Patent Application
20170098257
Not Applicable
Not Applicable
Not Applicable
There are numerous price optimization systems for a general merchandise store or grocery stores, but the retail motor fuel store has different requirements. The price of the main product these stores sell, gasoline, is announced to the world and competitors on large, visible signs. This is different than any other retail outlet and requires different price optimization systems. There have been attempts to create a price optimization routine for these stores but they have not been highly successful for a variety of reasons, including the failure to understand that these stores often have very seasonal traffic patterns and that these systems pick a price for the fuel without giving the user an explanation of what his choices are.
In the retail motor fuel price management industry, when retailers price their fuel products like gasoline and diesel there are similarities to non-fuel retail pricing practices, but there are significant differences as well. On one hand, motor fuel products, like all retail products, must be priced according to perceived relative value compared to the competition, so retailers pay close attention to the price charged by the competition. Pricing strategies are carefully monitored by measuring the ongoing sales volumes, and prices are changed when needed. But unlike other retailers, fuel retailers must deal with a constantly changing replacement cost of fuel, and a much more public display of their fuel products pricing. In the 1960's, the replacement cost of fuel was relatively static, so fuel retailers could be successful simply by setting their retail fuel prices once for the month, and updating their retail fuel prices every 30 days. But now replacement costs are so volatile, retailers are likely paying a higher or lower price for each load of fuel they receive, and that can be as frequently as multiple times in one day. In addition, not only must the fuel prices be prominently displayed on the outdoor sign for all consumers to see and compare to the competition, but more and more consumers are browsing websites from their mobile devices to compare fuel prices so they can plan which fuel retailer to buy from based on their travel plans, especially when travelling out of town.
Additionally, the retail motor fuel price management industry has become more complex because of the introduction of rewards programs. Consumer buying behavior is now heavily influenced by the points consumers accrue by purchasing in-store merchandise from convenience stores and grocery stores. For example, if a consumer purchases $100 in groceries, that person may earn enough rewards points worth a $0.10 per gallon discount for gasoline at a participating fuel retailer. When a retailer introduces a fuel discount rewards program, it immediately impacts their fuel pricing strategy. Fuel rewards programs must constantly be reviewed to see how much impact they have on consumer behavior and fuel volume sales. When a competitor introduces a fuel rewards program, the retailer must be careful to identify what competitive price is being reported in their competitor surveys: the full price or the rewards price.
Another important characteristic of the retail motor fuel industry is that the overall retail motor fuels market is experiencing shrinking volumes. As retailers are competing for an ever-shrinking motor fuels volume market, competition is increasingly intense, and the right fuels pricing decisions are increasingly critical because there is less room for error by selecting the wrong price for any individual commodity. Motor fuel retailers need a solution that allows them to better understand the competitive nature of all the products they sell, in all the markets in which they compete, on every street corner where they have a store, the fuel pricing relationship with their competitors, and the overall price elasticity of motor fuels with their customers both on a per-product basis and as a product family.
One more aspect of the retail motor fuel industry that adds to the complexity of fuel pricing is regulatory compliance. The first common fuel pricing compliance issue is related to cost. Motor fuel retailers are often legally not allowed to sell fuel below cost. Consequently, the economic model optimization must be aware of cost on an individual product basis as well as across a family of products. The second issue motor fuel retailers face is related to price change frequency. Motor fuel retailers are often legally not allowed to make changes to their fuel prices more frequently than once every 24 hours, that means their fuel pricing system must allow for price changes to be made no more frequently than once in a 24 hour period when fuel retailers are operating in this context.
Other optimization patents already exist for the retail space, allowing optimized prices to be calculated for a product based on predicted sales volumes. However, none of these optimization models will work in the retail motor fuel price management industry because the retail motor fuel price management industry is so volatile in both cost and competitor price. Retail motor fuel cost calculations are not based on LIFO or FIFO accounting practices, but are instead based on the current published RACK cost of fuel by fuel supplier and terminal. This means retail motor fuel retailers always base their current margins on replacement cost, which is, current RACK cost, plus freight, tax and any other cost. In other words, current retail fuel margins are based not on the actual cost they paid for the fuel inventory they paid in the tanks, but on how much it would cost to fill an empty fuel tank at any moment in time. In some cases, fuel pricing is based on anticipated future replacement costs based on trends in the NYMEX commodities futures market, specifically the cost trend of a barrel of crude oil, whether it be Brent or WTI crude. Only by using the replacement margin that retail motor fuel retailers are able to survive in an industry where costs are so volatile. Existing optimization patents are also unusable in the retail motor fuels industry because the competitor prices change so dramatically and so frequently. Further, consumers are able to easily compare prices between retailers and buy based on price more easily than in other markets because the price of motor fuel is so prominently displayed on the store signs. This means motor fuel retailers must react quickly to competitor price changes in the market. This is especially true when a competitor introduces a rewards program and immediately has an impact on sales for the existing store.
Thus there exists a need for a fuel store(s) optimization system that takes the unique nature of the retail fuel stores environment into account.
A method of optimizing one or more retail fuel stores that overcomes these and other problems uses a system having a first computer in communication with a database. Remote computing devices are connected to the first computer by a communication system. A number of electronic signs receive an instruction over the communication system. The system creates a correlation matrix having a number fuel prices for each of the retail fuel stores, a reward discount for each of the retail fuel stores, and a number of competitor fuel prices at the first computer, a profit for the fuel prices for each of the retail fuel stores, and a volume for each of the fuel prices for each of the retail fuel stores. It also creates an economic model that receives a number of correlation coefficients from the correlation matrix at the first computer. A multi-store optimization process configures the economic model to determine optimal fuel prices for each of the retail fuel stores based on a total multi-store profit. The optimal fuel prices for each of the retail fuel stores based on a total multi-store profit is transmitted to the electronic signs and displayed. Thus the total multi-store profit is maximized.
The invention is directed to a system and method of optimizing one or more retail fuel stores that uses a system having a first computer in communication with a database. Remote computing devices are connected to the first computer by a communication system. A number of electronic signs receive an instruction over the communication system. The system creates a correlation matrix having a number fuel prices for each of the retail fuel stores, a reward discount for each of the retail fuel stores, and a number of competitor fuel prices at the first computer, a profit for the fuel prices for each of the retail fuel stores, and a volume for each of the fuel prices for each of the retail fuel stores. It also creates an economic model that receives a number of correlation coefficients from the correlation matrix at the first computer. A multi-store optimization process configures the economic model to determine optimal fuel prices for each of the retail fuel stores based on a total multi-store profit. The optimal fuel prices for each of the retail fuel stores based on a total multi-store profit is transmitted to the electronic signs and displayed. Thus the total multi-store profit is maximized.
This application hereby incorporates by reference U.S. patent application Ser. No. 12/250,273, entitled “System and Method for Controlling Outdoor Signs”, US patent publication number 20110246313.
Statistical Methodology: A range of prices are offered to provide strategic insight into the pricing options with a range of +/−$0.10. The range of pricing options in $0.01 increments plot the volume and profit from each point on the curve. Furthermore, the model suggests the profit maximization point within the curve. The models are based on logistic multiple regressions and secondarily a correlation matrix 34 based on historical identified competitor pricing. The variables in the economic model consist of the change in competitive price movement, competitive index, volume gallon sales by commodity, and wholesale cost changes, date, date range, day of week, commodity pricing, among others.
Logistic models are used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve; it is a generalized linear model used for binomial regression. Like many forms of regression analysis, it makes use of several predictor variables that may be either numerical or categorical. Logistic regression is used extensively in the medical and social sciences as well as marketing applications such as prediction of a customer's propensity to purchase a product or cease a subscription.
Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. Correlations can also suggest possible causal or mechanistic relationships; however, statistical dependence is not sufficient to demonstrate the presence of such a relationship. A correlation matrix 34 is connected to the economic model 32 and contains a plurality of correlation coefficients 36.
Formally, dependence refers to any situation in which random variables do not satisfy a mathematical condition of probabilistic independence. In general statistical usage, correlation or co-relation can refer to any departure of two or more random variables from independence, but most commonly refers to a more specialized type of relationship between mean values.
There is a caution while using a correlation matrix 34 for competitive pricing. First, it is only on the number of competitors specified. Secondly, correlation does not imply causation; therefore the amount of fuel volume and/or price change due to a single competitor may not necessarily lead to the amount of fuel volume and/or margin.
Validity and reliability of the model analysis must also consider correcting for non-normal data distributions, skewness, and heteroscedasticy and homoscedasticity. The economic model is formulated within a non-sterile environment with real-world dirty data provided by actual customers. The economic model provides solutions where there is non-normal data distributions.
The economic model 32 is configurable by a store optimization process 38. The store optimization process 38 includes a number of options such a fuel price versus profit or volume or for multiple fuel prices 40. A multi-store optimization process 42 configures the economic model 32 to determine a maximum profit across multiple stores of the same company by defining the optimal fuel price(s). This is particularly important when a company has two or more retail fuel stores that are close to each other and seen by consumers as alternatives or competitors. A replacement costs and profit process 44 provides fuel prices and profit calculations to the economic model 32 and the correlation matrix 34. A competitor price rewards process 46 determines if a reported price is like to be a rewards price. This information is passed to the correlation matrix 34. The economic model has a number of outputs which usually includes a proposed price. This proposed price(s) are checked against regulatory requirements by the regulator check process 48. Two of the important checks are that the proposed price is not below cost, which is prohibited in many states and that the timing of the proposed price is allowed. For instance, some states only allow stores to change their fuel prices once a day. A price change process 50 may propagate the proposed prices to the electronic signs 22a, 22b, 22c, where the displayed price will be updated automatically, or it may send a chart of the possible choices on the proposed changes to a user who will select the updated price to be propagated to the electronic signs 22a, 22b, 22c. There the proposed change may be approved manually or the user may receive a chart of the possible choices and select the updated price to be propagated to the electronic signs 22a, 22b, 22c.
The price optimization system presents a method for scheduling price changes 50 into the future, as either a onetime price change event, or a set of recurring price change events. Scheduled price changes may apply to an individual store or a region of stores. Scheduled price changes ensure compliance with regulations related to price change frequency during times of market price adjustment when motor fuel retailers need to increase prices to recover from cost increases, but cannot increase prices more frequently than a specified number of hours (most typically 24 hours). Scheduled price changes enable price optimization at specific times in a day or week by allowing repeating price specials to be scheduled, to bring in additional customer traffic to the store, and to build customer loyalty.
This system uses numerous computing devices and communication systems. All of these systems are physical and result in the use of energy, movement of electrons, and the changing states of transistors. A computer is an electronic circuit that is wired using software. Software is a set of wiring instructions that are converted into the native language of a computer by a complier (or interpreter). The native machine language changes voltages in the computer to configure switches, i.e., transistors, to wire the electronic circuit that is a computer. The output of the computer is electronic messages (changes in voltages), which eventually turn on and off various lights, store electronic voltages (charges or states of transistors) that are indicative of the information desired by the user. Everything described herein can be implemented in hardware without a computer, because a computer is hardware. The methods described herein are a new and useful processes, the system to implement these processes are new and useful machines. The invention, like all inventions is how these elements are combined together. Every invention in the history of the world is a unique combination of existing elements, since conservation of matter and energy mean that no one can create something out of nothing. Looking at the elements in isolation is both not allowed under the law and logically absurd.
Thus there has been described a fuel store(s) optimization system that takes the unique nature of the retail fuel stores environment into account.
The methods described herein can be implemented as computer-readable instructions stored on a computer-readable storage medium that when executed by a computer will perform the methods described herein.
While the invention has been described in conjunction with specific embodiments thereof, it is evident that many alterations, modifications, and variations will be apparent to those skilled in the art in light of the foregoing description. Accordingly, it is intended to embrace all such alterations, modifications, and variations in the appended claims.
The present invention claims priority on provisional patent application, Ser. No. 61/831,722, filed on Jun. 6, 2013, entitled “Additional Capabilities For A Price Optimization System For A Chain Of Retail Fuel Stores” and both are hereby incorporated by reference.
