AI Customer Analytics in 2025: Your Next Competitive Advantage

The next generation of customer analytics is here. With rising customer acquisition costs and intensifying competition, businesses are looking to AI to leverage hidden opportunities to reduce churn or expand revenue in existing customer data. Every month, your company generates hundreds to thousands of customer touchpoints: support calls, product interactions, survey responses and service tickets that collectively tell the story of your customer relationships. The challenge isn’t finding new data but rather unlocking intelligence already flowing through your business every day. 

Every customer interaction holds signals about satisfaction, emerging trends and expansion opportunities, but aggregating signals for true insight can remain elusive. Modern customer journeys span multiple channels, platforms and touchpoints, creating a web of interactions that traditional analytics infrastructure struggles to capture and analyze in real time. Until now, customer insights at scale required specialized data science skills, complex tools and long development cycles. 

The opportunity is to move customer success teams from reactive problem-solving to proactive action. The result? Teams can shift from building complex systems to building customer relationships. 

TS Imagine saved 4,000 annual hours and reduced AI costs by 30% by automating manual email processing and improved customer support workflows by categorizing tickets based on urgency and complexity.

From lagging to leading differentiation

It’s hardly a new concept for most companies to possess some version of customer analytics and sentiment analysis. However, the real differentiation today is the speed and agility with which your teams can access information and action spikes in customer churn or unexpected sales within a new market segment. Much of that speed is down to how quickly your teams can transform unstructured data at scale.  

For most businesses, the process to understand feedback requires a complex process of combining disparate data sources and using different AI tools to transcribe audio files, analyze sentiment and generate summaries before extracting meaningful insights. Now, you can simplify this to a few lines of code. The best part? Getting to production-ready solutions can be done in just days. 

Next-generation customer insight

Let's consider post-call analytics — a common customer intelligence use case for transforming recorded customer conversations into business intelligence on customer requests and complaints. 

Step 1: Efficiently transcribe calls at scale

The starting point for any post-call analytics workflow is converting audio into analyzable text with high quality, especially at massive scale. To provide some context, an enterprise customer supports operations that can take in over 100,000 calls per week, spanning an average of 30 minutes, that are often stored in object storage such as Amazon S3, Azure Blob Storage or Google Storage Services. That is a total of 3 million minutes of content that you need to easily and efficiently process each week before you can begin running any additional analytics.

To address this at scale, we built AI_TRANSCRIBE (in public preview), a SQL-native speech-to-text AI operator that allows for high-quality audio transcription, automatic speaker identification and word-level time stamps for audio files up to two hours in length. Not only is AISQL AI_TRANSCRIBE easy to use, but it also delivers comparable quality and better latency to popular commercial systems such as AWS Transcribe.  

The fully managed AI operator can process files directly from object storage with no data movement and no infrastructure management required — simply point to your audio files and get structured, ready-to-analyze text using SQL.

One of the UK’s largest tour operators is able to analyze over 2,000 calls a day to find customer intent.

Step 2: Access industry-leading quality for customer analytics

Once you have transcribed text from customer calls, the next step is understanding not just what customers are saying, but how they feel and what patterns emerge across thousands of conversations. Using Cortex AISQL, you can easily build AI transformation pipelines to get high-quality sentiment analysis, translate and categorize different calls and summarize key themes.  

To help understand customer sentiment and satisfaction levels, we built AI_SENTIMENT (generally available). AI_SENTIMENT is an AISQL task-specific operator purpose built to deliver state-of-the-art overall and granular aspect-based sentiment analysis across diverse content in different languages. It’s perfect for global businesses with customers across multiple markets. AI_SENTIMENT is fine-tuned to understand English, French, German, Hindi, Italian and Portuguese, preserving the context and nuance of what is said that would otherwise be lost in translation. 

Beyond simple positive or negative sentiment classification, AI_SENTIMENT provides granular aspect-based sentiment analysis that reveals how customers feel about specific aspects of your product or service. A single call transcript can show mixed sentiment about product quality while revealing negative sentiment about pricing or support response times, enabling precise, targeted interventions. 

To make it easy to translate, categorize call issues and summarize themes across thousands of transcribed conversations, we built a suite of scalable AISQL operators such as AI Translate (generally available), AI_CLASSIFY (public preview) and AI_AGG (public preview). 

Many businesses first localize transcripts using Cortex AI Translate to convert conversations into their primary business language before performing additional analysis. Then they use AI_CLASSIFY to automatically categorize calls to route them into the right product teams or label escalation levels. For example, you can classify calls such as [‘billing_issue’, ‘product_bug’, ‘feature_request’ or ’escalation] into categories. 

Finally, AI_AGG can synthesize insights and themes across thousands of categorized call transcripts like a SQL aggregation function. For example, AI_AGG enables organizations to easily “summarize the top billing issues by severity level” or “identify escalation themes by call category.” Whether identifying root causes of problems, extracting top feature requests by product area or highlighting satisfaction drivers, we can analyze in more detail using AI_SENTIMENT, and AI_AGG helps transform call transcripts into actionable business intelligence. 

RAC was able to reduce call handling times by 30% and improve insight turnaround time from months to two to three days.

Compounding benefits of customer analytics

Improving customer analytics creates a compounding effect as insights flow across teams — this drives higher retention and expansion revenue while generating more data for even deeper insights. 

• Product: Identify feature gaps, prioritize development roadmaps based on actual user pain points, and proactively address issues before they impact broader customer satisfaction and retention.
• Marketing: Understand what resonates most with customers, refine messaging based on real user language and pain points, and create targeted campaigns that address specific customer concerns while highlighting successful product experiences.
• Sales: Find upsell opportunities, understand objections before they arise, and provide prospects with relevant case studies and solutions based on real customer success patterns and resolved pain points.
• Data science teams: Build more accurate predictive models for churn and expansion, discover hidden patterns in customer behavior across the entire lifecycle, and create data-driven features that directly address the most impactful customer opportunities.

Reduce time to insight to action to customer value

Building customer analytics on Snowflake fundamentally transforms how your teams allocate their time and directly impacts your bottom line. By accelerating the journey from raw data to actionable insights, you can shift from delayed reactions to proactive interventions, develop more accurate predictive models and streamline operations with measurable effects on both revenue growth and customer satisfaction. This transformation enables your customer teams to evolve from reactive problem solvers to strategic revenue drivers, turning frustrated customers into loyal advocates while ensuring your technology investments deliver tangible returns. The companies that embrace this shift today will build the customer relationships that fuel tomorrow's growth, creating a sustainable competitive advantage in an increasingly crowded marketplace.

Ultimately, the less time you spend aggregating insights manually means you can spend more time on what matters for your business: building relationships, identifying opportunities and making informed decisions on real customer intelligence. Snowflake’s Cortex AI platform reduces the barriers for data and technology teams by simplifying AI-powered transformation across multiple data types, inconsistent formats and quality and evolving privacy requirements. 

Build call center analytics or get started with multimodal analytics now on Snowflake.