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AQM Rules Training Reference Guide

Overview

Traditionally, even the most fully staffed Quality Management departments manage only to review a small fraction of the entire calls their agents take.  While this random sampling continues to play an important role as there remain quality review questions best performed by analysts, there are now tools that allow the system to review 100% of the calls coming into the contact center, providing critical insights into trends as they are developing.   

These tools are possible through Automated Quality Management (AQM).  Through this, a variety of rules may be set up to have the system monitor every conversation for select criteria.  Words, phrases, emotion, crosstalk, and even the number of times an agent interrupts the caller are just a few of the attributes that may be used when setting up rules with AQM.   

This guide is reference material while a consultant works with the contact center to establish the most effective AQM rules for the business.

Audience

This document is intended for those who have purchased the Automated Quality Management module and are responsible for setting up rules and rule attributes within the Eleveo Workforce Optimization solution.

All information within this file is confidential and should not be shared without written permission. 

AQM Rule Architecture

Automated Rules are designed to equal 100%. This is true whether the rule consists of a single attribute equaling 100%, or if there are multiple attributes that add up to 100%. 

Basic Example:  

What attributes can be used when creating Automated Rules? 

Agent-related attributes - Impacts specific agents score only 

Customer-related attributes - Impacts the score of each agent within the conversation 

Conversation level attributes - Impacts the score of each agent within the conversation 

**Available options may vary based on the data available within your specific installation. 

Complex Example:  

AQM Rule Best Practices

Automated Rules are designed to equal 100%. This is true whether the rule consists of a single attribute equaling 100%, or if there are multiple attributes that add up to 100%.  

At this time, it is not possible to decrease the score based on an attribute, but rather only award a score based on the presence of the attribute. It's important to understand what a score of 100% means for each individual rule. Consider a naming convention that readily identifies 100% as a positive or negative outcome, using symbols to highlight this outcome. 

Create a set of rules that search for either a positive outcome or its opposite. 

AQM Example Cases 

Script Adherence 

Attribute(s): 

  • Speech Tag(s) 

Often within contact centers, agents have either required scripts they must follow or specific things they must ask for each conversation.  Using traditional quality, only a portion of conversations may be checked for these requirements.  With AQM, all conversations may be audited.  Such a tool can be useful in identifying troubling trends with specific agents and highlight the calls where the required phrases are missing for coaching purposes. 

Restricted Words (Agent) 

Attribute(s): 

  • Speech Tag(s) 

There may be many words, ranging from curse words to slang terms to simple internal lingo customers might not understand, that we wish our agents to avoid when speaking with them.  As with script adherence, traditional quality might only rarely capture such incidents on an agent's part.  With AQM, you can monitor every conversation and have the system monitor only the agent's side of the conversation for the terms you may find concerning. 

Agent Improved Customer Experience 

Attribute(s): 

  • Speech Tag(s) 

  • Customer Emotion 

We may be interested in tracking when our agents have taken angry or dissatisfied customers and improved their experience.  Do we have specific agents that excel at this?  Not only might identifying such calls be great to use for praising the employee, they also may be useful as coaching tools for other agents.  Is there something driving a trend within a queue a group of agents may be answering that we should be addressing?  Seeing a sudden increase in such types of calls across the entire group may help draw attention to something we otherwise might have overlooked.   

Agent Worsened Customer Experience 

Attribute(s): 

  • Speech Tag(s) 

  • Customer Emotion 

Inevitably, we will have customers who have unsatisfactory experiences with our agents.  Having the ability to track if there are specific agents who have a higher rate of these calls, however, can be particularly useful.  Quickly identifying the specific calls where these events occurred may also help with tracing patterns of behavior that may be exacerbating the customer's frustrations.  As with  
"Agent Improved Customer Experience", this can also help identify trends across the queue/group that may require attention. 

Process adherence:  

  • Compliance - EU specific – informing and asking the customers approval to record the call for quality and training purposes (not always included in the IVR messages) 
    Examples: call being recorded, call is recorded, for quality purposes,for training and quality purposes 

  • Verification attempt – a combination of speech tags based on the customer's specific requirements 
    Examples: verification, your customer number,  your name and surname, your account name, your company name, your phone number, your social security number, your subscription number, your full address, etc. 

  • Resolution confirmation - a combination of speech tags 
    Examples: Can you confirm your issue has been resolved, Did this answer your question, Did this resolve your issue, Does it work for you, Does the issue persist, Was this solution helpful, Was your issue resolved, etc. 

  • Professional Communication: 

  • Greeting -  Good morning, Good afternoon, Good evening, Hello, How can I assist you, How can I help you, How may I assist you today, What can I do for you?, Thank you for calling, Thank you for contacting us/[company name], etc. 

  • Professional communication during the call -
    A combination of speech tags & other attributes (number of interruptions, emotion, crosstalk ratio or duration) 
    Examples: happy to help, I am very sorry, I apologize for the inconvenience, I appreciate your patience, I completely understand , I imagine this can be challenging for you, I understand this can be frustrating, I will do my best , I would gladly look into it, of course, sorry, sorry to hear that, etc. 

  • Closing – a combination of speech tags  
    Examples: Have a good rest of the day, Have a great day, Have a great evening, Have a great week, Have a great weekend, I am glad I could help you, It was a pleasure helping you, It was a pleasure talking to you, Thank you for calling, Thank you for your call, Thank you for calling us, have a great day 

How do I identify Positive vs Negative?  

Begin the Rule Name with a simple identifier:  

  • Positive Rule Name begin with + [plus sign] 

  • Negative Rule Name being with - [minus/negative sign] 

Positive Set of Rules:

Negative Set of Rules: 

 Easily Identify in Searching: 

Easily Identify in Details: 

In this way, regardless of the name of the rule, the initial indicator (+ or -) shows whether a high score for this particular rule is good or bad.  

In some cases, you may consider a third indicator (Neutral).  For example, if there are rules you create that have neither a positive nor negative connotation but rather are simply being used to track the occurrence of some attribute.  

AQM Example Cases 

Script Adherence 

Attribute(s): 

  • Speech Tag(s) 

 Often within contact centers, agents have either required scripts they must follow or specific things they must ask for each conversation.  Using traditional quality, only a portion of conversations may be checked for these requirements.  With AQM, all conversations may be audited.  Such a tool can be useful in identifying troubling trends with specific agents and highlight the calls where the required phrases are missing for coaching purposes. 

Restricted Words (Agent) 

 Attribute(s): 

  • Speech Tag(s) 

There may be many words, ranging from curse words to slang terms to simple internal lingo customers might not understand, that we wish our agents to avoid when speaking with them.  As with script adherence, traditional quality might only rarely capture such incidents on an agent's part.  With AQM, you can monitor every conversation and have the system monitor only the agent's side of the conversation for the terms you may find concerning. 

Agent Improved Customer Experience 

Attribute(s): 

  • Speech Tag(s) 

  • Customer Emotion 

 We may be interested in tracking when our agents have taken angry or dissatisfied customers and improved their experience.  Do we have specific agents that excel at this?  Not only might identifying such calls be great to use for praising the employee, they also may be useful as coaching tools for other agents.  Is there something driving a trend within a queue a group of agents may be answering that we should be addressing?  Seeing a sudden increase in such types of calls across the entire group may help draw attention to something we otherwise might have overlooked.   

Agent Worsened Customer Experience 

 Attribute(s): 

  • Speech Tag(s) 

  • Customer Emotion 

 Inevitably, we will have customers who have unsatisfactory experiences with our agents.  Having the ability to track if there are specific agents who have a higher rate of these calls, however, can be particularly useful.  Quickly identifying the specific calls where these events occurred may also help with tracing patterns of behavior that may be exacerbating the customer's frustrations.  As with  
"Agent Improved Customer Experience", this can also help identify trends across the queue/group that may require attention. 

Process adherence:  

  • Compliance - EU specific – informing and asking the customers approval to record the call for quality and training purposes (not always included in the IVR messages) 
    Examples: call being recorded, call is recorded, for quality purposes, for training and quality purposes 

  • Verification attempt – a combination of speech tags based on the customer's specific requirements 
    Examples: verification, your customer number,  your name and surname, your account name, your company name, your phone number, your social security number, your subscription number, your full address, etc. 

  • Resolution confirmation - a combination of speech tags 
    Examples: Can you confirm your issue has been resolved, Did this answer your question, Did this resolve your issue, Does it work for you, Does the issue persist, Was this solution helpful, Was your issue resolved, etc. 

  •  Professional Communication: 

  • Greeting -  Good morning, Good afternoon, Good evening, Hello, How can I assist you, How can I help you, How may I assist you today, What can I do for you?, Thank you for calling, Thank you for contacting us/[company name], etc. 

  • Professional communication during the call -
    A combination of speech tags & other attributes (number of interruptions, emotion, crosstalk ratio or duration) 
    Examples: happy to help, I am very sorry, I apologize for the inconvenience, I appreciate your patience, I completely understand , I imagine this can be challenging for you, I understand this can be frustrating, I will do my best , I would gladly look into it, of course, sorry, sorry to hear that, etc. 

  • Closing – a combination of speech tags  
    Examples: Have a good rest of the day, Have a great day, Have a great evening, Have a great week, Have a great weekend, I am glad I could help you, It was a pleasure helping you, It was a pleasure talking to you, Thank you for calling, Thank you for your call, Thank you for calling us, have a great day.

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