Automated call monitoring tools have various strategic or technological shortcomings with regard to addressing customer satisfaction. For example, text-mining software is vulnerable to inaccuracy, sensitive to user-defined key words, and prone to human typing errors.

And notes entered by customer service reps often contain the solution to the problem (e.g., gave customer $5 credit), rather than the root cause of the trouble (e.g., customer stated that they were misinformed of service fee).

While more rigorous hands-on analysis, such as live monitoring of inbound calls, is very time consuming and expensive to make the process effective and actionable. A small sample of calls may give the listener insightful detail into particular customer concerns, but it may not be representative of the customer population as a whole. Conversely, the call may be representative of the customer population but, due to the broad range of issues covered in the calls monitored, may not be sufficiently detailed or actionable.

ROOT CAUSE ANALYSIS

Sound root cause analysis, on the other hand, can deliver a complete, statistically valid picture of customer service issues leading to actionable recommendations, and it can be implemented at a manageable cost.

Root cause analysis reveals the underlying causes behind customer dissatisfaction and leads to targeted actions to resolve them. It is based on the understanding that strategic, rather than merely operational, methods are required if companies are serious about solving the challenges of customer satisfaction.

The strategy follows the design and implementation of code-based intervention.

Code-based intervention enables targeted call monitoring and analysis by employing a coding schema for inbound calls that is then matched to actual call recordings. In this way, a customer-centric company can hypothesize about root causes of customer dissatisfaction and subsequently validate, quantify and tactically respond to these problems.

ANALYSIS IN ACTION

To see how root cause analysis works in action, let's look at how a wireless telecommunications operator implemented the strategy. The company was facing high customer service call volumes with declining customer satisfaction and average call per subscriber was above the industry benchmark.

With the cost of $3 to $5 per call, the customer service operation was a significant share of the company's general and administrative expense. Faced with declining margins and high churn, the operator decided to elevate their customer service operation from a tactical to a strategic level. The carrier charted a three-step process:

1. Create a coding schema for inbound calls, allowing the operator to categorize the root cause, prompting a customer call and to obtain a radar view of the issues. This component is known as a call reason tracker, which records call reason frequencies of a random representative sample of calls each month.

The application is designed to detect slight but statistically significant deviations among these call reasons from month to month. Data generated from this tool proffers a radar view ¡ª current and historical ¡ª of call reason patterns across the enterprise, allowing the carrier to identify major reasons for customer calls; anticipate growing problems in the customer base; and quantify the costs attributable to different call reasons.

2. Create a call library: a catalogued inventory of recorded calls, searchable by call reason or other target specifications. This enables targeted call monitoring and fills in the gap between the radar view of the call reason tracker and the deep dive of the call monitoring team.

3. Develop a statistically significant tentative causal model for the relationship between coded events and their value (i.e., hypothesize and prioritize). Once executives have studied the radar view of call reason results and identified issues or customer segments of concern, a call monitoring team performs deep dives into the issue, validating and quantifying hypotheses for root causes.

Armed with insight into the root causes of customer dissatisfaction, the telecommunications operator developed strategies/tactics to address and resolve the key customer service issues.

For example, it was assumed that there were specific recurring issues that drove frequent callers to contact the company regularly and therefore that they would have a substantially different call-reason profile than the rest of the base.

In fact, the opposite was true. While secondary analysis revealed that less than 4% of callers were responsible for more than 20% of calls, targeted call monitoring efforts revealed that these customers were calling for quite similar reasons as everyone else. The findings indicated that the root cause of many of these calls was personal habit on behalf of the customer rather than a particular reason. As a result, rather than having to address a recurring issue per se, the company singled out (through a review flag on customers' accounts) the frequent callers and resolved the problem.

Another project demonstrated the importance of hypothesis validation and quantification. A flaw in some forms of primary analysis is that anecdotal evidence may bring to the forefront problems that seem glaring but are actually not as crucial as other root causes. Without validation and quantification, the "problem of the moment" can often lead to an automatic reaction that fails to consider the full cost/benefit picture.

At the carrier in our example, balance and billing information provided across automated systems represented exactly this type of problem. The perception within the company was that update lags or unavailability of this information were prompting a high volume of calls.

The benefits of the root cause analysis strategy were that it offered a clean, exact way of locating the drivers for a problem such as automated system information, and also quantified the extent of the costs for which these problems were accountable.

Once the call monitoring analysis had given an estimate of the call volume that was attributable to this issue, a business case was constructed to weigh the savings that could be achieved through systems improvements against the costs of these improvements. The graph below illustrates the results of a deep dive targeted call monitoring effort that focused on two specific call reason categories: account management inquiry and account management change.

Once the call monitoring analysis had given an estimate of the call volume that was attributable to problems with the synchronization of account balances across systems, a business case was constructed to weigh the savings generated from fixing the problems versus the costs of these improvements. Since IT improvements often involve large amounts of fixed and variable cost, this case serves to illustrate the importance of targeted call monitoring determining where to focus human and capital resources.

Using inputs from the call monitoring effort it was determined specific fixes would generate cost savings in the range of $8 million to $10 million, while other fixes would likely produce cost savings of less than $25,000 due to the low frequency with which they occurred, thereby not justifying a costly IT fix.

Without targeted call monitoring to validate and quantify specific issues, problems were often identified through anecdotal feedback, leading to expenditures that outweighed the resulting cost savings.

THE END RESULT

Code-based intervention to improve customer satisfaction has produced substantial benefits for the wireless company. For less than the $1 million it cost to implement and operate this code-based intervention solution, the carrier has identified more than $60 million in variable cost savings within the first six months of implementation.

Analysis showed that 70% of the calls to customer service were potentially preventable with the help of automated solutions and streamlined processes. Just as important, the company avoided wasting money on ill-advised IT projects. The call library was used to make specific actionable recommendations for improvement in processes and prioritizing and quantifying the benefits of various technology projects.

For example, unsynchronized usage information across the various customer touch points was anecdotally believed to be a large driver of calls to care. However, research from the reason code tracker ¡ª the monthly call reason report ¡ª did not show it as a major call driver and the associated technology project was reprioritized.

Quantitatively, root cause analysis enables the company to optimize spending and maximize the returns on the investment. Qualitatively, the solution has added precision to primary research. The methodology and tools introduced by the solution's design allow for ongoing identification, verification, and measurement of actionable drivers of dissatisfaction. The methodology can be applied across the organization (such as to understand network problems and churn reasons).

A REVOLUTION WAITING TO HAPPEN

Companies facing mounting customer service costs and customer dissatisfaction challenges could stand to benefit from such a strategy. Within financial services, for example, code-based intervention could be employed to answer questions such as why customers are switching to other banks, or why customers feel that product offerings are not tailored to their investment needs. Cable companies could seek to understand why subscribers are unwilling to take upsold content or why broadband users are migrating to DSL.

Companies such as software distributors or appliance manufacturers that field technical support questions could use this combination of radar view and deep dives to draw attention to the most frequent call drivers and determine the underlying causes behind recurring problems. Other industries with extensive customer service operations, including insurance, utilities, and some governmental organizations, could achieve decreased costs and increased satisfaction with the ideas outlined above.

In short, by creating uniformity in the way that primary customer information is categorized and stored, code-based intervention can revolutionize customer service. Harvesting primary information becomes a secondary concern, allowing companies to focus on what really matters: identifying and addressing the root causes of customer dissatisfaction.