Steve Jobs and Apple famously proved, with the introduction of iPhones and iPads, the successful strategy of telling consumers what they need rather than asking them what they want. But, does this same axiom hold true when “improving” the design or formulation of existing products that consumers currently purchase, use and love?

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Artificial Intelligence is becoming common vernacular these days in general business as well as marketing research. But, a quick Google search of “Artificial Intelligence and Market Research” reveals that AI has been primarily relegated to data analytics.

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Getting pressure to do more research with less money? What if a simple methodology could save your company between 50-70% on quantitative insights spending while maintaining customization for individual marketing research needs? Well, good news, because such a methodology exists and has proven valuable for many forward-thinking companies...

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So you’re eagerly anticipating receiving scores from your latest marketing research study which you’ll use to guide a series of next step decisions to get your product to market.  But, have you thought about what benchmarks you’ll consider to help you UNDERSTAND whether you should “go” and move forward, or “no-go” and head back to the drawing board?

So, at the risk of stirring up an serious geek-debate among my fellow researchers...

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At Diagnostic Measurement Group, we like to shout loudly, whenever the spirit moves us, “Product performance is king!”  Then, we just roll with the awkward silence and stares that follow from non-marketers.  It’s because we believe deeply that a brand can compel consumers to trial, but ultimately, it is the product experience that determines repeat purchase and supports a sustainable business.

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If you’ve ever conducted consumer marketing research, chances are good you’ve had to decide whether to evaluate your test stimuli via monadic or sequential-monadic exposure.

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