How To Use Snowflake Cortex To Classify Customer Sentiment And Categorize Feedback By Theme And Automate It Using SQL

In today’s data-driven world, customer feedback is a goldmine for understanding satisfaction, pain points, and evolving needs. However, processing and classifying thousands (or millions) of open-ended responses is no trivial task. This is where Snowflake Cortex, the AI and ML engine in Snowflake, steps in — allowing us to extract actionable insights from unstructured text using simple SQL.

In this article, you’ll learn how to:

Use Cortex functions to analyze sentiment in customer feedback.
Categorize comments by themes using zero-shot classification.
Automate the entire pipeline inside Snowflake using pure SQL.
Apply results to drive better product, marketing, and support strategies.

What Is Snowflake Cortex?

Snowflake Cortex is a built-in service that lets you apply pre-trained AI models (including LLMs) to your data directly within Snowflake. Key highlights:

Native SQL interface: No Python or external libraries needed.
Use-cases include sentiment analysis, text summarization, zero-shot classification, and embedding generation.
Powered by foundation models like Meta’s LLaMA, Mistral, and more.

You don’t need ML expertise — just SQL knowledge and clean data.

Set Up The Feedback Dataset

Assume you have a table called customer_feedback:

Here’s an example of feedback:

Perform Sentiment Analysis Using Cortex

Snowflake Cortex provides a built-in sentiment analysis function:

Result:

id	feedback_text	sentiment
1	The mobile app is very slow and crashes often.	NEGATIVE
2	Excellent customer service! Helped me resolve my issue…	POSITIVE
3	I love the new features in the dashboard. Very useful.	POSITIVE
4	Your delivery is always late and the packaging is poor.	NEGATIVE

This function returns POSITIVE, NEGATIVE, or NEUTRAL and is very fast — suitable for high-throughput workloads.

Categorize Feedback Themes With Zero-Shot Classification

Next, we want to auto-label each comment with a theme: e.g., "Delivery", "App Performance", "Customer Service", etc.

Use the SNOWFLAKE.CORTEX.CLASSIFY() function:

Sample Output:

id	feedback_text	category
1	The mobile app is very slow and crashes often.	App Performance
2	Excellent customer service!	Customer Service
3	I love the new features in the dashboard.	Features
4	Your delivery is always late and the packaging is…	Delivery

Cortex uses zero-shot learning, meaning the model doesn’t require prior training on your domain. It intelligently maps feedback to the closest label based on semantics.

Automate The Pipeline In SQL Views Or Tasks

To make this scalable and repeatable, let’s build a materialized view:

Or automate it using a Snowflake Task:

sql

CREATE OR REPLACE TABLE analyzed_feedback (

id STRING,

sentiment STRING,

theme STRING,

analyzed_at TIMESTAMP

);

CREATE OR REPLACE TASK analyze_new_feedback
WAREHOUSE = compute_wh
SCHEDULE = ‘1 HOUR’
AS
INSERT INTO analyzed_feedback
SELECT
id,
SNOWFLAKE.CORTEX.SENTIMENT(feedback_text),
SNOWFLAKE.CORTEX.CLASSIFY(
feedback_text,
ARRAY_CONSTRUCT(‘Customer Service’, ‘Delivery’, ‘App Performance’, ‘Features’, ‘Usability’)
),
CURRENT_TIMESTAMP()
FROM customer_feedback
WHERE id NOT IN (SELECT id FROM analyzed_feedback);

This task checks every hour for new feedback and appends results to the analyzed_feedback table.

Visualize Sentiment & Themes

Now that analysis is stored, it can power dashboards.

For sentiment breakdown:

For theme frequency:

You can visualize this in Snowsight, Power BI, or Tableau for dynamic feedback tracking.

Summarize Feedback With Cortex

For long-form survey responses, use:

This will generate a concise version of each long comment — helpful for internal reviews or dashboards.

Testing And Evaluating Accuracy

Although Cortex models are very capable, it’s wise to:

Randomly sample and manually validate results.
Compare Cortex sentiment with a third-party model like AWS Comprehend or OpenAI.
Track misclassifications for tuning (you can include a “Miscellaneous” category or threshold confidence logic in a future enhancement).

Security And Cost Considerations

Cortex is billed per function call, but pricing is relatively modest. You can batch or deduplicate calls to reduce costs.
Data never leaves Snowflake, ensuring security and compliance with PII or sensitive feedback.
You can limit access using Snowflake’s role-based access control to prevent misuse of AI functions.

Building Feedback Loops

With this foundation, you can:

Notify customer success teams on negative sentiment cases.
Flag frequent “App Performance” feedback to engineering.
Export categorized data to Snowflake ML or external analytics tools for churn prediction or feature roadmap planning.

You can even push this into a chatbot or GenAI interface, letting product managers query:
“What are customers saying about delivery this month?”

Conclusion

Customer feedback is often trapped in messy spreadsheets or ignored emails. With Snowflake Cortex, you can turn raw comments into real-time business intelligence — all with the power of SQL.

To summarize, you’ve learned how to:

Classify sentiment in feedback using CORTEX.SENTIMENT.
Label themes using CORTEX.CLASSIFY and zero-shot learning.
Build automated, scalable pipelines with tasks and materialized views.
Visualize and act on this intelligence in downstream systems.

This requires no Python, no ML pipelines, and no external APIs — just your Snowflake data warehouse and a few Cortex functions.

As AI becomes increasingly integrated into modern data platforms, tools like Cortex are democratizing advanced analytics. Whether you’re a data engineer, analyst, or product manager, you now have the tools to unlock insights from text like never before.