Clicking through the Clickstream: A Novel Statistical Modeling Approach to Improve Information Usage of Clickstream Data by E-Commerce Entities

Success or failure of an E-commerce platform is often reduced to its ability to maximize the conversion rate of its visitors. This is commonly regarded as the capacity to induce a purchase from a visitor. Visitors possess individual characteristics, histories, and objectives which complicate the choice of what platform features that maximize the conversion rate. Modern web technology has made clickstream data accessible allowing a complete record of a visitor’s actions on a website to be analyzed. What remains poorly constrained is what parts of the clickstream data are meaningful information and what parts are accidental for the problem of platform design. In this research, clickstream data from an online retailer was examined to demonstrate how statistical modeling can improve clickstream information usage. A conceptual model was developed that conjectured relationships between visitor and platform variables, visitors’ platform exit rate, boune rate, and decision to purchase. Several hypotheses on the nature of the clickstream relationships were posited and tested with the models. A discrete choice logit model showed that the content of a website, the history of website use, and the exit rate of pages visited had marginal effects on derived utility for the visitor. Exit rate and bounce rate were modeled as beta distributed random variables. It was found that exit rate and its variability for pages visited were associated with site content, site quality, prior visitor history on the site, and technological preferences of the visitor. Bounce rate was also found to be influenced by the same factors but was in a direction opposite to the registered hypotheses. Most findings supported that clickstream data is amenable to statistical modeling with interpretable and comprehensible models.

Simulation of consumers decision-making process using agent-based model approach

This paper describes a model to simulate the decision-making process of consumers that adopts technology within a dynamic social network.The proposed model use theories and tools from the psychology of consumer behavior,social networks and complex dynamical systems like the Consumat framework and fuzzy logic.The model has been adjusted using real data,tested with the automobile market and it can recreate trends like those described in the world market.

Public Transport Service Provisions and Policy Implications for Columbarium Trips

Grave-sweeping is one of the popular special events in Asian cities,especiallyin Chinese societies,in which families express filial piety to their ancestors during two traditional grave-sweeping festivalsin a year.The extraordinary high travel demandis often attracted to columbaria within a relative short period around the festivals,and induces severe impacts to the local traffic.It is challenging for the government and private operators to formulatea public transport service setting to satisfactorily cater all the visitors'travel demand.This paper aims to proposean optimization frameworkto identifythe optimalprovisions of public transport services for columbarium trips to achieve consumer surplus or profit maximization.Numerical studies are carried out usingthe travel demand data collected from a selected columbarium,to examine the effects of different public transport service settings to the policyobjectivesin variouscases.The modelresults showthat the current situation is neither consumer surplus nor profit optimum.Improvement schemes are suggested in relation to allow various fares during different visit periods and provide multiple public transport feeder services to serve the visitors during the high travel demand period.