A big problem can occur when trying to operationalize CI before significant progress on customer information integration has been achieved. Often, in this stage of CI maturity, customer insight analysis is performed on data collected in an ad hoc manner, based on databases not readily available to the contact center.
The resulting customer insights are, while interesting, not easily actionable by the contact center if a targeted customer cannot be identified when they interact with the company. Very often, the results of a customer insight analysis will be descriptive parameters: age, income, buying behavior and position in customer lifecycle.
With a 360-degree view of the customer, all the databases used to develop this description would be available in a contact center business rules engine to classify an incoming caller. In the absence of the 360-degree view, the manager should request a mapping of these descriptive statistics into data points available in contact center databases.
Having the CI analysts hand the contact center a list of targeted accounts is not an optimal approach to customer identification. At worst, it requires manual identification and at best, it requires the maintenance of a static customer list, often outside the main customer database. It is much better to have target descriptor data points and let the business rules engine manage the selection of targeted customers.
Contact Routing: Operational
Once the identity of the customer has been established, customer insights drive the routing of some of the segments identified as requiring specialized handling. Credit risks (also known as deadbeats) are sent to credit and collections, regardless of what they might really want. The company will not sell to them until they pay on their bill. On the other end, very high-value customers are sent to a “high touch” agent group skilled to handle all the unique needs of their segment. In both cases, questions in the IVR/chat relating to needed resource skill can be bypassed, saving toll-free charges from deadbeats’ calls, and avoiding any hassle for the high-value customers.
An example of routing to credit and collections has already been provided. On the high-value customer side, an electric utility, through customer intelligence, discovered that while their very large industrial customers did drive revenue and kilowatt hours, and residential customers drove call volume into the service centers, it was the middle market customers that drove profit—the entrepreneur that owns five fast food restaurants, the mom and pop grocery stores, the car dealerships, for example.
These customers had very similar need profiles: quick turnaround for service turn-on and turn-offs, security lighting, how to reduce their bill and quick response to outages. There was very little in the way of credit and collections or bill dispute interactions.
These customers were identified and their interactions went to a specialized group of highly trained agents, some of whom were newly-minted electrical engineers, trained to handle all issues of that segment. This special treatment, coincidentally, also had the impact of reducing regulatory complaints since these mid-market business people are often well connected to Public Utility Commission commissioners and their staff.
Contact Routing: Technical
The generic term for the ability to intelligently route interactions is “skills based routing” (SBR). SBR is available on almost all ACD, email, chat, etc., systems as part of the standard packages. (It is best when all interaction channels are integrated and managed by a single routing rules engine).
Robust routing includes a capability called “variable routing.” This means that rather than providing a specific set of routing parameters, almost any data point can be identified as a routing variable. For example, customer intelligence can use demographic data, past purchase history, or any other defining variable to map to a specific treatment. Data points can be within any piece of the interaction-handling platform or from host connections.
Skills based routing is typically associated with ANI, CED from the IVR, from: e-mail address or chat identifier; SBR attempts to match customer need with appropriate skill. Host connectivity is most often used to simply “pop” a screen of customer information to the agent upon delivery of the interaction. Data points from the customer record are rarely used to specify what data to pop, and therefore are not often used as a routing variable, primarily because no customer intelligence exists to make these decisions. However, if host connectivity is already part of the routing platform, any customer data point can be used for pop and routing purposes.
