About the Customer
Industry: Retail & Manufacturing
Based in the USA, the client is a leading Fortune 500 manufacturing company, with net revenue of more than $60 billion and a plethora of around 23 manufactured products that create more than $1 billion each in yearly retail sales. It has multiple retail stores across the country.
Key Challenges
To serve customers in a better way, the company wanted to understand customer feedback on newly launched products from social media platforms like twitter. They are looking for in-depth analysis like: –
- Whether customers really liked the product.
- Whether the customers preferred the company’s product over other products.
- Whether the preferences changed over time.
The challenges they encountered while collecting customer feedbacks are as below: –
- The company focused on surveys which were collected periodically from the Sales Executive and Marketing campaign on twitter
- The information was in the form of thousands of individual texts and posts per year on Twitter Thus, digging out refined data was difficult.
- The company were not able to find
- Whether the text is about the company’s product or competitor’s product.
- Is the post about the consumer’s expectation or is the review about real experience from the product?
Basically, the company wanted to know how happy their customers are and what they can do to perk up with their issues.
Our Solution
After understanding the company’s problem, SPEC INDIA came up with the solution of Sentiment Analysis in R.
SPEC worked along with the customer to analyze the marketing campaign, feedback data and the tweets related to the products along with competitors’ brands.
Once the appropriate text was identified, sentimental analysis and visualization were performed to determine whether the text was positive or negative.
To come up with the best result, SPEC: –
- Converted tweet to a text format
- Combined the text for different brands with the same category of products
- Calculate the sentimental scores for the tweets
- Identify positive, negative, and neutral sentimental
- Developed visualization – Tag Cloud, Comparative Analysis
- Data factors considered – Landing Page, Converted, Group and Time Stamp
Tools & Technologies
Business Benefits Of Twitter Sentiment Analysis Using R
- Monitors the product and brand performance
- Optimize, evaluate the efficiency and measure the ROI of the marketing campaign
- Help the company to understand the customer’s need
- Enable to develop more insightful, data-based marketing strategy
- Reduce customer churn
- Track the overall customer satisfaction
- Boost sales revenue