Case Study: Skullcandy forecasts returns and reduces warranty costs with Qlik AutoML from Qlik

Skullcandy Using Qlik AutoML to get insights on returns and warranty records

Skullcandy, the lifestyle audio brand, wanted to proactively reduce returns and warranty costs by predicting early product failures and identifying which parts or functions were likely to fail. Facing thousands of warranty records and customer reviews, limited in-house ML expertise, and inconsistent data that made an initial 90-day launch forecast impractical, they needed a quick, affordable way to analyze large volumes of text and manufacturing data.

Partnering with BigSquid.ai and using Qlik AutoML plus a Python-based NLP pipeline, Skullcandy built models to forecast monthly returns by product and extract common failure themes from reviews and claims, feeding results into filterable dashboards. The solution enabled them to identify failing parts and factory correlations before market release, detect issues in real time, reconcile retailer return variances, reduce return-related costs, and plan to extend predictive analytics across the business.

Skullcandy

Mark Hopkins

Chief Information Officer