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 This application claims priority under 35 USC 119(e) based on provisional patent application No. 60/331,215 filed on Nov. 13, 2001.
 The present invention is directed to a method for selecting a site for a store such as a retail unit or restaurant, and in particular, to a method that parcels out the effect of advertising so that pure characteristics of the site can be used to determine whether the site should be selected, and one that allows for site selection based on the level of advertising.
 In the prior art, retail industry decision-making is becoming more sophisticated in nature, relying upon scientific methodologies and quantitative measures. Site selection is one of the most crucial decisions made in the chain retail (including especially restaurants) environment as it is capital intensive, has serious long term marketing implications, and is critical to corporate—franchisee relations. While there are a number of site selection tools in the prior art, the presently available tools forecast sales based on the sales of existing sites, and do not explicitly incorporate the effect of advertising into the site selection process. The problem with this approach is locating a new site in an area populated with other successful stores may not be solely related to site characteristics. A site selection finding that a geographic site has high sales may be a result of high advertising expenditures at that site. Consequently, putting a store at that site may only be successful if the same level of advertising is used. This flaw in prior art site selection tools is a result of the inability of the prior art tools to isolate the effects of advertising so that this effect can be removed from the site selection process.
 Accordingly, a need exists to provide site selection tools that are capable making site selection analyses without the effects of advertising skewing the site selection results. The present invention solves this need by providing a method which allows for site selection without the effects of advertising so that business owners have a more accurate picture of site characteristics without the effects of advertising.
 It is a first object of the present invention to provide a method of site selection for retail units.
 Another object of the invention is a method of site selection which parcels out advertising effects to better focus on other site characteristics for site selection purposes.
 Still another object of the invention is a method that is suitable for sites that include existing stores as well as sites that do not have stores.
 One other objective is a model that “controls” statistically for advertising, allowing comparison of sites with the effects of advertising held constant. The model also allows “what if” sales forecasts with different hypothetical advertising allocations.
 Other objects and advantages of the present invention will become apparent as a description thereof proceeds.
 The primary objective of the inventive method described is to identify, with a high degree of certainty, whether or not a site is suitable for development, the volume of business that can be expected, and the general degree of profitability to be expected at the site. The associated site-selection method compiles and analyzes data and presents findings in a user-friendly manner, allowing the user to adjust for “what if” conditions.
 Reference is now made to the drawings of the invention wherein:
 The invention involves a number of steps in order to arrive at an output that informs the business owner whether a particular site is desirable for development. A first mode of the invention addresses situations where other stores exist in the site being investigated for further development. A second mode addresses the situation where there are no other stores in the site.
 A first step in either mode is assembling of a database. The business owner or client supplies a sample of stores, or if possible the population of its stores, together with location information (latitude and longitude; these can be calculated from the address if necessary) and whatever store-specific information the system has. Often this includes store characteristics (size, model), and perhaps some local demographics. Many if not all of the variables are fixed items which are out of the control of the business owner.
 When using the first mode where stores exist in the site being investigated, store sales (or profit, or an alternative performance measure) over some period, preferably but not necessarily a year, must be included. A measure of advertising should also be included, typically target rating points or TRPs. Often these are available only for “market areas,” (DMA's). This is appropriate, as the advertising in a DMA affects sales for all stores in the DMA. These variables are augmented by census information available routinely. Data at the block group, tract and zip level are typically used. This data is available, for example in the commercial product MapPoint (MS), which is used, though many other mapping companies offer mapping capabilities augmented with census data. As the client desires, this data can be augmented with local business statistics available from Claritas or other sources. More detail leads to higher explanatory power in the regressions and hence better forecasts and better site selection.
 With this data at hand, a DMA average sales per store for each DMA containing 4 or more stores is constructed. While four stores are used, this number can be adjusted up or down if need be. Of course, for many chains there will be many DMA's without stores and this situation is addressed in the second mode of the invention.
 The next step involves a regression analysis and calculations using typical regression programs that are widely available. More specifically, the sales for each store are regressed on the assembled data including the DMA average for the sample of stores in DMA's with a reliable DMA average.
 As part of the analysis, insignificant variables are dropped. This step may require some experimentation, but in general those variables showing the lowest t-statistic after regression is completed are dropped. This leaves a grouping of remaining variables, which all have t-statistics generally greater than 1.5 in absolute value works. Of course, other values could be used as the cutoff to define which variables should be considered as part of the site selection.
 Once the insignificant variables are dropped, an equation for forecasting sales for sites in DMA's having stores already there is produced. In other words, sales could be predicted using the coefficients generated by the regression for each variable left in the equation.
 In the second mode of the invention, sales data is not available since the business does not have stores in the site being investigated. Therefore, all stores in the sample are used, and sales are regressed on the variable list, excluding the DMA average variable. After again selecting significant variables and dropping insignificant variables as done for the first mode, a forecasting equation is generated that is relevant to sites in DMA's where the chain does not have accumulated experience.
 The interpretation of the forecasting is done in two ways. One way involves predictions for a site on the basis of site characteristics alone. A second way involves the situation where there are already stores in the market, and their average sales are also used to predict sales at the new store.
 Forecasts for either mode are reported along with the regression standard error and probabilities associated with sales intervals e.g., Sales will be 1.0 to 1.25 million$/year with a probability 0.33). The interval of a year can vary, and be selected according to a client's needs.
 Another important aspect of the invention is accounting for or controlling for advertising. As noted above, if advertising is not accounted for, site selection may be based, at least in part on advertising allocation and give a misleading prediction as to store performance.
 Referring again to the second mode and the second regression described above wherein DMA average data is excluded, the regression includes variables controlling for the TRP or advertising allocation within the DMA. The regression uses three variables for the control; a dummy variable indicating whether the TRP allocation is zero, a variable indicating the level of TRPs, and a variable TRP-squared reflecting hypothesized TRPs. The point of the specification is use of a non-linear specification for the hypothesized TRPs so that its effect is allowed to be nonlinear. If the effect was linear then the logical conclusion is that all advertising expenditure should be concentrated at the same place, and such would not a useful recommendation for any business.
 The purpose of the suggested specification is to estimate the profitability of a site while holding constant the effects of advertising. Using the formula described above, the effect of advertising is zero up to some critical level, then positive. The formula for adjusting sales forecasts for incremental TRP level is: if the current TRP level at a site is zero, subtract the coefficient of the dummy variable indicating TRP zero, then add the estimated quadratic effect of the hypothesized level of TRPs. If the current level is nonzero, calculate the effect of TRPs at the current level, then subtract this amount from the calculated effect of the hypothesized TRP level. In effect, this formula is parceling out the effects of existing advertising in the sales forecast, either in situations where there is no advertising or a level of advertising at some defined level.
 With the formula, one can pick hypothetical TRPs and engage in “what if” analysis with respect to the effect on sales when the TRP is varied.
 The formula is adjusted so that “way out of sample” values are not exaggerated. In particular, negative effects at low, unobserved levels are excluded (set to zero) and effects at values greater than 1.25 times the maximum value observed in the sample are set constant at the estimated effect at 1.25 times the maximum value. The 1.25 level can be adjusted according to the client and the client's statistical sophistication.
 The specification described above is illustrated in the
 The specification or function is advantageous since it will allow a business owner to plug in TRPs for a site and forecast sales, independently of existing advertising effects.
 This technique offers advantages over current site selection analysis. That is, it is often the case that, if a chain is not interested in varying its advertising allocation, it decides that it is best to locate in areas where the advertising level is already high. However, this does not take into account the situation where it may be better to change the advertising allocation. Using the inventive method, a better decision can be made by considering the advertising allocation jointly with the site selection process. The inventive model allows management to consider the potential profit from opening additional stores in an area and adjusting the advertising allocation appropriately.
 In practice, the presentation of the results of practicing the inventive method is preferably driven by a Visual Basic front-end program linking with mapping software. One preferred example is MS MapPoint because it is convenient, and it has Active-X links. However, other mapping software could also be used. The front-end program could also be linked with financial performance display screen. The financial performance screen takes the sales forecasts given by the model and user-inputted site cost variables to provide multiple proforma profit and loss projections, break-even analyses, and net present value calculations (at user-chosen capitalization rates). A final observation relative to probable unit profitability will be made in this module. Default values are provided for key financial ratios using industry average values (for the restaurant industry these are assembled by Delolitte and Touche for the National Restaurant Association). It should be understood that once the sales forecast is made using the specification, any number of techniques can be used to show profitability based on the other data related to the business. Since generating a profitability in terms of a forecast of sales per year and a probability is within the skill on the art, a further description thereof is not deemed necessary for understanding of the invention.
 To illustrate, we show two schematic maps, Maps
 The second map in
 Comparing the two maps of
 However, the invention also has the ability to look at the effects of actual advertising, which can be much more informative when making site selection. Only actual advertising is used to determine profitability as shown in Map
 To relate Map
 Again, Map
 The two maps in comparison demonstrate that actual advertising levels are low compared to the national average and this is reflected in the profitability calculations. In addition, taking into account just actual advertising as shown in Map
 As noted above, another advantage of the invention is that the TRP can be forecast and the regression run to determine what effects may occur, the “what if” approach. Referring again to the graph above, plugging in estimates for TRP can generate an estimate as to incremental sales, and give one an idea of where they are on the curve for purposes of advertising.
 The mapping software is widely available and is only used for presenting the results of the patented method and any type available and known in the art are suitable for the invention.
 Another advantage in using TRPs in the evaluation of site selection is that TRPs are in the control of the business owner whereas site characteristics are typically fixed variables. Thus, while site characteristics can be isolated to determine their relative effects on profitability, advertising can also be controlled for site selection. While restaurants have been exemplified, any type of a retail unit can be sited with the invention.
 As such, an invention has been disclosed in terms of preferred embodiments thereof which fulfills each and every one of the objects of the present invention as set forth above and provides a new and improved method for site selection of retail units.
 Of course, various changes, modifications and alterations from the teachings of the present invention may be contemplated by those skilled in the art without departing from the intended spirit and scope thereof. It is intended that the present invention only be limited by the terms of the appended claims.