights, which can negatively impact decision-making.
- Lack of Clear Objectives: Without specific, measurable goals and a well-defined ROI framework, it's hard to assess the success of these projects and justify continued investment.
- Organizational Resistance: Change can be tough, and some team members may be resistant to adopting a more data-driven approach.
To address these issues, George Malim, Managing Editor at Vanilla Plus, interviews Mark Burnard, Global Service Line Owner, Data and Intelligence at Amdocs Cloud, about the power of "cloud-power." According to Mark, cloud-based solutions can help simplify legacy systems and efficiently deliver the insights needed for tailored customer journeys. Here's what he suggests for overcoming the challenges:
1. Migrate to the Cloud:
Moving to cloud-based systems offers scalable, flexible, and easy-to-integrate platforms for better leveraging data analytics tools.
2. Data Integration and Governance:
Implement platforms that integrate data from various sources and establish policies for data management to ensure quality and compliance.
3. Clear Objectives and ROI Metrics:
Set specific, measurable goals for customer experience projects and monitor their impact on crucial business outcomes like customer retention and revenue growth.
4. Cultural and Organizational Change:
Provide training to ensure teams understand the importance of data-driven insights, encourage innovation, and foster a culture that embraces data-driven decision-making.
5. AI and Machine Learning:
Utilize AI and machine learning to analyze large datasets and optimize personalized customer experiences.
6. Customer Journey Mapping:
Identify pain points in the customer journey and use data to improve the experience at each stage. By implementing these strategies, large enterprises can enhance their customer experiences and get a clearer ROI from their investments. Mark Burnard and Amdocs Cloud likely highlight these points in their discussions about optimizing data-driven customer experience projects.
- Acknowledging the challenges faced by big organizations in achieving return on investment from projects aimed at delivering data-driven customer experiences, Mark Burnard, Global Service Line Owner, Data and Intelligence at Amdocs Cloud, suggests migrating to cloud-based systems to leverage scalable, flexible, and easy-to-integrate platforms for better data analytics.
- In the course of his interview with George Malim, Managing Editor at Vanilla Plus, Mark Burnard also emphasizes the importance of data integration and governance, clear objectives and ROI metrics, cultural and organizational change, AI and machine learning, and customer journey mapping as effective strategies for improving data-driven customer experiences and seeing a clearer return on investment.
