A New Framework for Evaluating Customer Interactions

Today we propose a new framework for analyzing and improving customer interactions within your organization.  Rather than rely on quantitative and descriptive measurements, such as rings to answer, we suggest shifting the focus to the quality of interaction and prescriptive measurements.  In this framework, calls are sampled with call recording, and the dialog is benchmarked.  Things like: customers having to repeat themselves, and correct responses to questions, can be measured.

What is Your #VocalQ?

Recorded conversations can be compared against customer surveys so supervisors can discover why an interaction went well or went awry.  Supervisors can listen for a tone that is professional and empathetic.  A qualitative score is determined and the best conversations then saved for future customer service training.   This framework for measuring customer interactions – both in a descriptive and prescriptive manner – is outlined below:

Measurement Criteria

Quantitative

Qualitative

Prescriptive Measuring metrics that directly effect customer satisfaction and taking action on them.

Example:  Average wait times exceeding prescribed limit

Action: Automatically put more agents in the queue

Monitoring actual conversations in the customer engagement and using voice triggers

Example: Recognizing words that indicate positive experience and saving conversation

Action: Measuring other agents against the positive interaction

Descriptive Data that describes the details about the call

Example: Call Detail Logs (answer time, time of call, duration, etc.)

Data or recordings that describe the details about customer conversations

Example: Measuring how long it took the customer to get their issue resolved.

An example of using this framework:

Record the first request of customers, and then capture the response of the service representative.  The reaction of a representative to a hostile customer is often key to defusing a tough situation.  Phrases like “I am sorry a mistake was made” versus “I am sorry I made a mistake” can steer the conversation in a different direction.   The context is what is important and prescriptive/qualitative data about what was said helps improve the engagement.

The ability to take immediate action on words that we call “voice triggers” is another benefit of this framework:

Supervisors can identify critical words and phrases in conversations.  Phrases such as “system outage” or “I am down” are used to trigger text messages to supervisors who can automatically add customer service agents into the queue.  The ability to address problems immediately improves the processes and procedures at the frontline that drive the business.

What is your Vocal Q?

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