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Enhancing UX with Customer Review Tagging using NLP and ML for a Prominent Online Jewelry Retailer and Manufacturer

Enhancing UX with Customer Review Tagging using NLP and ML for a Prominent Online Jewelry Retailer and Manufacturer

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    Synopsis

    Technology Stack

    Python

    Natural Language Processing (NLP)

    NLTK

    Gensim

    SpaCy

    Client Overview

    The Client is the Largest Manufacturer and Exporter of Colored Gemstones & Studded Jewelry.

    Challenge

    The Client required a Digital Channel System to gather customer requirements and gain customer satisfaction and get away with the method of consolidating the customer reviews by performing manual activities.

    Solution

    • Q3 Team developed a Digital Channel System using NLP & Machine Learning to guide the revenue lifecycle from customer acquisition to upsell and retention, and tracks each consumer experience from start to finish and predict the kind of engagement.
    • Natural Language Processing (NLP) is used to model the various topics, extract the named entity recognition and apply Machine Learning algorithms to detect the sentiment and classify the department the review is intended towards.
    • Python was used for extracting feedback data through web scrapping and social media feeds, open source libraries NLTK, Gensim and SpaCy were used for text processing and modelling.

    Outcome

    Increase in ROI by 30%
    Reduced manual efforts in reviews extraction.
    Improved customer satisfaction
    Better customer segmentation & recommendations

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