fbpx
Detecting Consumer Signals in the 90% Economy

Detecting Consumer Signals in the 90% Economy

Media Coverage

Our CEO Ryohei Fujimaki, Ph.D. discusses the role #AI plays in adapting the new normal of a 90% economy via @datanami https://bit.ly/2SGlMet

Consumers flocked to Amazon.com and other e-commerce sites during COVID-19, and that’s changed the way machine learning-based forecasting methods work for companies in the retail and consumer goods sectors. In May, companies will be looking to data and AI to guide their decision-making as businesses re-open. Retail and consumer goods are experiencing significant volatility, which is causing the industry to change a lot.

The U.S. economy is doing better than China, but it is far from ordinary. The Economist says the 90% economy is one of financial hardship and fear of the COVID-19 lockdown. Consumers will avoid large crowds, ridesharing, and public transportation, and trade shows this year. They will also shop locally. A pandemic of COVID-19 will alter consumer behavior. Some businesses will fail in the next several months. IBM’s study of COVID-19 shows that there are long-term implications for industries like retail, transportation, and travel. Artificial intelligence will help companies adapt to the new normal of a 90% economy.

“Analytics models are becoming less accurate because no single method can capture the real trend,” Fujimaki told Datanami. Fujimaki recommends that companies watch their AI model and retrain it if it becomes less accurate. Model maintenance is more critical than ever, Fujimaki says, and customers should run multiple machine learning models to see more accuracy. AI models adjust to changes in consumer sentiment in the long run, but short-term answers might not be as accurate. Read the rest of the article at Datanami.

dotData
dotData

dotData Automated Feature Engineering powers our full-cycle data science automation platform to help enterprise organizations accelerate ML and AI projects and deliver more business value by automating the hardest part of the data science and AI process - feature engineering and operationalization. Learn more at dotdata.com, and join us on Twitter and LinkedIn.