The typical use cases of Artificial Intelligence (AI) in the consumer product industry have been mainly in trend prediction, demand forecasting, supply chain, distribution, production, and maintenance; the main reason has been the availability of huge amounts of structured data. Product Innovation is a harder problem and the proof lies in the fact that the success rate of new product innovation is in the lower double digits; traditional product innovation methods use limited data and are highly dependent on the skillset and experience of the team working on it.
For better & faster product innovations process, AI can be used in the following ways:
Understanding Consumer Behavoiur
The first step in product innovation is consumer understanding; there are two data sources for this: Internal company data in the form of sales data, historical consumer data, online purchase behavior, etc; External Public data in the form of online conversations.
Today, we live in a world where people are constantly sharing glimpses of their life, their thoughts, and opinions everyday through text and images. These are unmoderated, unbiased opinions about products, places, services, or events. Till now it was not possible to make sense of this noise but recent advancements in technology have made it possible to analyze and understand this big data to derive meaning and context, without human intervention. The drop in the cost of computing and advancement in Artificial Intelligence in food technology has made it possible to structure, distill, and analyze this noise to identify and understand what people are thinking and asking for.
This information becomes very valuable as it is based on millions of data points, not just a few hundreds as is the case in typical consumer research. This information can be used for identifying consumer needs and emerging trends in real-time.
Technology that makes it possible is Natural Language Processing (NLP) & Computer Vision. Natural Language Processing (NLP) is a sub-field of Artificial Intelligence that is focused on enabling computers to understand and process languages in a way humans will do. Computer vision allows extracting unspoken information from images. Computer Vision, another sub-field of Artificial Intelligence, enables computers to see, identify, and process images in the same way that human vision does, and then provide appropriate output. Predictive analytics can then be used to predict the future trajectory of the trends.
Build Winning Product Concepts
New consumer product development and maintaining an innovation process is a time-consuming and laborious process often driven by gut feeling and intuition. Artificial Intelligence can help us to identify white space opportunities and then generate new product concepts to capture the white space opportunity.
Millions of product concepts can be created using algorithmic combination of n-gram features which would have been impossible by a human approach.
Screen & Prioristise Winning Concepts
The concepts generated by the algorithm can then be screened and prioritized based on the consumer understanding and trend identification done. Predictive analytics here can predict which products will be successful tomorrow even before the products hit the market and accelerate food technology