Multi-Channel Voice of Customer (VoC) Data Analysis

(07.02.2024- Present)

Abstract

In today's digital landscape, understanding customer sentiments and preferences across various channels is crucial for organizations to enhance customer experience and drive business growth. The Multi-Channel Voice of Customer (VoC) Data Analysis project aims to address the challenges of aggregating, processing, and extracting actionable insights from customer feedback dispersed across diverse channels such as emails, reviews, and social media platforms. The project leverages AI-powered solutions to streamline data ingestion, cleaning, sentiment analysis, topic modeling, and visualization, enabling organizations to gain valuable insights from customer interactions.

Solution

The project offers a comprehensive solution to handle VoC data across multiple channels:

Multi-Channel Data Ingestion: Robust systems are developed to ingest unstructured data from diverse channels using APIs provided by the DevRev platform.
Data Cleaning and Preprocessing: AI-driven algorithms are implemented to clean, preprocess, and denoise the collected data, removing irrelevant information, noise, and duplicates.
Sentiment Analysis: Machine learning and natural language processing models are employed to perform sentiment analysis on the denoised data, categorizing customer sentiments into positive, negative, or neutral.
Topic Modeling: Latent Dirichlet Allocation (LDA) models are used to perform topic modeling on the data, identifying meaningful categories or themes based on customer feedback.
Visualization: Interactive visualizations are generated to present actionable insights derived from sentiment analysis and topic modeling, facilitating easy interpretation and decision-making.

Methodology

Data Ingestion: Data is ingested from multiple channels such as emails, reviews, and social media platforms using APIs provided by the DevRev platform.
Data Cleaning: Text data is cleaned, preprocessed, and denoised using AI-driven algorithms to remove noise, special characters, and irrelevant information.
Sentiment Analysis: Machine learning models are trained to perform sentiment analysis on the cleaned data, categorizing customer sentiments into positive, negative, or neutral.
Topic Modeling: Latent Dirichlet Allocation (LDA) models are applied to identify topics or themes within the customer feedback data, enabling organizations to understand prevalent issues and trends.
Visualization: Interactive visualizations such as bar plots and word clouds are generated to visualize sentiment distributions and topic clusters, facilitating easy interpretation and decision-making.

Tech Stack

Python: Programming language used for development.
DevRev Platform APIs: APIs used for data ingestion from multiple channels.
NLTK (Natural Language Toolkit): Library used for text preprocessing and sentiment analysis.
Gensim: Library used for topic modeling with Latent Dirichlet Allocation (LDA).
Matplotlib: Library used for data visualization.
Tweepy: Library used for fetching tweets from Twitter.
BeautifulSoup: Library used for web scraping reviews from online platforms.

Contributors