Brand Data Strategy Guideline 2025 | How to Use AI for Market, Consumer & Competitor Analysis

Here's how you should be doing market, consumer, and competitor analysis from now on!

"Which products are customers responding to more right now?"

"In what ways are competitors' products better than ours?"

"Can these reviews alone tell us how every customer feels?"

These are questions nearly every brand has wrestled with at some point.

After all, market, consumer, and competitor analysis is essential to a brand's growth and survival.

But there aren't many clear guides on what data to collect, how to analyze the data you've gathered, and where to put those results to use.

Over the past two years, DALPHA has worked alongside more than 200 brands to solve exactly these challenges in the field. We've poured that experience and know-how into this guideline, offering an even more advanced approach.

• Repetitive data collection and analysis can now be handled by AI. Monitoring competitor prices every day and compiling reviews into reports is no longer work that people need to do.

• Instead, with recent advances in AI technology, we've entered an era where AI can perform even the insight extraction and interpretation once thought to be something only humans could do. AI analyzes thousands of reviews to grasp the flow of consumer reactions, identifies points of differentiation from competitors, and even suggests marketing messages and product improvement directions.

Even at this very moment, the gap between brands that leverage AI and those that don't keeps widening.

Secure a leading edge with the <Brand Data Strategy Guideline 2025>.

※ Available for download at the bottom of the page.

Here's what you'll find in the guideline

1. Brand strategies that get ahead in the AI data era

   1. The rise of LLMs and AI Agents, and the transformation of brand business

   2. Where does the key to a brand's expansion and success lie?

2. 'The era of reading 'feelings' as numbers has arrived

   1. There are many places that analyze performance - performance data

   2. Few places analyze the reasons - customer/product data

   3. Why are brands failing to make use of it?

3. What you analyze becomes your strategy

   1. SNS data - in search of the very latest trends

   2. Open community data - in search of the raw voice of consumers

   3. Online news and media articles - in search of the external environment and competitive trends

   4. Open markets and commerce platforms - in search of real market and consumer reactions

4. Hands-on data collection strategies: How do brands secure their data?

   1. Manual methods

   2. Leveraging platform business centers/data centers

   3. Leveraging official APIs

   4. Direct crawling

   5. Combined collection strategy - a TikTok ad performance analysis case

5. From data analysis and interpretation to real-world application

   1. Setting the direction of analysis: structured vs. unstructured

   2. What comes after analysis matters more: automated insight extraction and reporting

   3. The importance of specialized design

6. Use cases across the beauty, fashion, food, and living industries

7. Checklist: Is our organization good at data analysis?

Here's a sample of the guideline.

Download the guideline for your brand's growth

👉 Download the Brand Data Strategy Guideline 2025

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※ This guideline was created by the DALPHA team, which helps companies adopt and leverage AI.

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