What is Root Cause Analysis?

Tethr’s Root Cause Analysis is designed to help you understand how categories (or key events) found on your interactions impact a specific outcome, such as talk time metrics or a score. The analysis engine supports key driver (regression) and correlation analysis. The newly enhanced root cause analysis tool will equip you with actionable information you can use to resolve issues at the root.

Root cause analysis makes it easy to:

• Perform a targeted and customized analysis 
• Identify influential behaviors and categories
• Predict and quantify changes

Analysis configuration options

Analysis types

1. Key driver - measures the influence a category has over an outcome type
   • Pinpoint which actions are affecting outcomes the most, enabling targeted coaching and process improvements
2. Correlation -  measures a positive or negative relationship between categories and an outcome type
   • Understand trends and patterns that might not be immediately evident, aiding in more comprehensive strategic planning

Outcome types

1. 1. Talk time based - Length, Silence time, Talk time
   2. Boolean/Numeric based - Category, Custom field, Score 

Elements of root cause analysis 

(Example - Analysis: Key driver, Outcome: Length) 

1. 1. 1. List of categories included in the analysis 
      2. Total annualized cost of the category, calculated using monthly volume and the average cost per minute 
         • Only for time based outcomes
      3. Time added to an interaction each time the category is detected 
         • Only for time based outcomes; Boolean/numeric outcome data, the analysis measures and predicts value change
      4. Volume of the total category hits found in analysis
      5. Summary of the impact of the selected category on handle time. Click on a category in the analysis to display its summary 
      6. View interactions where the selected category was detected

Popular use cases 

• Average handle time analysis - Understand the category drivers of AHT (using any talk time outcome type)
• Score analysis - Understand the category drivers of a custom QA score or any of Tethr’s OOTB score
• Custom field analysis - Understand the category drivers of agent or customer asynchronous data 
  • Custom field data must be configured as numeric or boolean
• Category frequency analysis - Understand the relationships between categories 
• Correlation -> Key driver analysis - Understand the positive or negative relationships categories have with an outcome type, then use that data to create a targeted key driver analysis against the same outcome type

Requirements & limitations

• 1. Create and filter Tethr report for targeted analysis
  2. # maximum interactions: <400K
  3. # minimum interactions: 10k+
  4. # categories: 100 or less