Making sense of unstructured customer review data with text analysis (NLP)
The ecommerce platform was amassing reviews but was not effectively using the information to know what the customers were saying. They wanted to use the information to understand the customers and identify problems on their platform. Going a step further, they wanted to understand the correlation between star ratings and the review content – to understand how customers were using the star ratings and generate actionable insights from the review data.
Actionable insights from reviews. At scale.
We developed a daily report that would collect reviews provided in the last 24 hours and generate insights for the strategy team
- About 68% of 3 star ratings are associated with negative reviews
- 40% of 1 & 2 star ratings were negative feedback on delivery time
- 18% of 5 star rating were appreciative of the discounted offers on products
With the study, the ecommerce client is able to generate instant, actionable insights and intervene negative reviews before they can snowball into a brand crisis.
Unstructured text reviews. No standardized insights
The ecommerce client was using star rating to look at customer feedback. They were ignoring the information hidden in 4 and 5 star ratings, and looking at 1 and 2 star ratings, manually. Their assumption was that almost all negative reviews were associated with lowest 2 rating scale.
They were also not making use of the reviews associated with promotions, delivery time and attributes.
Their biggest issue was lack of clarity on the insights that text analysis could provide, and if such a solution can be automated and is scalable. They had deployed a team to analyse the reviews manually, and were aware of the value the reviews provided. We proposed a web based platform utilizing natural language processing for automated extraction of insights from review data on a daily basis, beyond just sentiment of reviews.
Not just customer feedback. Measure of brand attributes
The client was initially looking at customer reviews to identify and address the negative reviews being received. They lacked a holistic analysis of review data to generate insights about their brand.
With our integrated approach of text analysis based insights from reviews, and correlating the star rating with review text, we are able to identify that:
a. A high proportion of 3 star ratings were negative
b. Every 2 in 5 reviews were on client brand attributes like value for money, trust and reliability
c. Operational issues like delivery and return were associated with certain geography & delivery clusters
The client is able to analyse reviews within a day now and pass on to the specific teams for actions
