We’re analyzing fuel transactions (regular, midgrade, premium, and diesel) and c-store transactions from more than 21,000 stations and c-stores. Our lookback window is the previous 110 weeks to create comparisons from the previous two years. 

We’re tracking the following metrics, each aggregated to the average site on the average day: transactions, revenue, units sold, revenue per transaction, revenue per unit, units per transaction, sign price, rack price, profit margin (fuel only), Upside offer, pump to c-store ratio, number of sites used to calculate a measure, and number of retailers used to calculate a measure.

Not right now. Our team is working on making the dashboard available to the public. First, we need to better understand what’s most valuable to retailers to help guide our next steps.

No. In order to protect retailer privacy, we’re only making the aggregated, anonymized data available.

We’re happy to help you with insights related to your region! Reach out to our industry experts for an answer to a specific question. Journalists are also welcome to contact our press team.

In short, we need to exclude some regions from our analysis in order to protect retailer privacy. Here’s some more context:

As we aggregate the data, we conduct a few checks to ensure that no retailers are overrepresented in their market. For that reason, if a retailer’s sites make up more than 25% of all sites on Upside within a given area, we’ll exclude some of their sites to ensure they fall below that 25% threshold, or exclude that region entirely from our analysis. 

Additionally, we need to ensure that we have enough sites in every market to accurately represent the landscape. If we don’t have at least 25 sites on Upside overall in a market, we exclude the affected geography completely. These checks ensure that the data is anonymous and reflects the truth in each region. 

The data from our tracker is only useful if it is reliable and accurately reflects the real world. In order to assure data quality, we enforce a minimum number of sites in an area to ensure averages are representative of the region. We filter the data down to the item level to properly define transaction types. Overall, we are committed to publishing the most accurate, relevant data possible.