A retail brand utilized supervised learning approach to extract opinion in social posts.
The retail brand has a very active and engaged fan base on Twitter and Facebook. They keep the fans engaged with regular fun and participative videos and social posts. The users themselves create a lot of posts discussing various offers, events, products and brands. They also provide some feedback on products like comfort, pricing and sometimes tag competitors in their posts. The client decided to organize the user generated content to generate actionable insights.
User generated content is an insights goldmine
The client used social media mostly as a customer service platform addressing user complaints. They now use social media for valuable insights on their customers.
- The brand and its top competitors have no difference in sentiment distribution
- Negative sentiments are associated with variety and price
- There are significant number of posts mentioning more than one brand (36%)
The analysis was setup as a monthly report that enables the client to monitor opinion over time and analyse the impact of corrective strategies.
High volume. High velocity.
The retail brand is doing a good job engaging users with participative and fun posts. They have an active fan base, who share their opinion and tag the brand. The retailer was making limited use of the data being generated on social media to address user complaints and queries. Given the high volume of social posts, it was impossible to look at them manually, and a technology driven solution was needed to extract the social posts and extract sentiment.
A big challenge in setting up a sentiment mining solution was the volume of data being generated on a daily basis. The client has a limited need for a state of the art data Infrastructure till date, as they mostly dealt with sales and marketing data. They were not big on digital marketing.
With the shift in focus on social media data driven insights, they opted for a cloud based solution.
He, who has a longer bait catches the bigger fish.
Sentiment analysis helps extract the opinion and polarity of emotions from social media posts.
With state of the art supervised learning approach, the retail brand is not able to extract valuable insights including:
a. A better understanding of their brand strength and where they outperformed competitors
b. Insights on negative opinions associated with price and the variety of product offerings
c. Insight into the path to purchase and how users compared similar products from competitors to make a purchase decision
Sentiment analysis is the first step in using social media to generate user level and aggregated insights.