April 09, 2025
For many retailers, quickly understanding customer behavior, optimizing inventory and responding to market trends is essential. Yet, many face challenges with fragmented data sources, making it hard to gain a comprehensive view of their business. Traditional reporting methods are often slow and lack the flexibility needed for real-time decision-making, leading to missed opportunities, inefficient operations and reduced profitability.
Google Cloud’s Conversational Analytics API addresses these challenges by allowing business users to make ad-hoc data queries through natural language—no coding required. With self-service analytics at their fingertips, users can make faster, more informed decisions.
What is the Google Cloud Conversational Analytics API?
The Conversational Analytics API offers a natural language interface that lets users query data stored in BigQuery and Looker using conversational input. This means users no longer need to write complex SQL or navigate intricate dashboards. Instead, they can ask questions like, “What were our top-selling products last quarter?” and get quick, accurate answers.
The API powers conversational analytics agents capable of interpreting natural language inputs through:
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Generative AI with agentic functionality: Using tools like Google Agentspace, AI models can autonomously act on user input, generate reports, answer advanced queries or proactively offer insights.
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Looker’s semantic modeling layer: This standardizes business metrics and definitions, improving the accuracy of natural language queries. It ensures AI understands user intent while simplifying the complexity of reasoning.
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Extensible API architecture: Developers can integrate conversational analytics into custom applications—building chatbots, embedding into platforms or crafting new experiences tailored to business needs.
Semantic layers enhance query accuracy
A key feature of the API is its semantic layer, which acts as a bridge between raw data and business users. It enhances query accuracy and context by translating technical data into business-friendly language.
The semantic layer helps:
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Translate technical terms: For example, converting ‘cust_id’ into ‘Customer ID’
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Define relationships: Establishes links between tables like orders, customers and products
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Standardize metrics: Ensures consistency in key calculations, such as revenue
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Improve accuracy: Acts as a governance layer, reducing data errors in generative AI queries. Internal testing shows that Looker’s semantic layer cuts data errors by two-thirds compared to ungoverned tables. This is especially valuable for complex queries involving multiple joins and calculations.
Want to learn more?
The Google Cloud Conversational Analytics API is transforming how businesses interact with data. By merging natural language input with powerful tools like BigQuery, Looker and generative AI, it enables real-time insights and agile decision-making — without a single line of code.
To see this in action, visit the Valtech booth #1870 at Google Next for a live demo. Discover how conversational analytics can elevate your retail strategy and unlock competitive advantages.
