Key to Success:
- A robust combination of human expertise and machine learning to analyze customer sentiments accurately and at scale
- A framework that identifies a customer wish list of product attributes to build customer-centric products
- Timely and validated pragmatic insights and recommendations
A multibillion dollar retailer wanted to grow its private label business to gain competitive advantage in the market. The brand was seeking to build customer-centric, private-label products in highly competitive categories — identifying that customer reviews and ratings are useful data sources for understanding customer needs.
There was an overview of top-level themes emerging from customer reviews, however, deeper insights were needed quickly and at scale to differentiate and compete better. Given the dynamism of the industry, the constantly changing customer needs and the large number of categories, the client needed expertise and a technology-enabled solution to analyze its customer sentiments at scale.
Using a combination of machine learning algorithms and human expertise, we analyzed customer sentiments across various data sources, like product reviews on the retailer’s website, product reviews from competitor websites, and internal sales data. Leveraging natural language processing (NLP), we extracted keyword phrases from customer reviews, grouped them into themes and classified them as positive or negative customer sentiments. Our experts validated the output and generated timely insights and recommendations.
Our in-depth analysis provided the retailer with a customer wish list of product attributes. These pragmatic insights informed decisions to build customer-centric private label products.