Fine-Tuning CEM to Produce LoyaltyFine-Tuning CEM to Produce Loyalty

Measuring and processing big data relating to customer satisfaction and tweaking customer experience management is universal.

Creating customer loyalty is profitable, and customer churn is a negative cost. This especially true for communications service providers (CSP) such as wireless providers. Collecting, collating, and processing customer requirements, aspirations, and complaints can transform the CSP into a big data processor. Although there can be many problems dealing with a CSP, network service quality remains the biggest driver of customer dissatisfaction.

This is the conclusion of a survey performed by IBM and reported in a white paper titled, " Driving Customer Loyalty Through Network Service Quality ." Customer satisfaction of network service quality out polled every other factor measured, including billing -- one of my personal pet peeves.

Although the report covered wireless service providers, the lessons learned can be applied to any customer service center. Measuring and processing big data relating to customer satisfaction is universal.

IBM's 2014 study of over 4,000 C-level executives across worldwide groups of multiple industries found a strong correlation between organizations that perform well and a focus on customer experience management.

CEM involves three elements. First, the organization must use relevant insights about the customer. The organization has to understand the customer and his or her evaluation of and response to the experience. This involves approaches to customer care, policies, and finally network quality itself. Not all things measured will be useful, but you may not know what data is important until you analyze how the organization treats the customer.

Second, the CSP must use those insights to develop actions to satisfy the customer. Who determines the insights can affect the conclusions drawn. Whether the actions taken should be at the sole discretion of the marketing and/or sales organization is an open question. This is where IT can help by providing flexible tools to determine the actions to be taken.

Third, and probably most important, is to measure if the actions produced the desired results. This is where big data enters the picture.

The report mentions that 67% of CSPs believe that they have adequate or good visibility into network service quality. A small number, 16%, think they have excellent visibility. Those who have pursued CEM strategy for over a year (24%) reported success, which compares to 5% who have less than one year of operation. It appears that just creating the CEM environment is not enough. It needs to be tuned and improved over time. Those who measure network quality (IT) need to work closely with those responsible for customer satisfaction. The first day of operation does not automatically deliver the desired results.

The report demonstrated that there a multiple CEM mechanisms to analyze customer loyalty:

Data collection security and delivery methods from third-party sources are limiting factors. Can the organization trust the third-party source's security controls?

Up to this point, most organizations have not had to process big data. It is becoming apparent that big data analysis is the next step, but there are challenges to this approach. Customer loyalty data is probably in different formats, from different sources, measuring different metrics. Data may come from focus groups, emails, Web interactions, mobile apps, third-party data collectors, messaging systems, Twitter, Facebook, and other social media sites.

The second challenge is to create service quality data sets that can be used with the loyalty data to produce meaningful conclusions. Both of these challenges mean that most organizations have work to do, as is shown in the following chart from the report. Each survey respondent was allowed to report his or her three highest priority efforts.

Like other organizations, most CSPs are proceeding to expand their CEM systems to better understand how customer loyalty is measured and processed.

The diversity of data sources presents problems for internal network operations. I am referring to the organization's own network, not the network that is servicing customers. My recent blog The Network Impact of Big Data generated well over 100 retweets, demonstrating a high interest in the network problems produced by big data collection.

My greatest concern is not the accuracy of the data sources or the information formats. The external sources of loyalty data all have different levels of security -- in access, content, and transmission. I would be cautious about whom to collect information from. Their security policies and procedures may not be those of my network. Due diligence is called for before accessing third-party networks for customer loyalty data.