Customer-Centric Information Architecture For Efficient Customer Insight

Traditionally, many large service businesses, have focused narrowly on direct operational needs like order handling & invoicing, when designing their information architecture. This way they have developed account-centric data structures. A real Customer could have more than one accounts, the records of which were unlinked in the customer database. In this case, more than one Customer records, would exist for the same real Customer. This data model would not reflect accurately the relationship of the Customer to the Business. Moreover this information architecture would often involve loosely coupled or isolated databases, thus developing departmental 'information silos'. For example the faults call center database, would not integrate to the order handling database. Therefore the Customer interaction history would be fragmented in various isolated systems, serving specific Customer touch points (CTPs). The information architecture described above, does not support the Customer holistic view, which is needed in order to provide quality Customer service or analyze efficiently the Customer behavior.

Any analysis on Customer data which are stored in an account-centric structure is problematic. For example, one might want to calculate a simple Customer value ranking based on the last quarter invoiced amounts. However, this would rather be an account value ranking, than a Customer value ranking, since the analysis would probably not aggregate all accounts related to a specific Customer. Business wise, it is erroneous to carry out Customer analysis on the account level, since this analysis may give an incomplete picture about a Customer. Furthermore, one might want to perform a recency analysis based on Customer interaction history. This analysis will not be effective, if the Customer interaction history can not be consolidated in a single database. Any CRM interaction which is not based on the Customer holistic view, can not be optimised. For example a CTP handling sales inquiries and orders, can not perform efficient cross & up selling without the Customer holistic view, which allows the call agent to assess the profile of the Customer and handle the case accordingly. Businesses active in highly competitive environments can not afford not to develop Customer insight. CRM systems have been developed in order to efficiently manage the Customer interface and capture & exploit Customer contact history. However CRM systems integrate with other operational systems in order to support end-to-end processes. These operational systems have to align to a Customer-centric information architecture, in order to achieve the Customer holistic view. Having realized the paramount need to develop Customer insight, Businesses have started reorganizing their information architecture and gradually developing their Customer-centric information assets.

The process is gradual because legacy data structures and account-centric data, inherit their properties to the new systems, during migration projects. In order to avoid the inheritance of the undesirable properties, records of the same Customer should be identified, if possible, and merged in order to realize the new customer-centric structure. The resulting 'Customer tree' is a structure which incorporates all accounts and products, related to the same real Customer. This business need has been identified by vendors active in the data quality niche market. They started offering record 'matching & merging' functionality, in order to develop and maintain customer-centric information assets (such products are Trillium, Firstlogic, Ascential). Being able to view 'one face of the Customer' is of paramount importance to operational as well as analytical CRM. Copyright 2006 – Kostis Panayotakis