Customer intelligence (CI) is the process of gathering and analyzing information regarding customers; their details and their activities, in order to build deeper and more effective customer relationships and improve strategic decision making.
Contents
CI and CRM
Customer intelligence is a key component of effective customer relationship management (CRM), and when effectively implemented it is a rich source of insight into the behaviour and experience of a company's customer base.
As an example, some customers walk into a store and walk out without buying anything. Information about these customers/prospects (or their visits) may not exist in a traditional CRM system, as no sales are entered on the store cash register. Although no commercial transaction took place, knowing why customers leave the store (perhaps by asking them, or a store employee, to complete a survey) and using this data to make inferences about customer behaviour, is an example of CI.
Process
Customer Intelligence begins with reference data – basic key facts about the customer, such as their geographic location.
This data is then supplemented with transaction data – reports of customer activity. This can be commercial information (for example purchase history from sales and order processing), interactions from service contacts over the phone and via e-mail.
A further subjective dimension can be added, in the form of customer satisfaction surveys or agent data.
Finally, a company can use competitor insight and mystery shopping to get a better view of how their service benchmarks in the market.
By mining this data, and placing it in context with wider information about competitors, conditions in the industry, and general trends, information can be obtained about customers' existing and future needs, how they reach decisions, and predictions made about their future behavior.
Benefits
Customer Intelligence provides a detailed understanding of the experience customers have in interacting with a company, and allows predictions to be made regarding reasons behind customer behaviors.
This knowledge can then be applied to support more effective and strategic decision making – for example, understanding why customers call makes it easier to predict (and plan to reduce) call volumes in a contact centre.