[MUSIC PLAYING]
PRESENTER: Hello, and welcome to this UiPath Task Mining demo.
Today we will walk you through a task mining study in a contact
center environment.
You and your agents are hard at work solving
your customers most critical and time sensitive questions,
and your success depends on a consistent set of resolutions.
And while each customer may be unique,
their needs are often very similar.
It could be checking on the status of their purchase,
helping them regain access to their account,
or even updating their contact information.
While repetitive, these tasks often
include a lot of process variation driven
by many factors, which can include
the order in which information is entered into your CRM
or other business critical applications,
the resources used to guide agents
and operational procedures, or even where and when
your team thinks the process begins and ends.
Understanding these variations is a challenge
and interviewing agents to uncover these variables
is costly and time consuming.
But to automate nearly any aspect of your contact center,
you'll need detailed documentation
of your team's expertise to uncover which
tasks are worth automating.
UiPath Task Mining helps you discover the tasks best suited
for automation by using AI to identify and aggregate
agent workflows.
And then with the help of your process SMEs,
identify task candidates that will
have high-automation potential and impact to your business.
This is done by collecting agent desktop interactions
within pre-selected applications and then recording
each step in the background, such as screenshots,
mouse clicks, and keystrokes.
Let's look at how deploying a task mining study
can give you visibility into your processes
and expand your automation pipeline.
To get started, we will log in to the Task Mining study
facilitator to configure the study.
From here we will be able to create
a list of approved applications that we will want to record.
Enable PII Data Masking, which is pre-configured to detect
and obscure information such as social security numbers,
addresses, and banking details.
Additionally, we will designate which agents will take place
in the study.
The participating agents will then
be sent an email with instructions
on how to download and install the application
on their local machine.
Once the study is active, the agent
can see which applications are being recorded
but can otherwise continue their work uninterrupted.
All of the standard contact center applications
can be recorded, including the age and soft phone or chat
interactions.
Once the study has concluded, the recordings
will be processed by the machine learning model.
And an email notification will be
sent to the Task Mining study facilitator when
the results and process visualization are
ready for review.
This could take several hours depending on the length
and depth of the study.
To best interpret the results, you
will want to include a process SME, which can provide
additional layers of analysis.
Let's now take a look at the summary dashboard
where you can get instant visibility
into key metrics, such as the total number of recorded hours,
the number of actions collected, the applications used,
and most importantly, the number of repetitive tasks
identified with automation potential.
As we scroll down through the dashboard
we find additional details, including the total time spent
on each task, the number of users who performed
that task, the number of traces, or in other words,
the different ways that task was completed by study
participants, and additional metrics that will help
to determine task complexity.
Now, at the top of the list is the task
that has been identified as the most promising automation
candidate.
And when opening this task we are
presented with a process map of the most frequent path
in the process model.
By selecting one of the captured traces
we can then view its process flowchart,
which includes important information such as the start
and end point, a description of each step,
and its duration and frequency.
In the Filter pane we can also fine tune
the level of visible detail.
Let's click into this step of the process where
we can review the specifics and add or update information,
such as a new step title.
In this case, we will rename it to UiBank application.
Great.
Now we've updated our task trace and are
ready to export it to a process definition document,
also referred to as a PDD, by clicking on the Export Trace
button in the bottom corner.
With this PDD your team can make adjustments
to account for any desired changes
you want to see in the process document.
Upon completion of a Task Mining study,
you will have gained a deeper understanding of how
your contact centers processes are being completed
by your agents, identification of your optimal process
candidates, and the process documentation needed
for developers to create an automation.
Additionally, by submitting your finalized PDD
to Automation Hub, your automation development team
will be able to make informed prioritization
decisions by capturing the expected benefits in Automation
Hub, such as average handle time, talk time and hold time,
or even the reduction of after-call work.
With Task Mining by UiPath, you can
continue to discover high-priority automation
opportunities and process improvements
to enhance your customer and employee
experience simultaneously.
To start your own Task Mining study,
visit UiPath.com/TaskMining.