The organization aims to automate the identification of dissatisfied customers based on the ticket
description. To achieve this, leveraging natural language processing (NLP) capabilities is the most
efficient method. Appian provides connected systems that allow integration with external NLP
services. These services can analyze text data (such as ticket descriptions) to determine the
sentiment or classify the text into predefined categories (like "dissatisfied customer").
Natural Language Connected System:
Appian can integrate with third-party NLP platforms such as Google Cloud Natural Language, AWS
Comprehend, or Azure Text Analytics via connected systems.
These services analyze the text provided in the ticket description to detect sentiment, keywords, or
specific categories indicating dissatisfaction.
Based on the analysis, the system can automatically assign the appropriate team to handle the case.
Why Not Other Options?:
B . Decision Table: While decision tables are useful for rule-based decisions, they are not suitable for
interpreting unstructured text like ticket descriptions.
C . Image Analysis Connected System: This option is irrelevant as the task involves text processing,
not image analysis.
D . SAIL Form: SAIL forms are primarily used for user interface creation and are not intended for text
analysis or classification.
Implementation in Appian:
Create a connected system to integrate with the chosen NLP service.
Configure the NLP service to analyze the text data and return the sentiment or classification results.
Based on the results, use process models to route the ticket to the appropriate team for resolution.
