Ecommerce

Customer feedback sentiment analysis for a leading eCommerce market player
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The Situation

One of the best-known Central European e-commerce merchants (under NDA) was looking to create a tool intended to analyze the customer’s feedback on goods purchased through their web marketplace. The business goal was to increase customer loyalty, drive business changes, and deliver appropriate return on investment. Customer experiences fall into three basic categories: positive, negative or neutral. The goal was to perform an in-depth sentiment analysis and detect the tone of voice for every single customer’s comment/review/post in social media pertaining to the purchased product.

The Solution

The client hired us for building their Dedicated Data Science Team in our R&D Center in Kyiv, Ukraine.

After data cleaning and munging , an initial step of words tokenization was applied. After this, an NLTK tools was used to define synonyms, semantics, and overall mood of feedbacks; aligned with scores given by these particular feedback authors, a section of manual business logics was brought in: language specifics, abbreviation, collocations and vernacular expression played a significant role in overall semantic analysis. Alongside NLP and semantic analysis, Data Science techniques were applied: based on the data from social network that each customer logged in through, a set of demographic features was involved to model, leading to a complex analytical solution.

The Result

We have set up a dedicated team comprised of highly-skilled analysts, mathematicians and software developers.

This solution helped the Client to define and upgrade their marketing and sales strategy, which resulted in 10% revenue increase within one year after the deployment.

The Client noted the overall high level of delivery and solution architecture.

Team Solution

Dedicated Data Science Team

Team Solution

NLP, Text Mining, Data Science

Team Solution

E-commerce

Team Solution

Python, NLTK, NLP

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