Prediction of remaining time on site for e‐commerce users: A SOM and long short‐term memory study

With the development of information technology, online transactions and e‐commerce are gradually replacing conventional consumption patterns. To obtain a competitive advantage, industries proactively engage in digital transformations and the management of e‐commerce platforms. Faced with changes in market patterns, e‐commerce channels and online advertising firms hope to extend users' website browsing duration/time on site to enhance the effects of product promotion and the likelihood of advertisement clicks. The greatest challenge in predicting time on site is that clickstream data are not mutually independent, and short‐, mid‐, and long‐term data may intervene in a time series. Such timing dependence increases difficulty of capturing or learning the characteristics of website users for ordinary prediction models and leads to confusion and deviation during model construction. Accordingly, this study proposed a prediction method integrating self‐organizing map (SOM) and long short‐term memory (LSTM). The SOM method was initially applied to categorize website members into groups based on similarities in browsing behavior, and the LSTM prediction model was subsequently developed using the webpage browsing data of each group. The performance of the proposed method is evaluated by comparing the prediction with the results of three competing approaches (SOM with support vector regression, SOM with multilayer perceptron, and single LSTM) on the clickstream data provided by a leading online retailer specializing in selling skin care and cosmetics products in Taiwan. The Wilcoxon signed‐rank test validated the proposed SOM‐LSTM model outperforms competing approaches in remaining time‐on‐site prediction. This study serves as a first attempt to systematically predict remaining time on site for e‐commerce users in terms of empirically verifying a hybrid approach which integrates SOM and LSTM techniques.