Customer Experience is the priority of business leaders, any size, any vertical. Today customers want to be independent, they want access to self-serve solutions, they do not like to be sold things, they love to buy solutions to their problems and needs, thus a key to delivering amazing customer experience is to quickly resolve a customer’s problem – ideally on the first contact, even better if through self-services.
Artificial Intelligence implementations in contact centers are the primary way to improve first contact resolution (FSR) and drive customer experience and retention to the point that some analysts predict that a huge percentage of the customer interactions can be resolved by well-designed bots.
While I certainly believe that bots will be more and more powerful in the next future, my pragmatic suggestion, based on my field experience, would be slightly different, and the reason why I’m saying that is the incredible business case represented by Cognitive Contact Centers.
Let’s check some numbers together. Even assuming the monthly cost of an agent being $1000, optimizing by just 10% the efficiency of the Contact Center will trigger a huge benefit for the customer and even more in case of larger Contact Centers.
But what do we mean by 10% optimization?
Quite often that means an Artificial Intelligence BOT is able to successfully handle 10% of the incoming calls/chats, from beginning to end, without engaging the agent and therefore improving the scalability of the Contact Center or giving back time to agents to deal with the most complex cases. This approach requires building a BOT able to successfully manage the entire conversation with the customer and therefore potentially sophisticated, even complex so the first approach to Artificial Intelligence I advise is a different one.
In the vast majority of cases the first part, let’s say at least 10%-20% of the time, of a call to a Contact Center, is about data collection, name, the reason for calling, service id, etc. and this is something highly repetitive, a dialog very structured and therefore much easier to automate with a simple BOT just meant to collect the DATA.
Once all the necessary inputs are collected saving 10-20% of the agent time, the BOT can hand over the most complex part of the call to the agent passing those DATA and the CONTEXT so that the agent can move forward from there.
Artificial Intelligence Use Case and Solution
Together with Marco, in the below video, we offer an example of the SALES and TECHNICAL journey to skyrocket business efficiency in a Cisco Contact Center Enterprise solution with Google Artificial Intelligence DialogFlow platform.
We cover both sales and technical aspects, connecting the dots, helping account managers to scout opportunities, and engineers to design solutions.
The first half is about the use case and the incredible business value of the solution offered by combining the best of both worlds, while in the second part we go through the technical details of the solution.
Watch the video:
If you have any questions, feel free to reach to Massimiliano Caranzano and Marco Pirrone at:
Contributing Author: Maraco Pirrone
After his computing studies, Marco has spent 8 years in SCADA industrial research developing innovative software for realtime applications and computer graphics.
He moved then to the telco industry leveraging his software background to develop contact center applications.
Marco has been working for Cisco for the last 20 years, as a leading technical specialist worldwide, contributing to his passion and interdisciplinary skillset in the continuous innovation of Customer Experience.
All seasoned with a visceral passion for jazz and classical music, supported by a degree in classical piano.
For More Information
Video on SalesConnect: Cisco Artificial Intelligence in Contact Centers: Chat Translation Assistance
Video on YouTube: Cognitive Contact Centers Voice Transcription Translation