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System for automated distribution of support tickets to engineers assuring maximum customer satisfaction

IP.com Disclosure Number: IPCOM000250146D
Publication Date: 2017-Jun-06
Document File: 2 page(s) / 34K

Publishing Venue

The IP.com Prior Art Database

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TITLE: System for automated distribution of support tickets to engineers assuring maximum customer satisfaction

Abstract

Work of support teams serving many customers is governed by complicated rules where many factors

count. Typically support manager needs to deal with support skill gaps, customer skills, escalations,

minimize tickets' backlog, make sure engineers are not overloaded, assure there are no delays, etc., but

at the end only one goal needs to be achieved - customer satisfaction.

One of the more important activities is correct assignment of new support tickets to engineers in a way

which allows to maximize the customers' satisfaction. Current article provides a solution which can do

this in almost fully automated way.

We use neural network which learns on past support tickets to assigns best support engineer to work on

new tickets in such a way that customers' satisfaction is maximized. It also allows support manager to

steer/adjust the system parameters to make sure hot situations are properly covered and support

engineers are not overloaded. The system also allows support manager for manual assignment in cases

which can not be automatically covered be the tool providing all possible supporting information based

on which he/she can make the best decision.

The vectors being inputs for the analysis would consist of the following fields:

1. Customer contact email address

2. Company name (unique customer identifier)

3. Company current sentiment (based on sentiment analysis of all currently opened tickets)

4. Product area to which ticket is opened (code component) or alternatively ticket title

5. Ticket severity

6. Ticket priority

7. Escalation. For example, this can mean if critical situation is assigned. (true/false)

8. Number of tickets currently in backlog (this allows for correlation between backlog size and

customer satisfaction)

9. Support engineer name (or unique identifier)

10. Number of tickets currently owned by the engineer

Please note that points 3, 8 and 10 are not trivial. Thanks to them we can eliminate time factor from the

considerations and focus on satisfaction only. Also analyzing current customer satisfaction from all

opened tickets allows to take into considerations and hot situations and escalations. Please note that all

this can be determined from archive of all past support tickets.

The analysis based on the input vector needs to determine the predicted custo...