AU2007202292A1 - Method for planning and evaluating in-store advertising for a retail entity - Google Patents

Description

AUSTRALIA

Patents Act 1990 ORIGINAL COMPLETE SPECIFICATION STANDARD PATENT Invention title: METHOD FOR PLANNING AND EVALUATING IN-STORE ADVERTISING FOR A RETAIL ENTITY The following statement is a full description of this invention, including the best method of performing it known to us: METHOD FOR PLANNING AND EVALUATING IN-STORE ADVERTISING FOR A RETAIL ENTITY C Background of the Invention Planning, evaluating and revising advertising strategies is an ongoing challenge for a retail entity. Traditionally, such advertising analysis is product or service-specific.

Such traditional approach focuses on what types of products and/or methods of advertising would be the most attractive to different demographic groups of consumers. One example of traditional advertising analysis utilizes interest data C- 10 the types and numbers of consumers that show an interest in a particular advertisement rather than the number of advertised products actually sold as a result of the advertisement. Another example of traditional analysis utilizes productspecific sales data the difference in sales of a particular product when advertised and when not advertised.

The limitation of both of these approaches is that they attempt to predict the results of a complicated system using only one or two factors. Such one- or twodimensional strategy fails to take into account, for example, the effect one advertised feature has on the sales of other products sold by the retail entity, the total sales of the retail entity, the number and types of customers attracted to the retail entity or the long term consequences of these effects. In reality, the success a diverse retail entity enjoys from an advertising strategy is based upon much more than increase in sales for a single item. Success is maximized only when the advertising strategy targets the most efficient mix of attracting customers to the store, directing customer traffic through the store, targeting a broad spectrum of customer price sensitivities, attracting customers to products not featured in the advertisement, rewarding customers for their loyalty and maximizing overall return.

In addition to being too limited in scope, as stated above, the traditional approach is inefficiently broad in other respects. For large interstate retail entities, an entity-wide analysis is inefficient, because consumers in different geographical regions tend to have different needs, purchase different products, be exposed to different alternative shopping venues and have different price sensitivities. Customer habits also differ chbm A0108579542v1 305853976 with time. For example, customer needs and desires, as well as product availability, often change with the seasons.

Sunmmary of the Invention The present invention meets these challenges by analyzing the complex interactions among a broad array of advertising and sales data, while maintaining the ability to scale the analyses to particular geographical regions, periods of time, groups of products, and/or groups of consumers.

It is therefore a first aspect of the present invention to provide a method for planning and evaluating advertising for a retail entity comprising the steps of: collecting, with the assistance of a computer, a first set of advertisement publication data for a plurality of featured products published by the retail entity over a first period of time, where the advertisement publication data includes data concerning the featured products in the advertisements, data concerning the type of advertisement, data concerning the term of the advertisement, and/or data concerning the region of the advertisement; collecting, with the assistance of a computer, a first set of transaction data for a plurality of transactions conducted by a plurality of consumers purchasing a plurality of products from the retail entity over the first period of time; analyzing, with the assistance of a computer, the first set of advertising publication data with respect to the first set of transaction data over the first period of time; and classifying, with the assistance of a computer, the plurality of featured products into advertising roles based upon the analyzing of the first set of advertising publication data with respect to the first set of transaction data, where the plurality of advertising roles include featured products that attract consumers to a particular part of a retail entity, featured products that promote attracting a mixture of consumers of different consumer classifications to purchase the featured products, featured products that promote a balance of different types of products purchased by a consumer, featured products that promote an overall increase in sales by the retail entity, and/or featured products that promote a combination of two or more of the above 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: chbm A0108579542v1 305853976 collecting, with the assistance of a computer, transaction data for a plurality of consumers from transactions conducted with the retail entity over a first period of time and a second period of time; collecting, with the assistance of a computer, advertising data for at least one product featured by an advertisement, where the advertising data includes at least the identity of the product featured and an association between the advertisement and the second period of time; (c) evaluating, with the assistance of a computer, the transaction data associated with the featured product in comparison at least between the first period of time and the second period of time to determine: the effectiveness of the advertisement attracting consumers to conduct transactions with the retail entity, the effectiveness of the advertisement promoting transactions for consumers of a first category versus a second category, and/or the effectiveness of the advertisement increasing revenues for one or more of the product featured and the products not featured; (d) compiling results of the evaluating step and displaying the results, with the assistance of a computer, to a user in the form of a graphical user interface; and (e) providing the user, by the graphical user interface, a tool for selecting one or more products to feature in future advertisements based upon the compiled results.

Upon reading the following specification, with reference to the attached figures, and the appended claims; one of ordinary skill in the art will recognize that the present invention involves many additional aspects and advantages.

Description of the Drawings FIG. 1 shows a general flow chart diagram of a method according to an exemplary embodiment of the present invention; FIG. 2 shows a graphical user interface providing output according to an exemplary embodiment of the present invention; FIG. 3 shows an alternate graphical user interface providing output according to an exemplary embodiment of the present invention; FIG. 4 shows an additional graphical user interface providing output according to an exemplary embodiment of the present invention; and chbm AOI08579542vl 305853976 FIG. 5 shows a spreadsheet output according to an 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 the advertisement and "transaction data" associated with transactions occurring before, during and/or after the advertisement has run; classifying the "reach," "balance" and "return" on such advertisements based upon the analysis; and using the classifications to establish a future advertising strategy. From the synthesized publication and transaction data, the method enables user to deduce the most productive combination of "featured products" to be ran for discrete geographical locations to achieve a desired combination of "reach," "balance" and "return." "Publication data" as will be described further below, is typically advertising data collected from the retail stores themselves; and includes data concerning the featured products, the type of advertisement, the term of the advertisement, the region of the advertisement and the like. "Transaction data" as will be described further below, is typically shopping purchase data that is collected from "frequent shopper cards," "loyalty cards" and the like carried by, or associated with each customer.

A "featured product" or "feature" refers to a particular product and/or service that is featured in the retail entity's advertising; and can also refer to a group of products that is advertised all variations of a product line that is on sale, such as all variations of a soup or soft-drink brand/company). Although exemplary embodiments of the present invention pertain to featured products ran in an advertising circular for a given store or stores; featured products may alternatively be found on television ads, radio ads, in-store displays, in-store promotions, electronic ads (email, internet and the like) and other types of advertisements or promotions as will be apparent to those of ordinary skill in the art.

"Reach," "balance" and "return" refer to different economic effects that advertising featured products may have on store transactions. "Reach" is the effect of attracting chbm A0108579542v1 305853976 customers not only to particular store, but to a particular location or locations within the store (reach can be calculated as the percentage of consumers in a particular consumer category(s) that transacted for one or more of the featured products); "balance" is the effect of attracting the most productive mixture of customers of (-i different consumer classifications (consumer personality classifications such as price ,I sensitivities, demographic classifications or a combination of both such as lifestyle classifications) to the most productive mixture of products; and "return" is the effect (-i O of maximizing percentage and dollar sales of not only a featured product, but of all (-i the products offered by the retail entity; or, in other words, the effect of maximizing 10 the return on investment. As used herein, the term "product" includes not only (-i consumer products that can be purchased in a retail store, but also any other product, service, or thing of value that can be furnished by a business to a consumer.

In an exemplary embodiment of the present invention, the publication data collected includes "feature dimensions," which can be information regarding the commodity, sub-commodity, manufacturer, and/or size of a particular featured product. In a more detailed embodiment, publication data includes "advertising dimensions," which can be information regarding the base price (which can be MSRP, the standard price charged by the retail entity or an estimated/calculated price approximating such a base price), feature price (advertised price), advertisement display and/or advertisement execution of the particular feature. Advertisement execution may include a range of dates of execution of a particular advertisement and/or a list of individual store locations where that advertisement was executed. As each retail store of a retail entity plans and executes advertising, publication data may be recorded in a centralized database, accessible by one or more of the retail stores and/or administrative offices of the retail entity. Such centralized database may provide access to publication data for an individual store, a group of stores located in a particular geographical division, or the for all stores of the retail entity. This ability to view and analyze data at varying organizational levels of the retail entity is referred to as "granularity." "Transaction data" refers to data relating to any transaction or interaction between the consumer and the business. In an exemplary embodiment, transaction data includes "shopping purchase data," which can be information regarding a chbm A0108579542vl 305853976 consumer's shopping history, including the identity of products and quantities thereof that the consumer has purchased. As used herein, the term "products" includes not only consumer products that can be purchased in a retail store, but also any other product, service, or thing of value that can be furnished by a business to a consumer. Shopping purchase data can be collected using a unique identification code resident on tag or card, for example, commonly known as a "frequent shopper card" or "loyalty card," carried by (or otherwise assigned to) each consumer. Such cards or tags contain the unique identification codes stored by a bar code, magnetic media, or other data storage device and can be read by an electronic device and variance manners that are well known to persons skilled in the art. Such unique identification codes can be resident on items other than cards, of course. For example, such unique identification code can be resident on RFID mechanisms, key fobs, and the like.

When a consumer goes through the checkout process at a store and the products being purchased are scanned, the unique identification code of the consumer's frequent shopper card, for example, can be read by an electronic device. The store's computer system can then compile a record of the products being purchased during this particular sale and associate that list with unique identification code of the consumer. By repeating this process each time the consumer visits the store and makes purchases, the store can build a cumulative record of a particular consumers' shopping history, including the identification of products and quantities thereof that the consumer has purchased. The compiled record of consumer shopping history can be stored in a database and analyzed as discuss herein. The "consumer" whose shopper history is profiled can be an individual person or a household, therefore, consisting of a group of persons residing at the same address or using the same credit card account, or even a business or government entity.

In an alternative embodiment, a consumer's shopping purchase data can be associated with the consumer using other consumer identification information (such as a telephone number, store credit card, bank credit card, or checking account number) instead of codes from frequent shopper cards, RFID tags, or similar items.

In this manner, the details of a particular transaction can be matched to the chbm A0108579542v1 305853976 consumer's previous transactions, thus facilitating the continuing addition of.

transactional information to each consumer's record in the database.

Each consumer's record in the database can comprise of a plurality of transaction entries or records, one for each transaction by that consumer. For each of these consumer records, there is provided, in the exemplary embodiment: a code identifying the SKU/product(s) purchased by the customer for the transaction; a code identifying the particular transaction or "basket"; a code identifying the customer or household for which the transaction is attributed; a code identifying the store in which the transaction occurred; data concerning the quantity of product purchased and the amount spent; data concerning the date, time, etc. of the purchase; and any other data or codes, such as a code indicating a geographical region for the purpose, as could be useful to generate reports based upon such transactional data.

The code in the transaction record identifying the SKU/product can be used to retrieve details pertaining to that product from a separate database containing a plurality of "product records" for each product. For each "product record" in the product database, there is provided, in the exemplary embodiments: Product grouping or categorization data or code; product UPC data; manufacturer or supplier data or code; and any other data or codes, such as suggested retail price data, that could be useful to generate reports based upon a combination of transaction data and product data.

The code in the transaction record identifying the customer or household for the transaction can be used to retrieve details pertaining to that household from a separate database containing a plurality of"household records," one for each household. For each "household record," there may be provided, in the exemplary embodiment: data and/or codes pertaining to the customer's demographics, shopping history, shopping preferences, and any other data or codes as could be useful to generate reports based upon the combination of transaction data and customer/household data.

In the code in the transaction record identifying the store in which the transaction occurred can be used to retrieve details pertaining to that store from a separate chbm A0108579542v 305853976

I

database containing a plurality of "store records," one for each store. For each "store record," there is provided, in the exemplary environment: store name data; store location data or code; and any other data or codes as could be useful to generate reports based upon a combination of transaction data and store data.

As will be appreciated by those of ordinary skill, the above-described database record structures are only exemplary in nature and that unlimited combinations of database records and hierarchies are available to cross-reference transaction information, product information, customer/household information, store information, location information, timing information, and any other appropriate information with one another. Additionally, one of ordinary skill will appreciate that the invention is not limited for use with retail store transactions and 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 database structures and hierarchies will be adapted for generating reports of such alternate transaction data.

FIG. 1 shows a flow diagram illustrating an exemplary method of the present invention. Individual retail stores 12 of a retail entity are grouped into geographical divisions 14. Each division works differently, so each division separately provides the system with data in the form of product tables 16 listing the different products offered by the stores within that division. These product tables 16 identify the product identity, commodity and sub-commodity of each product sold by the stores in that division. The system then classifies each product according to customercentric departments 18, where each department represents specific spatial locations of products within the store. For example, the salad bar, fresh meats, produce, canned foods, dairy products, laundry sections, paper products, pet foods, soft drinks, bag snacks, frozen entrees, frozen deserts, cereal, etc. are each examples of customer-centric departments that may be defined for a typical supermarket. As will be appreciated by those of ordinary skill "customer-centric" for this classification is based upon how the customers see the retail entity as opposed to how the retail entity might see itself.

In addition to providing product tables, each geographical division plans the featured products to be advertised by the stores within its division. These plans may then reviewed and revised by the individual stores who enter "Weekly Ad Coding" chbm A0108579542v1 305853976 Sdata 20 representing finalized plans into the system. Such revisions may also be made at the sub-region level or by an over-seeing body. The Feature Grouping Engine 22 of the system takes the individual store Weekly Ad Coding and N, supplements such information with respect to featured products, display information, price, delivery method and the like. From here, the supplemented ad C coding data and the customer-centric department data are synthesized with the CI analysis rules (described below) to create raw publication data 23.

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C The system also collects transaction data 24 from each store using any of the methods described above. As introduced above, the transaction data will include the CI 10 information regarding how many customers bought the featured products, what other products were bought, when the products were purchased, and the like. The raw publication data 23 and raw transaction data 24 are then sent to the Metrics Engine 26, which builds standard metrics used to by the Summarization Classification Engine 28 to analyze the transaction data in light of the publication data (classify each featured product according its economic effect; how the featured product promotes reach, balance, and/or return). In a more detailed exemplary embodiment, the system uses two types of metrics individual feature metrics and cumulative metrics. The individual metrics analyze the raw data for each featured product, including the price of the featured product when featured and not featured, and calculate the relevant "appeal segment," "basket size," "uplift," and "penetration" of each feature. Cumulative metrics analyze cumulative statistics regarding any or all featured products and also provide a base line analysis for all products offered.

"Appeal segment" refers to any attribute segment of consumers that can be emphasized to attract the segment of consumers to a retail entity, or to a portion of the retail entity, such as price sensitivity consumer, for example. The group into which a particular consumer is placed will be determined from characteristics about that consumer that can be ascertained from the consumer's shopping history.

Because a consumer's shopping history, including the identity of products and quantities thereof that the consumer has purchased, provides valuable insight into the consumer's lifestyle, financial means, and other important characteristics, it allows consumers to be divided into groups according to various selection criteria.

chbm A0108579542vl 305853976 C-i The consumer group into which a particular consumer is placed may also be based upon demographic data and/or personality data, which may or may not be ascertained from the consumer's transaction history. Demographic data may include, N' but is certainly not limited to, age data, income data, geographic data, and education-

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level data. Personality data (also referred to as the consumer's "transaction (N personality") may include, but is certainly not limited to, price sensitivity, negotiation (N tendencies, coupon usage, attention to promotions, loyalty, attention to product (-i O locations or configurations, and the like. Those of ordinary skill in the art will (-i appreciate the numerous sources for such demographic and/or personality data. In a 10 more detailed embodiment, products are categorized as follows: a "low-end" product (-i is a one typically purchased by very price sensitive consumers; a "middle" or "broad" product is a one typically purchased by mainstream consumers; a "high-end" product is a 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 sensitivity consumer groups. By analyzing the similarities or differences between the consumer groups that purchase a product when featured as opposed to when not featured, the system is able to analyze the balance effect of each feature. Additional support for categorizing consumers in these manners can be found in co-pending patent application Serial No. 10/955,946, filed September 30, 2004; the disclosure of which is incorporated herein by reference.

"Basket size" refers to the number of items purchased in any one transaction between one consumer and one store. In a more detailed embodiment, basket size is divided into three categories: small, medium and large. The system takes basket size of each transaction in which a feature is purchased, and calculates the median basket size for each individual feature. By determining median basket size for each feature, the method is able to determine how the feature fits into the broad appeal of store were consumers only coming into the store to get a deal on that particular item, or were consumers shopping for other products as well? "Uplift" refers to the increase in revenue that resulted from a particular product being featured. Uplift can be expressed as a percentage "sales uplift," which shows the percentage increase, if any, in sales as a result of the product being featured, or "dollar uplift" which shows the increase in revenue, if any, as a result of the product chbnm A0108579542v 305853976 11 being featured. The combination of sales uplift and dollar uplift provide measures of "return" for the product features.

"Penetration" refers to the number of customers "reached" by the feature. It may be (Ni C expressed as "customer penetration," which represents the percentage of all of the store's customers who purchased the featured item during the specified time period.

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N. Finally, "Associated sales" is a weighted version of penetration. For example it can represent the percentage of total spend the customers who purchased a feature (Ni represent. In this example, if all of the customers of a particular store spent a total of $100,000.00 during the time period, and all of the customers who purchased the (-i feature spent a total of $50,000.00 during the time period, the associated sales for that feature is 50%. The weights can also be weighted with respect to other attributes (other than total sales as given in the above example) such as weighting according to: basket size, demographics, price sensitivity and the like. This can be calculated for an individual store, for a division, or for the entire retail entity, and is how the method determines the reach effect of each feature.

While the "individual metrics" analyze data on an individual-feature level, "cumulative metrics" analyze data from larger groups of products. The present invention uses cumulative metrics to generate broad data for all products featured in a given period of time, as well as baseline data regarding all products. For example, the cumulative metrics may include the number of customers purchasing any featured product, the total sales of all featured products purchased, and/or the total uplift of all featured products purchased. As a baseline, it would then additionally calculate the total number of customers purchasing any product, the total sales of all products purchased, and/or the total uplift of all products purchased.

The Summarization Classification engine 28 also classifies each featured product into meaningful "type" classifications for use by personnel of the retail entity. In a more detailed exemplary embodiment of the present invention, the featured products are divided into three general "type" classifications "anchors," "helpers," and "unknown." "Anchors" are featured products that promote the economic effects of reach and return, while "helpers" are featured products that promote the economic effects of balance and return. An "unknown" classification is used when chbm AOtO8579542vl 305853976 the system has not been provided with enough publication and/or transaction data for a particular feature to meaningfully analyze its economic effect(s). Anchors are essentially staple items for the particular retail entity with high repurchase rates. For example, an anchor product for a grocery store may be bread, milk, and/or colas; while anchor products for an electronics store may be DVDs, CDs and batteries.

Helpers complement the anchors by driving balance. Helper products will have slightly more limited reach than anchors; products that are commonly purchased, but not virtually every time the store is visited. For example, in a grocery store helpers may include detergents, paper products, fish, frozen foods; and in an electronics store helpers may include accessory items, DVD players, CD players and speakers.

In an even more detailed embodiment, the anchor and helper classifications are further divided into sub-classifications. For example, anchor featured products may be divided into "reward anchors," "incentive anchors," "traffic anchors," "big ticket anchors," "low end anchors" or other types of anchors such as demographically categorized anchors. Reward anchors are featured products that reward consumers for purchasing staple items that they would purchase regardless of advertisement.

Milk is an example of a reward anchor. Incentive anchors are high end products when not featured, but when featured, encourage individuals who generally purchase only mainstream or low end products to "buy-up." Consistent buying-up of the featured product causes the product to be re-designated a mainstream or low end product when featured. Traffic anchors are featured products that draw consumers into the store for the specific purpose of purchasing the featured item, thus driving consumers into and through the store. A typical example of a traffic anchor is cola, as many consumers tend to "shop around" for weekly specials on the soft drink. Big ticket items are products with a relatively high advertised price and relatively low associated sales; so a big ticket anchor would be a big ticket item that becomes an anchor when featured.

Helpers may also be divided into the same types of sub-categories, such as "low end helpers," "high end helpers," and "store helpers" Low end and high end helpers are featured products that help boost return from low end and high end products respectively; and store helpers are featured products that boost return from broad chbm A0108579542vl 305853976 and middle items. In an even more detailed embodiment, the system may qualify the classifications, depending upon how effective the feature is at producing the relevant economic effect/s. For example, a feature that modestly boosts return for broad and middle items may be designated a "weak store helper." While the system maintains minimum criteria for each classification, the specific criteria may be based upon differences in sales, preferences and economics among different divisions, or even different individual stores.

After the system summarizes the raw data and classifies each feature, it uses a "final tool" 30 to deliver the synthesized information to each user. In a detailed embodiment of the present invention, the final tool is a graphical user interface. As will be described below and as shown in Figs. 2-4, the graphical user interface organizes the information so it may be accessed easily by a user.

Referring back to Fig. 1, the raw data and classifications are monitored by the system's "benchmarking tracking" component 32. This component provides periodic analyses that are used to modify the rules governing the grouping of featured products and the summarization of data and classification of featured products. Such periodic modification is important, as the behavior of consumers changes, e.g. with the seasons, with the growth of a particular geographical region, with the introduction of new products on the market, etc. In a more detailed embodiment of the present invention, the "benchmarking tracking" component 32 produces a "quarterly analysis" 33. This "quarterly analysis" is used to modify the rules quarterly. In an alternative more detailed embodiment of the present invention, analyses are continuously produced and the rules are continuously modified as needed. In another alternative more detailed embodiment of the present invention, a user can observe the benchmarking tracking and analyses and trigger the modification of rules at any time.

In addition to varying the time period between modification of the rules, the present invention may perform modifications at varying levels of granularity. In a more detailed embodiment of the present invention, the rules are modified independently for each geographical division 14 of retail stores. In an alternative more detailed embodiment, the rules are modified independently for each retail store. Such storespecific rules are referred to as "store specific metrics lookups" 34.

chbm AO108579542vl 305853976 SFIGS. 2-4 show an exemplary embodiment of a graphical user interface for displaying the results of the analysis are provided. A user can choose to view information specific to a particular geographical division and a particular time period. The graphical user interface allows the results of the analysis to be shown in three different formats in the exemplary embodiment: a weekly performance format, a C1 period trending format and a features database format. Displays for each of these C formats can be brought up by activating the respective corresponding button on the O graphical user interface: the Weekly Performance button 36, the Period Trending button 38 or the Features Database button The display depicted in FIG. 2 is the Features Database display. This displays how each of the featured products performed during a given period. The first column "Division" 42 identifies the geographical Division for which the user has chosen to view information. The second column "Week" 44 displays the time period in which the displayed data was collected. Just as advertising similar products in different geographical divisions produce different effects, so may advertising similar products during different periods of time.

The third, fourth and fifth columns display the identity of the featured product "Feature" 46, the customer-centric department or "Loyalty Department" to which the feature belongs 48, and the classification "Type" given to the featured product by the system 50. The third column 46, or feature column, identifies the specific feature analyzed. By clicking the "view feature name" button, a user can toggle back and forth between viewing the feature name, 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 of a featured product). It is also within the scope of the invention that this toggle could allow a display of all UPCs of the feature and not just the dominant one. The fourth column 48 identifies to which customer-centric department the featured product belongs. For example, "Big K Diet Root Beer" belongs to the soft drinks loyalty department. The classification Type of each feature is displayed in the fifth column 50. Note that, in this embodiment, the classification types are generated by the Summarization Classification engine 28 as discussed above, which divides the classification Types into sub-classification and provides chbm A0108579542v 305853976 qualifiers. For example, if a feature was classified as a weak store helper, this would mean that the feature modestly boosted sales of mainstream products.

The remainder of the columns shown in FIG. 2 display processed data that was used by the system in classifying each feature. The sixth column "Store Dist." 52 informs the user what percentage of stores in the division offer the featured product; columns seven through nine "Price Point Change" 54, "Base Price" 56 and "Front Price" 58 provide information on the price difference between the product when featured and when not featured; columns ten and eleven "Base Position" 60 and "Front Position" 62 provide classification of the type of consumer that purchases the product at the base price versus the type of consumer that purchases the product at the advertised price; column eleven "Basket Size" 64 indicates the number of other products purchased with the featured product; column fourteen "Cust. Pen." 70 lists the percentage of customers that have purchased the featured product; and the fifteenth column "Assoc. Sales" 72 lists the percentage of total sales that the featured product represents as compared to the other products. Column twelve "Sales Uplift" 66 and column thirteen "Dollar Uplift" 68 express the increase in sales of a product when featured versus when not featured. Sales uplift 66 represents the percentage increase in the number of a particular product sold when the product is featured versus when the product is not featured. Dollar uplift 68 represents the increase in revenue generated by sales of a particular product when the product is featured versus when the product is not featured.

The graphical user interface provides the base price (the price of the product when not featured), the front price (the price of the product when featured) and the percentage difference in the two prices. It also classifies the product by price sensitivity at its base and front prices. These columns allow the user to see whether the product appealed to different groups/categories of customers when featured as opposed to when not featured. For example, when not featured, some products appeal to consumers with high-end price sensitivity, but appealed to consumers with middle price sensitivity when featured. Because some products appeal to a different appeal segment when featured as opposed to when not featured, such information is vital to analyzing current and predicting future "balance" effects of particular features.

chbm A0108579542v 305853976 16 Another feature of the graphical interface shown in FIG. 2 is that it allows the user to create an Ad Evaluation Template that allows the user to compare proposed features against the predetermined goals of reach, balance and return. By choosing from C' prior featured products, by clicking on division column for the relevant feature (or by dragging the featured items own into the evaluation table 74) the user can create CI a proposed advertising plan in table 74. For example, each user may provided with C1 goals or criteria specifying approximate numbers of each Type 50 that it desires to be (Ni O included in a particular week's advertising plan to achieve the desired "reach," "balance" and "return" effects. Such criteria may be provided at varying levels of 10 granularity. In a more detailed embodiment of the present invention, such criteria (Ni are independently provided for each geographical division 14. In an alternative more detailed embodiment, such criteria are independently provided for each individual retail store 12. By referencing such criteria, a user may evaluate each proposed advertising plan and adjust the plan to meet specific "reach," "balance" and "return" needs.

Once the user has added the combination of proposed featured products into the table 74, the user can then click on the Create Ad Evaluation Template button 75 and the proposed features deposited into the table 74 will be exported into a spreadsheet file, referred to as a matrix template, as shown in Fig. 5. The matrix template allows the user to look across all product 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 showing how such features have performed in the past. The matrix also includes sections for additional comments that can be added by the user and additional space to evaluate the overall movement toward the three goals of reach, balance and return. This data allows the retail entity to see which features have the strongest potential to reach consumers, create additional sales and include all categories of customers. This information allows the retail entity to balance its investments each week (or other advertising period) to maximize the mix of features needed to meet predetermined goals. While the exemplary embodiment of the present invention performs this matrix template evaluation step manually with the assistance of a computerized spreadsheet tool, it is certainly within the scope of the invention to allow the software to automate the selection and evaluation process further (if not completely).

chbm A0108579542vl 305853976 SFIG. 3 shows the "Weekly Performance" screen of the exemplary graphical user interface. This screen provides the user with raw data, percentage data and graphical data reflecting the reach, balance and return effects experienced by a particular geographical division (or any other selection or combination of retail entities) for a particular period of time. In the exemplary embodiment, the user can choose to C view data from different geographical divisions and/or different weeks by clicking on C the "select division" drop-down box 76 and/or the "select week" drop-down box 78.

C1 The "overall weekly performance" graphic 80 provides the user with overall "reach," "penetration" and "uplift" data. A total percentage "reach" 82 is provided along with C 10 the numeric data from which the percentage was derived the total number of households 84 who shopped at any of the retail stores within the selected geographical division 76 during the selected week 78 and the number of those households 86 that purchased any of the products featured during the selected week 78. Reach is also represented from a revenue perspective by the percentage of "associated store sales" 88. This figure represents what is known as the "halo effect," and is calculated by finding the percentage of "total store sales" 90 that were sales to customers who purchased one or more features the "total associated store sales") 92. Reach is broken down further to show the percentage of very price sensitive and price sensitive customers 94 who were engaged by the weekly features, by finding the percentage of "total VPS/PS households" 96 who purchased one or more features the "total VPS/PS engaged by front page features") 98 during the selected week 78. The "sales penetration" percentage 100 represents the percentage the "total front page feature sales" 104 were of the "total store sales" 102. Finally, "uplift" represents the increase in revenue for featured products experienced as a result of advertising. Fig. 3 shows a percentage "sales uplift" 106, as well as providing dollar figures for both "base sales for front page features" 108 and "total uplift dollars over base dollars" 110.

Fig. 3 also displays reach and uplift data broken down by appeal segments in its "how did my features perform by segment?" graphic 112. A reach chart 116 displays the total number households in each appeal segment that shopped, the total households that were engaged by one or more features in each appeal segment, and the percentage of households in each appeal segment that were "reached" by the chbm A0108579542vl 305853976 Sfeatures. The percentage reach data for each appeal segment compared to the total percentage reach is also displayed via a bar and line graph 118. An uplift chait 120 displays base sales, front page feature sales, and the percentage uplift for each appeal segment, as well as total base sales, total front page sales and the total uplift percentage. Base sales and front page sales for each appeal segment are also C compared via bar graph 122.

(-i FIG. 4 shows the "Performance Trending" screen of an exemplary graphical user interface. The reach period trending graph 123 allows a user to determine whether the stores within a particular geographical division 126 experienced higher total sales N 10 during weeks in which they experienced higher total reach. Again, the display is broken down by geographical division in the exemplary embodiment, but the invention is flexible enough to break down the display by other combinations of retail entities, periods, product categories, etc. Graphic 124 states the last week for which the data has been reported. The x-axis 132 of graph 123 represents the individual weeks constituting the analyzed period. The bar graph y-axis 128 represents total store sales and the line graph y-axis 130 represents percentage reach.

By overlaying total sales with reach data, this screen allows a user to evaluate the effect of reach on the total sales of stores in a particular geographical division over an extended period of time.

Having described the invention with reference to exemplary embodiments, it is to be understood that the invention is defined by the claims and it not intended that any limitations or elements describing the exemplary embodiment set forth herein are to be incorporated into the meanings of the claims unless such limitations or elements are explicitly listed in the claims. Likewise, it is to be understood that it is not necessary to meet any or all of the identified advantages or objects of the invention disclosed herein in order to fall within the scope of any claims, since the invention is defined by the claims and since inherent and/or unforeseen advantages of the present invention may exist even though they may not have been explicitly discussed herein.

chbm A0108579542vl 305853976

Claims (24)

1. A method for planning and evaluating advertising for a retail entity comprising the steps of: collecting, with the assistance of a computer, a first set of advertisement publication data for a plurality of featured products published by the retail entity over a first period of time, the advertisement publication data including data taken from a group consisting of data concerning the featured products in the advertisements, data concerning the type of advertisement, data concerning the term of the advertisement, and data concerning the region of the advertisement; collecting, with the assistance of a computer, a first set of transaction data for a plurality of transactions conducted by a plurality of consumers purchasing a plurality of products from the retail entity over the first period of time; analyzing, with the assistance of a computer, the first set of advertising publication data with respect to the first set of transaction data over the first period of time; and classifying, with the assistance of a computer, the plurality of featured products into advertising roles based upon the analyzing of the first set of advertising publication data with respect to the first set of transaction data, the plurality of advertising roles including one or more advertising roles take from a group consisting of featured products that attract consumers to a particular part of a retail entity, featured products that promote attracting a mixture of consumers of different consumer classifications to purchase the featured products, featured products that promote a balance of different types of products purchased by a consumer, featured products that promote an overall increase in sales by the retail entity, and featured products that promote a combination of two or more of the above advertising roles.

2. The method of claim 1, further comprising the step of establishing a first advertising strategy based upon the advertising roles. chbm A0108579542v1 305853976

3. The method of claim 1, further comprising the steps of: collecting, with the assistance of a computer, a next set of advertising publication data for a second plurality of featured products used by the retail entity over a next period of time; collecting, with the assistance of a computer, a next set of transaction data for a plurality of products sold by the retail entity over the next period of time; analyzing, with the assistance of a computer, the next set of advertising publication data with respect to the next set of transaction data over the next period of time; and classifying, with the assistance of a computer, the second plurality of featured products into advertising roles based upon the analyzing of the next set of advertising publication data with respect to the next set of transaction data.

4. The method of claim 1, wherein output of the analyzing step includes one or more of the following categories of output: a total percentage sales uplift; a percentage sales uplift for each featured product; a total dollar uplift; a dollar uplift for each featured product; a total customer penetration; a customer penetration for each featured product; total associated sales percentage; and an associated sales percentage for each featured product.

5. The method of claim 1, wherein the step of collecting publication data includes the steps of: generating a list of featured product categories relevant to the retail entity; chbm A0108579542v 305853976 21 assigning each of a plurality of featured products offered by the retail entity to one or more of the feature product categories; generating a list of advertising dimensions relevant to the retail entity; assigning each of a plurality of in-store advertisements used by the retail C 5 entity to one or more of the advertising dimensions. C- 6. The method of claim 5, wherein the list of feature dimensions includes one or 0 C more of the following feature dimensions: -a commodity category; a sub-commodity category; a manufacturer category; a department category; and a size category.

7. The method of claim 6, wherein the step of generating a list of featured product categories further includes mapping featured products into one or more of a plurality of consumer loyalty departments based upon locations of similar products within a retail store of the retail entity.

8. The method of claim 5, wherein the list of advertising dimensions includes one or more of the following advertising dimensions: a product code dimension; a base price dimension; a feature price dimension; a display dimension; and an advertisement execution dimension.

9. The method of claim 10, wherein the advertisement execution dimension includes one or more of the following: a range of dates of execution; chbm A0108579542v 305853976 c1 a list of locations of execution.

12. The method of claim 1, wherein the step of collecting publication data is limited to collecting publication data from a plurality of retail stores located in (Ni one or more subsets of geographical divisions of the retail entity. N 5 13. The method of claim 1, wherein the step of collecting publication data is limited to collecting publication data from a single retail store of the retail entity.

14. The method of claim 1, wherein the step of collecting transaction data includes the steps of: generating a list of product categories relevant to a retail entity; assigning each of a plurality of products sold to one or more of the product categories; generating a list of transaction dimensions relevant to a retail entity; assigning each of a plurality of transactions to one or more of the transaction dimensions; generating a list of consumer categories relevant to a retail entity; and assigning each of a plurality of consumers to one or more of the consumer dimensions. The method of claim 14, wherein the list of product categories include one or more of the following product categories: a commodity category; a sub-commodity category; a manufacturer category; a department category; and a size category.

16. The method of claim 15, wherein the step of generating a list of product categories further includes mapping the product categories into one or more chbm AO108579542vi 305853976 23 of a plurality of loyalty departments based upon locations of similar products within a retail store of the retail entity.

17. The method of claim 14, wherein the list of transaction dimensions include one or more of the following transaction dimensions: a basket size; a number of features purchased; a total cost of products purchased; an average cost of product purchased; a total cost of features purchased; an average cost of features purchased; and a list of products purchased.

18. The method of claim 17, wherein the basket size transaction dimension includes one or more of the following sizes: a small basket size; a medium basket size; and a large basket size.

19. The method of claim 14, wherein the list of consumer categories include one or more of the following: a list of customer-based consumer categories; and a list of feature-based consumer categories. The method of claim 19, wherein the customer-based consumer categories include one or more of the following sub-categories: a price sensitivity sub-category; and a loyalty sub-category. chbm A0108579542v1 305853976

21. The method of claim 20, wherein the price sensitivity sub-category includes one or more of the following: a not price sensitive category; a mainstream category; a price sensitive category, a very price sensitive category; and an unknown price sensitivity category.

22. The method of claim 19, wherein the feature-based consumer categories include one or more of the following: a total number of consumers who purchased any feature; a total cost of all features purchased; and a total uplift of all features purchased.

23. The method of claim 1, wherein the step of collecting transaction data is limited to collecting transaction data from a plurality of retail stores located in one or more geographical divisions of the retail entity.

24. The method of claim 1, wherein the step of collecting transaction data is limited to collecting transaction data from a single retail store of the retail entity. The method of claim 1, wherein the advertising roles include one or more of the following roles: an anchor role; and a helper role. The method of claim 26, wherein the anchor role includes one or more of the following types: a reward anchor; an incentive anchor; and chbm A0108579542v1 305853976 a traffic anchor.

27. The method of claim 25 wherein the helper role includes one or more of the following types: a low end helper; C, 5 a high end helper; a mainstream helper; and a store helper. C 1 28. The method of claim 1, further comprising a step of generating a report with the assistance of a computer.

29. The method of claim 28, wherein the report includes: at least a portion of the publication data; at least a portion of the transaction data; and output concerning at least a portion of the advertising roles. The method of claim 29, wherein a user may access the report through a graphical user interface.

31. The method of claim 30, wherein the graphical user interface organizes the report into one or more of the following categories: a geographical division; a range dates of execution; a universal product code for each featured product; a name of each featured product; a retail department for each featured product; an advertising role for each featured product; a price point change for each featured product; chbmA0108579542vl 305853976 a price sensitivity consumer category at a base price for each featured product; a price sensitivity consumer category at a feature price for each featured product; an average basket size for a basket containing each featured product; a percentage sales uplift for each featured product; a dollar uplift for each featured product; a consumer penetration percentage for each featured product; and an associated sales percentage for each featured product.

32. The method of claim 30 further including the step of creating an advertisement evaluation template.

33. The method of claim 32, wherein the step of creating an advertisement evaluation template includes the steps of: assembling publication data, transaction data, advertising roles and effectiveness data for a combination of one or more features from one or more periods of time; analyzing the assembled data against a desired spread of reach, balance and return.

34. A computerized method for evaluating and/or planning advertisements for a retail entity, comprising the steps of: collecting, with the assistance of a computer, transaction data for a plurality of consumers from transactions conducted with the retail entity over a first period of time and a second period of time; collecting, with the assistance of a computer, advertising data for at least one product featured by an advertisement, the advertising data including at least the identity of the product featured and an association between the advertisement and the second period of time; chbm A0108579542v1 305853976 cto evaluating, with the assistance of a computer, the transaction data associated with the featured product in comparison at least between the first period of time and the second period of time to determine one or more of the following: the effectiveness of the advertisement attracting consumers to conduct transactions with the retail entity, the effectiveness of the advertisement promoting transactions for consumers of a first category versus a second category, and the effectiveness of the advertisement increasing revenues for one or more of the product featured and the products not featured; compiling results of the evaluating step and displaying the results, with the assistance of a computer, to a user in the form of a graphical user interface; and providing the user, by the graphical user interface, a tool for selecting one or more products to feature in future advertisements based upon the compiled results. chbmA0108579542v1 305853976