In 2026, the U.S. economic engine is running on complex gears. Between shifting tariff policies, fluctuating interest rates, and a recalibrated global supply chain, producers of consumer durable goods are facing a "signal-to-noise" problem.
To bring clarity to the chaos, Customer Analytics analyzed over 700 variables to isolate the primary drivers of consumer behavior. We’ve distilled these into a predictive model that provides a high-certainty roadmap for U.S. durable goods spending through 1Q 2027.
A breakdown of the six macro-economic drivers that correlate to PCE for durable goods that inform a predictive model for producers of consumer goods.
How to move beyond reactive planning and start anticipating shifts in consumer spending before they hit your P&L.
How we leveraged AI and machine learning to turn real-time data inputs into a high-fidelity snapshot of market demand.
Practical frameworks for applying these indicators to your business across supply chain, inventory management, finance, and marketing.


Director of Strategic Analytics, Customer Analytics
As Director of Analytics at Customer Analytics, Qingtian (QZ) Zhou leads an integrated U.S.–India analytics delivery framework that applies advanced modeling to real-world business and policy problems. His methodology fuses economic logic with computational precision, diagnosing systems from first principles rather than fitting them to inherited models.
QZ holds a master’s degree in applied economics from the University of Minnesota (UMN), where he also pursued a PhD before deciding to focus on real-world data and analytics. Prior to joining CA, he worked in research and development for the highly prestigious IPUMS program at the UMN’s Institute for Social Research and Data Innovation.