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Customer knowledge databases

Customer Knowledge is a generic term for information about the customer. It includes transaction data, browsing histories, CRM and sales systems, and customer service records. It can also include linking social media, external data and modelling and analytic scoring. A database for customer knowledge as a whole links all these elements together for both analysis, but also to present a single view of the customer to sales or service staff.

Information pools will be a mix of tight and loose data - some data in a well-defined and controlled format and others more ad-hoc freeform. Combining the two requires good relational and non-relational database tools to sift and blend the different data sources.

Customer databases can be discussed in two parts. Web, or cloud based businesses where most customer data is pooled and linked across systems, and more traditional businesses, particularly in B2B markets where a single account may have several contacts, several addresses and information will be spread across a number of operational and transaction databases.

Even in web-based businesses, a single pooled database is uncommon. For operational reasons, data will be split across different systems. So, for instance, log and visit journey data will be held separately from log-in and account data, and chat and service records are often separate again. Consequently, a level of data blending will be required to build a picture of an individual, or to develop machine learning algorithms.

As such a customer database has to be much more flexible than at traditional transaction based database, though it must also have connections into the transaction databases to process orders, product supply and invoicing. And for those in a B2B world, the database needs to be flexible enough to support complex business relationships where there are potentially multiple contacts, multiple customer sites, and groups and networks of businesses.

The ideal of a customer knowledge database is not just about pooling existing data sources. Ideally it also allows information to be captured by field staff to allow notes and information to be shared by sales and non-sales teams. This means that access is most easily managed with a web-based interface with appropriate access control.

As the data requirements move and change and flexibility is required, a customer knowledge database has to be open to change and to a wide variety of data types and formats without the tightness of a standard relational database. At the heart of a ck-database is often a more loosely defined key-value or noSQL database that can grow over time, but that allows for structures and standards to be applied to the data.

For one-off projects, scaffolding a fast lightweight database structure, that can be addressed by search-engine software may be sufficient. Or to use simple structures that can be coded and queried for statistical analysis, or machine learning. However, for longer term use, systems are needed to pull snapshots for analysis, and to provide feed information for customer dashboards. Designing data structures and APIs can help standardise and systematise the different data types.

For instance, data collected into the database also needs to feed into sales and marketing planning software for instance in order to set appointments, schedule follow up calls, or use event-triggered marketing to return and recontact the customer. CRM solutions such as Salesforce.com allow for external connectors to legacy systems, and to bring in data from systems like SAP or Oracle.

Analysts will need to be able to pull off data in bulk, run queries and extract data for modelling or deeper analysis and then reapply the data as flags, classification and scores back to the main database. Design of the customer knowledge system therefore requires thought and planning.

Formally modeling the data that you have, how it links and then drawing out the front-end uses that are needed from the data is the first step in developing a customer knowledge system. From this appropriate software tools can be used to query, integrate and visualise the data relationships.


For help and advice on building customer, competitor or marketing knowledge systems contact info@dobney.com


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