The typical use cases of Artificial Intelligence (AI) in consumer product industry has been mainly in prediction, demand forecasting, supply chain, distribution, production and maintenance; the main reason has been the availability of huge amount of structured data. Product Innovation is a harder problem and the proof lies in the fact that success rate of new product innovation is in lower double digits; traditional product innovation methods use very less data and are highly dependent on the skill set and experience of the team working on it.
For better & faster product innovations, AI can be used in the following ways:
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 behaviour 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 opinion 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 analyse and understand this big data to derive meaning and context , without human intervention. The drop in cost of computing and advancement in Artificial Intelligence technology has made it possible to structure, distill and analyse 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 few hundreds as it is the case in a typical consumer research. This information can be used for identifying consumer needs and emerging trends in real time.
Technology that makes it possible are 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 to extract 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 product concept generation and maintaining an innovation pipeline 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 & Prioritise Winning Concepts:
The concepts generated by the algorithm can then be screened and prioritised 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.