02/09/2026

A Brand Manager is preparing for a quarterly business review and needs clear answers to seemingly simple questions:

• How is my brand performing compared to competitors?
• Has our market share improved in the premium segment?
• What is driving the recent drop in customer satisfaction?
• How do we compare in price perception versus Brand B?

The answers exist. They are somewhere in the organization.

In brand tracker studies.
In retail audit reports.
In pricing analyses.
In consumer research decks.
In Excel sheets from sales.
In PowerPoint presentations from research agencies.
In archived PDFs on the internal document repository.

The challenge is not the lack of data.
The challenge is finding, connecting, and interpreting it fast enough to act.

From the outside, brand management appears strategic and creative. Internally, however, a significant portion of time is spent on information retrieval and consolidation.

A typical analysis cycle looks like this:

• Searching the internal document repository for the latest research wave.
• Opening multiple versions of similar files.
• Comparing numbers across different reports.
• Copy-pasting charts into one summary presentation.
• Calling Consumer Insights colleagues to confirm methodology differences.
• Re-checking if newer data exists.

Even answering a straightforward question like:

“What attributes are most strongly associated with our brand this year?”

may require reviewing three separate studies and manually reconciling the findings.

More complex questions demand even more effort:

“How has our brand performance evolved over the last three years compared to key competitors, and what has influenced these changes?”

Answering this means:

• Extracting historical market share data
• Reviewing pricing shifts
• Checking promotional intensity
• Analyzing brand perception metrics
• Linking them to category trends.

This process can take hours, sometimes days.

By the time insights are consolidated, the market has already moved.

To address this bottleneck, we implemented an AI-powered Research Information Management Agent, a conversational assistant designed specifically for marketing teams.

The purpose is simple:
Centralize all research knowledge and make it instantly accessible through natural language.

Instead of searching for files, brand managers ask questions.

Instead of navigating folders, they receive unified answers.

Instead of manually connecting insights, they see the full picture in seconds.

Let’s return to our Brand Manager (BM) preparing for the review.

BM opens the AI assistant and types:

“What attributes are currently most associated with our brand in the Croatian market?”

Within seconds, the AI responds with a concise summary:

• top three brand associations
• comparison to the previous year
• benchmark versus key competitors
• direct links to source reports.

BM continues:

“How has our brand performed compared to brand X over the past three years?”

The system analyzes market share data, price positioning, and customer satisfaction metrics across studies and returns:

• a performance comparison trend
• key inflection points
• identified drivers (price repositioning, distribution expansion, campaign impact).

Finally, BM asks:

“What influenced the drop-in customer satisfaction in Q2?”

The AI connects survey findings with product complaints, pricing adjustments, and competitor activity.

In less than 15 minutes, BM has a data-backed narrative ready for leadership discussion.

Behind the simplicity of a chat interface lies a robust data foundation powered by Snowflake.

Snowflake enables the marketing organization to move from document storage to intelligence management by:

• centralizing thousands of research files (PDF, Word, Excel, PowerPoint)
• integrating structured and unstructured data into one governed environment
• scaling seamlessly as research volume grows
• allowing AI models to analyze data securely where it is stored.

Instead of exporting files into separate AI tools, Snowflake ensures that AI operates directly within the enterprise data environment - securely, reliably, and with full access control.

This transforms Snowflake from a storage solution into a strategic intelligence platform.This includes:

• Orchestration Migration – Moving from monolithic ETL/ELT tools to modern frameworks such as Apache Airflow or Dagster.
• Modern Data Ingestion – Replacing legacy tools with modern services that add value through CDC (Change Data Capture).
• Logic Refactoring – Gradually extracting logic from outdated tools so every step creates new business value.
• Data Governance – Establishing ownership, quality standards, and mapping data activities to measurable business outcomes.

Beyond technology, the company also acts as a facilitator of change - breaking down silos and building a common language between business, application, and data teams.

The impact is tangible:

• Time to insight drops by over 90% - hours of searching become seconds of reading.
• Decision-making accelerates - campaigns can be adjusted in near real time based on fresh insights.
• Analysis depth increases - cross-study comparisons that were previously too time-consuming become standard practice.
• Insights become easier to communicate - the solution can be extended with visualization capabilities, allowing results to be translated into clear and intuitive visual formats. Brand managers can quickly generate structured, presentation-ready outputs for leadership or external partners.
• Employee satisfaction improves – brand managers focus on strategy, positioning, and creativity, not file management.

Perhaps the most significant shift is how time is allocated. Marketing discussions shift from: “Do we have that data somewhere?” to “What does the data tell us, and what should we do next?”

Previously, the process looked like this:

• 80% data preparation
• 20% analysis, presentation, and strategic discussion

Now, the balance is reversed:

• 20% preparation
• 80% insight generation, interpretation, and communication

Brand managers spend less time searching and consolidating, and more time understanding what the data means for their brand and how to act on it.

The result - marketing discussions evolve from operational updates to strategic conversations focused on growth.

This solution delivers value across multiple dimensions:

• operational efficiency through automation of data procurement
• cost optimization without increasing analytical headcount
• improved cross-functional alignment between marketing, sales, and the research team
• and stronger competitive responsiveness.

It demonstrates how modern data platforms are evolving.

With Snowflake as the foundation, AI agents become embedded into daily workflows - not as experimental tools, but as practical business enablers.

Marketing teams are not struggling because they lack research.
They are struggling because research is fragmented, manual, and slow to operationalize.

By combining an AI Research Information Management Agent with the scalable, secure infrastructure of Snowflake AI Data Cloud, organizations transform thousands of documents into a living, conversational intelligence system.

The result is simple but powerful:

Faster insights.
Better decisions.
Stronger brands.

And a marketing team is finally able to focus on what it was hired to do - build growth.