Machine learning and AI techniques are widely discussed within the innovation and market research community, often with questions circulating about best uses, applications, and the role of the human researcher. Machine learning and AI techniques refine the goldmine of available customer textual feedback into a detailed and comprehensive database of actionable insights.
These latest advances in AI make innovators work harder, better, faster and stronger.
- Harder: AI for qualitative insights allows us as innovators to spend our time on value-add activities. The machine works harder, so we can work smarter. Automated software can scrape web content, sometimes totaling up to over one-million records. The AI algorithm does the heavy lifting by reducing the mountain of textual insights into a manageable list. Innovators and research analysts can thus spend their time on synthesizing the data, formulating insights and developing strategies based on the implications.
- Better: AI allows us to examine the complete universe of insights, to be sure we’re not missing anything. More than ever, customers are interacting with our brands and voicing their opinions. They are leaving product reviews, sharing on social media, and talking with others in forums. While AI allow us to capture, extract and analyze consumer opinions volunteered at the moment of truth, research shows that it also does a better job of surfacing the more nuanced, less frequently mentioned needs. Research shows that those less frequently mentioned needs, often not discovered through traditional methods, may be what leads to truly disruptive innovation.
- Faster: Timing is everything, particularly when it comes to innovation. Often there isn’t enough room in the schedule for several months of traditional research. Traditional respondent screening, recruitment and data collection are expensive and time consuming. Machine learning doesn't require the traditional lengthy recruitment process, so it can often surface a database of detailed and comprehensive insights quickly, sometimes in as little as two weeks!
- Stronger: AI methods only continue to improve. Through deep learning, machines can get smarter and more efficient after every instance of being used. Over time, the algorithms require less training, get better at identifying unique and nuanced needs and become stronger in their ability to explore adjacent categories. Some machines can even overlays sentiment analysis and frequency of mention to pinpoint focus areas for innovation and potential hidden opportunities.