Fine-grained social media information has significant predictive power in forecasting colour and fit demands months in advance of the sales season, and could thus assist in making the initial shipment quantity decision, says a new study.
- Despite challenges like short product lifetimes, long manufacturing lead times and constant innovation of fashion products, social media information can enable efficiency and increased revenue.
- The predictive power of including social media features, measured by the improvement of the out-of-sample mean absolute deviation over current practice ranges from 24% to 57%.
THE FINDINGS: Perhaps a first of its kind study that explores and demonstrates the value of social media information in fashion demand forecasting in a way that is practical and operable for fashion retailers, the researchers partnered with three multinational retailers—two apparel and one footwear—and combined their data sets with publicly available data on Twitter and the Google Search Volume Index.
- They implemented a variety of models to develop forecasts that could be used in setting the initial shipment quantity for an item, arguably the most important decision for fashion retailers.
- The study threw up consistent results across all three retailers and demonstrated the robustness of the findings over market and geographic heterogeneity, and different forecast horizons.
- Changes in fashion demand are driven more by ‘bottom-up’ changes in consumer preferences than by ‘top-down’ influence from the fashion industry.
FUNDING & RESEARCH: The study — The Value of Social Media Data in Fashion Forecasting — published in the Manufacturing & Service Operations Management journal, was supported by Wharton School Fishman-Davidson Center for Service and Operations Management, the Wharton School Baker Retailing Center, and the Wharton School Risk Management Center Russell Ackoff Doctoral Student Fellowship.
