AI-driven insights for innovative, never-before-seen products

"AI is just based on existing data. How can it generate useful insights for a new product? Surely this can’t work for my upcoming product; there isn’t anything like it in the market."
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When we speak to our customers, we hear these kinds of concerns a lot. Frankly, this is a real problem for any statistical or AI approach – predicting the future is notoriously hard!

At CreationSpace, we take a unique approach to predicting consumer preferences for new and existing products. Our AI models find and learn from patterns in data, breaking down products into multiple dimensions and retrieving relevant consumer preferences for each dimension or combinations of them to make meaningful predictive analyses.

Instead of focusing on a product as a whole, we distinguish specific features, ingredients, and other attributes used in the development and marketing processes. (This is actually how Amazon’s search function works!) These features are compared with similar features of existing products, allowing us to pull in consumer perspectives for them. By combining insights across all relevant features and the perspectives that consumers have on them, our synthetic consumers provide “feedback” on your new product innovations.
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For example, let’s say it’s 2010 and you want to develop a new non-alcoholic beer. There really aren’t many similar players or product equivalents out there yet. Here’s how you can use VOCS (our Voice-of-Consumer Simulator) to get instantaneous insights:
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1. Describe your new product and the persona you want to target
   1. Product: taste, alcohol level (i.e., <0.5% ABV), nutritional information, price, etc.
   2. Persona: who you aim to target (e.g., pregnant women or athletes looking for refreshing alternatives to beer)
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2. VOCS extracts the key attributes of your product and persona to identify the most relevant data, including specific use cases (e.g., your beer alternative)
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3. Leveraging proprietary Bain frameworks, we create synthetic consumers that learn and pull from this data
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4. Your questions are passed to each relevant synthetic consumer, whose responses are are summarized in an easy-to-view interface
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5. Relevant quotes and summary statistics representing the perspective of the synthetic consumer can also be highlighted, making it easier to understand why specific product attributes are desired by consumers

Applying this technology, brand marketers and innovation managers can generate early insights on never-before-seen products, all grounded in real world data.

Of course, any approach leveraging past data has its limits and we recognize this. With CreationSpace, you can generate instantaneous insights to validate your hypothesis, reducing reliance on time-intensive, costly surveys and focus groups. This can enable your teams to iterate faster and run smaller research efforts, if needed, to validate results with live consumers.