S-MALL Application

Turning Concrete Malls into Smart Malls (S-MALL):
A web-based analytics application for visualizing and mapping in-mall customer journeys and shopping behaviours

Team S-MALL: Chen Yun-Chen | Chiam Zhan Peng | Zheng Bijun

Overview

Application

User Guide

Poster

APPLICATION OVERVIEW

Movement Analysis

Member Analysis

Association Analysis

APPLICATION DESCRIPTION

Part1: Movement Analysis

Visualization	Methodology & Technique	Usage
	Chart type: Line chart & trellis plot R Package: ggplot2, plotly Interactivity: Use selectInput to control plot and segment by different timelevels, eg. Date, day of week, and hour	Line chart without trellis (segment=None) can be used to analyze the daily/weekly/hourly pattern of footfalls. Trellis plot can be used to detect the cycling pattern over time.
	Chart type: Chord diagram R Package: chorddiag Interactivity: Set specific datetime using selectIput and sliderInput to view the traffic transfer across floors. Hover to each floor to see the destination of its outflow traffics.	Analyze traffic flow across floor for specific datetime selection
	Chart type: Hexagonal binning map R Package: hexbin, ggplot2, plotly Key parameter setting: number of bins is set to 50 Interactivity: Set specific datetime using selectIput and sliderInput to view the traffic density on each floor. Click on specific hexbin to investigate members identity.	Analyze traffic density of floors for specific datetime selection Drill down to shoppers’ identity based on interested density area

Part2: Member Analysis

Visualization	Methodology & Technique	Usage
	Chart type: Sunburst diagram R Package: sunburstR Interactivity: Set radio button to view the journey of different member segments. Set minimum dwell time using sliderInput to exclude passing-by floors. Hover to see the path and relevant statistics.	Investigate popular shopping path of members based on floors.
	Chart type: Treemap R Package: treemap, highCharter Interactivity: Set radio button to view the journey of different member segments. Click on floor level to drill down to store level.	Analyze members’ average dwell time on each floor and store.
	Chart type: Boxplot R Package: plotly Key parameter setting: number of bins is set to 50 Interactivity: Click on the treemap to get the relevant boxplot for selected floor.	Analyze dwell time distribution of each store based on floor selection on treemap.
	Chart type: Bar chart R Package: plotly Interactivity: Select on the boxplot distribution to view the demographic of interested members. Set the ‘profile count by’ parameter to decide the y-axis of bar plots.	Analyze members’ profile based on selection of their dwell time distribution. For example, we may analyze the high time spender of a specific store to see if they are of similar demographic.

Part3: Association Anlaysis

Visualization	Methodology & Technique	Usage
	Chart type: Bar chart R Package: ggplot2, plotly Interactivity: Set date range using radio button to see the plot of different months.	Investigate popular shopping path of members based on floors.
	Chart type: Scatter plot R Package: arules, ggplot2, plotly Interactivity: Set date range using radio button to generate association rules based on different month’s transactions. Set parameters (support/confidence/min items) to generate valid associations rules and render plot. Hover over the bar to fade out non-relevant rules in the quadrant. Hover over the network to fade out non-relevant rules in the quadrant.	Analyze the competitive position of different rules based on set parameters.
	Chart type: Network R Package: visNetwork Interactivity: Set date range using radio button to plot rules of different months. Set parameters (support/confidence/min items) to generate valid associations rules and render plot.	Visualize the associations among departments.