Customer Analytics – Measuring and Evolving the Customer Experience - Part I

In part one of this two part series on customer experience and analytics for financial companies, we’ll explore the nature of the customer journey, disconnects caused by silos, and how to justify and measure projects for remediation 

Complexity and the Customer Journey

As more customers transact most or all of their business online, financial organizations are tasked with stitching together a seamless customer journey from what in many cases is a series of disparate systems. One recent customer experience project with a financial firm dealt with over 50 platforms for managing customer investments. For some transactions, most steps could be accomplished online. For more complex redemptions, changes in beneficiary or transactions with tax implications, the customer needed to call and speak with a customer service representative.

Some tasks that could not be accomplished online required a single call to the center agent, while others required transfers to specialist for callbacks. On occasion, the call center agent needed to interact with a back office transfer agent. In extreme cases, the transfer agent required support from a special “hotline” of top tier specialists who had even greater levels of training, expertise, and system access. Imagine the customer experience: beginning a process online, running into a roadblock, searching for help, sending an email request, calling in, being transferred, getting placed on hold, leaving messages, calling back, having to repeat basic identifying information repeatedly and so on.

Rather than a smooth paved highway, that journey is filled with bumps, detours and side ramps that slow the process, increase support and transaction costs and damage the customer relationship.

Each step and task may require a different department, a different system, process, technology, cost center, procedure and so on. So much for a smooth seamless journey – the journey is bumpy and inefficient because internal systems and processes are disconnected.

It seems obvious at face value that this poor experience will likely result in a range of bad things – from ill will, to higher costs, to lost business, to missed opportunities. Why isn’t this fixed? Why would this sad state exist?               

The Cost of Remediation – Is it Worth It?

No one wants to offer a poor customer experience. Sometimes this arises due to changes in the business, in offerings, in technologies – in any number of variables. The reason the problem is not fixed lies in the complexity of the processes that underlie the customer journey and the costs of dealing with such complexity. There are not always simple solutions and it can be difficult to pinpoint an intervention that has the greatest impact for an investment. One approach is to measure an aspect of the customer’s interaction and correlate that with a process that is designed to support that interaction. By focusing resources precisely on that process, and measuring a specific dimension of a behavior, it is possible to test cause and effect relationships and create a linkage of customer behaviors to internal systems and processes.

Call centers are rich in data, metrics and baselines. In many cases, the time to resolution is a key metric and a great deal of effort is placed on minimizing this metric. However, that emphasis on quickly getting through the call and ending the incident can have unintended consequences. If shorter time per incident data were all that was considered, then an agent with the best metric might become a benchmark. If those short call times meant that customers did not fully resolve their issue, had to call back or felt rushed and were not satisfied with the experience, a feedback survey would reveal the more complete picture.

Complex Scenarios Require Data Aggregation

This simple example can be extended to more complex scenarios and interactions. Frequently data is aggregated across multiple interactions, processes, systems and departments. One interaction might consist of several steps or a customer might provide feedback after a complex transaction is complete. In those situations, information needs to be mined or combined, integrated and analyzed and then correlated with multiple internal systems and processes. For example, a loan application might entail initial submission online, telephone verification of information and perhaps a visit to a branch for completion. Different systems may describe the details of a customer in different ways. A web site might target consumer in a particular demographic and capture data for marketing purposes that may described differently (or not be present at all) in systems that are further downstream in the process.

Attempting to aggregate and process data from disparate systems using different customer attributes will require the ability to model the customer with terminology and descriptors that are “harmonized” – that is, mapped to a common set of terms and concepts. Once these data inconsistencies are addressed, it is then possible to look at the impact of an intervention across a process or journey. The next piece of the puzzle is around measuring the external customer interaction through various unstructured data feedback sources – the Voice of the Customer sources that require text analytics approaches with similar modeling approaches.

Go to Part 2.

Meet the Author
Seth Earley

Seth Earley is the Founder & CEO of Earley Information Science and the author of the award winning book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable. An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions. He has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance.