Flash Forward: Flash.AI™ is Changing the Game in Pack Design Testing

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It has been just 10 months since we launched Flash.AI™ – a revolutionary way to test pack designs leveraging powerful AI image recognition technology and Behaviorally’s unrivaled database of consumer behavior data on pack design metrics – and clients love it!

We have tested  1,000+ designs across 60 categories globally in a matter of minutes each time. And up to 85% of these times, Flash.AI™ accurately predicted pack design metric scores. It is cost-effective and time-efficient.

Brands get clear direction on early-stage designs, a pulse on the competitive landscape, or benchmark designs in new or adjacent categories.

Flash.AI is blazing a path towards the future of design testing. Here are a few examples of the solution in action:

Early-Stage Design Screening

  • A new-to-market bottled water brand saved 4 weeks of pre-screening research by reducing from 9 designs to 4 for further development. Each of the designs was found to meet the “taste appeal” metric and a winner emerged through our PackFlash® validation tool.
  • A Salty Snack brand in a pack redesign sprint saved 8% in design testing expenses while sharpening its focus to two lead contenders that outperformed expectations on the “visibility on shelf” metric.
  • An OTC consumer drug company used Flash.AI as the fast-filtering step, reducing 6 pack design choices to 4. Rich diagnostics did identify low performance on several key metrics. Further qualitative research helped refine the pack designs and a quick validation with PackFlash improved performance of the pack that is currently in-market.

Competitive Intelligence

  • An intimate care brand saw its closest competitor “beautify” their pack for a more upscale look. The Flash.AI personal care model provided fast and inexpensive guidance to inform a business case for their design strategy. With up to 85% accuracy, Flash.AI clarified where the competitor achieved a “better than other brands” score, proving a business case for a redesign.
  • A dominant brand in the packaged food category saw competition mounting from Private Label brands. The brand needed evidence of pack design impact versus stores-owned brands and Flash.AI metrics predicted “value perception”, “high quality”, and “trust” as key metrics for success. Results identified the packs that worked and where private-label pack designs made them vulnerable.
  • An iconic beverage brand saw competition from the trend of natural soda brands. Variants of the current packs were tested against the competition to identify the most compelling design. In a matter of minutes, Flash.AI confirmed all potential designs outperformed natural soda competitors on key consumer benefits such as “taste appeal” and “best brand”. The key directional guidance identified was the need to “stand out better on shelf” for the next phase of design.


  • An oral care brand was restaging its packaging. They used Flash.AI to gather learnings and benchmark the current packaging design relative to the competition. The benchmarking results demonstrated that the brand clearly owned significant consumer benefits relative to competitors, including “quality”, “value”, and “trust”. Further qualitative research identified which visual equities to keep and enhance for visibility.
  • A traditional dairy spreads brand had introduced a plant-based line extension leveraging PackFlash. A few months later, the company decided to enter an adjacent category using a similar design. Flash.AI provided fast, accurate, and cost-effective metrics confirming design strength against competitors on shelf in the new category.
  • A leading home improvement brand was beginning its packaging redesign initiative. The company needed to benchmark its product against the leading competitors, understand the strengths and limitations of its existing designs, and validate the role of the pack in influencing purchase behavior. They used Flash.AI in the first phase of work to establish the elements that must be kept stable and those to change. The initiative saved four weeks in the design sprint and 30% of the cost of traditional research.

Brands are rapidly adopting Flash.AI as the preferred method of early pack screening and cost-effective measurement of pack potential to influence shopper choice.

Contact us today to learn how you, too, can harness the power of AI image recognition to improve your odds to drive shopper growth!


Nisha Yadav is the Senior Vice President, Omni Shopper Lead at Behaviorally. She has over 15 years of experience as a marketing strategist offering insight-led, brand, product, and customer strategy to storied brands. Before joining Behaviorally, Nisha’s career included progressive roles leading marketing strategy teams within the WPP network.  

Connect with her on LinkedIn.

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