Accelerating business growth for an Indonesian transportation service brand with route optimization
Data & AI
ADA designed a comprehensive strategy for a transpotation service brand's growth using various data sources & analytics, RFM analysis, and consumer insights.
The client is a transportation service leader in Indonesia. As concern over the COVID-19 pandemic escalated, the client engaged with ADA to leverage our advanced data analytics capabilities. This brought forth clarity on the mobility pattern and profiles of the commuters in various points of interest (POI), as well as validating several assumptions on their passenger segments.
1. Derive insights from XACT, ADA’s proprietary DMP
ADA provided a comprehensive strategy using various data sources from XACT, client’s passenger, and telco data.
2. Recency, Frequency, and Monetary (RFM) analysis
This is an analysis based on customer value, represented in their recency of using the service, frequency of usage, and monetary spend. We wanted to discover customer loyalty by analysing their usage and spend to provide clarity on passenger comparison between business and non-business users.
3. Point of Interest (POI) analysis
We analysed the top 600 visited POI (office, malls, transport hub, etc.) in Indonesia. This analysis assessed the polygonal area of POI for granular details into the footfall density of potential customers.
4. Commuter density analysis
We identified the density of commuters at different areas during specific time intervals with the capability of identifying home and work location, as well as POI overlay to visualise only target passengers.
The Execution 1. Analyse consumer RFM metrics
We analysed consumer RFM metrics throughout 12 months to study the revenue distribution and contribution of users, as well as to assess the difference pre- and post-COVID pandemic.
2. Analyse consumer Point of Interest (POI) metrics
We segmented 3 types of taxi / shuttle stands for each POI. All visitors’ information seen within the top 600 most-visited POIs in Jakarta and satellite cities areas were extracted and categorised.
3. Mobility Analytics Dashboard
The density of commuters within various POI was configured alongside the selected time. The data was cross-referenced with taxi / bus stands and shuttle pick up points. Demographic filters from XACT were added for richer profiling and insights.
The client’s passengers were skewed towards business users and high affluence groups, contributing to 72% of their annual revenue. Our client explored loyalty programmes to better leverage their user base.
13 out of the 60 office POIs listed had the client’s taxi stand within reasonable reach. This indicates significant business expansion opportunity to provide their services across the other 47 POIs, as well as optimising route planning to strategically locate or relocate existing transportation services.