A rather theoretical piece from Carlos, but the concept makes sense, and this is how I would have expected CRM to evolve. 

Customers will form their customer service opinion around the product or brand – not necessarily a specific dealership’s service department. This dynamic underscores the importance of maintaining good performance across all dealerships in the channel. Furthermore, the manufacturer must account for the entire customer service picture, which may extend beyond the service department to sales, financing and other departments.

Source: Customer Service as Marketing through Statistical Modeling

While he is speaking of car dealerships, the concept and alignment of sales and service is entirely relevant to Banks.

An understanding of a dealer channel’s causal networks provides the necessary and often missing basis to apply statistical process control to continually reduce variation and improve dealership processes.

Often, people make inferences about simple causal relationships, focusing on a single cause and effect, for example, how reducing the number visits required to diagnose and complete a repair may affect customer satisfaction.

A causal network represents a more sophisticated set of relationships, providing deeper understanding and control with the ability to predict the consequences of actions that have not yet been performed.

Then the theory sets in, but it makes sense.

Structural equations modeling (SEM) provides a statistical method to develop a causal network of exceptional service and quantify how each relevant variable affects service.

It is an advanced statistical technique to study the simultaneous impact of several independent variables on a specific outcome variable. Each outcome variable of significant interest would have its own model.

For example, if the goal of dealer operations is improving customer service, a model might be created for the customer satisfaction index outcome variable.

The examples for the causal network could be applied to opening an account, or mortgage, and the after sales service.

In one study performed for an automotive manufacturer, the first fixed visit (FFV) was an outcome variable used to measure the effectiveness of parts and service. This variable measured how often a vehicle was repaired correctly after it was first serviced. In the resulting causal network, ratings for the technical infrastructure, the service manager, and the dealership itself directly impacted FFV. Each of these variables was affected by additional unknowns.

The variables affecting the service manager attribute may include specialized training that the manager has taken, the employee satisfaction and the manager’s relationship with the parts department. The model can be refined further with multiple causal networks. For example, an SEM could be run on employee satisfaction to see what variables can be indirectly leveraged to boost the service manager rating and affect FFV.

This is good stuff.  If the CRM system could collect this information, and derive this type of causal relationship, it could predict when to have the banker offer something better or different to make up for a mistake, or even better make different decisions concerning a customer to ensure that a bad service experience does not occur.

Relevance to Bankwatch:

I have complained liberally about the implementation and capabilities of CRM, and where they lack.  This kind of intelligence is one way CRM could link the enormous volumes of data Banks’ have with the customer interactions in a way that would dramatically enhance loyalty.