Building High-Quality Data for Personalised AI Fitness Product Recommendations

Korea's fitness industry is thriving. With over 4.2 million people actively engaged in fitness and bodybuilding, and an athleisure market exceeding KRW 7 trillion (approximately USD 5 billion), the opportunity for innovation has never been greater. Yet despite this growth, most platforms still struggle with a fundamental challenge: connecting users with products that genuinely match their fitness goals and lifestyles.

A leading fitness recommendation platform in Korea recognised this gap and saw an opportunity. They envisioned an AI-powered service that could deliver truly personalised product suggestions, from supplements to gym wear, based on real user behaviour and preferences. But personalisation at scale requires one critical ingredient: high-quality training data that accurately represents how people think, train, and shop. That's where Tictag stepped in.

The Challenge: From Vision to Validated Data

Building an effective AI recommendation engine isn't just about collecting data, it's about collecting the right data. The platform needed structured, machine-learning-ready datasets that captured genuine fitness preferences, workout habits, and purchasing motivations across diverse user segments.

Our Approach: Quality Over Quantity

Tictag designed and executed a targeted data collection campaign focused on capturing authentic consumer insights:

Data Collection Results:

• Gathered over 1,500 structured responses about fitness preferences, habits, and product needs, 158% above the initial target
• Validated and cleaned 1,037 high-quality datasets (approximately 65% validation rate), ensuring every data point met strict quality standards
• Structured responses to reveal meaningful correlations between lifestyle factors, workout goals, and purchasing behaviour

Every dataset was human-verified by our network of expert Taggers across Asia, ensuring cultural accuracy and real-world reliability, critical factors for AI models serving the Korean market.

The Impact: Data That Drives Results

The results speak for themselves:

• Smarter AI Recommendations: The platform's recommendation engine now delivers more accurate, personalised fitness product suggestions tailored to individual user profiles and goals.
• Enhanced User Experience: By combining AI-powered recommendations with community feedback, the platform creates a trusted environment where users discover products that genuinely work for them.
• Actionable Marketing Insights: The structured data revealed new audience segments and purchasing patterns, helping fitness brands identify untapped opportunities and refine their targeting strategies.

From Raw Data to Real Results

This partnership demonstrates what's possible when quality data meets AI innovation. Tictag didn't just collect responses; we delivered validated, structured datasets that empowered our client to build an AI recommendation system that their users actually trust.

In the competitive fitness technology space, the difference between good and great recommendations often comes down to the quality of your training data. When your AI understands what users truly want, everyone wins: platforms deliver better experiences, brands reach the right customers, and users discover products that help them achieve their goals.

Ready to build your next high-quality AI training dataset? Whether you're developing recommendation engines, computer vision models, or conversational AI, Tictag delivers the human-verified data your models need to perform.

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