£470 + VAT
- Developing and managing customer-related databases
- Structured and unstructured data
- Data integration, warehousing and marts
- Knowledge management
- Using customer-related data
- Three ways to generate analytical insight
- Privacy issues
- Sales and marketing directors, managers, executives, practitioners and staff.
- Senior and mid-level managers who are involved in customer relationship management (CRM) programmes and system implementations, whether in a marketing department, the sales force or the service centre.
- Contemporary CRM professionals who sell products or services, to consumers or businesses.
- Senior managers who realise that profitable customers are their company’s greatest asset and seek guidance to retain them.
- Those who wish to understand the CRM landscape that covers the vastness between operational and strategic CRM, with an overview of customer-related data and data mining.
- Managers looking to take customer-centred strategies to the next level.
Upon completion of this course, you will be able to understand:
- The central role of customer-related data in the achievement of CRM outcomes.
- The importance of data quality to CRM performance.
- Issues that need to be considered in developing a customer-related database.
- How data integration contributes to CRM performance.
- The purpose of a data warehouse and data mart.
- Uses of knowledge management systems in CRM.
- How analytical CRM supports strategic and operational CRM.
- How analytics support customer management strategy and tactics, throughout the customer lifecycle, in the sales, marketing, and customer service functions.
- How standard reports, OLAP and data mining generate insights for CRM users.
- The data mining works in a number of ways: by describing and visualising, classification, estimation, prediction, affinity grouping and clustering.
- The types of analytics that apply to structured, unstructured and ‘big’ data.
- The three V’s of big data.
- Why it is important to understand the differences between nominal, ordinal, interval and ratio data before selected analytical procedures.
- The types of regulatory constraints that regulators impose to ensure the privacy of customer data.