Call Center Analytics: Real Workflows and Insights

An e-commerce product analyst needed to extract specific product attributes like battery, screen, sound, durability, price, and usability from unstructured Amazon review text. The resulting dashboard visualizes automated extraction results, transforming raw text into structured sentiment signals to overcome manual extraction struggles and junk reviews. A stacked bar chart reveals the polarity of these mentions categorized into positive, neutral, and negative, showing that while price and usability drive high positive feedback, durability shows a disproportionately high ratio of negative mentions.

What it shows:

Provides a structured view of product performance independent of unreliable star ratings.

A social media analyst uses this dashboard to transition from a universal posting schedule to platform-specific engagement windows. The centerpiece is a heatmap plotting average engagement rates across days and hours, with explicit annotations marking a Friday morning peak and a Saturday evening dead zone. By consolidating these variables into a single view, the dashboard solves the problem of fragmented spreadsheet pivot tables, providing concrete evidence that optimal timing is a secondary lever highly dependent on the specific platform.

What it shows:

Delivers documented evidence for quarterly planning by identifying optimal engagement windows.