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Using OI as a Team Leader or Supervisor for Agent Coaching
Using OI as a Team Leader or Supervisor for Agent Coaching

A walk through of how Team Leaders or Supervisors can use OI to help with targeted agent coaching. Includes a training video.

Andrew Cox avatar
Written by Andrew Cox
Updated over 9 months ago

Summary

This support page will help you confidently navigate OI as a team leader or supervisor to drive data-based continuous improvement and coaching in your teams.

Below is a step by step guide but we have also included a video training 👆if you prefer to view it that way.

You will learn how to:

  • Find your team

  • Navigate the OI filters

  • See the performance of your team

  • Interpret the Agent Profile

  • Identify coaching opportunities

Step 1 - Find my Team

From the Dashboard, click the ‘Performance’ page.

Click ‘Show me how’ at the bottom of ‘Approach two: Review agent performance by metric’

You can now see all agents sorted by metric, the default metric is Average Handle Time (but you can change this).

Now, let’s apply filters to find the agents in your team.

Click on the filters bar on the left hand side of the screen.

Click on the icon with 3 horizontal lines.

You will now see the Filters screen.

To filter to your team, click on ‘Org Structure ‘ and then find your name and select it (note it will show highlighted in purple when selected - make sure no other team leader names are also highlighted in purple).

At this stage, you can click on ‘Apply filters’ to see the agents in your team, however if you want to sort your team by a metric other than Average Handle Time, click on ‘Metric’.

Here you can select any one metric you would like to sort agent performance by.

Please see this article to learn about the different metrics and definitions.

Once you click ‘Apply filters’ you will be taken back to the agent performance page for your team.

You can save this view by clicking on the love heart in the filter window (left of screen), it will show up on your dashboard home page under ‘Saved Views’. This means you don’t need to apply filters every time you want to see your team.

Tips:

1. If you don’t see your team's name in the ‘Org structure’ results the agent roster may need to be updated. As a trick, you can use the ‘Agent’ field in the filters panel to manually select all of the agents in your team, then save that view (see above instructions).

2. In the filter pane (left of screen), click on the cube icon to make sure you have the right ‘model’ selected for your department or functional area.

Agent performance by metric is sorted from High to Low as a default. When you hover over the bar next to the agent name, you can see more information about that particular agent.

In this example, agents with the longest Average Handle Time are at the top of the screen and the lowest Average Handle Time are at the bottom.

When you click on the bar, it will take you to that Agent’s Profile.

Navigating the Agent Profile

There are various sections within the Agent Profile.

Time Series

You can adjust this to look at data and performance over different time periods. For example if you use 3 months of historical data to identify a coaching opportunity, once you’ve coached the agent, you can then look at the most recent week of data to see how they’re doing.

Agent Performance Overall

Here you can see how the agent performs for key metrics versus everyone else. Note that if you click on the drop down arrow next to the metric, you can change the metric.

Volume handled by agent

Here OI automatically identifies what inquiry types the agent is handling. When you hover over one, you can see information about that inquiry, based on the agent.

When you click one of the inquiries, the Agent Performance Overall, will show you how the agent performs for that inquiry relative to everyone else.

For example:

Here we can see how this agent performs when a customer calls because they’re having an issue ‘Logging into my Internet Banking/App’.

This agent's Average Handle Time is higher than ‘All’, their Average Predicted Resolution is lower than ‘All’ and their Average Agent Effectiveness is higher than ‘All’ (a lower score is better for Agent Effectiveness). This is a clear coaching opportunity.

Inquiry impact on Average Handle Time

This view shows you exactly which inquiries are impacting any given metric. In this example, calls relating to ‘Logging into my Internet Banking/App’ are driving this agent’s Average Handle Time up more than any other inquiry. This is the inquiry to focus on to reduce this agent’s overall AHT.

Note, by using the Filters, you can change the metric, to see which inquiries are impacting Sentiment, Satisfaction, Resolution, Effectiveness etc.

Agent’s metrics over time

Here you can see how any of the agent’s metrics (the lines) are changing over time. You can use the drop down arrow (shown underneath ‘Monthly’ in the top right hand corner) to change the metrics. The bars represent volume.

How to use the Agent Profile for Coaching

Best Practice Playbook - Steps

  1. Identify one specific performance opportunity

    1. This should be a single inquiry

    2. Identify a focus metric to improve for the inquiry (i.e. AHT)

  2. Agent coaching

    1. Show the agent the data

    2. Share why you have identified the inquiry for coaching

    3. Discuss the metric they need to improve for that inquiry

    4. Agree a goal to improve the metrics (it should be SMART)

      1. I.e. by x date, my goal is to reduce my Average Handle Time by 10% for inquiries where customers call because they’re having an issue ‘Logging into my internet Banking/App’

    5. Ask the agent what they can work on to meet their goal (i.e. skill gaps)

    6. Agree a plan and date to check in and re-review the data and progress

  3. Check in

    1. When you meet with the agent, re-review the data

    2. Focus on more recent data i.e. the last week

    3. You should be filtering to the inquiry they’re focused on

    4. Provide feedback on their progress

  4. Complete

    1. Repeat the process until the agent has met their goal

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