Fuel Pricing Model - Update January 20 th, 2011

Agenda 1.Overview of model 2.Results over the past year 3.Going forward

Agenda 1.Overview of model 2.Results over the past year 3.Going forward

Optimally price to increase volume and/or profits  Understand what customers are willing to pay under different conditions We analyzed how fuel sales were affected by over 100 variables including the weather, days of week, special events, and fuel price … and we found some interesting results. For example:  Customers care more about how quickly price changing, than about price itself  Customers care about rain only on Sundays  People decide whether a price is high or low by comparing it to the highest price in the short term (~ 1month) and the long term (~ 1yr)  And their long-term memory is 3X as important as their short-term memory We decided to follow a two step approach:  First maximize volume, then fine-tune the price according to data We implemented the model across three sites on February 1 st, 2010, and on a fourth site on May 1 st, 2010 Overview of the fuel pricing project Objective Research and Data Analysis Strategy

Our Pricing Model Simple Inputs Our pricing model – simple inputs

Our Pricing Model Simple Inputs Our pricing model – simple inputs

Our pricing model – clear outputs

Station managers report: “It takes the guesswork out of pricing” “It’s easy to use” “people at the pumps say we’re doing something different with our prices… and whatever it is, it’s working”

Agenda 1.Overview of model 2.Results over the past year 3.Going forward

Running the model for a significant time period  As of now, we have run the model for 11 full months : 2/1/2010 – 12/31/2010  We can compare results to those over the same time period over the previous year Comparing volume  How has total volume sold changed as a result of the model?  How does volume growth at MVV compare to average growth in the market?  Greater volume leads to greater convenience store sales Comparing profits  Increasing total profits is ultimate goal of the pricing model  Total profits are defined as tax revenue plus fuel profit (price – cost) Testing the model

Volume ―We saw a 64% increase across our three sites… 54% 96% 58% 64% … And almost doubled volume at one of our sites % change: volume over 2/09-12/09 vs. 2/10-12/10 6.5%

Total profit ―We saw a 45% increase across the sites 30% 77% 42% 45% Individual performance ranged from a 30% to a 77% total profit increase % change: profits over 2/09-12/09 vs. 2/10-12/10

45% How would the average 45% profit lift affect sites of different sizes? Sample performance/month A 45% increase could result in 100K extra profits for a smaller site, and over 900K for larger sites Extra Profits

Agenda 1.Overview of model 2.Results over the past year 3.Going forward

How does our model compare with other pricing tools in the market? Major competitors include PriceIt! Pro (from MPSI) and PriceAdvantage (from Skyline products) – Market prices range from 1-2 cents/gallon – We considered using them, but decided we had to build our own to meet our needs We believe we have developed a more effective tool to optimize fuel profits: Greater flexibility Flexible definition of fuel profit allows a holistic/relevant view of business –Can included additional expenses: credit card fees, operational expenses –Can include additional revenue: fuel tax dollars, c-store conversion Pricing methodology responds to market and recommends optimal price –Current tools allow fuel managers to price at cost +, competitor +/-, or to maximize profit given volume objectives –But how do you know if the volume or margin you chose optimizes price? Can break-out tax revenue from fuel revenue Can tailor strategy to maximize either or both Better price optimization Customized for tribes We plan to bring the pricing model to the tribal market by 2011, and to the broader market by 2012

Pricing Model Timeline

MVV is looking for beta-test partners Criteria for participation include: – Type of site: we are looking for a balanced set of test sites – Willingness to share data: the model requires volume, and pricing data – Commitment to the process: partners must commit to using the model and being part of the testing process (e.g. implementing changes, reporting problems, etc) – Patience and time: there will be setbacks along the way… While we’re hoping there is a lot of interest, limited MVV resources will only allow us to partner with a few tribes during the beta test period – Interested tribes will receive the model for free during the beta period If you are interested please contact