Given the incredible amounts of data being collected about customers, what are the technologies available to handle, the models used to parse it, the issues that are part of the handling of it. How does it get accessed in real time? Etc.

 

 

Number of Pages: 10

 

 

What Should Be Included and Why?

 

 The strategy on Customer Data Management .. popularly known as CDI or MDM -- primarily "Gold Copy of Customer " .. The fundamental block for success of any Customer experience model is around identifying the customer with the core attributes . The challenge faced by most customers is most of them do not even know which part of the enterprise holds a 360 degree view of the customer .. we are not talking about merging customer data but about the importance of a Single View of Customer

 

 

 (Joel Lindstrom)The challenge is that customer data lives in many different systems--Accounting/Finance/ERP, CRM, Enterprise Data Warehouse, etc, many of which employees in sales and customer service don't have access to, giving them a limited view of the customer.

A good CRM system can be used to give end users a 360 degree view of customers including not only the sales/marketing/customer service data, but it can also can provide a glimpse of relevant data from these other systems so they only have to look in one place for customer data.

For example, we frequently use Scribe Insight with clients who have GP/Oracle/SAP to reflect accounting/financials data in CRM.  Scribe has adapters for most leading CRM products.  So when a customer service rep is on the phone witha client, they can see their order history and can also see if there are any unpaid invoices.  This data is not generated in CRM, but it is a great tool to make this data excessible to end users. 

 

 

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Vendors Who Could Be Showcased in this Chapter

 

(Joel Lindstrom)

 Scribe Insight

C360

 

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    Anonymous:Paul, I'd like to see a play here for the role of data warehousing and the need to build a strong analytical foundation. Need not reference Teradata, although selfishly I'd like a mention. A core analytical foundation would consist of technologies, people skills, and processes. So, technology would consist of ETL, data warehousing, and analytical technologies, and stretch into business activity management, business process automation technologies for more mature customers. There's also the "enterprise fit" story of how these technologies fit in the larger enterprise (EAI, operational systems etc.) Processes would include data modeling, data management, security, data and information governance, building of analytical models etc. Skills could include; database administration, knowledge of statistics and data mining techniques, data modeling, application development on the technical side, and on the business side an understanding of how these techniques and technologies drive business value
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