Scroll down to read about a recent data commercialisation initiative Datamine has been working on with Gaspy.





Gaspy is a phone app designed to help users combat New Zealand’s rising fuel prices (or at least avoid them).  The app’s 400,000+ users enter the current prices at whichever gas stations they pass, creating a real-time database of retail fuel prices.  This effort gathers thousands of raw price records per day, covering 1500+ stations nationwide. 

The Gaspy team wanted to go one step further and make extracts of this incredibly useful data available for interested parties, but they weren’t sure where to start or how to do it themselves.  They enlisted Datamine as their official data agent help to clean, structure and enhance the fuel price data (both historically and in real-time moving forward), as well as to commercialise it on their behalf.


The first place Datamine started was ensuring data quality.  With any crowdsourced database, there are guaranteed to be errors, such as an incorrectly entered price, a lag in price update or a rural station that no one has entered data for.  Datamine assesses the accuracy of real-time records and removes outliers and anomalies (which account for only 0.2% of the average daily records).  For missing price data, we use custom algorithms to impute and correct data gaps using statistical clustering and marker site movement, meaning we’re constantly assessing validity to ensure the database is as accurate as possible.  Our final step to commercialising Gaspy’s data was to size the addressable market and develop a targeting strategy to go after it.


Aside from the initial time investment to set up the commercialisation strategy, the partnership has required very little investment from Gaspy.  As a result, the company will continue to receive recurring monthly revenue from these customers at no cost to the business for as long as the data is valuable to consumers.  This partnership has enabled both Datamine and Gaspy to add value to a number of organizations who have been using this data to get market insights and improve their own strategies.  A great example of this is the New Zealand Travel Authority who use the data for their quarterly reporting around the Auckland regional fuel tax.

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