The Digital Ensemble that Helps Virtual Agent Create Better Experiences

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Regardless of what industry you are in, I have to imagine you are in some way being driven to improve productivity by:

• increasing the velocity at which things get done
• taking advantage of your data assets to help the organization work smarter
• ultimately improving the experience customers and employees have on an intraday basis

Now that I introduced some stress, let's attempt to remove it.

Take a moment to think about your favorite band.

The way they sound, the emotion they invoke, the experience they create.

That experience is created by that group of musicians playing together - an ensemble (which is a French work that means "together").

Musical ensembles work together to blend their sounds and skills to create a better experience for their audience.  The same is true of theatrical ensembles.  And the same can be true of digital ensembles that impact the service experiences customers and employees have with our organizations.

Traditionally, service experiences have been negatively impacted by an over reliance on manual resolutions to routine requests and a ton of time and money wasted on repetitive tasks.

Many organizations are attacking this traditional challenge in a more modern way by offering some form of chatbot (if you're not familiar with chatbots check out this post).  The Now Platform offers a chatbot experience called Virtual Agent.   Virtual Agent allows our customers to:

• provide an always on, 24/7 self-service channel for customers, partners, and employees
• reduce costs by automating routine and repetitive tasks
• augment service staff, thus increasing productivity on higher priority tasks

On its own, Virtual Agent can improve experiences, but blend it together with some of ServiceNow's in-platform intelligence capabilities and Virtual Agent becomes the front man of a digital ensemble that delivers better experiences in your organization.

The first of these capabilities is the in-platform machine learning engine which automates and eliminates some repetitive manual tasks and provides targeted recommendations based on historic and current data.

How does in-platform machine learning complement Virtual Agent?

Out of the box, Virtual Agent can fully automate and intelligently deflect a number of different use cases - like checking the status of a ticket, providing an answer from the knowledge base, ordering an item from a catalog - but eventually the customer or employee may have to use the Virtual Agent to create a ticket.  To do this, Virtual Agent will ask the customer or employee for a short description of their issue.  Based on that short description, the in-platform machine learning will kick in and automatically categorize and assign that ticket - removing those repetitive triaging steps and accelerating the tickets path towards resolution.  In addition to that, when the ticket lands in an agent's queue, the machine learning engine will surface similar open and resolved tickets for the agent to help further streamline the path to resolution ultimately creating a better experience for the agent and more importantly the customer.

The in-platform analytics offering, known as Performance Analytics, rounds out this digital ensemble.  Performance Analytics is designed to help align different stakeholders in the organization and provide visibility into improvement opportunities.

How does in-platform analytics complement Virtual Agent?

In many ways, but for purposes of this blog let's focus on three.

First, there is an out of the box dashboard that gives us visibility into the usage, adoption and quality of the different Virtual Agent conversations.

This will allow us to understand things like:

• Which conversations are consistently being transferred to live agents? - so we can focus on improving the dialog in those conversations and decrease the amount of times an actual agent has to get involved.
• Which areas of the organization are leveraging the Virtual Agent less than others? -to give us direction on where we may need to run some more internal adoption campaigns.

Second, we can use the in-platform analytics to help us identify future Virtual Agent topics that will have the largest impact by analyzing trends in a few areas:

• Top search terms
• Top incidents/cases that been resolved on the first contact
• Most viewed knowledge articles

In all of these cases getting visibility into volume-based metrics is important, but getting an understanding of the trends in these areas is also key.  You'll want to make investments in areas in which you are seeing an increase in demand rather than a decrease.

Finally, and probably most importantly, is using Performance Analytics to communicate the higher level business outcome information about the impact the Virtual Agent is having.  Implementing Virtual Agent(or any chatbot) is the type of project that will have a great deal of visibility (and potential scrutiny) in most organizations.  People at all levels of the organization are going to want to know whether it is having the anticipated impact.

You can use Performance Analytics to communicate the answers to questions like:

• Are we seeing the shift from our more costly channels to Virtual Agent?
• Are we seeing the expected decrease in resolution times and is that having a positive impact on CSAT, ESAT, or NPS scores?

Each of these capabilities on their own can have a positive impact on service experiences you're providing, but together this digital ensemble lead by Virtual Agent can create better experiences and deliver measurable improvements throughout your organization.

Other Virtual Agent Blog Posts:

Even Virtual Agents are Working from Home

Is "chatbot" a misnomer? Let's think about what creates successful chat experiences

How to Chat about Chatbots

NOW Intelligence & Virtual Agent - Better Together