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Purpose of functional area #
The purpose of the Data Analytics / Data Science team as understood here is to ensure insight and understanding of what is going on with the customer base. Data Analytics team ensures that insights are obtained, customer behavior can be predicted, key hypotheses can be validated and results reported; and finally, that data-driven CVM initiatives are automated.
Inputs: How it impacts CVM #
- Provides the key insights that support core decisions made by the CVM team
- Determines the effectiveness of CVM activities by making high quality analytical models
- Delivers the automation that allows high productivity of CVM team
- Determines the quality of automated CVM processes
- Affects stability of automated CVM processes
CVM work related to function #
- Owns the business requirements for analytics needed by CVM function
- Utilizes data analytics team for
- analytics,
- reporting,
- personalization/recommendations,
- campaign management,
- predicting customer behavior,
- customer insights,
- impact tracking
- Provides feedback on analytical insights
- Owns and initiates major analytical projects related to CVM
Outputs: How CVM impacts this area #
- CVM dictates the requirements for all data analysis work related to CVM domain.
- Changes to how CVM initiatives are designed & implemented may break previously created analytical processes resulting in refactoring workload for Data Analytics team
Key handshakes to consider #
Collaboration with data analysis team is foundational for CVM initiatives, as data analysis provides the most crucial sets of inputs for:
- Developing customer segments
- Interpreting customer segment behavior and needs
- Predicting customer churn and tailoring retention campaigns.
- Generating personalized offers;
- Tracking the impact;
- etc
Impact of collaboration quality on CVM team results #
HIGH – lack of alignment with Data Analytics team results in a severe impact on the performance of CVM.
