Between 1997 – 2006, the average consumption of Compressed Natural Gas (CNG) as a source of fuel for motor vehicles grew at a rate of approximately 8.1 % annually. Over 80% of motor vehicle owners moved to CNG due to its price competitiveness. However, deregulation of its cost per kilogram, governmental tariffs, gas shortage and the cumulative effect of energy crisis brought the costs up and the profit margins gradually declined to a level where it became difficult for the CNG industry to compete with their gasoline counterparts.

A progressive oil and gas company that was engaged in the CNG sector approached us to help them grow despite the challenges within their line of business. The rules were simple: Devise a growth and business strategy that helps them outlast competition, and an analytics strategy to help drive their business decisions particularly when it came to acquiring CNG filling stations across the region that were doing poorly in sales and risked shut down.

This case focuses on the analytics strategy we devised for our client.

During the government of Pakistan Peoples’ Party (PPP), Gas infrastructure development CESS (GIDC) was imposed on commercial gas consumers including fertilizer producers, power plants run by textile mills and CNG stations. It was done to fund the laying of gas pipelines including Iran-Pakistan, Turkmenistan-Afghanistan-Pakistan-India (Tapi) and liquefied natural gas (LNG) pipelines. CNG stations had to perform consistently well in order to (a) remain profitable and (b) pay their share of GIDC or risk government penalties and closure. GIDC data was maintained by Oil & Gas Regulatory Authority (OGRA) and included highly valuable parameters such as the identification factors for a CNG station, their location, their consumption (in cubic hectometer, Hm3) and status (whether active or inactive).

For our analysis, GIDC data was obtained from OGRA and joined with the data from SUI Northern Gas Pipelines Limited (SNGPL) which consisted of current and historical billing for a CNG station. Additionally, news sources were mined and corpora analyzed for regional news concerning the CNG sector.

With the helped of our created data repository, we mapped all CNG stations across Punjab and divided them into regional clusters. Within these clusters, the consumption was analyzed to figure if the demand was enough to warrant an acquisition within. Example for three regions is shown below:

Cluster 1 - Rawat





Cluster 2 - Gujar Khan





Cluster 3 - Jhelum

Our team dug deeper within clusters using the data we collected and found who the market leaders were in terms of sales revenue, who were on the risk of getting closed yet showed good earning potential in the past and finally those who were just getting by.

Financial forecasting was done for the average market players and the ones risking closure using an Auto-Regressive Moving Average (ARMA) model that makes the present value of a time series (in this case a month’s revenue) dependent on the past values. Our data driven analysis was supplemented with internal research on global and national macroeconomic trends and text mining on news corpora to figure out regional views on CNG market.

Together, this mix of research and analytics helped us devise an informed CNG filling station acquisition strategy for our client. While the results cannot be disclosed in full, our client now has a footing within nearly all profitable clusters and is beating the nearest competitor by a considerable margin.



