COMPUTER SYSTEM TO PLAN AND EVALUATE PUBLICITY AT THE POINT OF SALE FOR A TIMELY SALES ENTITY
Field and Background of the Invention: The planning, evaluation and review of advertising strategies is a continuous challenge for a retail entity. Traditionally, this advertising analysis is specific to the product or service. This traditional approach focuses on what types of products and / or advertising methods would be the most attractive to different demographic groups of consumers. An example of traditional advertising analysis uses data of interest, the types and numbers of consumers that show an interest in a particular ad, rather than the number of advertised products actually sold as a result of advertising. Another example of traditional analysis uses sales data specific to the product, the difference in sales of a particular product when it is offered and when it is not offered. The limitation of these approaches is that they try to predict the results of a complicated system using only one or two factors. This one- or two-dimensional strategy fails to take into account, for example, the effect of a feature advertised in the sales of other products sold by the retail entity, the total sales of the retail entity, the number and types of clients attracted to the retail entity or the long-term consequences of these effects. In reality, the success enjoyed by a diverse retail entity of a published strategy. It is based on much more than the increase in sales of a single item. Success is maximized only when the advertising strategy focuses on the most efficient mix of attracting customers to the store, directing customer traffic through the store, focusing on a broad spectrum of customer price sensitivities, to attract customers to products not offered in advertising, to reward customers for their loyalty and to maximize total re-entry. In addition to being too limited in scope, as noted above, the traditional approach is inefficiently broad in other respects. For large interstate retail entities, an analysis across the entity is inefficient, because consumers in different geographic regions tend to have different needs, buy different products, expose themselves to different alternative purchasing clauses and have different price sensibilities. The habits of the clients also differ with time. For example, the needs and desires of customers, as well as the availability of products, change frequently with the seasons.
Brief Description of the Invention The present invention meets these challenges by analyzing the complex interactions between a broad array of advertising and sales data, while maintaining the ability to scale the analyzes to particular geographic regions, particular periods of time, particular groups of products, and / or particular groups of consumers. Therefore, it is a first aspect of the present invention to provide a method for planning and evaluating advertising for a retail entity comprising the steps of: (a) collecting, with the aid of a computer, a first data set advertising publication for a plurality of offered products published by the retail entity | during a first period of time, where advertising publication data includes data regarding the products offered in the advertisements, data regarding the type of advertising, data regarding the term of advertising, and / or data regarding the advertising region; (b) collecting, with the aid of a computer, a first set of transaction data for a plurality of transactions carried out by a plurality of consumers who purchase a plurality of products from the selling entity. retail during the first period of time; (c) analyzing, with the aid of a computer, the first set of advertising publication data with respect to the first set of transaction data during the first period of time; and (d) classifying, with the help of a computer, the plurality of products offered in advertising roles based on the analysis of the first set of advertising publication data with respect to the first set of transaction data, where the plurality of roles advertising includes products offered that attract customers to a particular part of a retail entity, products offered that promote the attraction of a mix of consumers of different consumer classifications to buy the products offered, products offered that promote a balance of different types of products purchased by a consumer, products offered that promote a total increase in sales by the retail entity, and / or products offered that promote a combination of two or more of the previous advertising roles. It is a second aspect of the present invention to provide a computerized method for evaluating and / or planning advertisements for a retail entity, comprising the steps of: (a) collecting, with the aid of a computer, transaction data for a plurality of consumers of transactions carried out with the retail entity during a first period of time, and a second period of time; (b) collect, with the help of a computer, advertising data for at least one product offered by an advertisement, where the advertising data includes at least the identity of the product offered and an association between the advertising and the second period of time; (c) evaluate, with the help of a computer, the transaction data associated with the offered product in comparison to at least between the first period of time and the second period of time to determine: the effectiveness of the advertising that attracts customers to carry out transactions with the retail entity, the effectiveness of advertising promotes transactions for consumers of a first category versus a second category, and / or the effectiveness of advertising that increases revenues for one or more of the products offered and products not offered; (d) compiling the results of step (c) of evaluation and displaying the results, with the help of a computer, to a user in the form of a graphical user interface; and (e) provide the user, through the graphical user interface, with a tool to select one or more products to offer in future advertisements based on the compiled results. In reading the following specification, with reference to the appended figures, and the appended claims; one skilled in the art will recognize that the present invention comprises many additional aspects and advantages.
Brief Description of the Figures Figure 1 shows a general flow diagram of a method according to an example embodiment of the present invention; Figure 2 shows a graphical user interface that provides output according to an exemplary embodiment of the present invention; Figure 3 shows an alternative graphical user interface that provides the output according to an example embodiment of the present invention; Figure 4 shows an additional graphical user interface that provides the output according to an example embodiment of the present invention; and Figure 5 shows a spreadsheet output according to the exemplary embodiment of the present invention.
Detailed Description of the Invention The present invention provides a method for planning and evaluating advertising (and in a specific embodiment, store circulars and related advertisements) for a retail entity by collecting and analyzing "publication data" related to advertising. and "transaction data" associated with transactions that occur before, during and / or after the advertising has run; classify the "scope", "balance" and "re-entry" of these advertisements based on the analysis; and use the classifications to establish a future advertising strategy. From the synthesized publication and transaction data, the method allows the user to deduce the most productive combination of "offered products" to be executed for discrete geographical locations to achieve a desired combination of "reach", "balance" and "re-entry". . The "publication data" as will be more fully described below, are typically advertising data collected from the retail stores themselves; and include data with respect to the products offered, the type of advertising, the term of advertising, the advertising region and the like. The "transaction data" as will be further described below, are typically purchase acquisition data that is collected from the "frequent buyer cards", "loyalty cards" and the like owned by, or associated with, each customer. An "offered product" or "characteristic" refers to a particular product and / or service that is offered in the advertising of the retail entity; and it can also refer to a group of products offered (for example, all variations of a product line that is for sale, such as all variations of a brand / company of soups or soft drinks). Although the exemplary embodiments of the present invention refer to products offered executed in an advertisement circular for a specific store or stores; the products offered can be found alternatively in television commercials, radio announcements, exhibitions at the point of sale, promotions at the point of sale, electronic advertisements (email, Internet and similar) and other types of advertising or promotions such as it will be evident to those skilled in the art. The "scope", "balance" and "re-entry" refer to different economic effects that the advertising of products offered in store transactions may have. "Reach" is the effect of attracting customers not only to a particular store, but to a particular location or locations within the store (the scope can be calculated as the percentage of customers, in a particular category (s) of customers, which make transactions for one or more of the products offered); "Balancing" is the effect of attracting the most productive mix of customers from different customer classifications (ratings of customer personalities such as price sensitivities, demographic rankings or a combination of both such as lifestyle classifications) to the mix more productive products; and the "re-entry" is the effect of maximizing the percentage and sales in dollars not only of an offered product, but of all the products offered by the retail entity.; or in other words, the effect of maximizing re-entry in the investment. As used herein, the term "product" includes not only consumer products that can be purchased at a retail store, but also any other product, service, or item of value that can be supplied by a business or a client. In an exemplary embodiment of the present invention, the collected publication data includes "characteristic dimensions", which may be information regarding the merchandise, secondary merchandise, manufacturer and / or size of a particular offered product. In a more detailed form, the publication data includes "advertising dimensions" which can be information with respect to the base price (which can be MSRP, the standard price charged by the retail entity or an estimated / calculated price that is approximate to this base price), property price (advertised price), advertising exposures and / or advertising execution of the particular property. The advertising execution may include a variety of execution dates of a particular advertisement and / or a list of individual store locations where this advertisement is executed. As each retail store of a retail entity plans and executes the advertising, the publication data can be recorded in a centralized database, accessible by one or more of the retail stores and / or retail stores. administrative offices of the retail entity. This centralized database can provide access to publication data for an individual store, a group of stores located in a particular geographic division, or for all stores in the retail entity. This ability to open and analyze data at various organizational levels of the retail entity is referred to as "granularity". "Transaction data" refers to data with respect to any transaction or interaction between the consumer and the business. In a particular embodiment, the transaction data includes the "purchase acquisition data", which may be information regarding the consumer's purchase history, which include the identity of products and quantities thereof that the consumer has purchased. As used herein, the term "product" includes not only consumer products that may be purchased in a retail store, but also any other product, service or item of value that may be supplied by a business to a retail store. consumer The purchase acquisition data can be: collected using a unique identification code residing on a label or card, for example, commonly known as a "frequent buyer card" or "loyalty card", owned by (or assigned from another mode a) each client. These cards or labels contain the unique identification codes stored by a bar code, magnetic means, or other data storage device and can be read by an electronic device and in various ways that are well known to the person skilled in the art. . Of course, these unique identification codes may reside on different items of cards. For example, this unique identification code can reside in RFID mechanisms, bracelet keys and the like. When a consumer goes through the verification process in a warehouse and the products that are purchased are scanned, for example, the unique identification code of the consumer's frequent buyer card can be read by an electronic device. The warehouse computer system can then compile a record of the products that are purchased during this particular sale and associate that list with the unique consumer identification code. By repeating this process every time the consumer visits the store and makes purchases, the store can accumulate a cumulative record of the particular purchase history of the consumers, including the identification of products and quantities of the same that the consumer has purchased. The compiled record of the consumer's purchase history can be stored in a database and analyzed as analyzed in this. The "consumer" whose purchase history is outlined may be an individual or a family group, therefore, consisting of a group of people residing at the same address or using the same credit card account, or even a business or government entity. In an alternative mode, the consumer purchase acquisition data may be associated with the consumer using other consumer identification information (such as a telephone number, store credit card, bank credit card, or credit card number). checks) in place of frequent buyer card codes, RFID tags, or similar items. In this way, the details of a particular transaction can be matched to the consumer's previous transactions, thus facilitating the continuous addition of transaction information to each customer record in the database. Each customer record in the database may comprise a plurality of entries or transaction records, one for each transaction by that customer. For each of these consumer registers, an example code is provided that identifies the SKU / product (s) purchased by the customer for the transaction.; a code that identifies the particular transaction or "basket"; a code that identifies the customer or family group for which the transaction is attributed; a code that identifies the warehouse in which the transaction was presented; data regarding the quantity of the product purchased and the amount spent; data regarding the date, time, etc., of the purchase; and any other data or codes, such as a code that indicates a geographic region for the purpose, as it may be useful to generate reports based on this transaction data. The code in the transaction record that identifies the SKU / product can be used to retrieve details regarding this product from a separate database that contains a plurality of "product records" for each product. For each "product registration" in the product database, it is provided, in the example modalities: data or codes of grouping or categorization of products; UPC data of products; data or codes of the manufacturer or supplier; and any other data or codes, such as suggested retail price data, which may be useful for generating reports based on a combination of transaction data and product data. The code in the transaction record that identifies the customer or family group for the transaction can be used to retrieve details regarding that family group from a separate database containing a plurality of "family group records", one for each family record. For each "family group registration", you can provide, in the example mode: data and / or codes regarding demographics, purchase history, purchasing preferences and any other data or customer codes as they may be useful for generate reports based on the combination of transaction data and customer / family group data. In the code in the transaction log that identifies the store in which the transaction has been filed can be used to retrieve details regarding that store from a separate database containing a plurality of "warehouse records", one for each store. For each "warehouse record", in the example mode: warehouse name data is provided; data or warehouse location codes; and any other data or codes as it may be useful for generating reports based on a combination of transaction data and warehouse data. As will be appreciated by those skilled in the art, the database registration structures described above are only exemplary in nature and that unlimited combinations of records and database hierarchies are available to cross-reference transaction information, information of products, customer information / family groups, warehouse information, location information, synchronization information, and any other information appropriate to one another. Additionally, one skilled in the art will appreciate that the invention is not limited to use with retail store transactions and that the invention can be used with most (if not all) types of transactions (such as financial / banking transactions, insurance transactions, service transactions, etc.), where the structures and hierarchies of the database will be adapted to generate reports of this alternative transaction data. Figure 1 shows a flow diagram illustrating an exemplary method of the present invention. The individual retail stores 12 of a retail entity are grouped into geographical divisions 14. Each division works differently, so that each division provides the system separately with data in the form of tables 16 of products that list the different products offered by the stores within that division. These product tables 16 identify the identity of the product, the merchandise and the; secondary merchandise of each product sold by the stores in that division. The system then classifies each product according to departments 18 centric to the customers, where each department represents specific spatial locations of the products within the warehouse. For example, the salad bar, fresh meats, products, canned foods, milk products, laundry sections, paper products, pet food, soft drinks, bagged snacks, frozen starter meals, frozen desserts, cereals, etc., Examples of downtown departments are customers that can be defined for a typical supermarket. As will be appreciated by those skilled in the art "centric to customers" for this classification, it is based on how customers see the retail entity as opposed to how they can see the retail entity themselves. In addition to providing tables of. products, each geographical division plans the products offered that are going to be announced by the stores within its division. These plans can then be reviewed and reviewed by the individual stores when entering the "weekly ad encoding" data that represents the completed plans in the system. These revisions can also be done at the level of sub-regions or by an inspection body. The system feature grouping machine 22 takes the coding of weekly advertisements from the individual store and supplements this information with respect to the advertised products, display information, price, distribution method and the like. From here, the data of coding of complemented advertisements and the data of the central departments to the clients are synthesized with the analysis rules (described below) to create data 23 of unmodified publications. The system also collects transaction data 24 from each store using any of the methods described above. As discussed above, the transaction data will include information regarding how many customers buy the products offered, which other products were purchased, when the products were purchased, and the like. The unmodified publication data 23 and the unmodified transaction data 24 are then sent to the metric machine 26, which accumulates the normal metrics used by the adding and sorting machine 28 to analyze the transaction data in view of the data of publication (classifies each product offered according to its economic effect, that is, how the product offered promotes reach, balance and / or re-entry). In a more detailed example mode, the system uses two types of metrics, the individual feature metrics and the cumulative metrics. The individual metrics analyze the unmodified data for each product offered, including the price of the product offered when it is offered and not offered, and calculates the relevant "attractive segment", the "basket size", the "elevation" and the " penetration "of each characteristic. The cumulative metric analyzes the cumulative statistics with respect to any or all of the products offered and also provides a baseline analysis for all the products offered. The "attractive segment" refers to any fragment of consumer attributes that can be emphasized to attract the consumer segment to a retail entity, or to a portion of the retail entity, such as consumer sensitive to price, as an example. The group in which a particular consumer is placed will be determined from the characteristics about that customer that can be determined from the customer's purchase history. Because a customer's purchase history, which includes the identity of the products and quantities of the same that the customer has purchased, provides valuable understanding of the lifestyle, financial means and other important characteristics of the client, allows customers to be divided into groups according to several selection criteria. The group of clients in which a particular client is placed can also be based on demographic data and / or personality data, which can be determined or not from the client's transaction history. Demographics may include, but are not limited to, age data, income data, geographic data, and educational level data. Personality data (also referred to as the "transaction personality" of the client) may include, but are not limited to, price sensitivity, negotiation trends, use of coupons, attention to promotions, loyalty, attention to locations or configurations of products, and the like. Those skilled in the art will appreciate the numerous sources for this demographic and / or personality data. In a more detailed mode, the products are categorized as follows: a "low end" product is one typically purchased by consumers who are very price sensitive; an "intermediate" or "broad" product is one purchased ^ typically by prevailing consumers; a "high end" product is one typically purchased by a consumer that is not price sensitive; and a "broad" product is one purchased by members of a number of price sensitive consumer groups. When analyzing the similarities or differences between consumer groups that buy a product when offered as the opposite of when it is not offered, the system is able to analyze the equilibrium effect of each characteristic. Additional support for categorizing consumers in these ways can be found in co-pending patent application serial number 10 / 955,946, filed on September 30, 2004; the description of which is incorporated herein by reference. The "basket size" refers to the number of items purchased in a transaction between a consumer and a store. In a more detailed mode, the basket size is divided into three categories: small, medium and large. The system takes the basket size of each transaction in which a characteristic was purchased, and calculates the average basket size for each individual characteristic. In determining the average basket size for each characteristic, the method is able to determine how the characteristic fits into the broad appeal of the store. Do customers come only to the store to buy an amount of that particular item? Customers buy other products too? "Elevation" refers to the increase in re-entry that resulted from a particular product being offered. The elevation can be expressed as a. percentage of "sale elevation", which shows the percentage increase, if any, in sales as a result of the product offered, or "elevation in dollars" that shows the increase in income, if any, as a result of the product that is offered. The combination of lifting sales and raising in dollars provides measures of
"re-entry" for the characteristics of the products. "Penetration" refers to the number of clients "reached" by the characteristic. It can be expressed as "customer penetration", which represents the percentage of all store customers who bought the item offered during the specified period of time. Finally, "associated sales" is a weighted version of penetration. For example, it may represent the percentage of the total spent by consumers who purchased a property represented. In this example, if all customers in a particular store spent a total of $ 100,000.00 during the time period, and all customers who purchased the property spent a total of $ 50,000.00 during the time period, the sales associated with this feature are of fifty %. The weights can also be weighted with respect to other attributes (different from total sales as given in the previous example) such as the weighting according to: basket size, demography, price sensitivity and the like. This can be calculated for a particular store, for a division, or for the entire retail entity, and is how the method determines the scope effect of each characteristic. While the "individual metric" analyzes data at a level of individual characteristics, the "cumulative metric" analyzes data from large product groups. The present invention uses cumulative metrics to generate broad data for all products offered in a given period of time, as well as baseline data with respect to all products. For example, the cumulative metric may include the number of customers who buy any offered product, the total sales of all the offered products purchased, and / or the total elevation of all offered products purchased. As a baseline, you will then additionally calculate the total number of customers who buy any product, the total sales of all products purchased, and / or the total elevation of all purchased products. The addition and classification machine 28 also classifies each product offered into significant "type" classifications for use by retail entity personnel. In a more detailed example embodiment of the present invention, the products offered are divided into three general "type" classifications, "anchors", "assistants", and "unknown". The "anchors" are offered products that promote the economic effects of the scope and re-entry, while the "assistants" are offered products that promote the economic effects of balance and re-entry. An "unknown" classification is used when the system has not been provided with sufficient publication and / or transaction data for a particular characteristic to meaningfully analyze its economic effects. Anchors are essentially essential items for the retail entity with high buyback rates. For example, an anchor product for a grocery store may be bread, milk and / or cola; while the anchor products for a store of electronic products can be DVDs, CDs and batteries. Helpers complement the anchors by boosting the balance. The helper products will have a slightly more limited range than the anchors; that is, products that. They are commonly purchased, but not virtually every time the store is visited. For example, in a grocery store, helpers can include detergents, paper products, fish, frozen foods; and in an electronic products store the assistants can include auxiliary items, DVD players, CD players and speakers. In an even more detailed modality, the classifications of anchors and helpers are further divided into sub-classifications. For example, products offered as anchor can be divided into "reward anchors", "incentive anchors", "anchors of traffic", "anchors of large labels," "low end anchors" or other types of anchors such as anchors categorized demographically Reward anchors are products offered that reward customers for buying essential items that they would buy despite the publication Milk is an example of a reward anchor: incentive anchor products are high end products when they are not offered, but when they are offered, they encourage individuals who in general buy only prevailing or low-end products to "buy." Consistent purchase of the product offered causes the product to be re-designated as a prevailing or low-end product when it is offered. offered to guide customers in the store for the specific purpose of purchasing the item offered, thereby propelling customers in and through the A typical example of a traffic anchor is cola refreshment, since many consumers tend to "go to the store" weekly several times for refreshments. Large-label items are products with a relatively high advertised price and relatively low associated sales; so that a large tag anchor will be a large tag article that becomes an anchor when it is advertised. Assistive products can also be divided into the same types of sub-categories, such as "low end assistants", "high end assistants", and "warehouse helpers". Low end and high end assistants are offered products that help reinforce the re-entry of low end and high end product; respectively; and warehouse products are offered products that reinforce the re-entry of large articles e! intermediate In an even more detailed modality, the system can qualify the classifications, depending on how effective the characteristic is in producing the relevant economic effects. For example, a feature that modestly reinforces re-entry for broad and intermediate items can be designated as a "weak warehouse assistant". While the system maintains minimum criteria for each classification, the specific criteria can be based on differences in sales, preferences and economy between different divisions, or even different individual stores. After the system summarizes the. data without modifying and classifying each characteristic, uses a "final tool" 30 to distribute the synthesized information to each user. In a detailed embodiment of the present invention, the final tool is a practical user interface. As will be described later and as shown in Figures 2-4, the graphical user interface organizes the information so that it can be easily accessed by a user. With reference back to Figure I, the unmodified data and classifications are monitored by the "reference tracking" component 32 of the system. This component provides periodic analyzes that are used to modify the rules that govern the grouping of products offered and the sum of data and the classification of products offered. This periodic modification is important, since it changes the behavior of customers, for example, with the seasons, with the growth of a particular geographical region, with the introduction of new products in the market, etc. In a more detailed embodiment of the present invention, the "follow-up reference" component 32 produces a "quarterly analysis" 33. This "quarterly analysis" is used to modify the rules quarterly. In a more detailed alternative embodiment of the present invention, analysis and continuous modification are continuously produced, as needed, by the rules. In another more detailed alternative embodiment of the present invention, a user can observe the reference tracking and analyzes and activate the modification of the rules each time. In addition to varying the period of time between the modification of the rules, the present invention can make modifications to varying levels of granularity. In a more detailed embodiment of the present invention, the rules are modified independently of each geographic division 14 of retail stores. In a more detailed alternative mode, the rules are modified independently for each retail store. These store-specific rules are referred to as "store-specific metrics searches" 34. Figures 2-4 show an example mode of a graphical user interface for displaying the results of the analyzes that are provided. A user may choose to view information specific to a particular geographical division and a particular period of time. The graphical user interface allows the results of the analysis to be shown in three different formats in the example mode: a weekly completion format, a trend format per period and a feature database format. The displays for each of these formats can be uploaded by activating the respective corresponding button in the graphical user interface: the weekly performance button 36, the trend button 38 per period or the feature database button 40. The display shown in Figure 2 is the display of the characteristics database. It shows how each of the products worked during a certain period. The first "division" column 42 identifies the geographical division for which the user has chosen to view the information. The second column "week" 44 shows the period of time in which the displayed data is collected. Just as announcing similar products in different geographical divisions produces different effects, it may be the announcement of similar products during different periods of time.
The third, fourth and fifth columns display the identity of the "characteristic" 46 of the product offered, the department centric to the customer or "loyalty department" to which the characteristic corresponds, and · the "type" of classification given to the product. offered by the system 50. The third column 46, or the characteristic column, identify the specific characteristic analyzed. By clicking on the "see feature name" button, a user toggle back and forth between seeing the name of the feature, as it appears in the advertisement, and the dominant UPC for each feature group (the dominant UPC is the UPC that accounts for the highest sales for an advertised product). It is also within the scope of the invention that this alternation may allow the display of all the UPC's of the characteristic and not only of the dominant one. The fourth column 48 identifies which customer centric department the advertised product corresponds to. For example, "Big K Dietary Root Beer" corresponds to the soft drink loyalty department. The type of classification of each characteristic is displayed in the fifth column 50. It is pointed out that, in this modality, the types of classification are generated by the machine 28 of addition and classification as analyzed above, which divides the types of classification in sub. -classification and provides qualifiers. For example, if a characteristic was classified as a weak warehouse assistant, this will mean that the property modestly reinforced the sales of the. prevailing products. The rest of the columns shown in Figure 2 show processed data that was used by the system in the classification of each characteristic. The sixth column "Dist. De Almacén" 52 informs the user what percentage of the stores in the division offer the offered product; the seventh to ninth columns "Price Point Change" 54, "Base Price" 56 and "Front Price" 58 provide information on the price difference between the product when it is offered and when it is not offered; columns ten and eleven "Base Position" 60 and "Frontal Position" 62 provide classification of the type of consumer who buys the product at base price versus the type of consumer who buys the product at the advertised price; column eleven "Basket Size" 64 indicates the number of other products purchased with the offered product; column fourteen "Client Pen" 70 lists the percentage of customers who have purchased the offered product; and the fifteenth column "Associated Sales" 72 lists the percentage of total sales that the product offered represents in comparison to the other products. Column twelve "Elevation of Sale" 66 and column thirteen "Elevation in Dollars" 68 expresses the increase in sales of a product when offered versus when it is not offered. The sales increase 66 represents the percentage increase in the number of a particular product sold when the product is offered versus when the product is not offered. The increase in dollars 68 represents the increase in income generated by the sales of a particular product when the product is offered versus when the product is not offered. The graphical user interface provides the base price (the price of the product when it is not offered), the front price (the price of the product when it is offered) and the percentage of the difference in the two prices. It also classifies the product by price sensitivity in its base and front prices. These columns allow the user to see if the product is attractive to different groups / categories of customers when offered as the opposite of when it is not offered. For example, when it is not offered, some products are attractive to consumers with high end price sensitivity, but are attractive to consumers with moderate price sensitivity when they are offered. Because some products are attractive to a different attractive segment when offered as the opposite of when they are not offered, this information is vital when analyzing current "balance" effects and predicting the future of particular characteristics.
Another feature of the graphical interface shown in Figure 2 is that it allows the user to create an ad evaluation template that allows the user to compare the proposed features against the predetermined reach, balance and re-entry objectives. By choosing from the products offered above, by clicking on the division column for the relevant characteristic (or by dragging the offered articles themselves into Evaluation Table 74) the user can create a proposed advertising plan in Table 74. For example, each user can be provided with objectives or criteria that specify the approximate numbers of each type that he wants to be included in a particular weekly advertising plan to achieve the desired "reach", "balance" and "re-entry" effects . These criteria can be provided at varying levels of granularity. In a more detailed embodiment of the present invention, these criteria are provided independently for each geographic division 14. In a more detailed alternative embodiment, these criteria are provided independently for each individual retail store 12. By referring to these criteria, a user can evaluate each proposed advertising plan and adjust the plan to meet the specific needs of "reach", "balance" and "re-entry". Once the user has added the combination of offered products proposed in Table 74, the user can then click on button 75 to create ad evaluation template and the proposed features deposited in Table 74 will be exported to a file of spreadsheet, referred to as a matrix template, as shown in Figure 5. The matrix template allows the user to look through all the products to see how the proposed features perform together. The matrix template includes rows / spaces for the proposed features and columns for the key metrics associated with the proposed features that show how these features have played in the past. The matrix also includes sections for additional comments that can be added by the user and additional space to evaluate the complete movement towards the three scope, balance and re-entry objectives. This data allows the retail entity to see which features have the strongest potential to reach consumers, create additional sales and include all customer characteristics. This information allows the retail entity to balance its investments each week (or other advertising period) to maximize the mix of features necessary to meet predetermined objectives. While the exemplary embodiment of the present invention performs this matrix template evaluation step manually with the aid of a computerized spreadsheet tool, it is certainly within the scope of the invention to allow the software to automate further (if not is that completely) the process of selection and evaluation. Figure 3 shows the "weekly performance" screen of the sample graphical user interface. This screen provides the user with natural data, percentage of data and graphic data that reflect the effects of reach, balance and re-entry experienced by a particular geographical division (or any other selection or combination of these retail entities) during a period particular of time. In the example mode, the user can choose to see data from different geographical divisions and / or different weeks by clicking on the box 76 of calda below "select division" and / or the box 78 of falling down "select week " Graph 80 of "full weekly performance" provides the user with complete data of "reach", "penetration" and "elevation". A total percentage of
"scope" 82 is provided together with the numerical data from which the percentage was derived, the total number of family groups 84 who purchased at any of the retail stores within the geographic division 76 selected during week 78 selected and the number of these family groups 86 who purchased any of the products offered during the selected week 78. The scope of an income perspective is also represented by the percentage of "associated warehouse sales" 88. This figure represents what is known as the "halo effect", and is calculated by finding the percentage of "total warehouse sales". "90 that were the sales to customers who purchased one or more characteristics (ie, the" total associated warehouse sales ") 92. The scope is further decomposed to show the percentage of customers 94 very price-sensitive (" VPS " ) and price-sensitive ("PS") 94 who were matched by the weekly characteristics, finding the percentage of "total VPS / PS family groups" 96 who purchased one or more characteristics (ie, the "VPS / characteristics"). PS coupled by the front page ") 98 during week 78 selected. The percentage 100 of "sales penetration" represents the percentage of the "total sales of the front page features" 104 that were the "total warehouse sales" 102. Finally, the "elevation" represents the increase in income for products offered, experienced as a result of advertising. Figure 3 shows a percentage of "sale elevation" 106, as well as provides figures in dollars for both "base sales for front page features" 108 and "total dollars of elevation above base dollars" 110. Figure 3 it also presents the data of reach and elevation decomposed by attractive segments in its graph 112 of "how do you perform my characteristics by segment?". A scope graph 116 presents the total number of family groups in each attractive segment that was purchased, and the total family groups that were matched by one or more characteristics in each attractive segment, and the percentage of family groups in each segment of the group. Attractiveness that was "achieved" by the characteristics. The range percentage data for each attractive segment compared to the total reach percentage are also presented by a bar and line graph 118. An elevation graph 120 presents base sales, sales of front page features, and percentage of elevation for each attractive segment, as well as total base sales, total front page sales, and total percentage of elevation. Base sales and front page sales for each attractive segment are also compared using bar graph 122. Figure 4 shows the "performance trend" screen of an example graphical user interface. The reach period trend graph 123 allows a user to determine whether stores within a particular geographic division 126 experienced higher total sales during the weeks in which they experienced greater total reach. Again, the presentation is broken down by geographical division into the example mode, but the invention is flexible enough to decompose the presentation by other combinations of retail entities, periods, product categories, etc. Graph 124: indicates the last week for which the data have been reported. The x 132 axis of graph 123 represents the individual weeks that constitute the period analyzed. The y-axis 128 of the bar graph represents the total sales of the warehouse and the y-axis 130 of the line graph represents the reach percentage. When placing the total sales with the reach data, this screen allows a user to evaluate the effect of the reach on the total sales of the stores in a particular geographical division over an extended period of time. Having described the invention with reference to example modalities, it is to be understood that the invention is defined by the claims and it is not proposed that any of the limitations or elements describing the exemplary embodiments set forth herein will be incorporated into the meanings of the claims unless these limitations or limitations are met. elements are explicitly listed in the claims. Likewise, it will be understood that it is not necessary to comply with any or all of the advantages or identified objects of the invention described herein in order to fall within the scope of any of the claims, since the invention is defined by the claims and since there may be inherent and / or unanticipated advantages of the present invention although they may not have been explicitly discussed herein.