Ideas & Debate

Utilise Big data for viable innovations

ai

“Necessity is the mother of invention” is a famous proverb often used to explain situations where new inventions and innovations have developed out of the need to find solutions to new and challenging realities.

In fact, history suggests that the greatest drivers for inventions and innovations were in times of war, famine, pandemic, and death.

The Covid-19 pandemic, especially, saw a huge number of new user innovations gain wide public interest, with people creating ventilators from scuba diving equipment or espresso machines out of frugality and necessity.

This is also true with the global climate crisis which has prompted people to work collaboratively to find solutions to challenges posed by the phenomenon.

Innovation research has long shown that it is the “lead users”, not the companies or manufacturers, who are the real pioneers. These individuals, who are actively invested in the activity, create many radically new products and service models that are then popularised and commercialised.

Many sports that are mainstream today, such as skateboarding, mountain biking and windsurfing were developed and pioneered by those who first participated in them: the lead users, who worked together to build their own equipment, techniques, rules, and competitions for years before bigger producers got involved.

Eric von Hippel in his book, Free Innovation, notes that the first personal computers were developed by lead users. So were the first personal 3D printers. Even trends in hair styles, from mohawks to bright colours because someone dared to try something new. New apps being built into smartphone and smartwatches today were also done first by hackers.

One key factor that cuts across ancient and new user-led innovations is that they matter to users themselves since they help them meet their personal needs and address particular problems they face. These individuals, who are actively invested in the activity, create many radically new products and service models that are then popularised and commercialised.

But to make these techniques as valuable as possible, it is important for organisations to learn how to incorporate the user-designed innovations into their corporate innovation culture that is largely concentrated on internal idea-sourcing.

Companies, innovation consultants and market researchers should no longer assume that it is solely their task to design the “next big thing” for consumers.

Organisations that work in product development must quickly adapt their innovation processes to this new reality. They must develop new ways to systematically find, screen and commercialise lead user-developed innovations as well as create new product concepts in-house.

To achieve this they must understand how consumers are creating innovative solutions to satisfy their needs. This is a critical step towards creating consumer-centric innovations especially in this age when the ability to innovate in an agile and sustainable manner has never been more essential for survival of organisations.

Although searching for Lead User Innovations lacks practical value due to the costs associated with sourcing such innovations.

But the good news is that it has been found that applying semantic Artificial Intelligence (AI) algorithms to the universe of user-generated social data can significantly improve the efficiency and expense of identifying commercially promising Lead User Innovation whitespaces in consumer goods.

A recent Research & Development study conducted with Eric von Hippel from the Massachusetts Institute of Technology has shown that promising user innovations can be found in any consumer product or service fields within a week or two of using a dedicated semantic AI model.

Based on this study, Ipsos in Kenya launched the Innovation Spaces methodology to give research professionals easy access to the analytical toolkit and consumer data to turn the internet into an ongoing innovation mine.

This approach has helped us understand how leveraging social data can provide actionable, powerful insights on unmet needs and innovation opportunities.

The Innovation Spaces method makes it practical to identify user-developed need-solution pairs on massive scale at a very early stage in the innovation development process.

Compared to other early stage innovation research techniques, a critical advantage of this method is its “bottom-up” nature, which enables us to detect user innovations within the entire category landscape (as well as the corresponding emerging needs) within days, thanks to an AI-powered execution process.

This means our clients can now continuously decode innovation opportunities as they arise and effectively establish a truly consumer-centric corporate innovation strategy.