Conversational Analytics in Looker: Reinventing Sales Intelligence: The Product Insight Agent

As business intelligence evolves, organizations are transitioning from static dashboards to dynamic, AI-driven insights. By leveraging semantic models, Looker conversational analytics, and Looker’s governed data foundation, the Product Insight Agent enables sales and marketing teams to enhance performance visibility — from operational reporting to predictive insight generation.

Conversational Analytics – Overview

Looker Conversational Analytics transforms how business teams interact with data. Instead of navigating dashboards or writing queries, users can submit natural-language questions and receive governed analytical responses. Within Looker AI agent, users can access the Conversational Analytics interface, select the relevant Explore, and engage an AI Agent that understands business context and data structure.

For example, users may query:

• A. “Display sales by product category for the past six months.”
• B. “Compare monthly revenue for top-performing brands.”

The system interprets the request, applies appropriate governance and data logic, and delivers structured insights — including visualizations and narratives — in near real-time. This enables faster decision-making and reduces reliance on technical expertise.

Product Analysis — “Inside the Growth Journey of Trend Chart”

Consider a retail organization operating in the fashion and lifestyle sector. A business analyst evaluates performance trends to support sales, supply chain, and category leadership teams. The following key questions guide insight generation:

1. Last Six Months Sales

Question: What are the sales figures for the last six months?

Use / Purpose:

• A. To analyze sales performance over the last six months and identify the top-performing months.

2. Distribution Centers Managing in Last 6 Months

Question: What are the top 5 distribution centers by total sales in the last six months?

Use / Purpose:

• A. Track distribution efficiency and location performance.
• B. Helps logistics and supply chain teams manage high-demand products

3. Top Product Categories in Leading Distribution Centers

Question:Among the top 5 distribution centers from the last six months of sales, which three product categories recorded the highest sales?

Use / Purpose:

• A. Helps in strategic product allocation, demand forecasting, and optimizing category-level inventory planning across high-performing centers.

4. Total Sales by Month and Gender :

 Question: Visualize total sales over the last five months, grouped by month and categorized by gender.

Use / Purpose:

• A. To identify sales trends or seasonal patterns in male vs. female categories.
• C. Supports inventory and marketing decisions for targeted campaigns.

Conclusion: Power of Conversational Analytics

Evaluating six-month sales data enables organizations to identify high-performing periods, leading distribution centers, and top product categories. Gender-based trends further support targeted planning and resource allocation.

Looker Conversational analytics transforms the way teams interact with data. Instead of spending hours navigating dashboards, applying filters, and interpreting charts, users can now simply ask questions in natural language and receive instant, insight-rich answers.

By combining AI-driven understanding, dynamic visualization, and contextual explanations, conversational analytics bridges the gap between data and decision-making — enabling organizations to move from reporting to real-time intelligence.

In short, it helps users:

• A. Get faster insights from complex data,
• B. Ask deeper follow-up questions naturally, and
• C. Act quickly on business opportunities and performance issues.

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