WO2019046833A1 - Systèmes et procédés de conception de promotion intelligente dans des détaillants de briques et de mortier avec notation de promotion - Google Patents

Systèmes et procédés de conception de promotion intelligente dans des détaillants de briques et de mortier avec notation de promotion Download PDF

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Publication number
WO2019046833A1
WO2019046833A1 PCT/US2018/049284 US2018049284W WO2019046833A1 WO 2019046833 A1 WO2019046833 A1 WO 2019046833A1 US 2018049284 W US2018049284 W US 2018049284W WO 2019046833 A1 WO2019046833 A1 WO 2019046833A1
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Prior art keywords
promotion
test
promotions
consumer
price
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Inventor
Jamie Rapperport
Michael Montero
David Moran
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Eversight Inc
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Eversight Inc
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Priority claimed from US15/990,005 external-priority patent/US20180341965A1/en
Application filed by Eversight Inc filed Critical Eversight Inc
Priority to CA3071719A priority Critical patent/CA3071719A1/fr
Priority to EP18851705.6A priority patent/EP3676783A4/fr
Publication of WO2019046833A1 publication Critical patent/WO2019046833A1/fr
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0242—Determining effectiveness of advertisements
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements

Definitions

  • the present invention relates generally to promotion optimization methods and apparatus therefor. More particularly, the present invention relates to computer-implemented methods and computer-implemented apparatus for the generation of and testing of promotions within brick and mortar retailers leveraging electronic pricing displays. In some embodiments, the promotional generation utilizes intelligent design criteria to maximize promotion experimentation.
  • Promotion refers to various practices designed to increase sales of a particular product or services and/or the profit associated with such sales.
  • the public often associates promotion with the sale of consumer goods and services, including consumer packaged goods (e.g., food, home and personal care), consumer durables (e.g., consumer appliances, consumer electronics, automotive leasing), consumer services (e.g., retail financial services, health care, insurance, home repair, beauty and personal care), and travel and hospitality (e.g., hotels, airline flights, and restaurants).
  • Promotion is particularly heavily involved in the sale of consumer packaged goods (e.g., consumer goods packaged for sale to an end consumer).
  • promotion occurs in almost any industry that offers goods or services to a buyer (whether the buyer is an end consumer or an intermediate entity between the producer and the end consumer).
  • promotion may refer to, for example, providing discounts
  • promotion (using for example a physical or electronic coupon or code) designed to, for example, promote the sales volume of a particular product or service.
  • One aspect of promotion may also refer to the bundling of goods or services to create a more desirable selling unit such that sales volume may be improved.
  • Another aspect of promotion may also refer to the merchandising design (with respect to looks, weight, design, color, etc.) or displaying of a particular product with a view to increasing its sales volume. It includes calls to action or marketing claims used in-store, on marketing collaterals, or on the package to drive demand.
  • Promotions may be composed of all or some of the following: price based claims, secondary displays or aisle end-caps in a retail store, shelf signage, temporary packaging, placement in a retailer circular/fly er/coupon book, a colored price tag, advertising claims, or other special incentives intended to drive consideration and purchase behavior. These examples are meant to be illustrative and not limiting.
  • CPG consumer packaged goods
  • price discount is employed as an example to explain the promotion methods and apparatuses herein. It should be understood, however, that promotion optimization may be employed to manipulate factors other than price discount in order to influence the sales volume.
  • factors other than price discount may include the call to action on a display or on the packaging, the size of the CPG item, the manner in which the item is displayed or promoted or advertised either in the store or in media, etc.
  • the retailer such as a grocery store
  • the promotion may be specifically targeted to an individual consumer (based on, for example, that consumer's demographics or past buying behavior).
  • the discount may alternatively be broadly offered to the general public. Examples of promotions offered to general public include for example, a printed or electronic redeemable discount (e.g., coupon or code) for a specific CPG item.
  • Another promotion example may include, for example, general advertising of the reduced price of a CPG item in a particular geographic area.
  • Another promotion example may include in-store marking down of a particular CPG item only for a loyalty card user base.
  • promotion optimization when the consumer decides to take advantage of the discount, efforts are continually made to minimize promotion cost while maximizing the return on promotion dollars investment. This effort is known in the industry as promotion optimization.
  • a typical promotion optimization method may involve examining the sales volume of a particular CPG item over time (e.g., weeks).
  • the sales volume may be represented by a demand curve as a function of time, for example.
  • a demand curve lift (excess over baseline) or dip (below baseline) for a particular time period would be examined to understand why the sales volume for that CPG item increases or decreases during such time period.
  • Figure 1 shows an example demand curve 102 for Brand X cookies over some period of time.
  • Two lifts 1 10 and 114 and one dip 1 12 in demand curve 102 are shown in the example of Figure 1.
  • Lift 1 10 shows that the demand for Brand X cookies exceeds the baseline at least during week 2.
  • the promotion effort that was undertaken at that time e.g., in the vicinity of weeks 1-4 or week 2
  • marketers have in the past attempted to judge the effectiveness of the promotion effort on the sales volume. If the sales volume is deemed to have been caused by the promotion effort and delivers certain financial performance metrics, that promotion effort is deemed to have been successful and may be replicated in the future in an attempt to increase the sales volume.
  • dip 112 is examined in an attempt to understand why the demand falls off during that time (e.g., weeks 3 and 4 in Figure 1). If the decrease in demand was due to the promotion in week 2 (also known as consumer pantry loading or retailer forward- buying, depending on whether the sales volume shown reflects the sales to consumers or the sales to retailers), this decrease in weeks 3 and 4 should be counted against the effectiveness of week 2.
  • discount depth e.g., how much was the discount on the CPG item
  • discount duration e.g., how long did the promotion campaign last
  • timing e.g., whether there was any special holidays or event or weather involved
  • promotion type e.g., whether the promotion was a price discount only, whether Brand X cookies were displayed/not displayed prominently, whether Brand X cookies were features/not featured in the promotion literature.
  • Brand X marketers may make the mistaken assumption that the costly promotion effort of Brand X cookies was solely responsible for the sales lift and should be continued, despite the fact that it was an unrelated event that contributed to most of the lift in the sales volume of Brand X cookies.
  • the milk may have been highlighted in the weekly circular, placed in a highly visible location in the store and/or a milk industry expert may have been present in the store to push buyers to purchase milk, for example.
  • Brand X marketer can ascertain that most of the lift in sales during the promotion period that spans lift 1 14 comes from new consumers of Brand X cookies, such marketer may be willing to spend more money on the same type of sales promotion, even to the point of tolerating a negative ROI (return on investment) on his promotion dollars for this particular type of promotion since the recruitment of new buyers to a brand is deemed more much valuable to the company in the long run than the temporary increase in sales to existing Brand X buyers.
  • aggregate historical sales volume data for Brand X cookies when examined in a backward-looking manner, would not provide such information.
  • Attempts have been made to employ non-aggregate sales data in promoting products may employ a loyalty card program (such as the type commonly used in grocery stores or drug stores) to keep track of purchases by individual consumers.
  • a loyalty card program such as the type commonly used in grocery stores or drug stores
  • the manufacturer of a new type of whole grain cereal may wish to offer a discount to that particular consumer to entice that consumer to try out the new whole grain cereal based on the theory that people who bought sugar-free cereal tend to be more health conscious and thus more likely to purchase whole grain cereal than the general cereal-consuming public.
  • Such individualized discount may take the form of, for example, a redeemable discount such as a coupon or a discount code mailed or emailed to that individual.
  • Some companies may vary the approach by, for example, ascertaining the items purchased by the consumer at the point of sale terminal and offering a redeemable code on the purchase receipt. Irrespective of the approach taken, the utilization of non-aggregate sales data has typically resulted in individualized offers, and has not been processed or integrated in any meaningful sense into a promotion optimization effort to determine the most cost-efficient, highest-return manner to promote a particular CPG item to the general public.
  • advertising budgets are often spent reactively rather than proactively. For example, cookies have been used to track browsing history and generate ads for products that consumers have been searching for. Such reactive strategies have limited scope and ignore a substantial amount of unexploited promotional opportunities.
  • electronic tags are deployed throughout the retail space. These tags are wirelessly coupled to a server system, allowing for real time and simultaneous updating of pricing and other promotional variables. These tags enable expansive testing of base pricing, promotion optimization, and sell through criteria. [0030] For base price testing a composite sales-margin goal or obj ective for each category of products within the retail space is received from the retailer, along with business and product-price rules. Deviations in the base price that are still within the sales-margin goal are then tested for the most profitable price. Once identified the base price for the item may be updated within not only the particular retailer, but within a wider retailer group.
  • Testing of the price may be completed by updating the electronic tags at a time when few consumers are present. In addition to updating the base price, testing may be performed on a wide range of promotional variables to determine what sorts of values for these variables yield the most effective promotions.
  • the promotional variable values may include any of price, deal structure type, color, imagery, phrasing, smell and sound.
  • price elasticity for individual products can likewise be tracked through price adjustment testing. This elasticity information, along with sell through volume and time goals, may be leveraged to schedule sell through pricing for products.
  • Tracking the consumers can include tracking any of a wireless signal emanating from a shopping cart, wireless signal emanating from an electronic display mounted to the shopping cart, wireless signal emanating from a mobile device belonging to the consumer, image tracking of the consumer and biometric data of the consumer.
  • Figure 1 shows an example demand curve 102 for Brand X cookies over some period of time.
  • Figure 2A shows, in accordance with an embodiment of the invention, a conceptual drawing of the forward-looking promotion optimization method.
  • Figure 2B shows, in accordance with an embodiment of the invention, the steps for generating a general public promotion.
  • Figure 3A shows in greater detail, in accordance with an embodiment of the invention, the administering step 206 of Figure 2 from the user's perspective.
  • Figure 3B shows in greater detail, in accordance with an embodiment of the invention, the administering step 206 of Figure 2 from the forward-looking promotion optimization system perspective.
  • Figure 4 shows various example segmentation criteria that may be employed to generate the purposefully segmented subpopulations.
  • Figure 5 shows various example methods for communicating the test promotions to individuals of the segmented subpopulations being tested.
  • Figure 6 shows, in accordance with some embodiments, various example promotion-significant responses.
  • Figure 7 shows, in accordance with some embodiments, various example test promotion variables affecting various aspects of a typical test promotion.
  • Figure 8 shows, in accordance with some embodiments, a general hardware/network view of a forward-looking promotion optimization system.
  • Figure 9 shows, in accordance with some embodiments, a block diagram of a brick and mortar retailer that employs electronic tags to provide near real time promotional testing.
  • Figure 10 shows, in accordance with some embodiments, an example illustration of an electronic tag system deployed within a retailer space.
  • Figures 1 1 A-C show, in accordance with some embodiments, an example illustration of user specific electronic displays for use in a retailer.
  • Figure 12 shows, in accordance with some embodiments, a flowchart of an example method for the generation and testing of promotions within a brick and mortar retailer space.
  • Figure 13 shows, in accordance with some embodiments, a flowchart of an example method for the determination of optimal base pricing in a brick and mortar setting.
  • Figure 14 shows, in accordance with some embodiments, a flowchart of an example method for the determination of optimal promotion pricing in a brick and mortar setting.
  • Figure 15 shows, in accordance with some embodiments, a flowchart of an example method for the determination of optimal sell-through pricing in a brick and mortar setting.
  • Figure 16 shows, in accordance with some embodiments, a flowchart of an example method for the personalized promotion in a brick and mortar setting.
  • Figure 17 shows, in accordance with some embodiments, a flowchart of an example method for the dynamic supply of the personalized promotion in a brick and mortar setting.
  • Figure 18 shows, in accordance with some embodiments, an exemplary
  • Figure 19 shows, in accordance with some embodiments, a flow diagram illustrating Objective Driven Personalization for the Promotion Optimization Ecosystem of Figure 18.
  • Figure 20 shows, in accordance with some embodiments, a flow diagram illustrating Personalized Carousel Containers for the Promotion Optimization Ecosystem of Figure 18.
  • Figure 21 shows, in accordance with some embodiments, a flow diagram illustrating Pre-approved Offer Bank and Offer Template(s) for the
  • Figure 22 shows, in accordance with some embodiments, a flow diagram illustrating Promotion Allocation for the Promotion Optimization Ecosystem of Figure 18.
  • Figures 23A and 23B are example computer systems capable of implementing the system for design matrix generation and recommendation overlay.
  • the present invention relates to the generation of promotion activity for deployment in near real time within a brick and mortar retail space.
  • brick and mortar includes any physical retail space, and is exemplified by general retailers, such as Target and Walmart, specialty boutique retailers, supermarkets, such as Safeway, or the like.
  • the advantage of promotional testing in physical retailer spaces has traditionally not been possible due to consumer expectations, as well as the unreasonable burden of physically updating pricing signage within the retailer in a manner that allows for effective promotional testing.
  • This promotion activity may include intelligent promotional designs for most effective experimentation of promotions to more efficiently identify a highly effective general promotion.
  • Such systems and methods assist administrator users to generate and deploy advertising campaigns. While such systems and methods may be utilized with any promotional setting system, such intelligent promotional design systems particularly excel when coupled with systems for optimizing promotions by administering, in large numbers and iteratively, test promotions on purposefully segmented subpopulations in advance of a general public promotion roll-out.
  • the inventive forward-looking promotion optimization involves obtaining actual revealed preferences from individual consumers of the segmented subpopulations being tested through deployment in physical retail spaces.
  • the intelligent promotional design system excels, particularly within physical retail spaces.
  • the revealed preferences are obtained when the individual consumers respond to specifically designed actual test promotions.
  • the revealed preferences may be tracked in individual computer-implemented accounts (which may, for example, be implemented via a record in a centralized database and rendered accessible to the merchant or the consumer via a computer network such as the internet) associated with individual consumers, or may be collected at a physical retailer based upon transaction records. For example, when a consumer responds, using his smart phone, web browser, or in a physical store through completion of a transaction, to a test promotion that offers 20% off a particular consumer packaged goods (CPG) item, that response is tracked in his individual computer-implemented account, or in a transaction record.
  • Such computer- implemented accounts may be implemented via, for example, a loyalty card program, apps on a smart phone, computerized records, social media news feed, etc.
  • a plurality of test promotions may be designed and tested on a plurality of groups of consumers (the groups of consumers are referred to herein as "subpopulations").
  • the responses by the consumers are recorded and analyzed, with the analysis result employed to generate additional test promotions or to formulate the general population promotion.
  • the individuals shopping in the retailer may be considered a 'subpopulation' as they are self-selecting by geography, which provides insights into demographics, socioeconomic standing, etc.
  • the groups of consumers involved in promotion testing represent segments of the public that have been purposefully segmented in accordance with segmenting criteria specifically designed for the purpose of testing the test promotions.
  • a subpopulation is deemed purposefully segmented when its members are selected based on criteria other than merely to make up a given number of members in the subpopulation.
  • Demographics, buying behavior, behavioral economics, geography are example criteria that may be employed to purposefully segment a population into subpopulations for promotion testing.
  • a segmented population may number in the tens or hundreds or even thousands of individuals.
  • the general public may involve tens of thousands, hundreds of thousands, or millions of potential customers.
  • embodiments of the invention can exert control over variables such as demographics (e.g., age, income, sex, marriage status, address, etc.), buying behavior (e.g., regular purchaser of Brand X cookies, consumer of premium food, frequent traveler, etc), weather, shopping habits, life style, and/or any other criteria suitable for use in creating the subpopulations.
  • demographics e.g., age, income, sex, marriage status, address, etc.
  • buying behavior e.g., regular purchaser of Brand X cookies, consumer of premium food, frequent traveler, etc
  • weather e.g., shopping habits, life style, and/or any other criteria suitable for use in creating the subpopulations.
  • the subpopulations are kept small such that multiple test promotions may be executed on different subpopulations, either simultaneously or at different times, without undue cost or delay in order to obtain data pertaining to the test promotion response behavior.
  • the low cost/low delay aspect of creating and executing test promotions on purposefully segmented subpopulations permits,
  • each individual test promotion may be designed to test one or more test promotion variables.
  • These test promotions variables may relate to, for example, the size, shape, color, manner of display, manner of discount, manner of publicizing, manner of dissemination pertaining to the goods/services being promoted.
  • one test promotion may involve 12-oz packages of fancy-cut potato chips with medium salt and a discount of 30% off the regular price. This test promotion may be tested on a purposefully segmented subpopulation of 35-40 years old professionals in the $30,000-$50,000 annual income range.
  • Another test promotion may involve the same 30% discount 12-oz packages of fancy-cut potato chips with medium salt on a different purposefully segmented subpopulation of 35-40 years old professionals in the higher $ 100,000-$ 150,000 annual income range.
  • test promotions variables may vary or one or more of the segmenting criteria employed to create the purposefully segmented subpopulations may vary.
  • the test promotion responses from individuals in the subpopulations are then collected and analyzed to ascertain which test promotion or test promotion variable(s) yields/yield the most desirable response (based on some predefined success criteria, for example).
  • test promotions can also reveal insights regarding which subpopulation performs the best, or well, with respect to test promotion responses.
  • test promotion response analysis provides insights not only regarding the relative performance of the test promotion and/or test promotion variable but also insights regarding population segmentation and/or segmentation criteria.
  • the segments may be arbitrarily or randomly segmented into groups and test promotions may be executed against these arbitrarily segmented groups in order to obtain insights regarding personal characteristics that respond well to a particular type of promotion.
  • the identified test promotion variable(s) that yield the most desirable responses may then be employed to formulate a general public promotion (GPP), which may then be offered to the larger public.
  • GPP general public promotion
  • a general public promotion is different from a test promotion in that a general public promotion is a promotion designed to be offered to members of the public to increase or maximize sales or profit whereas a test promotion is designed to be targeted to a small group of individuals fitting a specific segmentation criteria for the purpose of promotion testing.
  • Examples of general public promotions include (but not limited to) advertisement printed in newspapers, release in public forums and websites, flyers for general distribution, announcement on radios or television, promotion broadly transmitted or made available to members of the public, and/or promotions that are rolled out to a wider set of physical retailer locations.
  • the general public promotion may take the form of a paper or electronic circular that offers the same promotion to the larger public, for example.
  • promotion testing may be iterated over and over with different subpopulations (segmented using the same or different segmenting criteria) and different test promotions (devised using the same or different combinations of test promotion variables) in order to validate one or more the test promotion response analysis result(s) prior to the formation of the generalized public promotion. In this manner, "false positives" may be reduced.
  • test promotion testing may involve many test promotion variables, iterative test promotion testing, as mentioned, may help pin-point a variable (e.g., promotion feature) that yields the most desirable test promotion response to a particular subpopulation or to the general public.
  • a variable e.g., promotion feature
  • test promotion may reveal that consumers tend to buy a greater quantity of potato chips when packaged in brown paper bags versus green paper bags.
  • That "winning" test promotion variable value e.g., brown paper bag packaging
  • test promotion variables such as for example with different prices, different display options, etc.
  • the follow-up test promotions may be iterated multiple times in different test promotion variable combinations and/or with different test subpopulations to validate that there is, for example, a significant consumer preference for brown paper bag packaging over other types of packaging for potato chips.
  • individual "winning" test promotion variable values from different test promotions may be combined to enhance the efficacy of the general public promotion to be created. For example, if a 2-for-l discount is found to be another winning variable value (e.g., consumers tend to buy a greater quantity of potato chips when offered a 2-for-l discount), that winning test promotion variable value (e.g., the aforementioned 2-for-l discount) of the winning test promotion variable (e.g., discount depth) may be combined with the brown paper packaging winning variable value to yield a promotion that involves discounting 2-for-l potato chips in brown paper bag packaging.
  • a 2-for-l discount e.g., consumers tend to buy a greater quantity of potato chips when offered a 2-for-l discount
  • that winning test promotion variable value e.g., the aforementioned 2-for-l discount
  • the winning test promotion variable e.g., discount depth
  • the promotion involving discounting 2-for-l potato chips in brown paper bag packaging may be tested further to validate the hypothesis that such a combination elicits a more desirable response than the response from test promotions using only brown paper bag packaging or from test promotions using only 2-for-l discounts.
  • As many of the "winning" test promotion variable values may be identified and combined in a single promotion as desired.
  • a combination of "winning" test promotion variables (involving one, two, three, or more "winning” test promotion variables) may be employed to create the general public promotion, in one or more embodiments.
  • test promotions may be executed iteratively and/or in a continual fashion on different purposefully segmented subpopulations using different combinations of test promotion variables to continue to obtain insights into consumer actual revealed preferences, even as those preferences change over time.
  • the consumer responses that are obtained from the test promotions are actual revealed preferences instead of stated preferences.
  • the data obtained from the test promotions administered in accordance with embodiments of the invention pertains to what individual consumers actually do when presented with the actual promotions. The data is tracked and available for analysis and/or verification in individual computer-implemented accounts of individual consumers involved in the test promotions.
  • This revealed preference approach is opposed to a stated preference approach, which stated preference data is obtained when the consumer states what he would hypothetically do in response to, for example, a hypothetically posed conjoint test question.
  • the actual preference test promotion response data obtained in accordance with embodiments of the present invention is a more reliable indicator of what a general population member may be expected to behave when presented with the same or a similar promotion in a general public promotion. Accordingly, there is a closer relationship between the test promotion response behavior (obtained in response to the test promotions) and the general public response behavior when a general public promotion is generated based on such test promotion response data.
  • embodiments of the inventive test promotion optimization methods and apparatuses disclosed herein operate on a forward-looking basis in that the plurality of test promotions are generated and tested on segmented subpopulations in advance of the formulation of a general public promotion.
  • the analysis results from executing the plurality of test promotions on different purposefully segmented subpopulations are employed to generate future general public promotions.
  • data regarding the "expected" efficacy of the proposed general public promotion is obtained even before the proposed general public promotion is released to the public. This is one key driver in obtaining highly effective general public promotions at low cost.
  • the subpopulations can be generated with highly granular segmenting criteria, allowing for control of data noise that may arise due to a number of factors, some of which may be out of the control of the manufacturer or the merchant. This is in contrast to the aggregated data approach of the prior art.
  • test promotions themselves may be formulated to isolate specific test promotion variables (such as the aforementioned potato chip brown paper packaging or the 16-oz size packaging). This is also in contrast to the aggregated data approach of the prior art.
  • test promotion response data may be analyzed to answer questions related to specific subpopulation attribute(s) or specific test promotion variable(s).
  • questions such as "How deep of a discount is required to increase by 10% the volume of potato chip purchased by buyers who are 18-25 year-old male shopping on a Monday?" or to generate test promotions specifically designed to answer such a question.
  • Such data granularity and analysis result would have been impossible to achieve using the backward-looking, aggregate historical data approach of the prior art.
  • a promotional idea module for generating ideas for promotional concepts to test.
  • the promotional idea generation module relies on a series of pre-constructed sentence structures that outline typical promotional constructs. For example, Buy X, get Y for $Z price would be one sentence structure, whereas Get Y for $Z when you buy X would be a second. It's important to differentiate that the consumer call to action in those two examples is materially different, and one cannot assume the promotional response will be the same when using one sentence structure vs. another.
  • the solution is flexible and dynamic, so once X, Y, and Z are identified, multiple valid sentence structures can be tested.
  • the solution delivers a platform where multiple products, offers, and different ways of articulating such offers can be easily generated by a lay user.
  • the amount of combinations to test can be infinite.
  • the generation may be automated, saving time and effort in generating promotional concepts. In following sections one mechanism, the design matrix, for the automation of promotional generation will be provided in greater detail.
  • the technology advantageously a) will constrain offers to only test "viable promotions", e.g., those that don't violate local laws, conflict with branding guidelines, lead to unprofitable concepts that wouldn't be practically relevant, can be executed on a retailers' system, etc., and/or b) link to the design of experiments for micro-testing to determine which combinations of variables to test at any given time.
  • an offer selection module for enabling a non-technical audience to select viable offers for the purpose of planning traditional promotions (such as general population promotion, for example) outside the test environment.
  • traditional promotions such as general population promotion, for example
  • the offer selection module will be constrained to only show top performing concepts from the tests, with production-ready artwork wherever possible.
  • the offer selection module renders irrelevant the traditional, Excel-based or heavily numbers-oriented performance reports from traditional analytic tools.
  • the user can have "freedom within a framework" by selecting any of the pre-scanned promotions for inclusion in an offer to the general public, but value is delivered to the retailer or manufacturer because the offers are constrained to only include the best performing concepts. Deviation from the top concepts can be accomplished, but only once the specific changes are run through the testing process and emerge in the offer selection windows.
  • the general population and/or subpopulations may be chosen from social media site (e.g., FacebookTM, TwitterTM, Google+TM, etc.) participants.
  • Social media offers a large population of active participants and often provide various communication tools (e.g., email, chat, conversation streams, running posts, etc.) which makes it efficient to offer promotions and to receive responses to the promotions.
  • Various tools and data sources exist to uncover characteristics of social media site members, which characteristics (e.g., age, sex, preferences, attitude about a particular topic, etc.) may be employed as highly granular segmentation criteria, thereby simplifying segmentation planning.
  • characteristics e.g., age, sex, preferences, attitude about a particular topic, etc.
  • embodiments of the invention apply also to online shopping and online advertising/promotion and online members/customers.
  • Figure 2A shows, in accordance with an embodiment of the invention, a conceptual drawing of the forward-looking promotion optimization method.
  • a plurality of test promotions 102a, 102b, 102c, 102d, and 102e are administered to purposefully segmented subpopulations 104a, 104b, 104c, 104d, and 104e respectively.
  • each of the test promotions (102a-102e) may be designed to test one or more test promotion variables.
  • test promotions 102a-102d are shown testing three test promotion variables X, Y, and Z, which may represent for example the size of the packaging (e.g., 12 oz versus 16 oz), the manner of display (e.g., at the end of the aisle versus on the shelf), and the discount (e.g., 10% off versus 2-for-l).
  • test promotion variables are of course only illustrative and almost any variable involved in producing, packaging, displaying, promoting, discounting, etc. of the packaged product may be deemed a test promotion variable if there is an interest in determining how the consumer would respond to variations of one or more of the test promotion variables.
  • test promotion 102e is shown testing four test promotion variables (X, Y, Z, and T).
  • test promotion 102a involves test variable XI (representing a given value or attribute for test variable X) while test promotion 102b involves test variable X2 (representing a different value or attribute for test variable X).
  • a test promotion may vary, relative to another test promotion, one test promotion variable (as can be seen in the comparison between test promotions 102a and 102b) or many of the test promotion variables (as can be seen in the comparison between test promotions 102a and 102d).
  • test promotions 102a and 102e there are no requirements that all test promotions must have the same number of test promotion variables (as can be seen in the comparison between test promotions 102a and 102e) although for the purpose of validating the effect of a single variable, it may be useful to keep the number and values of other variables (e.g., the control variables) relatively constant from test to test (as can be seen in the comparison between test promotions 102a and 102b).
  • other variables e.g., the control variables
  • test promotions may be generated using automated test promotion generation software 1 10, which varies for example the test promotion variables and/or the values of the test promotion variables and/or the number of the test promotion variables to come up with different test promotions.
  • segmentation criteria A, B, C, D which may represent for example the age of the consumer, the household income, the zip code, group of consumers shopping at a particular physical retailer, and whether the person is known from past purchasing behavior to be a luxury item buyer or a value item buyer.
  • segmentation criteria are of course only illustrative and almost any demographics, behavioral, attitudinal, whether self-described, objective, interpolated from data sources (including past purchase or current purchase data), etc. may be used as segmentation criteria if there is an interest in determining how a particular subpopulation would likely respond to a test promotion.
  • segmentation may involve as many or as few of the segmentation criteria as desired.
  • purposefully segmented subpopulation 104e is shown segmented using five segmentation criteria (A, B, C, D, and E).
  • the former denotes a conscious effort to group individuals based on one or more segmentation criteria or attributes.
  • the latter denotes a random grouping for the purpose of forming a group irrespective of the attributes of the individuals. Randomly segmented subpopulations are useful in some cases; however they are distinguishable from purposefully segmented subpopulations when the differences are called out.
  • One or more of the segmentation criteria may vary from purposefully segmented subpopulation to purposefully segmented subpopulation.
  • purposefully segmented subpopulation 104a involves segmentation criterion value Al (representing a given attribute or range of attributes for segmentation criterion A) while purposefully segmented subpopulation 104c involves segmentation criterion value A2 (representing a different attribute or set of attributes for the same segmentation criterion A).
  • purposefully segmented subpopulation may have different numbers of individuals.
  • purposefully segmented subpopulation 104a has four individuals (P 1-P4) whereas purposefully segmented subpopulation 104e has six individuals (P17-P22).
  • a purposefully segmented subpopulation may differ from another purposefully segmented subpopulation in the value of a single segmentation criterion (as can be seen in the comparison between purposefully segmented subpopulation 104a and purposefully segmented
  • subpopulation 104c wherein the attribute A changes from Al to A2
  • the values for attributes A, B, C, and D are all different.
  • Two purposefully segmented subpopulations may also be segmented identically (e.g., using the same segmentation criteria and the same values for those criteria) as can be seen in the comparison between purposefully segmented subpopulation 104a and purposefully segmented subpopulation 104b.
  • the purposefully segmented subpopulations may be generated using automated segmentation software 1 12, which varies for example the segmentation criteria and/or the values of the segmentation criteria and/or the number of the segmentation criteria to come up with different purposefully segmented subpopulations.
  • the test promotions are administered to individual users in the purposefully segmented subpopulations in such a way that the responses of the individual users in that purposefully segmented subpopulation can be recorded for later analysis.
  • an electronic coupon may be presented in an individual user's computer-implemented account (e.g., shopping account or loyalty account), or emailed or otherwise transmitted to the smart phone of the individual.
  • the user may be provided with an electronic coupon on his smart phone that is redeemable at the merchant.
  • this administering is represented by the lines that extend from test promotion 102a to each of individuals P1-P4 in purposefully segmented subpopulation 104a. If the user (such as user PI) makes a promotion-significant response, the response is noted in database 130.
  • a promotion-significant response is defined as a response that is indicative of some level of interest or disinterest in the goods/service being promoted.
  • the redemption is strongly indicative of user PI 's interest in the offered goods.
  • responses falling short of actual redemption or actual purchase may still be significant for promotion analysis purposes. For example, if the user saves the electronic coupon in his electronic coupon folder on his smart phone, such action may be deemed to indicate a certain level of interest in the promoted goods.
  • the user forwards the electronic coupon to his friend or to a social network site such forwarding may also be deemed to indicate another level of interest in the promoted goods.
  • weights may be accorded to various user responses to reflect the level of interest/disinterest associated with the user's responses to a test promotion. For example, actual redemption may be given a weight of 1, whereas saving to an electronic folder would be given a weight of only 0.6 and whereas an immediate deletion of the electronic coupon would be given a weight of -0.5.
  • Analysis engine 132 represents a software engine for analyzing the consumer responses to the test promotions. Response analysis may employ any analysis technique (including statistical analysis) that may reveal the type and degree of correlation between test promotion variables, subpopulation attributes, and promotion responses. Analysis engine 132 may, for example, ascertain that a certain test promotion variable value (such as 2-for-l discount) may be more effective than another test promotion variable (such as 25% off) for 32-oz soft drinks if presented as an electronic coupon right before Monday Night Football. Such correlation may be employed to formulate a general population promotion (150) by a general promotion generator software (160). As can be appreciated from this discussion sequence, the optimization is a forward-looking optimization in that the results from test promotions administered in advance to purposefully segmented subpopulations are employed to generate a general promotion to be released to the public at a later date.
  • the correlations ascertained by analysis engine 132 may be employed to generate additional test promotions (arrows 172, 174, and 176) to administer to the same or a different set of purposefully segmented subpopulations.
  • the iterative testing may be employed to verify the consistency and/or strength of a correlation (by administering the same test promotion to a different purposefully segmented subpopulation or by combining the "winning" test promotion value with other test promotion variables and administering the reformulated test promotion to the same or a different set of purposefully segmented subpopulations).
  • a "winning" test promotion value (e.g., 0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 + (0.05 * (1 +
  • test promotion 20% off listed price
  • another "winning” test promotion value e.g., packaged in plain brown paper bags
  • the test promotion that is formed from multiple "winning" test promotion values may be administered to different purposefully segmented subpopulations to ascertain if such combination would elicit even more desirable responses from the test subjects.
  • test promotions Since the purposefully segmented subpopulations are small and may be segmented with highly granular segmentation criteria, a large number of test promotions may be generated (also with highly granular test promotion variables) and a large number of combinations of test promotions/purposefully segmented subpopulations can be executed quickly and at a relatively low cost.
  • the same number of promotions offered as general public promotions would have been prohibitively expensive to implement, and the large number of failed public promotions would have been costly for the manufacturers/retailers.
  • a test promotion fails, the fact that the test promotion was offered to only a small number of consumers in one or more segmented subpopulations, or a limited number of physical locations for a limited time, would limit the cost of failure. Thus, even if a large number of these test promotions "fail" to elicit the desired responses, the cost of conducting these small test promotions would still be quite small.
  • test promotion variables may be administered concurrently or staggered in time to the dozens, hundreds or thousands of segmented subpopulations.
  • large number of test promotions executed improves the statistical validity of the correlations ascertained by analysis engine. This is because the number of variations in test promotion variable values, subpopulation attributes, etc. can be large, thus yielding rich and granulated result data.
  • the data-rich results enable the analysis engine to generate highly granular correlations between test promotion variables, subpopulation attributes, and type/degree of responses, as well as track changes over time.
  • these more accurate/granular correlations help improve the probability that a general public promotion created from these correlations would likely elicit the desired response from the general public. It would also, over, time, create promotional profiles for specific categories, brands, retailers, and individual shoppers where, e.g., shopper 1 prefers contests and shopper 2 prefers instant financial savings.
  • Figure 2B shows, in accordance with an embodiment of the invention, the steps for generating a general public promotion. In one or more embodiments, each, some, or all the steps of Figure 2B may be automated via software to automate the forward-looking promotion optimization process. In step 202, the plurality of test promotions are generated.
  • test promotions have been discussed in connection with test promotions 102a-102e of Figure 2A and represent the plurality of actual promotions administered to small purposefully segmented subpopulations to allow the analysis engine to uncover highly accurate/granular correlations between test promotion variables, subpopulation attributes, and type/degree of responses in an embodiment, these test promotions may be generated using automated test promotion generation software that varies one or more of the test promotion variables, either randomly, according to heuristics, and/or responsive to hypotheses regarding correlations from analysis engine 132 for example.
  • the segmented subpopulations are generated.
  • the segmented subpopulations represent randomly segmented subpopulations.
  • the segmented subpopulations represent purposefully segmented subpopulations.
  • the segmented subpopulations may represent a combination of randomly segmented subpopulations and purposefully segmented subpopulations.
  • these segmented subpopulations may be generated using automated subpopulation segmentation software that varies one or more of the segmentation criteria, either randomly, according to heuristics, and/or responsive to hypotheses regarding correlations from analysis engine 132, for example.
  • step 206 the plurality of test promotions generated in step 202 are administered to the plurality of segmented subpopulations generated in step 204.
  • the test promotions are administered to individuals within the segmented subpopulation and the individual responses are obtained and recorded in a database (step 208).
  • automated test promotion software automatically administers the test promotions to the segmented subpopulations using electronic contact data that may be obtained in advance from, for example, social media sites, a loyalty card program, previous contact with individual consumers, or potential consumer data purchased from a third party, etc.
  • the test promotions may be administered via electronic pricing tags displayed within a physical retail location. Such physical test promotions may be constricted by deployment time due to logistic considerations.
  • the responses may be obtained at the point of sale terminal, or via a website or program, via social media, or via an app implemented on smart phones used by the individuals, for example.
  • step 210 the responses are analyzed to uncover correlations between test promotion variables, subpopulation attributes, and type/degree of responses.
  • the general public promotion is formulated from the correlation data, which is uncovered by the analysis engine from data obtained via subpopulation test promotions.
  • the general public promotion may be generated automatically using public promotion generation software which utilizes at least the test promotion variables and/or subpopulation segmentation criteria and/or test subject responses and/or the analysis provided by analysis engine 132.
  • step 214 the general public promotion is released to the general public to promote the goods/services.
  • promotion testing using the test promotions on the segmented subpopulations occurs in parallel to the release of a general public promotion and may continue in a continual fashion to validate correlation hypotheses and/or to derive new general public promotions based on the same or different analysis results. If iterative promotion testing involving correlation hypotheses uncovered by analysis engine 132 is desired, the same test promotions or new test promotions may be generated and executed against the same segmented subpopulations or different segmented subpopulations as needed (paths 216/222/226 or 216/224/226 or 216/222/224/226). As mentioned, iterative promotion testing may validate the correlation hypotheses, serve to eliminate "false positives" and/or uncover combinations of test promotion variables that may elicit even more favorable or different responses from the test subjects.
  • Promotion testing may be performed on an on-going basis using the same or different sets of test promotions on the same or different sets of segmented subpopulations as mentioned (paths 218/222/226 or 218/224/226 or 218/222/224/226 or 220/222/226 or 220/224/226 or 220/222/224/226).
  • FIG. 3 A shows in greater detail, in accordance with an embodiment of the invention, the administering step 206 of Figure 2 from the user's perspective.
  • the test promotion is received from the test promotion generation server (which executes the software employed to generate the test promotion).
  • the test promotion may be received at a user's smart phone or tablet (such as in the case of an electronic coupon or a discount code, along with the associated
  • step 304 the test promotion is presented to the user.
  • step 306 the user's response to the test promotion is obtained and transmitted to a database for analysis.
  • FIG. 3B shows in greater detail, in accordance with an embodiment of the invention, the administering step 206 of Figure 2 from the forward-looking promotion optimization system perspective.
  • the test promotions are generated using the test promotion generation server (which executes the software employed to generate the test promotion).
  • the test promotions are provided to the users (e.g., transmitted or emailed to the user's smart phone or tablet or computer, shared with the user using the user's loyalty account, displayed in the physical retailer).
  • the system receives the user's responses and stores the user's responses in the database for later analysis.
  • Figure 4 shows various example segmentation criteria that may be employed to generate the purposefully segmented subpopulations.
  • demographics criteria e.g., sex, location, household size, household income, etc.
  • buying behavior category purchase index, most frequent shopping hours, value versus premium shopper, etc.
  • past/current purchase history e.g., channel (e.g., stores frequently shopped at, competitive catchment of stores within driving distance), behavioral economics factors, etc.
  • the examples of Figure 4 are meant to be illustrative and not meant to be exhaustive or limiting.
  • one or more embodiments of the invention generate the segmented subpopulations automatically using automated population segmentation software that generates the segmented subpopulations based on values of segmentation criteria.
  • Figure 5 shows various example methods for communicating the test promotions to individuals of the segmented subpopulations being tested.
  • the test promotions may be mailed to the individuals, emailed in the form of text or electronic flyer or coupon or discount code, displayed on a webpage when the individual accesses his shopping or loyalty account via a computer or smart phone or tablet, and lastly display on an electronic pricing tag within a retailer's store.
  • Redemption may take place using, for example, a printed coupon (which may be mailed or may be printed from an electronic version of the coupon) at the point of sale terminal, an electronic version of the coupon (e.g., a screen image or QR code), the verbal providing or manual entry of a discount code into a terminal at the store or at the point of sale, or purchase of an item in a physical location that has the promotion displayed.
  • a printed coupon which may be mailed or may be printed from an electronic version of the coupon
  • an electronic version of the coupon e.g., a screen image or QR code
  • the examples of Figure 5 are meant to be illustrative and not meant to be exhaustive or limiting.
  • One or more embodiments of the invention automatically communicate the test promotions to individuals in the segmented subpopulations using software that communicates/email/mail/administer the
  • Figure 6 shows, in accordance with an embodiment, various example promotion-significant responses.
  • redemption of the test offer is one strong indication of interest in the promotion.
  • other consumer actions responsive to the receipt of a promotion may also reveal the level of
  • interest/disinterest may be employed by the analysis engine to ascertain which test promotion variable is likely or unlikely to elicit the desired response. Examples shown in Figure 6 include redemption (strong interest), deletion of the promotion offer (low interest), save to electronic coupon folder (mild to strong interest), clicked to read further (mild interest), forwarding to self or others or social media sites (mild to strong interest), stopping to look at an item within the store (mild interest), and picking up the item in a physical store but ultimately not purchasing the item (strong interest). As mentioned, weights may be accorded to various consumer responses to allow the analysis engine to assign scores and provide user-interest data for use in formulating follow-up test promotions and/or in formulating the general public promotion.
  • low interest may be afforded a score of -0.75 to -0.25
  • mild interest could be afforded a score weight of 0.1 -0.5
  • strong interest may be afforded a score of 0.5-0.8
  • purchase of the product may be afforded a score of 1.
  • Figure 6 are meant to be illustrative and not meant to be exhaustive or limiting.
  • Figure 7 shows, in accordance with an embodiment of the invention, various example test promotion variables affecting various aspects of a typical test promotion.
  • example test promotion variables include price, discount action (e.g., save 10%, save $1, 2-for-l offer, etc.), artwork (e.g., the images used in the test promotion to draw interest), brand (e.g., brand X potato chips versus brand Y potato chips), pricing tier (e.g., premium, value, economy), size (e.g., 32 oz, 16 oz, 8 oz), packaging (e.g., single, 6-pack, 12-pack, paper, can, etc.), channel (e.g., email versus paper coupon versus notification in loyalty account).
  • discount action e.g., save 10%, save $1, 2-for-l offer, etc.
  • artwork e.g., the images used in the test promotion to draw interest
  • brand e.g., brand X potato chips versus brand Y potato chips
  • pricing tier e.g., premium
  • one or more embodiments of the invention involve generating the test promotions automatically using automated test promotion generation software by varying one or more of the test promotion variables, either randomly or based on feedback from the analysis of other test promotions or from the analysis of the general public promotion.
  • Figure 8 shows, in accordance with an embodiment of the invention, a general hardware/network view of the forward-looking promotion optimization system 800.
  • the various functions discussed may be implemented as software modules, which may be implemented in one or more servers (including actual and/or virtual servers).
  • a test promotion generation module 802 for generating the test promotions in accordance with test promotion variables.
  • a population segmentation module 804 for generating the segmented subpopulations in accordance with segmentation criteria.
  • test promotion administration module 806 for administering the plurality of test promotions to the plurality of segmented subpopulations.
  • an analysis module 808 for analyzing the responses to the test promotions as discussed earlier.
  • Module 810 for generating the general population promotion using the analysis result of the data from the test promotions.
  • module 812 representing the software/hardware module for receiving the responses.
  • Module 812 may represent, for example, the point of sale terminal in a store, a shopping basket on an online shopping website, an app on a smart phone, a webpage displayed on a computer, a social media news feed, etc. where user responses can be received.
  • modules 802-812 may be implemented on one or more servers, as mentioned.
  • a database 814 is shown, representing the data store for user data and/or test promotion and/or general public promotion data and/or response data.
  • Database 814 may be implemented by a single database or by multiple databases.
  • the servers and database(s) may be coupled together using a local area network, an intranet, the internet, or any combination thereof (shown by reference number 830).
  • Test promotions may also be administered via printing/mailing module 850, which communicates the test promotions to the users via mailings 852 or printed circular 854.
  • the example components of Figure 8 are only illustrative and are not meant to be limiting of the scope of the invention.
  • the general public promotion, once generated, may also be communicated to the public using some or all of the user interaction devices/methods discussed herein.
  • Testing is said to be automated when the test promotions are generated in the manner that is likely produce the desired response consistent with the goal of the generalized public promotion.
  • embodiments of the invention optimally and adaptively, without using required human intervention, plan the test promotions, iterate through the test promotions to test the test promotion variables in the most optimal way, learn and validate such that the most result-effective set of test promotions can be derived, and provide such result-effective set of test promotions as recommendations for generalized public promotion to achieve the goal of maximizing profit for the sale of the newly created brand of potato chips.
  • the term "without required human intervention” does not denote zero human intervention.
  • embodiments of the invention do not exclude the optional participation of humans, especially experts, in various phases of the adaptive experimentation and optimization processes for automated promotion testing if such participation is desired at various points to inject human intelligence or experience or timing or judgment in the adaptive experimentation and optimization processes for automated promotion testing process.
  • the term does not exclude the optional nonessential ancillary human activities that can otherwise also be automated (such as issuing the "run” command to begin generating test promotions or issuing the "send" command to send recommendations obtained).
  • FIG. 9 shows, in accordance with some embodiments, a block diagram 900 of a brick and mortar retailer 920A-D that employs electronic tags 910 to provide near real time promotional testing.
  • the E-tags may include simple low power "electronic paper" displays large enough to display pricing of the product.
  • the E-tags also include receivers that allow for updating the displays remotely.
  • a server 940 located within the retailer, and coupled to the Wi-Fi within the store, is used to control the prices shown on the E-tags.
  • a database 980 provides the server information regarding promotional variables that are to be altered to effectively test promotions within the retailer.
  • E-tags may include a monochromatic display large enough for merely displaying product price
  • more advanced E-tags may enable more dynamic display properties and additional display real estate.
  • images and other promotional variables contemplated in the above discussion of promotional testing e.g., images, various more complex promotional structures, etc.
  • E-tags that are limited to displaying minimal information. This is done for clarity purposes, and is not intended to be limiting.
  • the systems and methods discussed herein are equally applicable to more dynamic displays and incorporating a wide array of promotional variables.
  • E-tag manufacturers include, but are not limited to: Altierre, Display data,Pricer, SES-imagotag, and Teraoka Seiko.
  • E-tags even advanced models, are generally limited to a color display of a given size. As holographic displays become practical, such technologies may be employed within E-tags and be tested as a promotional variable. Likewise, E-tags with non-visual outputs, such as audio cues, smells, etc. could be employed. One could envision, for example, that in the potato chip isle that a display could emit the smell of BBQ potato chips when a consumer is in proximity. The exact scent, and intensity, could constitute two additional promotional variables that are subject to testing.
  • the local server 940 may perform the processing required to determine promotional variable for testing, and plan the administration of the testing.
  • a remote server 960 that connects to various retailers 920A-D via a network 950.
  • the network 950 may include a private corporate network, or other local area network.
  • the network could alternatively include a wide area network, such as the Internet or cellular network, or some combination thereof.
  • a remote server comprising multiple parallel processing units may be better suited for generating the promotional testing plans than local servers that may be more limited in their processing capabilities.
  • a centralized server is capable of coordinating activity among the various retailers 920 A-D.
  • some retailers 920B-D may be located within a similar geographic region 970.
  • chain retailers have already identified regional clusters of stores. These stores are typically treated in a similar manner, and employ joint advertisements, common pricing and often joint management. This allows for a more consistent user experience, regardless of which store the user chooses to patronage.
  • the present system may likewise allow for common testing among regional store clusters.
  • certain variables may wish to be varied between the regionally clustered stores in order to specifically test specific variable values. Specific variable testing may be helpful when fine tuning pricing or promotions after bulk variable value decisions have been already made.
  • the ability to test variables, in a limited manner, between retailers in a single geographic region 970 is particularly helpful since the consumers to these retailers are presumably the same customer segment. Even when variables are altered between retailers in a single geographic region, it is important that the vast majority (95% or more) of the pricing and other variables remain consistent between the stores. If there are larger inconsistencies between the stores, the ability to compare a variable values across the retailers may be limited.
  • Figure 10 shows one such example illustration 1000 of electronic tag deployment within a supermarket style retailer. This may include item specific tags 1022-1052, large signage displays 1010, medium end-cap style promotional placards 1060, small-to- medium signage at checkout or self-checkout kiosks.
  • FIG. 1 1 A shows a possible use case where the electronic display follows the user 1 180, by coupling directly with the shopping cart 11 10 as a heads up display, mobile display monitor, tablet style device, proj ector, 3D display or even holographic projector (collectively referred to as a display) 1120, or even as a worn accoutrement 1160, such as google glasses or the like.
  • a display holographic projector
  • Figures 1 IB and 11 C the displays 1 130 and 1140, respectively, are illustrated as being mounted in different places on the shopping cart 11 10.
  • the digital display may be permanently fastened to the shopping cart.
  • the display is dock-able, allowing the user to affix the display on the cart when they enter the retailer, and remove it for charging and safe keeping before leaving the store. The removal of the display could be completed by the cashier upon checkout, or may be the responsibility of the user in some cases.
  • the display may incorporate an radio frequency identification (RFID) chip that triggers the theft prevention system to reduce the chance that the device is inadvertently removed from the retailer/left on the cart.
  • RFID radio frequency identification
  • Such an RFID can also be used to track the user around the retailer.
  • prices and promotions relevant to the products nearby may be transmitted to the device for display (from a local server). This may be accomplished via a Wi-Fi signal or other wireless transmission media.
  • the mobile digital display can have reduced processing and storage capabilities since it is merely displaying what it is told to by the server.
  • RFID or other proximity transmitters may be located throughout the retailer, allowing the mobile display to be location aware. In the case of google glasses or other display owned by the user, it may be desirable that the display is controlled by the device rather than by an external server system. The device would require an executable program for querying a database on what promotions to display based upon its perceived location within the store.
  • each shopping cart includes an RFID in order to track user movements throughout the store, even if they do not have an attached mobile digital display.
  • cameras or other optical tracking could be utilized to monitor user movements.
  • a user's location can be tracked with a fairly high degree of success (via amplitude and triangulation from sensors located throughout the store).
  • Figure 12 shows a flowchart 1200 of an example method for the generation and testing of promotions within a brick and mortar retailer space using the systems described in Figures 9-1 ID.
  • This process starts with the definition of retailer geographic clusters (at 1210) which, as previously discussed, are typically predefined by the retailer chain.
  • the base pricing of goods are then optimized for within this region (at 1220).
  • Figure 13 provides a more detailed flow diagram of this process of defining optimal base prices.
  • the price changes preferably, are updated over night when the store is closed. For 24 hour retailers, this may be set to a low volume period, and all prices in the store may be updated at the same time. In some cases, a grace period of an hour (or other acceptable timeframe) may be provided by the 24 hour retailer after a price update. Consumers who complete their purchase within this grace period will be afforded the lower of any price that was displayed for the item. For example is ice cream was offered at $3.99 and frozen pizza at $9.99 at 1 1 :59pm, and the price changed to $4.99 and $9.50 for the ice cream and pizza, respectively, at 12:01am, if the consumer purchases the items before 1 :00am the prices charged would be $3.99 and $9.50 respectively. Few consumers will bother altering their shopping behavior to go at very late hours for such a benefit, thereby limiting losses to the retailer.
  • the transaction data for the items is collected (at 1330). This includes sales volumes over time, changes in basket composition, etc. This data may be collected for a set period (such as one or two days for large volume items) or may be tied to a transaction number. For example, some items are deemed very low volume, such as shoe polish in the grocery store. Under normal circumstances, volumes for such a product are measured in the single digits per week. The item itself costs the retailer money to stock (given the loss of shelf space) but may be deemed valuable to the retailer by providing a "one stop shop" for consumers. For such an item, modifying the price for a few days (or even weeks) may be insufficient to gain statistically useful information regarding the promotional variable change.
  • the transaction volume, margin and profit from the testing period may be compared against the baseline price (at 1340). If the margin is still within an acceptable range of the target margin, and there is a statistically significant increase in volume and/or profit, then the baseline may be adjusted to the tested price (at 1350). The method then considers whether to continue testing for different base prices (at 1360). Only after a number of unsuccessful testing periods (ones where the base price remains the same after analysis) is the system sure the "best" base price has been reached. At this point the base pricing may be rolled out to a wider set of retailer settings (at 1380). Of course ongoing testing may always be undertaken, especially as underlying costs or the competitive landscape evolve.
  • the pricing may again be adjusted by a smaller degree (at 1370) and retested in the store from the last 'best' price. For example, assume the price of apples is currently $1.49 each, and the price is adjusted to $1.35. There is a margin drop, but it is still within a range that is deemed acceptable by the retailer. Volumes during the testing period don't change much, however, so overall profit actually reduces. The base price thus remains at $1.49, but is now retested at $1.65 each. Again, this is an acceptable margin, and cases a minor reduction in volume. However the profit is higher by a statistically relevant amount (over 95% confidence), so the updated base price is now $1.65.
  • the price is then adjusted to $1.69 by the system and analysis repeated.
  • the profit now drops due to price elasticity causing a reduced volume.
  • the base remains at $1.65 and is then tested at $1.59.
  • sales recover sufficiently to make this preferred (statistically significant profit increase and still within margin range) over the previous price.
  • the ideal base price is $1.62. Any more or less of a price change results in a lower profitability in this example.
  • This base price may then be disseminated to a wider set of stores within the retailer's chain, particularly to stores serving similar consumer types.
  • the method may optimize for the ideal promotion conditions (at 1230).
  • Figure 14 shows a flowchart of such a process. Much of the procedure and methodologies described previously may likewise be employed for in-store promotional testing. Where available, different promotion types (e.g., percent off, buy-one-get-one, reduced price, etc.) may be employed. Where the electronic tags allow, the testing of different images, color schemes, sounds, smells, and videos may all be tested for impact.
  • any promotional variable is typically updated (at 1410) when the store is closed, or during the lowest traffic period of time for 24 hour retailers. Unlike base price optimization, however, the variation of a promotional variable is not necessarily beholden to a particular margin requirement, or limited to a specific percentage change.
  • the data for this change is collected (at 1420) for a statistically relevant period of time (either set time or by transaction count). Profit levels for the promoted item are computed (at 1430), and the process repeats for a different variable (at 1440). In some cases there may be a retailer requirement that an item is promoted only a certain percentage of the time and/or there is a 'cool down' period between promotions. Any such constraints will be taken into consideration between subsequent promotions.
  • the profit for the new promotion is calculated (at 1450) and a determination is made if additional promotions are desired (at 1460). For many items, dozens or even hundreds of promotion variations are desirable to fully explore the test space of the promotion variables. The 'winning' promotion variable values may be collected and employed together from one promotion to the next to determine the 'best' set of promotional conditions. Only after exhausting much of the promotional space can the 'best' promotion values are fully identified. The usage of electronic tag signage allows such activity that would be cost prohibitive and unable to be completed (regardless of staffing levels) in real-time otherwise.
  • variable values that maximize profitability have been all identified (at 1470) they are combined with other winning variable values for general promotions across all retailers in a geographic area or even across all retailers in the chain (at 1480).
  • the process may continue by determining optimal sell through pricing (at 1240).
  • Figure 15 shows a more detailed flowchart of this process for determination of optimal sell-through pricing in a brick and mortar setting. It should be noted that unless sell through activity is anticipated for a product, this process may be skipped or deferred until a sell through event is necessitated. The reason for this is sell through policies, including typically progressive and deep discounting, may accomplish a volume goal, but usually underperforms on other metrics like profitability. When there is a supply glut, a need to clear out inventory to make room for additional product, or possible expiration of product, then such sell through activity may be desired. But routinely, sell through activity is not necessarily desirable for durable year-round goods.
  • the promotional testing showed that a particular display color (in instances where the electronic tags are color capable) results in larger sales levels
  • this variable value may be incorporated into the sell through activity.
  • the promotional variables already tested provides at least a baseline idea of volume lifts associated with various pricing points (and other promotional variables).
  • sell through goals may be met using variable values similar to the optimized promotion variables. In such situations the profit may be maximized (or close to maximized) while meeting the sell through volume goals.
  • the sell through volumes are larger than what is achievable using values for the promotional variables that are at, or near, the optimized values for promotion optimization.
  • the testing of sell through proceeds by making progressively deeper pricing discounts to the item's price (at 1530), and collecting sales information for the items (at 1540). Using this data, a complete price elasticity curve for the item can be generated (at 1550). This can be used in the future to estimate and plan for future sell through events. For example assume the price elasticity curve is as follows in graph 1.
  • the system may design a pricing schedule over this period that achieves this goal, while maximizing overall profit.
  • This scheduling generates an equation for the profit, and measure the area under the curve for differing prices over the sell through period.
  • the price can be altered only every 2 days (as dictated by a business rule of the retailer). This means that there are a maximum of 4 different prices over the sell through period. The process would conclude setting the price at $3 for the initial 5 days, followed by a price of $2 for the final two days. This would result in a sell through of the 500 units over the seven day period, while maximizing profit at $760 over this promotion period.
  • the final step is the rolling out of pricing policies to a larger set of retailer establishments (at 1260). This may include merely rolling out these pricing and promotion findings to other retail stores that are similar (historical transaction trends are similar), or may be rolled out to a wider segment of brick-and-mortar retail locations.
  • the first is to compare transaction histories of the retailers and use clustering algorithms (such as least mean squares or distance algorithms) to determine retail locations that have similar historical sales patterns.
  • the degree of similarity between "close” stores and "different” stores may be an adjustable threshold set by the retailer.
  • the retailer may indicate that all stores should be clustered into a certain number of groups, and the most similar stores are clustered accordingly.
  • the clustering may be based upon reaction to varying promotion variables. Two stores, for example, may have very different historical transaction records, but may have similar volume lifts based upon the altering of particular promotional variables for a items. While baseline preferences of the consumers of these stores are very different, how the consumers behaviors alter in response to promotional activity may be similar. These stores are thus very similar, from the perspective of reaction to price/promotion activity, than stores that may have more similar historical transactions.
  • clustering algorithms already known in the art, may be employed to determine which stores have similar reactions to changes in promotional variable values.
  • FIG. 16 shows one flowchart 1600 of an example method for such personalized promotion in a brick and mortar setting. This process is dependent upon tracking the user/consumer through the retail space (at 1610). As previously discussed, such tracking may be done by a shopping cart sensing signals throughout the retail space or, more commonly, through an array of sensors within the retail space. These sensory can track a signal (e.g., RFID, Bluetooth, wireless ISM band radio signal, etc.) being emitted from a shopping cart, or a device commonly carried by virtually every consumer (e.g., a cell phone).
  • a signal e.g., RFID, Bluetooth, wireless ISM band radio signal, etc.
  • image recognition, or other biometric data may be leveraged to track the consumers throughout the retail space.
  • the location data may be combined with data known about the user, in-store behaviors, and the like, to present the user with personalized promotions as they move through the store (at 1620).
  • Figure 17 provides a more detailed view of this sub process, where the known data regarding the shopper is initially collected (at 1710). In some cases the consumer/user is a blank slate, with no known information regarding this individual. Other times the user may be connected to a larger retailer infrastructure, with a loyalty application loaded on their phone, or other mechanism for identifying the individual. Such applications may be programmed to ping the retailer when entering the location with an identified for the user. Users are likely to opt in for such services due to the monetary savings, and more personalized shopping experience, they realize as a result.
  • the user's identity information may be matched with prior purchases, selections on the retailer's loyalty application, and other publically available information to determine what products the user typically purchases. Promotional variable values that have worked particularly well for the user may also be identified.
  • the user's movements through the store may also be used to track if the user has interest in particular items (at 1720). For example, if the user enters an aisle with cereal, and pauses for a moment at a particular location, the user can be assumed to be looking at, or even grabbing one of a limited number of items from the shelf.
  • the user's known attributes and movement data may then be combined (at 1730) to generate the best possible personalized promotions for this particular user (at 1740). For example, if a user is known to purchase milk and cereal in the same shopping trip, and sometimes purchases milk and a high margin cookie on selective trips, the system may determine in real-time that after stopping near the cereal the user will be present in the milk aisle in the future.
  • the electronic tag When in this aisle, the electronic tag may then present the user with a deal related to savings on the cookie brand of preference for the user, when purchased with milk.
  • the user likely was not considering purchasing the cookies when entering the retailer, but may be persuaded to increase their overall spend within the store, on higher margin items, based upon this electronic tag display.
  • the efficacy of these personalized promotions may be tracked at the point of sale (at 1630). This data may be appended to the user's account/profile, when available. Even for user's who do not have such a persistent identity, the promotions that are more effective may be retained and reused for shoppers with similar movements throughout the retail space. In such a manner the personalized promotions may be refined over time (at 1640) such that only the more effective promotions are displayed to a given user. For example, in aggregate, it may be determined that discounting cookies at the milk aisle is not particularly effective, but displaying a sale on buns when the user is in front of hotdogs and hamburger patties is effective, raising the sales of both the buns and meat products.
  • This efficacy tracking may be made even more powerful by being able to personalize the promotions down to the individual. For example, assume our user is influenced by buy-one-get-one-free sales at a disproportionate rate. Such promotions may be displayed to this user more often than other consumers in order to increase sales at the individual consumer level.
  • Figure 18 illustrates an exemplary Promotion Optimization Ecosystem
  • Third Party Servers 1870 operatively coupled to each other via Wide Area Network(s) (WAN) 1840.
  • Third Party Server(s) 1870 are operated by one or more of social media sites, search engines, Internet Service Providers (ISPs), and telecommunication service providers including POTS, Voice-over-IP (VoIP), Cable TV and satellite service providers.
  • Promotion Optimizer 1850 receives promotional optimization requests from promotors via the plurality of Promotor Communicators 1891-1899. These promotional requests are then optimized by Promotion Optimizer 1850.
  • the optimized promotions can be disseminated to the plurality of Consumer Communicators 1811-1819 by the Promotion Optimizer 150 and/or one or more of the Third Party Server(s) 1870. Note the promotion optimization functionality can be concentrated in one of the Promotion Optimizer 1850 and the Third Party Server(s) 1870, or distributed between the Promotion Optimizer 1850 and the Third Party Server(s) 1870.
  • Advertising has been shown to increase sales revenue, but because advertising budgets are limited, it is important to maximize the return on that budget; it may also be beneficial to not simply increase the short-term revenue related to a particular product or service, but to achieve an objective that may have a more strategic impact. Examples of this include increasing overall sales revenue (not simply revenue related to a single product or service), increasing overall market share, increasing sales per customer visit, increasing sales margin, increasing market penetration within a particular demographic, etc.
  • the disclosed Promotion Optimizer 1850 can automatically identify the right products, offers, offer attributes, consumer segments and individuals to achieve the objective.
  • Optimizer 1850 includes a model that uses inputs such as historical consumer behavior, user-to- product affinity and product-to-product affinity; the output of this model is set of advertisements that can auto-personalize the set of products, offer structures and targeted consumers. These highly targeted, automatically generated ads will maximize both the desired objective and the return on budget.
  • This process 1900 shown at Figure 19, includes first receiving the advertising budget from the retailer (at 1920) as well as the advertising objectives (at 1940). In any of the mechanisms of variable testing disclosed previously, the system can then personalize the promotions based upon the budget and objectives (at 1960).
  • Promotion Optimizer 1850 will present the individual with an offer for a product or service that has broad appeal; this could be an offer for a commodity product (such as milk or bananas) or a widely used service.
  • the individual In exchange for accepting the offer, the individual is required to provide personally identifying information. This information can be used later to target ads to that individual. Variations of this technique can also be used to get users to engage in a product offering flow where certain items in that offering may not, at the outset, be personalized.
  • ads are generated that highlight elements (the products on sale) within them, as shown in the example process 2000 of Figure 20.
  • ads are containers with individual elements in them are optimized.
  • An online carousel ad online may include five items.
  • each item within the ad can be uniquely tailored to an individual by using lookalike modeling. For instance, if a retailer who has hundreds of items on sale in a particular week, these items may be provided to the system for consideration (at 2020).
  • Optimizer 1850 can match the right subset of those sale items to an individual based on either what that individual has purchased before, or which products have the closest match to items that individual has purchased before (at 2040). This may be accomplished by looking at the historical Tlog and hash-key protected loyalty data of a retailer.
  • the sale items are then offered to the consumer within the personalized carousel container (at 2060).
  • Digital containers house optimally recommended set of products tailored toward the consumer where the overall combined impact of the containers drives a maximum increase in retailer objectives such as household (HH) penetration, unit volume, margin, new shoppers penetration, etc.
  • Containers could be carousels, windows on browser, different tabs, LTC assets, or set of linked ads. Returning to the physical space, these carousels may instead be the digital displays that are made available to the consumer as they navigate the store.
  • a single carousel includes products from multiple retailers. For example, a handyman browsing for power saws at Home Builder Supply may see a carousel displaying refurbished saws from Home Builder Supply, new saw blades from Tools Depot and recycled power tool dust-hoods from Recycle Universe.
  • Criteria for placement of these items may be based on advertiser's bid for ad space, location of local stores, delivery cost, delivery time and/or price. Obviously, such selection of offers may vary when implemented in a physical retailer, but within a shopping mall context, a similar "communal" advertisement space may be desirable.
  • Advertisers are typically very careful when generating new online offers, because there is a possibility that an error in the offer could cause problems either at a cash register or an online checkout. Because it is very important that an offer results in the correct discount, may promotions require new offers to be placed in a resting "bullpen" prior to publishing to ensure that when they are queried they can be instantly provided. This need to quality check offers acts as a bottleneck that can prevent on-the-fly creation of ads in response, for example, to a particular event.
  • Some embodiments include an approved offer bank that is pre-loaded with price promotions for personalization (at 2120) that includes multiple versions of a product offer, which will be matched with individuals (at 2140) only once they are called up, as shown in the process 2100 of Figure 21.
  • Other embodiments break out different "factors" (such as price level, images, claims, etc.) of an ad in containers so that they may be dynamically recompiled into different offer structures on the fly— this allows the creation of more ad variants without each factor needing to be individually tested before being added to the offer bank.
  • Certain embodiments may also include a continuously growing repository of offers that may have associated metadata. Attachment of metadata may occur by linking offers to events, experiments, audience targeted, etc. These types of repositories could be used as inputs to an allocation engine. [00189] In these embodiments, permutations of offers are generated in real time for execution. Benefits of the preferred embodiments include streamlining the retailer/manufacturer offer approval process dramatically by: 1) providing an already pre-approved bank of offers; and 2) decomposing the offers into their elemental components (product, discount depth, quantity, offer structure, stock imagery etc.).
  • preapproved offer components includes a combination of a loss leader with highly profitable items, such as premixing a laser printer with several toner cartridges, or premixing a staple such as milk with profitable cereals.
  • machine learning techniques are used to link the actions taken by ads (e.g., load to card, or redemption information offline), to change which products are surfaced to maximize objectives. For example, a particular product on sale may drive more revenue, a different product may drive more traffic, and yet a third product may drive bigger baskets, etc. Consumers can be scored by segments initially, and eventually these segments will be determined by a series of machine learning capabilities that are automated.
  • Promotion Optimizer 1850 auto-matches products to
  • Optimizer 1850 can surface combinations of products to consumers to maximize these objectives for any given shopping event or retailer goal.
  • Embodiments of the invention address retailers' needs to have their own curated offer sets that are designed for individuals, and that may be placed across multiple networks.
  • the disclosed embodiments allow a retailer to treat every product as potentially on sale (at 2220), and will map the matching of the product (and its corresponding promotion) to personalize every offer delivered digitally into one individually adapted offer set (at 2240).
  • This set can then persist across channels, adapted for the differences in advertising space.
  • a retailer's load to card app there might be a curated list that is ranked based on purchase probability by product category, with all categories represented and all 500 offers live.
  • third party media ad such as a social media ad, the number might be reduced to 50, and a set of 50 offers (different from the top 50) might be selected to show diversity across categories for third party media, and to maximize the diversity of offers.
  • Promotion Optimizer 1850 can provide a central solution to manage and optimize the promotion allocation across any channel ⁇ asset for a desired period. Two distinct levels of optimization will occur: i) within channel and asset
  • Distribution channels would be social media, LTC assets, web apps, mobile apps, digital circular, etc.
  • Promotion Optimizer 1850 may recognize the historical engagement of a user's click, browsing, purchasing of products and surfacing the relevant offer, through geo-location and store proximity, as the consumer enters a retailer's or a competitor's brick-and-mortar store location. In some embodiments, Optimizer 1850 may use location awareness and a shopper's purchase history to generate a push alert that notifies the shopper of the deals when they enter the store's location.
  • user friction is reduced by using a third party media advertising platform to link price promotions (like Load-to-ID) by pre- populating unique identifiers such as email, phone number, or loyalty number into permission fields.
  • capturing a user's loyalty card number can be used to link accounts between an advertiser and a retailer.
  • independent market effectiveness resources such as Nielsen + MT are used to enhance opportunity sensing and value mapping. Leveraging syndicated retail sales data (including data from competitors' sales), embodiments of the invention monitor product and offer performance over time to provide indicators for the manufacturer; these indicate whether optimization is required for their respective products based on metrics such as inward offer diversity, offer frequency, outward competitive offer frequency and diversity. In other words, Optimizer 1850 is able to monitor competitors' promotions, and will recommend similar promotions (if they are effective), or will generate tests of new promotions against other new or existing promotions to determine which would be more effective.
  • the Promotion Optimizer 1850 provides a nearly real time view into competitors' promotions; this allows a user to change their offers in an effort to draw sales away from a competitor, and vice versa, e.g., prevent competitors from drawing sales away from the user.
  • the user can modify promotion(s) in response one or more competitor prices, by matching the lowest competitor promotional price, matching the average competitor promotional price.
  • additional data including individual consumer-specific information such as incremental acquisition cost, e.g., inconvenience or extra time, for the consumer to purchase the competitors' products.
  • Figures 23A and 23B illustrate a Computer System 2300, which is suitable for implementing embodiments of the present invention.
  • Figure 23A shows one possible physical form of the Computer System 2300.
  • the Computer System 2300 may have many physical forms ranging from a printed circuit board, an integrated circuit, and a small handheld device up to a huge super computer.
  • Computer system 2300 may include a Monitor 2302, a Display 2304, a Housing 2306, a Disk Drive 2308, a Keyboard 2310, and a Mouse 2312.
  • Disk 2314 is a computer- readable medium used to transfer data to and from Computer System 2300.
  • Figure 23B is an example of a block diagram for Computer System
  • Processor(s) 2322 also referred to as central processing units, or CPUs
  • Memory 2324 includes random access memory (RAM) and read-only memory (ROM).
  • RAM random access memory
  • ROM read-only memory
  • RAM random access memory
  • ROM read-only memory
  • Both of these types of memories may include any suitable of the computer-readable media described below.
  • a Fixed Disk 2326 may also be coupled bi-directionally to the Processor 2322; it provides additional data storage capacity and may also include any of the computer- readable media described below.
  • Fixed Disk 2326 may be used to store programs, data, and the like and is typically a secondary storage medium (such as a hard disk) that is slower than primary storage. It will be appreciated that the information retained within Fixed Disk 2326 may, in appropriate cases, be incorporated in standard fashion as virtual memory in Memory 2324.
  • Removable Disk 2314 may take the form of any of the computer-readable media described below.
  • Processor 2322 is also coupled to a variety of input/output devices, such as Display 2304, Keyboard 2310, Mouse 2312 and Speakers 2330.
  • an input/output device may be any of: video displays, track balls, mice, keyboards, microphones, touch-sensitive displays, transducer card readers, magnetic or paper tape readers, tablets, styluses, voice or handwriting recognizers, biometrics readers, motion sensors, brain wave readers, or other computers.
  • Processor 2322 optionally may be coupled to another computer or telecommunications network using Network Interface 2340.
  • the Processor 2322 might receive information from the network, or might output information to the network in the course of performing the above-described promotion optimizations and administration within physical stores. Furthermore, method embodiments of the present invention may execute solely upon Processor 2322 or may execute over a network such as the Intemet in conjunction with a remote CPU that shares a portion of the processing.
  • a software program is assumed to be stored at any known or convenient location (from non-volatile storage to hardware registers) when the software program is referred to as "implemented in a computer-readable medium.”
  • a processor is considered to be “configured to execute a program” when at least one value associated with the program is stored in a register readable by the processor.
  • the computer system 2300 can be controlled by operating system software that includes a file management system, such as a disk operating system.
  • a file management system such as a disk operating system.
  • One example of operating system software with associated file management system software is the family of operating systems known as Windows® from Microsoft Corporation of Redmond, Washington, and their associated file
  • the file management system is typically stored in the non-volatile memory and/or drive unit and causes the processor to execute the various acts required by the operating system to input and output data and to store data in the memory, including storing files on the non-volatile memory and/or drive unit.
  • the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in a client-server network environment or as a peer machine in a peer-to-peer (or distributed) network environment.
  • the machine may be a server computer, a client computer, a personal computer (PC), a tablet PC, a laptop computer, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, an iPhone, a Blackberry, a processor, a telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.
  • PC personal computer
  • PDA personal digital assistant
  • machine-readable medium or machine-readable storage medium is shown in an exemplary embodiment to be a single medium, the term “machine-readable medium” and “machine-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions.
  • the term “machine-readable medium” and “machine-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the presently disclosed technique and innovation.
  • routines executed to implement the embodiments of the disclosure may be implemented as part of an operating system or a specific application, component, program, object, module or sequence of instructions referred to as "computer programs.”
  • the computer programs typically comprise one or more instructions set at various times in various memory and storage devices in a computer, and when read and executed by one or more processing units or processors in a computer, cause the computer to perform operations to execute elements involving the various aspects of the disclosure.

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Abstract

L'invention concerne des systèmes et des procédés pour optimiser des promotions au sien d'un espace physique de vente au détail. Des étiquettes électroniques sont déployées à travers l'espace de vente au détail. Ces étiquettes sont connectées sans fil à un système serveur, ce qui permet une mise à jour en temps réel et simultanée des prix et d'autres variables promotionnelles. Ces étiquettes permettent un test expansif du prix de base, une optimisation des promotions et la vente au moyen de critères. Des tests peuvent être effectués sur un large éventail de variables promotionnelles afin de déterminer quelles sortes de valeurs pour ces variables produisent les promotions les plus efficaces. L'élasticité des prix pour des produits individuels peut elle aussi être suivie par un test d'ajustement des prix afin de déterminer la vente par planification. De plus, en suivant les clients individuels à travers l'espace de vente au détail, des promotions personnalisées peuvent être présentées aux personnes.
PCT/US2018/049284 2017-09-01 2018-09-01 Systèmes et procédés de conception de promotion intelligente dans des détaillants de briques et de mortier avec notation de promotion Ceased WO2019046833A1 (fr)

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