Product & Concept Testing

If Peter L. Berger and Thomas Luckmann had been product managers instead of sociologists, they might have observed how we don’t discover reality in testing; we co-construct it with others. The stimuli you show, the task you set, the audience you recruit—together they create the new “reality” your team will deliver. The advent of AI, virtual prototyping, and synthetic data adds another dimension to the construction of testing reality: “How real is real enough to be predictive, and what threshold of realism optimizes the trade-off between investment and quality?”

These advances present tantalizing opportunities not only to virtualize products and concepts, but also to simulate potential purchasers and users. If this sounds like a bit too much like futuristic sci-fi, consider how these innovations address the all-too-mundane realities of today: how to create new products faster, cheaper, and better. “More” would be good, too. 

Along with thrilling new opportunities, product and concept testing technology also streamlines the familiar across types of offerings, from physical to digital, and across the lifecycle, from early concept screens to packaging and positioning through post-launch optimization, and even product retirement. No matter where you are in the development cycle, understanding emerging innovations will help you choose the tools and platforms that most improve your situation.

Consider recent developments such as:

• AI-assisted concept screening for early winnowing
• Synthetic data augmentation for low-incidence targets
• Conjoint/MaxDiff plus behavioral sims to go beyond stated intent
• Hybrid qualiquant such as video open-ends and emotion/attention signals
• IHUT logistics at scale and governance for privacy and IP protection

Don’t take reality as a given – reimagine your product and concept testing workflow and produce a better one. 

See for yourself how you can leverage new technology to accelerate from idea to evidence—without making customers your de facto QA department.

Who should attend?

• Leaders under pressure to ship faster without raising failure risk—and willing to trade polish for predictive signal when it’s justified.
• Teams debating suite vs. specialized stack, needing apples-to-apples ways to compare platforms on outcomes, not buzzwords.
• Researchers wrestling with data quality, low-incidence audiences, or sensitive IP, and seeking guardrails for AI/synthetic augmentation.