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Sentiment Analysis for E-commerce
Analyzing customer conversations is pivotal for understanding sentiment and improving business strategies.
Our Workflow feature streamlines this process, making it easy to derive valuable insights.
Here's a step-by-step guide:
Data Pulling: Start by pulling data containing conversation content using our Workflow feature.
Data Structuring: If your data isn't pre-structured with each conversation in a single row, utilize the "Ask AI about dataset" block to structure it accordingly. Instruct AI to extract essential fields like CustomerID.
Sentiment Analysis: Employ the "Ask AI about a row" block to classify content into ten sentiment categories effortlessly.
Organized Dataset: After analysis, you'll have an organized dataset with columns for "CustomerID," "Message," "Category," "Date," "Reason," and indication of a "Discount Code."
Filtering Insights: Apply a filter to focus exclusively on rows categorized as 'Abandonments,' ensuring you prioritize critical insights.
Seamless Sharing: Conclude the workflow by incorporating the "Send to Email" block, facilitating easy sharing of insights with stakeholders.