JP2007317209A - Method for planning and evaluating in-store advertising for retail entity - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method for planning, evaluating and revising an advertising strategy. <P>SOLUTION: This method includes 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, including data concerning the featured products in the advertisements, the type of the advertisement, the term of the advertisement, and/or the region of the advertisement; collecting 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 the first set of advertising publication data with respect to the first set of transaction data over the first period of time; and classifying the plurality of featured products into 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 advertising roles include featured products that attract consumers to a particular part of a retail entity, promote attracting a mixture of consumers of different consumer classifications to purchase the featured products, promote a balance of different types of products purchased by a consumer, promote an overall increase in sales by the retail entity, and/or promote a combination of two or more of the above advertising roles. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  Advertising strategy planning, evaluation and revision is an ongoing challenge for retailers. Traditionally, such advertising analysis is specific to a product or service. Such traditional approaches focus on what types of advertising products and / or methods will attract the most different consumers. One example of traditional advertising analysis utilizes interest data—the type and number of consumers interested in a particular advertisement—rather than the number of advertising products actually sold as a result of the advertisement. Another example of traditional advertising analysis uses product-specific sales data-the difference in sales of a particular product with and without advertising.

  The limitation of both these approaches is that they attempt to predict complex results using only one or two factors. Such one-dimensional or two-dimensional strategies can, for example, affect the impact of one advertised product on other products sold by the retailer, the total sales of the retailer, the consumer's attractiveness to the retailer. The number and type, or the long-term effects of these effects cannot be taken into account. In practice, the success that various retailers enjoy from advertising strategies is based on much more than a single product sales increase. Advertising strategies attract consumers to the store, direct consumer traffic into the store, target consumers with a wide range of price sensitivity, and consume on products not featured in advertising Success is maximized only when the most efficient combination of attracting consumers, rewarding consumers as rewards, and maximizing overall rewards is targeted.

  In addition to being too limited in scope as described above, traditional approaches are so broad that they are otherwise inefficient. Different geographies tend to have different needs, consumers buy different products, have alternative shopping locations, and have different price sensitivities. For large retailers who do business, the analysis as a whole is inefficient. Consumer habits also vary from time to time. For example, consumer needs and desires as well as product availability often change from season to season.

  The present invention analyzes complex interactions in a wide range of advertising and sales data while retaining the ability to scale the analysis to a specific geographic region, time period, product group and / or consumer group To solve these problems.

  Therefore, the first aspect of the present invention includes (a) data in an advertisement relating to a featured product, data relating to the type of advertisement, data relating to an advertisement period, and / or data relating to an advertisement area, and a plurality of featured products. Collecting, with the aid of a computer, a first set of distribution data marketed by a retailer over a first time period; and (b) the retailer performed by a plurality of consumers over a first time period. Collecting, with the assistance of a computer, a first set of transaction data for a plurality of transactions for purchasing a plurality of products from a first distribution for the first set of transaction data over a first period of time; Analyzing the data set with computer assistance; and (d) analyzing the first distribution data set with respect to the first transaction data set, Categorizing a plurality of featured products into an advertising role, with computer assistance, wherein the plurality of advertising roles include a featured product that attracts consumers to a particular part of the retailer, different consumer classifications A featured product that encourages a mix of consumers to be invited to purchase a featured product, a featured product that promotes a balance when one consumer purchases a different type of product, by a retailer Provide advertising planning and evaluation methods for retailers that include featured products that promote overall sales growth and / or featured products that promote a combination of two or more of the above advertising roles It is to be.

  According to a second aspect of the present invention, (a) collecting transaction data for a plurality of consumers from a transaction conducted with a retailer over a first period and a second period with the assistance of a computer; ) Collecting, with computer assistance, at least one advertising data about the featured product by advertisement, including at least the feature of the featured product and the relationship between the advertisement and the second period; (C) the effect of the advertisement that attracts the consumer to trade with the retailer, the effect of the advertisement that facilitates the transaction of the first category versus the second category of the consumer, and / or one or more In order to determine the effectiveness of the advertising to increase revenue for the featured and uncharacterized products of And (d) accumulating the results of the evaluation step (c) with computer assistance in a comparison between at least the first period and the second period; Displaying the results to the user in the form of a graphical user interface; and (e) a tool for selecting one or more products to be featured in future advertisements based on the accumulated results. Using the graphical user interface to provide a computerized method for planning and / or evaluating advertisements for retailers, including providing to a user.

  Upon reading the following specification with reference to the accompanying drawings and appended claims, those skilled in the art will recognize that the present invention includes many additional aspects and advantages.

  The present invention collects and analyzes “advertising data” relating to advertisements and “transaction data” relating to transactions that occur before, during and after the advertisement has been placed, and based on such analysis, “ Classified for “reach”, “balance” and “return” and using the classification to establish future advertising strategies, advertising for retailers (and specific In the embodiment, a method for planning and evaluating a store flyer and related advertisements is provided. In order to achieve the desired combination of “reach”, “balance” and “return”, the method allows the user to select the most profitable combination of “characterized products” to be posted for different geographical locations. , It becomes possible to infer from the integrated Idemitsu data and transaction data.

  “Idemitsu data”, described further below, is typically advertising data collected from the retail store itself, and includes data relating to featured products, types of advertisements, advertising periods, advertising regions, and the like. The “transaction data” described further below is typically purchase data carried by each consumer or collected from a “customer card”, “reward card”, etc. for each consumer. is there.

  A “featured product” or “feature” refers to a specific product and / or service featured in a retailer's advertisement and a group of products being advertised (eg, All variations of the lineup of products that are subject to sale, such as all variations of soup or soft drink brand / company. The exemplary embodiments of the present invention are suitable for featured products described in a given store advertising flyer, but alternatively, featured products can be television advertisements, radio advertisements, in-store displays, in-store promotions, It can be presented in electronic advertisements (email, internet, etc.) and other forms of advertisements or promotional means that will be apparent to those skilled in the art.

  “Reach,” “balance,” and “return” refer to different economic effects that featured product advertisements can have on store transactions. “Reach” refers to the effect of attracting consumers not only to a specific store, but also to a specific location in a specific store (reach refers to one or more featured products – a specific consumer In category-can be calculated as the percentage of consumers). “Balance” refers to the most profitable set of consumers that belong to different consumer classifications (consumer personality classifications such as price sensitivity, demographic classification or a combination of both, such as lifestyle classification) The effect of attracting a high rate set. “Return” is the effect of maximizing the sales rate and monetary sales of all products offered by the retailer as well as the featured product, that is, the effect of maximizing the return on invested capital. As used herein, the term “product” includes not only consumer products that can be purchased at retail stores, but also any other product, service, or value that a company can supply to consumers.

  In an exemplary embodiment of the present invention, the collected distribution data is information regarding the size of the commodity, product sub-commodity, producer and / or specific featured product. It includes a “featured product dimension”. In a more detailed embodiment, the distribution data includes an “advertising dimension”, which is a reference price (MSRP (manufacturer suggested retail price), a standard price charged by the retailer, or such a reference) Information that may be an estimated price / calculated price approximated to a price), a featured price (advertisement price), an advertisement exhibition, and / or an advertisement run for a particular featured product. An ad run may include a list of the duration of a particular ad and / or the location of an individual store where the ad was run. As each retailer's retailer plans and executes advertisements, the distribution data can be recorded in a central database accessible to one or more retailers and / or retailer's headquarters. Such a central database provides access to marketing data for an advertising store, a group of stores located in a particular geographic area, or all stores of a retailer. The ability of a retailer to view and analyze data at various organizational levels is referred to as “granularity”.

  “Transaction data” refers to data relating to any transaction or interaction between consumers and companies. In an exemplary embodiment, the transaction data includes “purchase data” that can be information about the consumer's purchase history, including the identity and quantity of the product purchased by the consumer. As used herein, the term “product” includes not only consumer products that can be purchased at retail stores, but also any other product, service, or value that a company can supply to consumers. Purchasing data can be unique, for example, in tags or cards well known as “customer cards”, “privilege cards” that each consumer carries (or else is given to consumers). It can be collected using an identification code. Such a card or tag includes a unique identification code stored by a bar code, magnetic media or other data storage device and can be read by various methods well known to electronics and those skilled in the art. Such a unique identification code can of course be inherent in an item other than the card. For example, such a unique identification code may be inherent in an RFID mechanism, key fob, etc.

  When the consumer goes through the checkout process in the store and the product to be purchased is scanned, for example, the unique identification code of the consumer's customer card can be read by the electronic device. The store computer system can then store a record of products purchased during this particular sale period and associate that list with the consumer's unique identification code. By repeating this process each time a consumer visits a store and makes a purchase, the store builds a cumulative record of a particular consumer's purchase history, including the identification number and quantity of the product purchased by the consumer. be able to. Accumulated consumer purchase history records are stored in a database and can be analyzed as discussed herein. The "consumer" of the profiled purchase history can be an individual or a household consisting of a group of people living at the same address or using the same credit card account, or even a business or government .

  In an alternative embodiment, the consumer purchase data may be replaced with a customer card, RFID tag, or other consumer identification information instead of a code from a similar item (eg, phone number, store credit card, bank credit). Card or current account number). In this way, transaction details can be easily added to each consumer record in the database because the details of a particular transaction can be matched with the consumer's previous transaction.

  Each consumer record in the database can include multiple transaction entries or records, each for each transaction by that consumer. In an exemplary embodiment, for each of these consumer records, the following data is provided: a code identifying the SKU (Minimum Inventory Management Unit) / product purchased by the consumer of the transaction, a specific transaction That is, a code that identifies the “basket”, a code that identifies the customer or household that made the transaction, a code that identifies the store where the transaction occurred, the amount of product purchased and the data paid, date of purchase, time of purchase, etc. And any other data or code, such as a code indicating the geographic region of purchase, that would be useful for generating reports based on such transaction data.

  The code in the transaction record that identifies the SKU / product can be used to retrieve details about that product from another database containing multiple “product records” for each product. In an exemplary embodiment, for each “product record” in the product database, the following data is provided: product grouping or categorization data or code, product UPC (Uniform Product Code) data, producer or supplier Recommended retail price data that may be useful for generating reports based on any data or code, and any other data or code, eg, a combination of transaction data and product data.

  The code in the transaction record that identifies the consumer or household of the transaction is used to retrieve details about the household from another database containing multiple “household records”, each for each household. be able to. In an exemplary embodiment, for each “household record”, the following data may be provided: data and / or codes regarding consumer demographics, purchase history, purchase preferences, and transaction data and consumption. Any other data or code that would be useful for generating reports based on person / household data combinations.

  The code in the transaction record that identifies the store where the transaction occurred may be used to retrieve details about the store from another database containing multiple “store records”, each for each store. it can. In an exemplary embodiment, for each “store record”, the following data may be provided: store name data, store location data or code, and a combination of transaction data and store data. Any other data or code that would be useful to generate a report based on.

  As will be appreciated by those skilled in the art, the database record structure described above is merely exemplary, and an unrestricted combination of database records and hierarchies can be used for cross-reference transaction information, product information, consumer / household information. , Store information, location information, time information, and any other suitable information. In addition to this, the present invention is not limited to use in retail store transactions, and the present invention (although not all) forms most types of transactions (eg, financial / banking transactions, insurance transactions, Those skilled in the art will appreciate that the database structure and hierarchy can be adapted and used to generate reports of such alternative transaction data (such as service transactions).

  FIG. 1 shows a flowchart illustrating an exemplary method of the present invention. The individual retail stores 12 of the retailer are grouped into geographic departments 14. Since each department operates differently, each department separately supplies data to the system in the form of a product table 16 listing the different products offered by stores within that department. These product tables 16 identify the identity of the product, the product, and the product raw material for each of the products sold by the department store. The system then classifies each of the products according to a customer counter 18 that each represents a spatial location of the product within the store. For example, salad bars, fresh meat, vegetables and fruits, canned foods, dairy products, cleaning corners, paper products, pet foods, soft drinks, bag confections, frozen foods, frozen desserts, cereals, etc., are each defined for a typical supermarket. An example of a customer counter that can be done. As understood by those skilled in the art, this category of “customers” is based on how customers view retailers versus how retailers can see themselves. is there.

  In addition to providing a product table, each of the geographic departments plan for featured products to be advertised by stores within that department. These plans can then be reviewed and revised by individual stores entering “weekly advertising coding” data 20 representing the finalized plan into the system. Such revisions may also be made at a sub-regional level or by a supervisory organization. The system's featured product grouping engine 22 receives weekly advertisement coding for individual stores and supplements such information regarding featured products, exhibition information, prices, delivery methods, and the like. From here, the supplemented advertisement coding data and customer counter data are integrated with analysis rules (described later), and raw spread data 23 is generated.

  The system also collects transaction data 24 from each of the stores using any of the methods described above. As introduced above, the transaction data will include information regarding how many customers have purchased the featured product, what other products have been purchased, when the product was purchased, and so forth. The raw spread data 23 and raw transaction data 24 are then sent to a metrics engine 26, which creates standard metrics that are used by the aggregation and classification engine 28 to generate transaction data. Analyzed considering broad data (characterized products are categorized according to their economic effects, ie how they promoted reach, balance, and / or return). In a more detailed exemplary embodiment, the system uses two types of metrics—individual characterization metrics and cumulative metrics. Individual featured metrics analyze the raw data for each featured product, including the price of the featured product, both featured and not featured, and the associated “ Calculate “appeal segment”, “basket size”, “increase” and “penetration”. Cumulative metrics analyze cumulative statistics for any or all featured products and provide a baseline analysis for all products offered.

  An “appeal segment” refers to any attribute segment of a consumer that can be focused on a retailer or part of a retailer to attract that consumer segment, for example, a price sensitive consumer. The group in which a particular consumer is placed could be determined from the consumer characteristics that can be determined from the consumer's purchase history. Consumer purchase history, including the identity and quantity of products purchased by consumers, provides valuable insights into consumer lifestyles, financial instruments, and other important characteristics, so consumer purchase history Consumers can be grouped according to various selection criteria. The consumer group in which a particular consumer is located can also be based on demographic and / or personality data that may or may not be determined from the consumer's transaction history. Demographic data is not limited in any way, but may include age data, income data, geographic data, and education level data. Personality data (also referred to as the consumer's “trading personality”) is not limited in any way to this, but is price sensitive, negotiating propensity, coupon usage, response to promotions and benefits, product location or placement. Reactions can be included. One skilled in the art will recognize that there are many sources of such demographic and / or personality data. In more detailed embodiments, products are categorized as follows: “Low-end” products are typically purchased by very price-sensitive consumers, “Middle” or “Broad” products are “High-end” products, typically purchased by the mainstream of consumers, are typically purchased by non-price sensitive consumers, “Broad” products are among many price-sensitive consumer groups Purchased by members. By analyzing similarities or differences between groups of consumers who purchase products when featured as opposed to not featured, the system analyzes the balance effect of each of the featured products. can do. Additional support for categorizing consumers in this way can be found in co-pending patent application Ser. No. 10 / 955,946, filed Sep. 30, 2004. That disclosure is hereby incorporated by reference herein.

  “Basket size” refers to the number of items purchased in any one transaction between a consumer and a store. In more detailed embodiments, basket sizes are divided into three categories: small, medium and large. The system receives a basket size for each transaction in which the featured product was purchased and calculates a median basket size for each featured product. By calculating the median basket size for each featured product, the method will show how the featured product fits the store's broad appeal-only for consumers to trade for that particular item. Did you come to the store or did the consumer buy another product? -Can be determined.

  “Increase” refers to an increase in revenue generated as a result of a particular product being featured. The increase is the “monetary increase” indicating the increase in revenue, if any, resulting from the product being featured, or the revenue increase, if any, resulting from the product being featured. ". The combination of sales growth rate and monetary increase provides a measure of “return” for the featured product.

  “Penetration” refers to the number of consumers “reached” by a featured product. Penetration can be expressed as “customer penetration”, which represents the percentage of all consumers in a store that have purchased a featured product during a specific period of time.

  Finally, “Related Sales” is a weighted penetration. For example, this may represent the ratio of the amount of featured product purchase by the consumer to the total consumption. In this example, all consumers at a particular store spent a total of $ 100,000 during that period, and all consumers who purchased a featured product spent a total of $ 50,000 during that period. In this case, the related sales for the featured product is 50%. For other attributes (other than total sales in the above example), weighting according to, for example, basket size, demographics, price sensitivity, etc. can also be made. This can be calculated for individual stores, departments or entire retailers, which is how the method determines the reach effect of each of the featured products.

  “Individual metrics” analyze data from individual featured product levels, whereas “cumulative metrics” analyze data from a large group of products. The present invention uses cumulative metrics to generate a wide range of data for all featured products over a given period, as well as baseline data for all products. For example, cumulative metrics can include the number of consumers who have purchased any featured product, the total sales of all featured products purchased, and / or the total increase in all featured products purchased. Then, as a baseline, the present invention additionally shows the total number of consumers who have purchased any featured product, the total sales of all featured products purchased, and / or the total increase in all featured products purchased. calculate.

  The aggregation and classification engine 28 also classifies each of the featured products into meaningful “type” classifications for use by retailer personnel. In a more detailed exemplary embodiment of the present invention, the featured products are divided into three broad “type” categories— “anchor”, “helper” and “undecided”. “Anchor” is a featured product that promotes the economic effect of reach and return, while “Helper” is a featured product that promotes the economic effect of balance and return. The “undecided” classification is used when the system does not provide sufficient distribution data and / or transaction data to meaningfully analyze the economic effects of a particular featured product. An anchor is a staple item with the highest purchase rate for a specific retailer in principle. For example, the anchor product for the grocery store can be bread, milk and / or cola, while the anchor product for the electronics store can be DVD, CD and battery. The helper supplements the anchor by promoting balance. Helper products are slightly more limited in reach than anchors. That is, they are products that are normally purchased, but most are products that are not always purchased with every visit to the store. For example, a helper product in a grocery store can be a detergent, paper product, fish, frozen food, and a helper product in an electrical store can be a DVD player, a CD player, and a speaker.

  In more detailed embodiments, the anchor and helper classifications are further divided into subclasses. For example, anchor characterization products include other types of anchors, such as “return anchors”, “incentive anchors”, “traffic anchors”, “expensive commodity anchors”, “low end anchors” or demographically categorized anchors. Can be divided. A reward anchor is a featured product that rewards consumers for purchasing a standard item that they would purchase with or without advertisements. Milk is an example of a reward anchor. Incentive anchors are high-end products when not featured, but when featured, they are typically “buy” for individuals who only purchase mainstream or low-end products. When a featured product becomes routinely bought out, the product, even when featured, causes it to mean a mainstream product or a low-end product. A traffic anchor is a featured product that draws the consumer into the store for the specific purpose of purchasing the featured product, thus driving the consumer into and into the store. A typical example of a traffic anchor is cola, where many consumers tend to “shop around” for this week's sale of soft drinks. High-priced products have a relatively high advertising price and are relatively far from special sales. Thus, expensive anchors would be expensive products that would be anchors when featured.

  Helpers can also be divided into the same type of sub-categories, eg, “low-end helpers”, “high-end helpers” and “store helpers”. Low-end helpers and high-end helpers are featured products that help boost returns from low-end products and high-end products, respectively, and store helpers are featured products that boost returns from broad and middle products. Even in more detailed embodiments, the system can be categorized by how effective the featured product was in producing the associated economic effect. For example, a featured product that only pushes returns from broad and middle products reasonably can be designated as a “weak store helper”. The system maintains a minimum standard for each of the classifications, but the specific standard should be based on sales, preferences and economic differences between different departments or even between different individual stores. be able to.

  After the system aggregates raw data and categorizes each of the featured products, the system uses the “final tool” 30 to deliver the integrated information to each of the users. In a detailed embodiment of the invention, the final tool is a graphical user interface. As described below and as shown in FIGS. 2-4, the graphical user interface organizes information so that the information can be easily accessed by the user.

  Returning to FIG. 1, the raw data and classification is monitored by the “benchmark tracking” component 32 of the system. This component provides a periodic analysis used to modify the rules that determine the grouping of featured products and the aggregation of featured product data and classification. Such periodic corrections are important because consumer behavior varies with, for example, the season, growth in specific geographic regions, new product launches into the market, and the like. In a more detailed embodiment of the present invention, the “benchmark tracking” component 32 generates a “quarterly analysis” 33. This “quarter analysis” is used to modify the rules on a quarterly basis. In a more detailed alternative embodiment of the present invention, the analysis is performed continuously and rule modifications are performed as necessary. In another more detailed embodiment of the present invention, the user can observe benchmark tracking and analysis and trigger rule modifications at any time.

  In addition to changing the rule modification interval, the present invention can make modifications in that it changes the granularity level. In a more detailed embodiment of the invention, the rules are modified independently for each of the retail store's geographic divisions 14. In a more detailed alternative embodiment, the rules are modified independently for each of the retail stores. Such a store-specific rule is referred to as a “store-specific metrics lookup” 34.

  Figures 2-4 illustrate an exemplary embodiment of a graphical user interface displaying analysis results. The user can choose to view information specific to a particular geographic department and a particular time period. In an exemplary embodiment, the analysis results can be displayed by a graphical user interface in three different formats: a weekly performance format, a period trend format, and a featured product database format. Each of these formats can be presented by selecting a corresponding button on the graphical user interface, weekly performance button 36, period trend button 38, and featured product database button 40.

  The display shown in FIG. 2 is a featured product database display. This display shows how each of the featured products has been in the predetermined period. The “department” 42 in the first column identifies the geographic department in which the user has chosen to view the information. “Week” 44 in the second column displays a period during which data to be displayed is collected. Just as advertising the same product in just different geographic sectors can produce different effects, advertising the same product in different time periods can produce different effects.

  The third, fourth, and fifth columns are “characterized product” 46 that is the feature of the featured product, “sales with special benefit” 48 to which the customer-facing sales floor, that is, the featured product belongs, and the classification given to the featured product by the system “Type” 50 is displayed. The third column 46, that is, the featured product column, identifies the particular featured product that was analyzed. By clicking the “View Specialized Product Name” button, the user can switch between the specialized product name shown in the advertisement and the main UPC for each specialized product group. UPC is the UPC that accounts for the highest sales of featured products.) It is also within the scope of the present invention that this switch will allow all UPCs of the featured product to be displayed, not just the main UPC. The fourth column 48 specifies the customer sales counter to which the featured product belongs. For example, “Big K Diet Root Beer” belongs to the sales floor with soft drink benefits. Each classification type of the featured product is displayed in the fifth column 50. It should be noted that in this embodiment, the classification type is generated by the aggregation and classification engine 28 that divides the classification type into subclasses and provides modifiers as described above. For example, if a featured product is classified as a weak store helper, this may mean that the featured product has only moderately boosted sales of mainstream products.

  The remaining columns shown in FIG. 2 display the processed data used by the system in each classification of the featured product. “Store Dist.” 52 in the sixth column informs the user of what percentage of stores in the department provided the featured product. “Price Point Change” 54, “Base Price” 56 and “Front Price” 58 in columns 7 to 9 are featured and not featured. Provide information about price differences in cases. The “Base Position” 60 and “Front Position” 62 in the 10th and 11th columns are classified into the types of consumers who purchase the product at the base price, and the consumption which purchases the product at the advertising price. Provide a classification of the person's type. The “BasketSize” 64 in the eleventh column indicates the number of other products purchased with the featured product. “Cust. Pen.” 70 in the 14th column indicates the percentage of consumers who purchased the featured product. “Assoc. Sales” 72 in the 15th column indicates the ratio of the total sales represented by the featured product as compared with other products. Column 12 “Sales Uplift” 66 and Column 13 “Dollar Uplift” 68 represent the sales increase of the product when it is featured versus when it is not featured. . Sales increase 66 represents the rate of increase of the number of products that are featured relative to the number of specific products sold when not featured. A monetary increase 68 represents an increase in revenue generated by sales of a particular product when it is featured versus when it is not featured.

  The graphical user interface provides the base price (product price if not featured), the front price (product price if featured) and the percentage difference between the two prices. It also classifies products by price sensitivity at base and front prices. These columns let the user know if the product has appealed to different consumer groups / categories when featured as opposed to not featured. For example, some products appeal to high-end price-sensitive consumers when not featured, but appeal to intermediate price-sensitive consumers when featured. Because some products appeal to different appeal segments when featured as opposed to not featured, such information may be the current and future expected “balance” of a particular featured product. It is vital for analysis of effects.

  Another feature of the graphical user interface shown in FIG. 2 is that it creates an advertising rating template that allows the user to compare featured product proposals against predetermined reach, balance and return goals. It is a point that can be. The user can create an advertising plan for table 74 by clicking on the department field of the relevant featured product (or dragging the featured product itself to evaluation table 74) and selecting from the previous featured product. Can do. For example, the user may specify an approximate number of goals for each of the type 50 that he / she wishes to be included in a particular weekly advertising plan to achieve the desired “reach”, “balance” and “return” effects. Or a criterion can be given. Such criteria can be provided in changing the granularity level. In a more detailed embodiment of the present invention, such criteria are given independently for each of the geographic divisions 14. In a more detailed alternative embodiment of the present invention, such criteria are provided independently for each of the retail stores 12. By referencing such criteria, the user can evaluate each of the advertising plans and adjust the plan to meet specific “reach”, “balance” and “return” needs.

  When the user adds the combination of featured product proposals to the table 74, the user can click the advertisement evaluation template button 75, and the featured product proposal added to the table 74 is a matrix as shown in FIG. Exported to a spreadsheet file called a template. The matrix template allows the user to look over all products and see how the featured product proposals together achieve a track record. The matrix template includes a row / blank for the featured product proposal, and a column for key metrics related to the featured product plan and indicating what performance the featured product has made in the past. The matrix also includes a section for additional comments that can be added by the user and additional space to evaluate the overall movement towards the three goals of reach, balance and return. With this data, retailers can see which featured products have the strongest potential to reach consumers, generate additional sales, and include all categories of consumers. With this information, retailers can balance their investment each week (or other advertising period) to maximize the set of featured products needed to achieve a given goal. In an exemplary embodiment of the invention, this matrix template evaluation step is performed manually with the help of a computerized spreadsheet tool, but further software selects and evaluates the process (not completely). In any case, automation is of course within the scope of the present invention.

  FIG. 3 shows a “Weekly Performance” screen of a typical graphical user interface. This screen provides the user with raw data, ratio data and graphical showing the reach, balance and return effects experienced by a specific geographic department (or any other retailer choice or combination) for a specific period of time. Provide data. In an exemplary embodiment, the user views data from different geographic departments and / or different weeks by clicking the “Select Department” drop down box 76 and / or the “Select Week” drop down box 78. Can be selected.

  The “total weekly performance” chart 80 provides the user with comprehensive “reach”, “penetration” and “increase” data. The total “reach” rate 82 is the numerical value from which the rate was derived—the total number of households 84 who shopped at the retail store in the selected geographic department 76 during the selected week 78, and Provided with the number of households 86 who purchased the featured product during the week 78. Reach can also be represented in terms of revenue by the “relevant store sales” rate 88. This figure represents the so-called “halo effect” and seeks the percentage of “total store sales” 90, which is sales to consumers who have purchased one or more featured products (ie, “all related store sales” 92) Is calculated by The reach is further subdivided and the “all VPS / PS households” 96 who purchased one or more featured products during a selected week 78 (ie, “ By looking for the ratio of “all VPS / PS” 98), the very price-sensitive (“VPS”) and price-sensitive (“PS”) that were interested in this week's featured products The percentage of customers 94 is shown. The “sales penetration” rate 100 represents the ratio of “total sales of featured products on the first page” 104 to “total sales of store” 102. Finally, “increase” represents the increase in revenue of the featured product resulting from the advertisement. FIG. 3 shows the “sales increase” rate 106 and provides dollar figures for both “basic sales of featured products on the first page” 108 and “total amount of increase over basic amount” 110.

  FIG. 3 also displays the reach data and the increase data subdivided by the appeal segment in the chart 112 of “What kind of performance did the company's featured product show for each segment?”. Reach table 116 shows the total number of households in each of the appealing segments bought, the total number of households interested in one or more featured products in each of the appealing segments, and the characterization in each of the appealing segments. Shows the percentage of households “reached” by product. Reach rate data for each of the appeal segments compared to the total reach rate is also displayed via a bar graph and line graph 118. The increase table 120 displays not only the total basic sales, the total sales of the first page, and the ratio of the total increase, but also the increase rate for each of the basic sales, the first-page featured product sales, and the appeal segment. The base sales and first page sales for each of the appeal segments are also compared via bar graph 122.

  FIG. 4 shows a “performance trend” screen of a typical graphical user interface. The user can determine from the reach period trend graph 123 whether the total sales of the stores in a particular geographic department 126 were high during the period when the total reach was high. Again, in this exemplary embodiment, the display is subdivided into geographic divisions. However, the present invention is sufficiently flexible that the display may be subdivided by other combinations such as retailers, time periods, products, product categories, etc. Graph 124 represents the latest week in which data was reported. The x-axis 132 of the graph 123 represents the individual weeks that make up the analysis period. The y-axis 128 of the bar graph represents the total sales of the store, and the y-axis 130 of the line graph represents the reach rate. By superimposing the total sales on the reach data, this screen allows the user to evaluate the effect of reach on the total sales of the store in a particular geographic department over an extended period of time.

  Although the invention has been described with reference to exemplary embodiments, the invention is defined by the claims and any limitation or element that describes the exemplary embodiments set forth herein may be It is to be understood that no limitation or element should be incorporated into the claims unless explicitly recited in the claims. Similarly, the invention is defined by the following claims, and inherent and / or unforeseeable advantages of the present invention may exist even if not explicitly discussed herein, It is to be understood that it is not necessary to meet any or all of the specified advantages or objectives of the invention disclosed herein to be within the scope.

2 shows a schematic flowchart of a method according to an exemplary embodiment of the present invention. FIG. 6 illustrates a graphical user interface that provides output in accordance with an exemplary embodiment of the present invention. FIG. Fig. 5 illustrates an alternative graphical user interface that provides output in accordance with an exemplary embodiment of the present invention. FIG. 6 illustrates an additional graphical user interface that provides output in accordance with an exemplary embodiment of the present invention. FIG. Fig. 4 shows a spreadsheet output according to an exemplary embodiment of the present invention.

Claims (32)

An advertising planning and evaluation method for retailers,
(A) Retail data for a plurality of featured products over a first period, including data in an advertisement for a featured product, data about the type of advertisement, data about an advertising period, and data about an advertising region. Collecting a first distribution data set distributed by a merchant with the assistance of a computer;
(B) collecting, with the aid of a computer, a first set of transaction data for a plurality of transactions for purchasing a plurality of products from the retailer conducted by a plurality of consumers over a first period of time;
(C) analyzing the first distribution data set with respect to the first transaction data set over a first period with computer assistance;
(D) with the aid of a computer, classifying the plurality of featured products into a plurality of advertising roles based on an analysis of the first distribution data set relating to the first transaction data set; ,
Including
The role of the plurality of advertisements is a featured product that attracts consumers to a particular part of the retailer, a feature that facilitates a mix of consumers belonging to different consumer categories to be attracted to purchase a featured product Two or more of the roles of advertising products, featured products that promote balance when one consumer purchases different types of products, featured products that promote overall sales increase by retailers, and The role of one or more advertisements obtained from a group that includes a featured product that facilitates the combination,
A method characterized by that. The method of claim 1, comprising:
(E) further comprising establishing a first advertising strategy based on the role of the advertisement. The method of claim 1, comprising:
(F) collecting, with computer assistance, a next distribution data set for a second plurality of featured products used by the retailer over a next period of time;
(G) collecting with computer assistance the next set of transaction data for a plurality of products sold by the retailer over the next period;
(H) analyzing for the next transaction data set over the next period with the aid of a computer, the next Idemitsu data set;
(I) classifying, with computer assistance, the second plurality of featured products into an advertising role based on an analysis of the next distribution data set with respect to a next transaction data set;
The method of further comprising. The method of claim 1, comprising:
The output of the analysis step is
Total sales growth,
Sales growth rate for each featured product,
Increase total amount,
Increased sales for each featured product,
Overall customer penetration,
Customer penetration for each of the featured products,
Percentage of related sales,
Percentage of related sales for each featured product,
A method comprising one or more of the following. The method of claim 1, comprising:
The said Hiroki data collection step includes
Generating a list of featured product categories relevant to the retailer;
Assigning one or more of the featured product categories to each of a plurality of featured products provided by the retailer;
Generating a list of advertising dimensions relevant to the retailer;
Assigning one or more of the advertising dimensions to each of a plurality of in-store advertisements used by the retailer;
A method comprising the steps of: The method of claim 5, comprising:
The list of sale item dimensions is
Product category,
Product raw material category,
Producer category,
Sales floor category,
Size category,
A method characterized in that it includes one or more of the sale item dimensions. The method of claim 6, comprising:
The step of creating a list of featured product categories is:
The method further comprises mapping the featured product to one or more of the plurality of consumer benefit counters based on the location of the same product within the retailer's retail store. The method of claim 5, comprising:
The list of advertising dimensions is
Product code dimension,
Basic price dimension,
Featured price dimension,
Display dimensions,
Ad execution dimension,
A method characterized in that it includes one or more advertising dimensions. 9. The method of claim 8, wherein
The advertisement execution dimension is:
Date range of execution,
List of execution positions,
A method comprising one or more of the following. The method of claim 1, comprising:
The distribution data collection step is limited to collecting distribution data from a plurality of retail stores located in one or more subsets of the retailer's geographic department. The method of claim 1, comprising:
The distribution data collection step is limited to collecting distribution data from a single retail store of the retailer. The method of claim 1, comprising:
The transaction data collection step includes:
Generating a list of product categories relevant to the retailer;
Assigning one or more product categories to each of the plurality of products;
Generating a list of transaction dimensions relevant to the retailer;
Assigning one or more transaction dimensions to each of the plurality of transactions;
Generating a list of consumer categories relevant to the retailer;
Granting one or more consumer dimensions to each of a plurality of consumers;
The method of further comprising. The method of claim 12, comprising:
The list of product categories is
Product category,
Product raw material category,
Producer category,
Sales floor category,
Size category,
A method comprising one or more of the categories. 14. The method of claim 13, comprising
The product category list generation step is:
The method further comprises mapping the product category to one or more of the plurality of privileged sales floors based on the location of the same product within the retailer's retail store. The method of claim 12, comprising:
The list of transaction dimensions is
Basket size,
The number of featured products purchased,
The total cost of the product purchased,
The average cost of the product purchased,
The total cost of the featured goods purchased,
The average cost of the featured goods purchased,
A list of purchased products,
A method comprising including one or more of the following transaction dimensions. The method of claim 15, comprising:
The basket size trading dimension is:
Small basket size,
Medium basket size,
Large basket size,
Including one or more sizes. The method of claim 12, comprising:
The list of consumer categories is
A list of customer-based consumer categories,
A list of featured product-based consumer categories,
A method comprising one or more of the following. The method of claim 17, comprising:
The customer-based consumer category is
Price sensitive subcategory,
Benefit subcategories,
A method comprising one or more of the following. The method of claim 18, comprising:
The price-sensitive subcategory is
Non-price sensitive category,
Mainstream category,
A price sensitive category,
Category with very high price sensitivity,
Category with unknown price sensitivity,
A method comprising one or more of the following. The method of claim 17, comprising:
The featured consumer-based consumer category is:
The total number of consumers who have purchased a featured product,
Total cost of all featured products purchased,
Total increase in all featured products purchased,
A method comprising one or more of the following. The method of claim 1, comprising:
The method of collecting transaction data is limited to collecting transaction data from a plurality of retail stores located in one or more of the retailer's geographic divisions. The method of claim 1, comprising:
The method of collecting transaction data is limited to collecting transaction data from a single retail store of the retailer. The method of claim 1, comprising:
The role of the advertisement is
The role of the anchor,
Helper role,
A method comprising one or more of the following. 24. The method of claim 23, comprising:
The role of the anchor is
Return anchor,
Incentive anchor,
Traffic anchor,
A method comprising one or more of the following types. 24. The method of claim 23, comprising:
The role of the helper is
Low-end helper,
High-end helper,
Mainstream helper,
Store helper,
A method comprising one or more of the following types. The method of claim 1, comprising:
A method further comprising the step of generating a report with the aid of a computer. 27. The method of claim 26, wherein
The report
At least a portion of the data
At least a portion of the transaction data, and at least a portion of the advertising role,
A method comprising the steps of: 28. The method of claim 27, wherein
A method wherein a user can access the report via a graphical user interface. 30. The method of claim 28, wherein
The graphical user interface displays the report as
Geographical division,
Execution date range,
Unified product code for each featured product,
The name of each featured product,
Retail floor for each of the featured products,
The role of advertising for each of the featured products,
Price point change for each featured product,
Price-sensitive consumer category at the base price for each featured product,
A price-sensitive consumer category at each featured price for each featured product,
The average basket size for the basket containing each of the featured products,
Sales growth rate for each featured product,
Increased amount for each featured product,
Consumer penetration for each featured product,
Percentage of related sales for each featured product,
A method characterized by organizing into one or more categories. 30. The method of claim 28, wherein
A method further comprising creating an advertisement evaluation template. 31. The method of claim 30, comprising
The advertisement evaluation template creation step includes:
Collecting, from one or more time periods, effect data for a combination of distribution data, transaction data, advertising roles and one or more featured products;
Analyzing the collected data relative to a desired range of reach, balance and return;
The method of further comprising. A computerized method for planning and / or evaluating advertisements for retailers,
(A) collecting transaction data for a plurality of consumers from a transaction conducted with the retailer over a first period and a second period with the assistance of a computer;
(B) Computer support for advertising data for at least one product featured by an advertisement, including at least a feature of the featured product and a relationship between the advertisement and the second time period. Collecting steps by
(C) the effect of the advertisement that attracts the consumer to trade with the retailer, the effect of the advertisement that facilitates the transaction of the first category versus the second category of the consumer, and one or more featured In order to determine one or more effects of the advertising that increases revenue for the product and the uncharacterized product, transaction data relating to the featured product is determined for at least a first period and a second Assessing with computer assistance in comparing between periods; and
(D) accumulating the results of the evaluation step (c) with the aid of a computer and displaying the results to the user in the form of a graphical user interface;
(E) providing a tool to the user via the graphical user interface to select one or more products to be featured in future advertisements based on the accumulated results;
A method comprising the steps of:

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