Source: http://www.patentsencyclopedia.com/app/20100114652
Timestamp: 2017-10-22 14:00:31
Document Index: 793290629

Matched Legal Cases: ['art 1400', 'art 1400', 'art 1500', 'art 1500', 'art 1800', 'art 1800']

Patent application number: 20100114652
An automated computer system for generating a geographically-localized media plan including a number of selected media buyable units (MBUs) is provided. The system implements the following functions: receiving client-defined information and a number of business rules; receiving a number of MBUs each having a relative value and including a first and second MBU associated with a first media product option and a third and fourth MBU associated with a second media product option; applying an optimization algorithm to the client-defined information, the number of business rules and the number of MBU relative values to obtain a number of selected MBUs included in a geographically-localized media plan; and outputting the geographically-localized media plan for use by a client in media planning. The first MBU associated with a first geography. The second MBU is associated with a second geography and the first geography, with the first geography being larger than the second geography and the first geography substantially covering the second geography.
1. An automated computer system for generating a geographically-localized media plan including a number of selected media buyable units (MBUs) and comprising a computer having a central processing unit (CPU) for executing machine instructions and a memory for storing machine instructions that are to be executed by the CPU, the machine instructions when executed by the CPU implement the following functions:receiving client-defined information and a number of business rules;receiving a number of MBUs including a first and second MBU associated with a first media product option and a third and fourth MBU associated with a second media product option, the first MBU associated with a first geography, the second MBU associated with a second geography and the first geography, the first geography being larger than the second geography, the first geography substantially covering the second geography, each of the number of MBUs having a relative value, and the first and second media options being part of a number of available media product options;applying an optimization algorithm to the client-defined information, the number of business rules and the number of MBU relative values to obtain a number of selected MBUs included in a geographically-localized media plan; andoutputting the geographically-localized media plan for use by a client in media planning.
2. The computer system of claim 1, wherein the applying function includes comparing a first MBU relative value to a second MBU relative value.
3. The computer system of claim 1, wherein the client-defined information includes a client-defined geography for the geographically-localized media plan and a number of required geographies, and the applying function includes applying the optimization algorithm to the client-defined information including the client-defined geography and the number of required geographies, the number of business rules and the number of MBU relative values to obtain the number of selected MBUs included in the geographically-localized media plan.
4. The computer system of claim 1, wherein the number of business rules include a number of penetration constraints, and the applying function includes applying the optimization algorithm to the client-defined information, the number of business rules including the number of penetration constraints and the number of MBU relative values to obtain the number of selected MBUs included in the geographically-localized media plan.
5. The computer system of claim 1, wherein the client-defined information includes a required market or store allocation budget, and the applying function includes applying the optimization algorithm to the client-defined information including the required market or store allocation budget, the number of business rules and the number of MBU relative values to obtain the number of selected MBUs included in the geographically-localized media plan.
6. The computer system of claim 1, wherein the machine instructions when executed by the CPU further implement the following functions:receiving a number of media planning scenarios including a first media planning scenario and a second media planning scenario, the first media planning scenario including a subset of the available media product options selected based on the client-defined information and the second media planning scenario including all of the available media options, and the applying step includes:for the first media planning scenario, applying the optimization algorithm to the client-defined information, the number of business rules and the number of MBU relative values associated with any of the subset of available media product options to obtain a number of selected MBUs included in a geographically-localized media plan based on the first media planning scenario; andfor the second media planning scenario, applying the optimization algorithm to the client-defined information, the number of business rules and the number of MBU relative values associated with any of the number of available media product options to obtain a number of selected MBUs included in a geographically-localized media plan based on the second media planning scenario.
7. The computer system of claim 6, wherein the client-defined information includes budget information.
8. The computer system of claim 1, wherein the optimization algorithm includes an objective function including a first term associated with MBU relative value and a second term associated with a penalty for geography over-penetration.
9. The computer system of claim 8, wherein the applying function includes maximizing the value of the objective function to obtain a number of selected MBUs included in the geographically-localized media plan.
10. The computer system of claim 1, wherein the optimization algorithm is an off-the-shelf algorithm.
11. The computer system of claim 1, wherein the number of business rules includes a minimum volume requirement associated with one of the available media product options and the applying function includes applying the optimization algorithm to the client-defined information, the number of business rules including the minimum volume requirement and the number of MBU relative values to obtain the number of selected MBUs included in the geographically-localized media plan.
12. The computer system of claim 11, wherein the applying function includes bundling a number of MBUs to obtain a bundled MBU for satisfying the minimum volume requirement.
13. The computer system of claim 1, wherein the first geography is a ZIP Code and the second geography is a carrier route.
14. The computer system of claim 1, wherein the MBU relative value is a function of an activation score and a household value score.
15. The computer system of claim 1, wherein the outputting function includes outputting the geographically-localized media plan for use by a client in integrated national media planning.
16. The computer system of claim 1, wherein the client-defined information includes a number of client-defined requirements.
17. An automated computer-implemented method for generating a geographically-localized media plan including a number of selected media buyable units (MBUs), the method comprising:receiving client-defined information and a number of business rules;receiving a number of MBUs including a first and second MBU associated with a first media product option and a third and fourth MBU associated with a second media product option, the first MBU associated with a first geography, the second MBU associated with a second geography and the first geography, the first geography being larger than the second geography, the first geography substantially covering the second geography, each of the number of MBUs having a relative value, and the first and second media options being part of a number of available media product options;applying an optimization algorithm to the client-defined information, the number of business rules and the number of MBU relative values to obtain a number of selected MBUs included in a geographically-localized media plan; andoutputting the geographically-localized media plan for use by a client in media planning.
18. An automated computer system for generating a plurality of geographically-localized media plans including a number of selected media buyable units (MBUs) and comprising a computer having a central processing unit (CPU) for executing machine instructions and a memory for storing machine instructions that are to be executed by the CPU, the machine instructions when executed by the CPU implement the following functions:receiving a plurality of media planning scenarios;receiving client-defined information and a number of business rules;receiving a number of MBUs including a first and second MBU associated with a first media product option and a third and fourth MBU associated with a second media product option, the first MBU associated with a first geography, the second MBU associated with a second geography and the first geography, the first geography being larger than the second geography, the first geography substantially covering the second geography, each of the number of MBUs having a relative value, and the first and second media options being part of a number of available media product options;applying an optimization algorithm to the client-defined information, the number of business rules and the number of MBU relative values to obtain a number of selected MBUs included in a geographically-localized media plan for each of the plurality of media planning scenarios; andoutputting the geographically-localized media plan for each of the media planning scenarios for use by a client in media planning.
19. The computer system of claim 18, wherein the plurality of media planning scenarios include at least two media planning scenarios selected from the group consisting of a client preference media planning scenario, a full portfolio media planning scenario and a flexed client preference media planning scenario.
20. The computer system of claim 18, wherein the outputting function includes outputting the geographically-localized media plan for each of the media planning scenarios onto a single report for use by the client in media planning.
21. The computer system of claim 16, wherein the plurality of media planning scenarios include a client preference media planning scenario, a full portfolio media planning scenario and a flexed client preference media planning scenario.
[0001]This application is related to U.S. patent application Ser. No. ______ filed on ______ and U.S. patent application Ser. No. ______ filed on ______. Both related applications are incorporated herein by reference in their entirely.
[0009]In one embodiment, an automated computer system for generating a geographically-localized media plan including a number of selected media buyable units (MBUs) is disclosed. The system includes a computer having a central processing unit (CPU) for executing machine instructions and a memory for storing machine instructions that are to be executed by the CPU. The machine instructions when executed by the CPU implement the following functions: receiving client-defined information and a number of business rules; receiving a number of MBUs (each of the number of MBUs having a relative value) including a first and second MBU associated with a first media product option and a third and fourth MBU associated with a second media product option; applying an optimization algorithm to the client-defined information, the number of business rules and the number of MBU relative values to obtain a number of selected MBUs included in a geographically-localized media plan; and outputting the geographically-localized media plan for use by a client in media planning. According to this embodiment, the first MBU is associated with a first geography and the second MBU is associated with a second geography and the first geography. The first geography is larger than the second geography and the first geography substantially covers the second geography. The first and second media options are part of a number of available media product options.
[0010]According to another embodiment, an automated computer-implemented method for generating a geographically-localized media plan including a number of selected media buyable units (MBUs) is disclosed. The method includes the following steps: receiving client-defined information and a number of business rules; receiving a number of MBUs (each of the number of MBUs having a relative value) including a first and second MBU associated with a first media product option and a third and fourth MBU associated with a second media product option; applying an optimization algorithm to the client-defined information, the number of business rules and the number of MBU relative values to obtain a number of selected MBUs included in a geographically-localized media plan; and outputting the geographically-localized media plan for use by a client in media planning. According to this embodiment, the first MBU is associated with a first geography and the second MBU is associated with a second geography and the first geography. The first geography is larger than the second geography and the first geography substantially covers the second geography. The first and second media options are part of a number of available media product options.
[0011]In yet another embodiment, an automated computer system for generating a plurality of geographically-localized media plans including a number of selected media buyable units (MBUs) is disclosed. The system includes a computer having a central processing unit (CPU) for executing machine instructions and a memory for storing machine instructions that are to be executed by the CPU. The machine instructions when executed by the CPU implement the following functions: receiving a plurality of media planning scenarios; receiving client-defined information and a number of business rules; receiving a number of MBUs (each of the number of MBUs having a relative value) including a first and second MBU associated with a first media product option and a third and fourth MBU associated with a second media product option; applying an optimization algorithm to the client-defined information, the number of business rules and the number of MBU relative values to obtain a number of selected MBUs included in a geographically-localized media plan for each of the plurality of media planning scenarios; and outputting the geographically-localized media plan for each of the media planning scenarios for use by a client in media planning. According to this embodiment, the first MBU is associated with a first geography and the second MBU is associated with a second geography and the first geography. The first geography is larger than the second geography and the first geography substantially covers the second geography. The first and second media options are part of a number of available media product options.
TABLE-US-00003 TABLE 3 Type Description Function A1 In or out of trade In/Out area A2 Distance from a site Distance Scaling Factor B1 Demographic Composite Demographic Index characteristics B2 Demographics and (Composite Demographic Index) * (Distance distance Scaling Factor) C Sales Average Sales per Household D Customer behavior Index Value of Behavioral Variable E Historical data of Index Value Of Response media promotion responses α Custom Custom Function
V. MAA Module
[0101]FIG. 13 depicts a process map 1300 relating to MAA module 208 according to one or more embodiments of the present invention. MAA module 208 receives data and information from various sources. For example, MAA module 208 may receive data and information, such as discovery data and information, output from the discovery module 202 and data and information, such as geographically localized data and HVSs, output from the targeting module 206, as generally depicted by arrow 1302. Other data and information may be received by the MAA module 208, such as data and information relating to media products and their availability. Such data and information may reside on a media inventory within data repository 205.
[0102]MAA module 208 may receive media product pricing data and information from pricing module 210. Such media product pricing data and information may reside on an HB media computer system or an ERP computer system, which may be part of data repository 205. MAA module 208 is configured to transmit data and information relating to selected media options and selected media buyable units, as depicted by arrow 1304.
[0103]MAA module 208 may be configured to calculate a household value score (HVS) of a geo-unit based on the household count in the geo-unit and the geo-score of the geo-unit. In one or more embodiments, the HVS is a number representing the relative value to a media campaign of reaching the geo-unit being examined. The HVS value is computed based on a function only of the geo-unit and is therefore independent of the media product. If the geo-score is a direct measurement or a count, then the HVS is the geo-score. If the geo-score is expressed as an index, over all households in the geo-unit, then the HVS is the geo-score times the household count.
[0104]FIG. 14 depicts a flowchart 1400 of steps that are executed by MAA module 208 according to one or more embodiments. It should be appreciated that the steps of flowchart 1400 can be modified, rearranged, and/or omitted according to the specific implementation of the present invention, and any step can be carried out by a user, a computer or in combination according to the particular implementation of the present invention.
[0105]According to block 1402, MBUs are identified. In one or more embodiments, an MBU is the lowest level of a media option that can be purchased by a client that covers a geographic area. A non-limiting example of an MBU is a newspaper insert in one or more ZIP Codes, which may make up a zone called "cluster 87" in the Boston Globe Sunday edition. An MBU for shared mail inserts may be a ZIP Code or ATZ. An MBU for solo mail inserts may be a postal carrier route. An MBU for a REDPLUM wrap may be a wrap zone. An MBU for a ZIC may be a ZIC zone. An MBU for an ROP may be the full run of a single newspaper. An MBU for a cooperative free standing insert may be a grouping of multiple delivery vehicles.
[0106]It should be appreciated that other MBUs are contemplated by one or more embodiments of the present invention. For example, a household may be a media buyable unit for one or more media product options contemplated by one or more embodiments. As another non-limiting example, an individual within a household may be an MBU.
[0107]MAA module 208 is configured to identify all buyable parents of a targeted carrier route. For each media product selected by a client during execution of discovery module 202, MAA module 208 retrieves all MBUs containing at least one targeted carrier route. If an exclusion file is stored in data repository 205, MAA module 208 is configured to reject MBUs associated with the media number and edition provided in the exclusion file.
[0108]According to block 1404 of FIG. 14, the availability of the media options associated with the MBUs is identified in block 1402. In one or more embodiments, media options refer to the distribution of media products associated with each MBU. In one or more embodiments, newspaper media products are defined on the edition level, and direct mail media products are defined at the direct mail distribution level. This step may also include eliminating any media products if such media products are not available within the client's requested date range.
[0109]According to block 1406, the inventory availability of zone media products, such as REDPLUM wrap and ZIC media products, is confirmed. In one or more embodiments, the exclusivity of REDPLUM wrap media products is also checked during this step.
[0110]According to block 1408, the available media options are de-duplicated by selecting the media product with the best date according to client preferences and business rules. The business rules may include in-home date and the client preferences may include before or after in-home date preferences stored in data repository 205 during execution of discovery module 202. It should be appreciated that in-home date refers to the date on which the particular media reaches the home. The delivery date is the in-home date for newspapers. A two day window is typically given as an in-home date range for media delivered by mail.
[0111]The best date may be selected according to the following prioritization: (1) client's preferred date; (2) best food day or direct mail package in-home date, and the day meets the client's directional preference of before or after the in-home date preference; (3) non-best food day and the day meets the client's directional preference; (4) best food day or direct mail package in-home date and the day does not meet the client's directional preference; and (5) non-best food day and the day does not meet the client's directional preference. In one or more embodiments, the best food day is the best day of the week for food purchases according to a number of grocers within a defined market.
[0112]According to block 1410, the PDI value is calculated. In one or more embodiments, PDI represents the conformance of the media option distribution date to the client's date preferences. In one embodiment, the PDI value ranges from 1.0 to 0.82, with the value decreasing relative to the alignment of the media option to the client's expressed date preferences.
[0113]According to block 1412, newspaper minimum buys for media products having pre-preprinted material, such as newspaper inserts, are validated. First, a number of available buyable units associated with a newspaper or edition are selected. The total circulation or purchase represented by the selected buyable units is determined. If the total circulation or purchase is within 90% (or some other defined percentage) of the minimum amount for the associated newspaper or edition, the selected buyable units are maintained. If the total circulation or purchase is less than 75% (or some other defined percentage) of the minimum amount, the number of selected buyable units associated with the newspaper or edition is removed from the list of available buyable units. If the total circulation or purchase is 75% (or some other defined percentage) or greater than the minimum amount, but less than 90% (or some other defined percentage) of the minimum amount, then available buyable units are added to the number of selected buyable units, as depicted in block 1414. In one embodiment, all available buyable units within a radius, such as a 5 mile radius, of the selected buyable units are identified. The identified buyable units are added to the selected buyable units to reach the required minimum of 90%. The radius may be expanded in order to meet the required minimum.
[0114]According to block 1416, pricing data is derived for all media options not explicitly excluded by the client. The pricing data is utilized by optimization module 212 so that optimized media plans are within budget constraints specified during execution of discovery module 202.
[0115]According to block 1418, an activation score is determined by MAA module 208. In one or more embodiments, an activation score means a relative score representing the value of a particular media product in relation to another media product in terms of the effectiveness in achieving a client's objectives. The activation score captures the level to which a media option achieves client objectives and household engagement with the selected media option. The activation score is a function of the particular media vehicle and is independent of geography.
[0116]FIG. 15 represents a flowchart 1500 depicting the steps for determining an activation score according to one or more embodiments of the present invention. It should be appreciated that the steps of flowchart 1500 can be modified, rearranged, and/or omitted according to the specific implementation of the present invention, and any step can be carried out by a user, a computer or in combination according to the particular implementation of the present invention.
[0117]Step 1502 is directed at determining a consumer purchasing frequency for a client. Non-limiting examples of consumer purchasing frequencies include ritual, reminder, research and consumer packaged goods (CPG). In one or more embodiments, the ritual consumer purchasing frequency includes those retailers who sell goods and/or services that a consumer may utilize relatively often, such as numerous times per month, without giving much thought to their specific choice of such goods and/or services. Non-limiting examples of clients, i.e., retailers, which may fall into the ritual consumer purchasing frequency include dry cleaners, grocery stores, packaged goods retailers, quick service restaurants and video stores. In one or more embodiments, the reminder consumer purchasing frequency includes those retailers who sell goods and/or services that a consumer may utilize periodically, although with a relative low frequency. Non-limiting examples of retailers that may fall into the reminder consumer purchasing frequency include auto services, carpet cleaning, optical stores, fine dining, healthcare, home services, professional services, specialty stores, sports and leisure and tires. In one or more embodiments, the research consumer purchasing frequency includes those retailers who sell good and/or services that a consumer will likely choose to research in advance of their purchase. Non-limiting examples of retailers that may fall into the research consumer purchasing frequency include auto dealers and manufacturers, consumer electronics retailers, department stores, financial services, home furnishings, home improvement stores, home remodeling business, mattress and bedding stores, real estate, satellite, soft goods, telecommunications and travel. In one or more embodiments, the CPG consumer purchasing frequency includes those retailers that sell goods that are consumable and need frequent replacement, such as food, beverages, and cleaning products.
[0118]Step 1504 is directed at determining base scores for each tier one product based on the applicable consumer purchasing frequency. In one embodiment, the base score data for the ritual, reminder and research frequencies are obtained from BIGresearch, LLC of Worthington, Ohio and the base score data for the CPG consumer purchasing frequency is obtained from NCH Marketing Services Ltd. of the United Kingdom. In one embodiment, the base scores for each tier product is an average of a number of base scores for each individual retailer within the applicable consumer purchasing frequency.
[0119]Step 1506 is directed at adjusting base scores based on client objective. In one embodiment, the client objective is selected from a number of client objectives, including conversion, retention, awareness, acquisition, frequency or ticket. In one embodiment, the conversion objective focuses on increasing share of a current customer spending by taking such a share away from the competitor. In one embodiment, the retention objective focuses on maintaining current customers and their current spending. In one embodiment, the awareness objective focuses on increasing "top of mind" position, such as the top three amongst all competition in that industry's accepted competitive frame. In one embodiment, the acquisition objective focuses on acquiring new loyal customers. In one embodiment, the frequency objective focuses on increasing frequency of purchases. In one embodiment, the ticket objective focuses on total spend per transaction.
[0120]In one embodiment, a two dimension matrix of the client objectives and the consumer purchasing frequency is constructed to obtain a table of rating of the relative effectiveness of individual media vehicles for each of the client objectives. Such tables can be constructed for each of the consumer purchasing frequency, such as ritual, reminder, response and CPG.
[0121]Table 4, which is set forth below, depicts the two dimensional matrix for the ritual consumer purchasing frequency according to one embodiment. The i of βi,j is the client objective and j of βi,j is the media product. β is selected from the group high, medium and low depending on market and/or client data and information.
[0122]Table 5, which is set forth below, depicts the two dimensional matrix for reminder consumer purchasing frequency according to one embodiment. The i of βi,j is the client objective and j of βi,j is the media product. β is selected from the group high, medium and low depending on market and/or client data and information.
[0123]Table 6, which is set forth below, depicts the two dimensional matrix for research consumer purchasing frequency according to one embodiment. The i of βi,j is the client objective and j of βi,j is the media product. β is selected from the group high, medium and low depending on market and/or client data and information.
[0124]Table 7, which is set forth below, depicts the two dimensional matrix for the CPG consumer purchasing frequency according to one embodiment. The i of βi,j is the client objective and j of βi,j is the media product. β is selected from the group high, medium and low depending on market and/or client data and information.
[0125]In one embodiment, a multiplier is associated with the high, medium and low ratings. In one embodiment, the multipliers for high, medium and low are 1.25, 1.0 and 0.85, although in other embodiments, these multipliers can be adjusted based on empirical data. With respect to each tier one product, the activation score is obtained by multiplying the base score and the multiplier.
[0126]Having described the determination of activation scores according to FIG. 15, the following provides an example of such a determination with values. Company X may desire a media plan for a back to school event with the client objective of Ticket. The Ticket objective may be most applicable because Company X may want to upsell existing customers. Company X is considered to fall in the ritual industry category. The base activation scores are obtained through market research and are averaged over a product category. The activation scores are represented as percentages with a value of between 0 and 1. The activation scores typically change over time, and are subject to periodic updates, such as monthly updates. The base activation scores for this example are set forth in the table below.
[0127]As the next step in the process, the ritual base scores may be adjusted based on the client objective to obtain the activation scores. The following table depicts the performance ratings for each tier one product for the ticket objective according to one embodiment.
[0128]For purposes of this example, the multipliers of 1.25, 1.0, and 0.85 were utilized for high, medium and low. As such, the following table shows the calculation of the adjusted activation score for each media product with tier one.
TABLE-US-00010 TABLE 10 Activation Media Product Base Score Multiplier Score Solo Saturation 0.5 0.85 0.425 Preprint in 0.8 1.25 1.0 Newspaper Preprint in Shared 0.9 1.25 1.125 Mail ShopWise Wrap 1.0 1.25 1.25 FSI 1.0 1.25 1.25 ROP 0.7 1.0 0.7
[0129]In one or more embodiments, the HVS and activation score are factors used in determining a media buyable unit ("MBU") score. Other factors are also used in the determination of an MBU score. For example, penetration, otherwise referred to as coverage, is the ratio of the number of media option units distributed and/or sold across a MBU's media footprint comprised of a number of geo-units to the number of base count households in the number of geo-units that constitute the media footprint. Equation 3 depicts penetration in algebraic terms.
[0130]Another factor considered in the MBU score determination is preferred date index ("PD" or "PDI"). In one or more embodiments, PDI means a scaling factor (multiplier) that captures how close a MBU's delivery date is to the client's preferred delivery date, assuming one has been specified. If the media option falls on the client's preferred in-home date, or no preferred date has been specified, then PDI equals 1. Otherwise, the PDI value is less than 1, decreasing for dates that are further away from the client's preferred date. In at least one embodiment, the PDI value is based on client preference, and may change over time.
[0131]Having thus defined the factors used in determining an MBU score, the following equation 4 depicts an algebraic relationship between activation, penetration and effective coverage according to one embodiment.
[0132]This equation is referred to as the APEC equation, which measures the value of each MBU considering all the geounits its touches. This value is otherwise referred to as the APEC score. In one or more embodiments, the APEC score captures the level to which a media option achieves the client's objectives and household engagement with the media option. The APEC score is determined by multiplying an MBU's activation score times its penetration times its PD times the sum of the HVSs for the geo-units touched by the MBU.
[0133]For each MBU within a particular geo-unit, the MBU score may be calculated using the following equation 5:
MBU Score = APEC Sell Price ( 5 ) ##EQU00003##
[0134]In one or more embodiments, the sell price refers to the cost associated with purchasing the media buyable unit with the particular geo-unit. For example, the sell price for a ZIC may be a wrap zone. As another example, the sell price for a newspaper insert may be a newspaper zone.
[0135]According to block 1420 of FIG. 14, an inclusion/exclusion flag associated with each available media product for each of a number of recommended media plans is set based on client preferences and business rules. In one embodiment, the number of recommended media plans includes three recommended media plans referred to as a client preferences recommendation, a full portfolio recommendation and a flexed client preference recommendation.
[0136]The MBU score is a relative score for a certain MBU as compared across a number of MBUs. Optimization module 212 utilizes the MBU scores to compare possible media product purchases based on a combination of factors and selects those MBUs that are most desirable to a client's objectives. In one or more embodiments, the optimization module 212 requires that metrics used in the optimization algorithm are multiplicative. For example, the difference between 4 and 1 is 4 times the difference between 2 and 1. A value of interest can often be restated as a metric that has the multiplicative quality.
[0137]FIG. 16 depicts a process map 1600 relating to optimization module 212 according to one or more embodiments of the present invention. Optimization module 212 receives data and information generated by MAA module 210 and stored in data repository 205. Such data and information includes, but is not limited to, geo-unit data and information, MBU data and information, constraint data and information, and client preference information, as generally depicted by arrow 1602. Geo-unit data and information includes, without limitation, geo-unit identifications, household counts and geo-scores. MBU data and information includes, but is not limited to, MBU identifications, media product definitions, media product footprints, media product editions, media numbers, delivery types, sell prices, penetration in footprint, PDIs and activation scores. Constraint information includes, without limitation, applicable minimum volumes for certain media products, required geo-units according to client's preferences and client budget. Client budget constraint information may include a total client budget, required market allocation budgets, and required store allocation budgets. Required geo-units may include information for required MBUs to cover a specific geography, such as home zips and/or other geo-units. In one or more embodiments, constraint information may also include newspaper groups for ROPs, saturation products limitations and penetration constraints. A non-limiting example of a saturation product limitation is a rule against the selection of two different saturation products covering the same geo-unit. Non-limiting examples of penetration constraints include (1) avoid total penetration beyond 100% for any given geo-unit and (2) avoid total newspaper penetration beyond 65% for any geo-unit.
[0138]Optimization module 212 is configured to select a number of MBUs to obtain an optimized media plan for each of a number of scenarios. In one embodiment, three scenarios are considered, namely, client preferences (scenario 1), full portfolio (scenario 2), and flexed client preferences (scenario 3). In one embodiment, a client's page position preference submitted during execution of discovery module 202 is utilized with scenarios 1 and 2. The lowest cost option page position (e.g., inside cover) is typically used for scenario 3.
[0139]In one or more embodiments, an objective function is utilized to execute the optimization process. One such objective function takes the form of equation 6 reproduced below:
[0140]The first term of the objective function is equal to the total APEC value associated with the selected MBUs. The second term of the objective function is equal to the penalties for geo-unit penetration that is considered too high, as defined by the client or other sources.
[0141]According to one optimization algorithm, the objective function is maximized such that the resulting optimized media plan includes as much APEC value as possible while minimizing the penalties by avoiding geo-unit penetration that is too high and satisfying client objectives and constraints, such as required geo-units and budgets.
[0142]xi is a decision variable, i.e. 0 or 1, that determines whether or not a particular MBUi is included in the optimized media plan. In one or more embodiments, the optimization algorithm assigns values to xi that make the value of the objective function as large as possible while satisfying all constraints being considered.
[0143]The second term of the objective function guides the optimization algorithm away from undesirable behavior, such as duplicating coverage of highly attractive geo-units. λj is the penalty associate with cumulative penetration above a certain percentage for geo-unit j. In one embodiment, the certain percentage is 100%, while it should be appreciated that in other embodiments, the percentage may be lower or higher. The value of λj is the HVS of geo-unit j times the smallest activation score out of all the MBUs that touch the geo-unit j. ρj is the penalty associated with cumulative newspaper penetration above a certain percentage for geo-unit j. In one embodiment, the certain percentage is 100%, while it should be appreciated that in other embodiments, the percentage may be lower or higher. The value of ρj is the HVS of geo-unit j times the smallest activation score out of all MBUs that touch geo-unit j. yj is the cumulative penetration above a certain percentage due to mail for geo-unit j. In one embodiment, the certain percentage is 100% because the penetration goal is typically 100% for mail, while it should be appreciated that in other embodiments, the percentage may be lower or higher. xz is the cumulative penetration above a certain percentage due to newspaper coverage for geo-unit j. In one embodiment, the certain percentage is 65% because the penetration goal is typically 65% for newspapers, while it should be appreciated that in other embodiments, the percentage may be lower or higher.
[0144]In one or more embodiments, optimization module 212 is configured to use a greedy-type algorithm with the objective function in order to obtain optimized media plans. The greedy-type algorithm looks at all available MBUs and picks the one with the biggest relative marketing value per client dollar spent. After picking one MBU, the greedy-type algorithm considers the remaining MBUs and selects the next best one in terms of marketing value per client dollar spent. The greedy-type algorithm also checks constraints as it selects MBUs to ensure that they will be satisfied.
[0145]In other embodiments, optimization module 212 may be configured to use other algorithms with the objective function in order to obtain optimized media plans. Non-limiting examples of other algorithms include Any algorithy used to solve mixed integer problems.
[0146]FIG. 17 depicts a schematic diagram illustrating the use of a greedy-type algorithm to obtain an optimized media plan. According to the client's objectives and constraints, four geo-units A, B, C and D are to be covered by a media plan footprint with a $10 budget. As depicted in FIG. 17, the optimization algorithm can select from six MBUs, i.e., MBU1, MBU2, MBU3, MBU4, MBU5 and MBU6, in order to satisfy the client's objectives and constraints. Table 11 lists the value, cost and value per cost for each MBU.
[0147]The greedy-type algorithm selects the MBU with the largest value/cost while satisfying the client's objectives and constraints. Accordingly, the greedy-algorithm selects MBU1 with a value/cost of 11.1. The greedy-type algorithm then selects an MBU from the remaining MBUs with the biggest value while satisfying the client's objectives and constraints. MBU2 and MBU4 satisfy the client's objective of selecting an MBU for geo-unit B. However, MBU4 does not satisfy the client's budgetary constraint because it is $3 and only $1 remains after the selection of MBU1. As such, MBU2 is selected. Therefore, the optimized media plan using the greedy-type algorithm includes MBU1 and MBU2, with a total value of 101.
[0148]FIGS. 18A and 18B depict a flowchart 1800 for implementing a greedy-type algorithm with the objective function to obtain an optimized media plan for one of the number of scenarios. FIGS. 18A and 18B may be referred to herein collectively as FIG. 18. It should be appreciated that the steps of flowchart 1800 can be modified, rearranged, and/or omitted according to the specific implementation of the present invention, and any step can be carried out by a user, a computer or in combination according to the particular implementation of the present invention. Optimization module 212 may be configured to execute the implementation to obtain optimized media plans for each of the number of scenarios being considered.
[0149]Block 1801 carries out the optimization pre-process step to calculate rates for the objective function. The inputs for the optimization pre-process step and the greedy-type algorithm implementation may include stored geographies, media options and client preferences as depicted by arrow 1803.
[0150]Decision block 1802 questions whether there are any required geo-units that are not yet covered by the media plan. If the answer is yes, then optimization module 212 proceeds to decision block 1804. If the answer is no, then optimization module 212 proceeds to decision block 1806.
[0151]Decision block 1804 questions whether any MBUs are unselected and feasible and touch a required geo-unit. If the answer is yes, then optimization module 212 proceeds to block 1808. If the answer is no, then optimization module 212 proceeds to decision block 1806.
[0152]Block 1808 selects an unselected and feasible MBU from those MBUs that touch a required geo-unit with the largest updated objective function value to price ratio. It should be appreciated that as MBUs are selected by the greedy-type algorithm, the objective function to price ratio may be updated to reflect a change in value due to removing the selected MBUs from the analysis. After carrying out the step identified in block 1808, optimization module 212 proceeds to decision block 1814.
[0153]Decision block 1806 questions whether there are any market or store allocation budgets that have not yet been satisfied. If the answer is yes, then optimization module 212 proceeds to decision block 1810. If the answer is no, then optimization module 112 proceeds to decision block 1812.
[0154]Decision block 1810 questions whether there are any unselected and feasible MBUs address a market or store allocation budget constraint. If the answer is yes, then optimization module 212 proceeds to block 1816. If the answer is no, then optimization module 212 proceeds to decision block 1812.
[0155]Decision block 1812 questions whether there are any MBUs that are unselected and feasible. If the answer is yes, then optimization module 212 proceeds to block 1818. If the answer is no, then optimization module 212 terminates the optimization algorithm.
[0156]Block 1816 selects an unselected and feasible MBU from those MBUs that touch a geo-unit contained in a market or store region with an unsatisfied allocation budget with the largest updated objective function to price ratio. It should be appreciated that as MBUs are selected by the greedy-type algorithm, the objective function to price ratio may be updated to reflect a change in value due to removing the selected MBUs from the analysis. After carrying out the step identified in block 1808, optimization module 212 proceeds to decision block 1814.
[0157]Block 1818 selects an unselected and feasible MBU with the largest updated objective function to price ratio. It should be appreciated that as MBUs are selected by the greedy-type algorithm, the objective function to price ratio may be updated to reflect a change in value due to removing the selected MBUs from the analysis. After carrying out the step identified in block 1808, optimization module 212 proceeds to decision block 1814.
[0158]Decision block 1814 questions whether the considered MBU is feasible to all constraints being considered. If the answer is yes, then optimization module 212 proceeds to block 1820. If the answer is no, then optimization module 112 proceeds to decision block 1822.
[0159]Block 1820 marks the considered MBU (or bundle) as selected and updates the values of other unselected and available MBUs and geo-unit coverage resulting from the selection. Subsequently, optimization module 212 proceeds to decision block 1802 to begin a new iteration.
[0160]Decision block 1822 questions whether the considered MBU passes all constraints except for a minimum volume requirement for a newspaper. If the answer is yes, then optimization module 212 proceeds to block 1824. If the answer is no, then optimization module 212 proceeds to decision block 1826.
[0161]Block 1824 builds a bundle of MBUs in an attempt to satisfy the minimum volume requirement. It should be appreciated that bundling refers to gathering together a number of MBUs for consideration as a bundled MBU for purposes of satisfying a minimum volume requirement. Subsequently, optimization module 212 proceeds to decision block 1828. Decision block 1828 questions whether the bundle is feasible to all constraints. If the answer is yes, then optimization module 212 proceeds to block 1820. If the answer is no, then optimization module 212 proceeds to block 1826.
[0162]Block 1826 marks the considered MBU (or the MBU bundle) as omitted. Subsequently, optimization module 212 proceeds to decision block 1802 to begin a new iteration.
[0163]As mentioned above, the greedy-type algorithm of FIG. 18 ends when no unselected and feasible MBUs exist, as depicted in decision block 1812. Subsequently, optimization module 212 outputs and saves a number of selected media products and a number of MBUs from the execution of the greedy-type algorithm, as depicted by blocks 1820 and 1832 of FIG. 18 and arrow 1804 of FIG. 13.
[0164]FIG. 19 depicts a process map 1900 relating to evaluation module 214 according to one or more embodiments of the present invention. Evaluation module 214 receives data and information from data repository 205. Such data and information may have been generated by discovery module 202, targeting module 204, MAA module 208 and/or optimization module 212. Arrow 1902 represents discovery data and information generated by discovery module 202, and transmitted to evaluation module 214. Such discovery data and information may include, without limitation, client budget constraint information, client objective information, industry category matrix information, and client preferences information. Arrow 1904 represents targeting data and information generated by targeting module 204, and transmitted to evaluation module 214. Such targeting data and information may include, without limitation, scored geographies, household counts, geo-scores and trade area information. Arrow 1906 represents MAA data and information generated by MAA module 208, and transmitted to evaluation module 214. Such MAA data and information may include all available media options for the geo-units, MBUs and the media footprint. Arrow 1908 represents optimization data and information generated by optimization module 212, and transmitted to evaluation module 214. Such optimization data and information may include an optimized list of MBUs to purchase for each scenario.
[0165]Evaluation module 214 is configured to generate media planning recommendation reports 1910 and charts from the received data and information. In one or more embodiments, the reports and charts are evaluated for presentation to the client. Non-limiting examples of reports that can be generated by evaluation module 214 include an executive report, a product detail comparison report, a common geodetail report.
[0166]FIGS. 20A and 20B depict an example of an executive summary report 2000 according to one or more embodiments of the present invention. Executive summary report 2000 may be output electronically in an electronic format, such as a portable digital file ("PDF") or an image file. In other embodiments, executive summary report 2000 may be output in a paper format by utilizing a printer device. In one or more embodiments, executive summary report 2000 includes header section 2002 and analysis section 2004.
[0167]The header portion 2002 includes an upper left area 2006, an upper right area 2008, and lower area 2010. The upper left area 2006 includes the client name and a target audience definition. As shown in FIG. 20A, upper right area 2008 includes information relating to the client's selections and exclusions of tier one media products from one or more media planning scenarios. Area 2008 identifies whether such products were selected as client preferred products during the execution of the discovery module 202, by including an "X" in the client preferred column 2012. Such preferred product indicators are used by evaluation module 214, which is configured to carry out client preferred and flexed client preferred analysis based on the preferred product selections.
[0168]Area 2008 also includes a percent of budget allocated column 2014 and an excluded product column 2016. The percent of budget allocated column 2014 shows the percent of budget allocated to each tier one media product. In one or more embodiments, column 2014 is optional, and applies only to those reports including results of the client preferred analysis or flexed preferred analysis. The excluded product column 2016 includes an "X" next to any media product that was specifically excluded from the full portfolio analysis by the client during execution of discovery module 202. The lower area 2010 of report 2000 includes the client budget, the client's objective and the promotional period used in the analysis.
[0169]The analysis portion 2004 includes a targeting footprint summary 2018, value of coverage analysis 2020, media spread analysis 2022, media coverage analysis 2024 and media spend analysis 2026. The analysis section 2004 may include data for a specified geometry, such as all geographies analyzed, specific market, or a specific store or specific client location. Targeting footprint summary 2018 contains a count (N) of deliverable addresses for all carrier routes within the trade area analyzed and includes one or more summarized values of the geoscores used in the analysis to rank the targeted geographies. The one or more summarized values include, without limitation, sum, minimum (Min N), maximum (Max N), average and weighted average (Weighted Ave N).
[0170]The value of coverage analysis 2020 includes a count of deliverable addresses for all carrier routes covered by the media product selected and within the targeting footprint. Analysis 2020 also includes one or more summarized values, such as sum, minimum, maximum, average or weighted average, for the geoscore used in the analysis to rank the targeted geographies. Such summarized values can be provided for a client's current buy (CB), client preference (CP), full portfolio (FP) and flexed client preference (FCP). In one or more embodiments, client's current buy refers to a client's current or past placement of media services. The summarized values can be used to compare a client's current buy, client preference, full portfolio and/or flexed client preference on a single report. The value of coverage analysis 2020 also includes total targeting footprint covered value divided by the count of deliverable addresses for all carrier routes.
[0171]Media spread analysis 2022 includes media circulation counts, such as solo mail (Sc Count), direct mail insert (Idm Count), newspaper insert (In Count), REDPLUM wrap (REDPLUM Wrap Count) and FSI (FSI Count), for each of the media products considered in each of the media planning scenarios.
[0172]Media coverage analysis 2024 includes media circulation counts for each of the media planning scenarios. The efficiency value (Percentage) is calculated by dividing the targeting footprint covered count by the total media spread. Analysis 2024 also includes a summary of the total number of store locations included in the analysis as well as the number of stores that have one or more carrier routes covered by a selected media product.
[0173]Media spread analysis 2026 contains the estimated spend in dollars ($) for each media planning scenario. Average cost per thousand ("CPM") is calculated by dividing the estimated spend by total media spread multiplied by 1,000. The CPM may not reflect an actual CPM rate for any one product. For example, the CPM for a REDPLUM wrap may be the cost of delivering an advertisement to 1,000 households as part of the wrap. As another non-limiting example, the CPM for a CSI may be the cost of distribution because the client will have already printed and delivered the insert. The % of budget (Percentage) is calculated by dividing the estimated spend by event budget listed in the event summary. The cost per households reached ($) is calculated by dividing the targeting footprint covered count by the estimated spend in dollars.
[0174]FIGS. 21A and 21B depict an example of a portion of a product detail comparison report 2100 according to one or more embodiments. Product detail comparison report 2100 may be output electronically in an electronic format, such as a portable digital file ("PDF") or an image file. In other embodiments, product detail comparison report 2100 may be output in a paper format by utilizing a printer device. In one or more embodiments, product detail comparison report 2100 includes header section 2102 and analysis section 2104.
[0175]The header portion 2102 includes an upper left area 2106, an upper right area 2108, and lower area 2110. The upper left area 2106 includes the client name and a target audience definition. As shown in FIG. 21A, upper right area 2108 includes information relating to the client's selections and exclusions of tier one media products from one or more media planning scenarios. Area 2108 identifies whether such products were selected as client preferred products during the execution of the discovery module 102, by including an "X" in the client preferred column 2112. Such preferred product indicators are used by evaluation module 214, which is configured to carry out client preferred and flexed client preferred analysis based on the preferred product selections.
[0176]Area 2108 also includes a percent of budget allocated column 2114 and an excluded product column 2116. The percent of budget allocated column 2114 shows the percent of budget allocated to each tier one media product. In one or more embodiments, column 2114 is optional, and applies only to those reports including results of the client preferred analysis or flexed preferred analysis. The excluded product column 2116 includes an "X" next to any media product that was specifically excluded from the full portfolio analysis by the client during execution of discovery module 202. The lower area 2110 of report 2100 includes the client budget, the client's objective and the promotional period used in the analysis.
[0177]Product detail comparison report 2100 may include a product detail section for a number of media products, such as the tier one media products identified in one or more embodiments. As depicted in FIGS. 21A and 21B, comparison report 2100 includes a product detail section for direct mail packages 2118, newspaper inserts 2120, REDPLUM wrap 2122, and ROP 2124. The analysis section 2104 may include data for a specified geometry, such as all geographies analyzed, specific market, or a specific store or specific client location. Regarding the newspaper insert detail section 2120, the abbreviations "F", "P" or "B" may be used to denote whether the newspaper circulation is free (e.g., no paid subscribers, paid (e.g., subscribers pay to receive, and also may include street sales) or both (e.g., newspapers and sold and given away). The "Edition DOW" column in the product detail sections 2118, 2120, 2122 and 2124 refers to either the newspaper edition or delivery day of the week. In one or more embodiments, the newspaper edition is selected from the following group: morning ("M"), evening ("E"), weekly ("W"), Saturday ("SAT") or Sunday ("SUN"). In one or more embodiments, mailed products will show a two-day delivery window.
[0178]Each detail section includes % of total distribution, targeted footprint circulation, total media circulation and efficiency for each media buyable unit present in columnar format. In one or more embodiments, such information is presented for each of the media planning scenarios, such as client preference, full portfolio and flexed client preference. % of total distribution is calculated by dividing the total media circulation for the media buyable unit by the total media circulation for the media product. The targeted footprint circulation represents the estimated circulation for each media product that meets a number of targeting objectives or parameters within the targeting footprint. The % efficiency is calculated by dividing the targeted footprint circulation by the media circulation.
[0179]FIG. 22 depicts an example of a portion of a common geodetail report 2200 according to one or more embodiments. The common geodetail report 2200 may be output electronically in an electronic format, such as a portable digital file ("PDF") or an image file. In other embodiments, common geodetail report 2200 may be output in a paper format by utilizing a printer device.
[0180]The common geodetail report 2200 includes a listing of specific media products and related information of a recommended media plan. As depicted by FIG. 22, each row of media product section 2202 represents a specific medial product within site i. Each row of media product section 2202 includes information related to the specific media product. As depicted in FIG. 22, the related information includes ZIP Code 2204 of each media product, city 2206 of each media product, media number 2208 of each media product, media name 2210 of each media product, ZIP Code circulation count 2212 for the ZIP Code associated with each media product, ZIP Code household count 2214 for each ZIP Code associated with each media product, non-duplicated ZIP Code household count 2216 for each ZIP Code associated with each media product, ZIP Code penetration percentage 2218 for each ZIP Code associated with each media product, zone or ATZ name 2220 associated with each media product, delivery type 2222 of each media product, targeting variable 1 index value 2224 for each media product, targeting variable 2 index value 2226 for each media product, targeting variable j 2228 for each media product, composite index 2230 for each media product, targeting variable K 2232 for each media product, site number 2234 for each media product, address of site i 2236, city of site i 2238, state of site i 2240, distance 2242 between the ZIP Code of each media product and site i, and direction 2244 from site i to the ZIP Code of each media product.
[0181]Non-limiting examples of targeting variables include household income of $60K+ and casual dining index. A non-limiting example of targeting variable K is median household income.
[0182]The total zip circulation count 2246 is provided for site i according to common geodetail report 2200. The total non-duplicated ZIP Code household count 2248 is provided for site i according to common geodetail report 2200. The total ZIP Code penetration percentage 2250 is provided for site i according to common geodetail report 2200.
[0183]The total zip circulation count 2252 is provided for all sites according to common geodetail report 2200. The total non-duplicated ZIP Code household count 2254 is provided for all sites according to common geodetail report 2200. The total ZIP Code penetration percentage 2256 is provided for all sites according to common geodetail report 2200.
[0184]While embodiments of the invention have been illustrated and described, it is not intended that these embodiments illustrate and describe all possible forms of the invention. Rather, the words used in the specification are words of description rather than limitation, and it is understood that various changes may be made without departing from the spirit and scope of the invention.
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