When will predictive models become more predictable?
Retailers and brands that used predictive models were forced to constantly retrain and make adjustments around their machine learning tools as historic data became unreliable during the pandemic. While the “new normal” is becoming more evident as vaccines roll out, changes in buying patterns are still volatile enough to continue to frustrate forecasting efforts.
“The question is: How quickly can we adapt and retrain ML models?,” said Dan Simion, VP of AI & analytics for Capgemini North America, in a recent interview with VentureBeat. “Not only do the models need to be rebuilt or redesigned based on new data, but they also need the right processes to be put into production at a pace that keeps up. Until there is some sort of stability, it will continue to be difficult for organizations to identify consistent trends.”
A recent Wall Street Journal article said the pandemic accelerated moves to lean more heavily on real-time live data, including website activity, to drive predictive software, although some companies abandoned pre-pandemic predictive models altogether.
“The problem in highly volatile times is that sales data of last week is not a good predictor of sales in the following week,” Alex Linden, research vice president at information technology research and consulting firm Gartner, told the Journal.
For its part, Walmart in a shift that predated the pandemic by several months, began tracking longer-term trends over several years and decreased its emphasis on the most recent year. Ravi Jariwala, a Walmart spokesman, told the Journal the lengthier data set “helped us normalize the data and more accurately perform demand forecasting.”
In a column for the Harvard Business Review, Angel Evan, president at Particle Inc., a data analytics firm, stated that the pandemic-driven challenges facing historic data should serve as a reminder that customer data is not limited to point-of-sale transactions. He wrote, “Retailers should consider data to be any information that is relevant to their customers’ behavior that can be ethically collected, organized, and studied for insights that decision-makers can rely on.”
- Companies Adjust Predictive Models in Wake of Covid – The Wall Street Journal
- How the pandemic challenges companies that use predictive models – VentureBeat
- Retailers Face a Data Deficit in the Wake of the Pandemic – Harvard Business Review
- How murky has COVID-19 made retail data? – RetailWire
DISCUSSION QUESTIONS: What challenges continue to limit the use of predictive models in supporting forecasting? Has the pandemic offered any lessons around the reliance on historical data or the capabilities of machine learning?