Abstract
The growing demand for customer success, and decision-makers’ adoption of AI and emerging technologies makes the biggest impact on organizations. What does it take to realize conversational IVR?
How to overcome the hurdles of the customer / IT service desk?
Sustaining a cost-effective customer / End User Computing Service as well as ensuring customer expectations with the right service is a tough assignment for the service desk leaders.
With the proliferation of digital engagement, customer expectations have changed with time. Customers want to interact with the service desk in a simplified manner, through a system that is intelligent enough to understand them naturally in their choice of language, which is available 24x7.
In furtherance to engineer and meet above customer expectation, a Smart Interactive Voice Response (Smart IVR) bolt-in solution is one of the options. This solution can address amplifying the customer experience and issues in traditional IVR
The bolt-in solution needs to be designed such that it can complement the existing telephony and IVR system.
The solution can be realized with a three-phase approach like any other IT project. This helps streamlining the investment / effort to achieve a clear outcome.
Discovery - The journey begins
Discovery is the fuel to competitive advantage; it is the driver that pushes for betterment of a product/service. It can also be said that discovery is the process of refinement of your problem statement, which brings out actionable points and the crux of the problem. During the discovery phase, the following activities play a key role to find out the set of use cases that generate high volume calls for the service desk. The activities are -
- Identify the call volume drivers
- Understand the technical landscape
- Understand SOP
- Shortlist the key use-cases
a. Identify call volume drivers
This process starts with analyzing the ticket dump, which typically contains details including the ticket category, sub-category, description, summary, intermediate comments and customer feedback. There are two standard ways of performing data analysis at this stage:
1. Filtering methodology:
Tickets are filtered, and grouped based on the category, sub-category etc. It’s quicker to achieve, albeit it doesn’t reveal a lot of information contained in your data. This method reveals the volumetric description of tickets and a graphical representation of the same.
2. Exploratory Data Analysis:
EDA is the standard data science practice to start the analysis process. Using ticket description, summary, intermediate agent comments and customer feedback details, the tickets can be put into right bucket. EDA reveals the hidden patterns in your data, which may or may not be visible at the first glance.
Once all the details are bucketed correctly, the volume for each category can be identified as an individual use case.
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