Abstract:
Retail sales forecasting is the process of estimating the number of future sales for a specific
product or products. However, producing reliable and accurate sales forecasts at a product
level is a very challenging task in the retail context. Many factors can influence observed sales
data at the product level, such as sales promotions, weather, holidays, and special events, all
of which causes demand irregularities. Sales promotions are one of the salient drivers in
generating irregular sales patterns. Sales promotions confound retail operations, causing
sudden demand changes not just during the promotion period, but also throughout the demand
series. As a result, three types of periods are relevant for sales promotions: normal,
promotional, and a post-promotional. However, previous research has mostly focused on
promotional and normal (i.e., non-promotional) periods, often neglecting the post-promotional
period. To address this gap, we explore the performance of comprehensive methods, namely
gradient-boosted regression trees, random forests, and deep learning in all periods. Moreover,
we compare proposed approaches with conventional forecasting approaches in a retail setting.
Our results demonstrate that machine learning methods can deal with demand fluctuations
generated by retail promotions while enhancing forecast performance throughout all time
periods. The base-lift model outperformed machine learning methods, although with more
effort necessary to cleanse sales data. Our findings indicate that machine learning methods can
automate the forecasting process and provide significant performance even with the standard
approach. Hence, our research demonstrates the way retailers can successfully apply machine
learning methods in forecasting sales.
Citation:
Chamara, H.H.H.R. (2022). Retail sales forecasting in the presence of promotions : comparison of statistical and machine learning forecasting methods [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21666