Multivariate Sales Forecast Model Towards Trend Shifting During COVID-19 Pandemic A Case Study in Global Beauty Industry

COVID-19 pandemic has changed the economic weather and business performance in multiple streams. The uncertainty condition caused by the pandemic needs to be carefully taken care by all companies and organizations due to rapid consumer trend shifting and volatile market condition. The sales and marketing strategy needs to be carefully taken during organizational decision-making process to avoid further loss. PT XYZ as one of the leading consumer goods in beauty industry experiences the same condition and challenge reflected by down-trend in the organization KPI. This research aims to introduce and provide predictive data analytics tools for enhancing sales forecast by comparing Random Forest and Neural Network as part of machine learning methods also Vector Autoregression (VAR) as conventional statistical forecasting methodology. As the result of this research, neural network returns better evaluation for skin care and Vector Autoregression for makeup category. Meanwhile data visualization is found necessary to provide additional factual information, includes the external factor, to support knowledge management for better rational decision-making process. © 2022 ACM.

How did COVID-19 change what people buy: Evidence from a supermarket chain

This research takes a retrospective view of the COVID-19 pandemic and attempts to accurately measure its impact on sales of different product categories in grocery retail. In total 150 product categories were analyzed using the data of a major supermarket chain in the Netherlands. We propose to measure the pandemic impact by excess sales – the difference of actual and expected sales. We show that the pandemic impact is twofold: (1) There was a large but brief growth at 30.6% in excess sales associated with panic buying across most product categories within a two-week period;and (2) People spending most of their time at home due to imposed restrictions resulted in an estimated 5.4% increase in total sales lasting as long as the restrictions were active. The pandemic impact on different product categories varies in magnitudes and timing. Using time series clustering, we identified eight clusters of categories with similar pandemic impacts. Using clustering results, we project that product categories used for cooking, baking or meal preparation in general will have elevated sales even after the pandemic.