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:

1. Data Pulling: Start by pulling data containing conversation content using our Workflow feature.

2. 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.

3. Sentiment Analysis: Employ the "Ask AI about a row" block to classify content into ten sentiment categories effortlessly.

4. Organized Dataset: After analysis, you'll have an organized dataset with columns for "CustomerID," "Message," "Category," "Date," "Reason," and indication of a "Discount Code."

5. Filtering Insights: Apply a filter to focus exclusively on rows categorized as 'Abandonments,' ensuring you prioritize critical insights.

6. Seamless Sharing: Conclude the workflow by incorporating the "Send to Email" block, facilitating easy sharing of insights with stakeholders.