Skullcandy leveraged Qlik AutoML and NLP to improve product quality, reduce returns, and control costs. Faced with limited data and no in-house data scientists, they used Qlik's predictive models and text analysis to forecast failure rates, identify problematic components, and improve product design pre-launch. Real-time review analysis enabled proactive issue resolution, while insights into retailer return discrepancies highlighted cost variances. These tools provided actionable insights, streamlined processes, and positioned Skullcandy to expand predictive analytics across departments for broader impact.

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