Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR 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

Definitions

• the present invention relates generally to price 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 and base pricing within brick and mortar retailers to determine an optimal price for goods.
• 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 (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/flyer/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. [0004] In addition to promotional activities, it is also desirable to perform
• base pricing e.g. non-promotional prices.
• MSRP retail pricing
• base prices are set based upon competitive analysis- a retailer may monitor competitor’s and match or beat the competitor’s price on some or all the goods in a store. Alternatively, some retailers may set a desired margin, or sales volume, for a good, and set prices accordingly.
• the base prices of goods in a brick-and-mortar store do not vary significantly due to logistical concerns of updating signage and point of sales (POS) databases, consumer expectation of generally consistent base prices, and the tendency that a retailer will continue patterns of behavior (e.g.,“this is what we have always done”).
• 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.
• CPG consumer packaged goods
• the retailer such as a grocery store
• 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.
• the consumer redeems the coupon or electronic code, the consumer is entitled to a reduced price for the CPG item.
• the revenue loss to the retailer due to the redeemed discount may be reimbursed, wholly or partly, by the manufacturer of the CPG item in a separate transaction.
• promotion and base price testing is expensive (in terms of, for example, the effort to conduct a promotion campaign, modify display prices and/or the per- unit revenue loss to the retail er/manufacturer 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 110 and 114 and one dip 112 in demand curve 102 are shown in the example of Figure 1.
• Lift 110 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.
• 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
• 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 when analyzing for promotions (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 cookies which many consumers view to be an equivalent substitute for Brand X cookies.
• Brand Y cookies being in short supply in the store, many consumers bought Brand X instead for convenience sake. Aggregate historical sales volume data for Brand X cookies, when examined after the fact in isolation by Brand X marketing department thousands of miles away, would not uncover that fact. As a result, 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.
• Brand X marketer can ascertain that most of the lift in sales during the promotion period that spans lift 114 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-retum manner to promote a particular CPG item to the general public.
• 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.
• optimizing base pricing of products within a physical retailer includes first collecting transaction logs for products in a set of physical retail spaces. These logs are validated, adjusted and elasticities between the products are computed. The adjustment may be responsive to the day, by retailer and by a host of external factors (e.g., weather). The adjustment may also include a normalization and filtering out of inaccurate log data. Elasticity is calculated by generalized linear models. A set of constraints are then received and used, along with the elasticities, to generate the optimal price for each product by the maximization of the following:
• Test values above and below the estimated optimal price for each product are then computed and subsequently evaluated in in three groups of retailers.
• the evaluating includes //-optimal designs via exchange algorithm and Box-Behnken design.
• Each of the retailers is randomly assigned to one of these three groups.
• the results of the evaluations are used to refine the elasticities that were calculated.
• the estimated optimal price may be subsequently updated using the refined elasticity. It can also be validated along with the test values and control price in four groups of retailers, each randomly assigned.
• the transaction logs include information that allows the comparison of a set of pricing instructions provided to the retailers against the actual pricing that occurs to confirm compliance with instructions. Transaction logs may be aggregated by day and by each retailer.
• 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 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;
• 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 11 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, a block diagram illustrating the system for base price optimization;
• Figures 19A and 19B show, in accordance with some embodiments, flow diagrams illustrating the method for base pricing optimization
• Figure 20 shows, in accordance with some embodiments, an illustration of an example rollout of a base price optimization test
• Figure 21 shows, in accordance with some embodiments, an illustration of an example elasticity matrix for the base price optimization test
• Figure 22 shows, in accordance with some embodiments, an illustration of a sales graph for the example rollout of the base price optimization test
• Figure 23 shows, in accordance with some embodiments, an illustration of an example refinement of the base price optimization test
• Figure 24 shows, in accordance with some embodiments, an illustration of a sales graph for the example refinement of the base price optimization test
• Figure 25 shows, in accordance with some embodiments, an illustration of an example of the completed base price optimization test.
• Figures 26A and 26B 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 and base price optimization 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 and base price 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 testing activity may include intelligent test designs for most effective experimentation of promotions and base pricing to more efficiently identify a highly effective general promotion and/or base prices.
• Such systems and methods assist administrator users to generate and deploy advertising campaigns, and optimize prices throughout the retailer. 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.
• FL-PO forward-looking promotion optimization
• 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 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, socio-economic standing, etc.
• the consumer actually redeems the offer one type of response is recorded and noted in the computer-implemented account of that consumer. Even if an action by the consumer does not involve actually redeeming or actually taking advantage of the promotional offer right away, an action by that consumer may, however, constitute a response that indicates a level of interest or lack of interest and may still be useful in revealing the consumer preference (or lack thereof).
• a consumer saves an electronic coupon (offered as part of a test promotion) in his electronic coupon folder or forwards that coupon to a friend via an email or a social website
• that action may indicate a certain level of interest and may be useful in determining the effectiveness of a given test promotion.
• a consumer stops to look at a product, or even pick up the product but chooses not to purchase it at the register, such activity, to the extent it is reliably measured, may indicate interest in the promotion despite the lack of a transaction being completed.
• Different types of responses/actions by the consumers may be accorded different weights, in one or more embodiments.
• 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. As the term is employed herein, 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
• 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., weather, 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.
• 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.
• the responses of these two test promotions if repeated in statistically significant numbers, would likely yield fairly accurate information regarding the relationship between income for 35-40 years old professionals and their actual preference for 12-oz packages of fancy cut potato chips with medium salt.
• 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 variable value e.g., brown paper bag packaging
• test promotion variables such as for example with different prices, different display options, etc.
• 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.
• 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.
• 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
• 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.
• 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 l02a, l02b, l02c, l02d, and l02e are administered to purposefully segmented subpopulations l04a, l04b, l04c, l04d, and l04e respectively.
• each of the test promotions (l02a-l02e) may be designed to test one or more test promotion variables.
• test promotions l02a-l02d 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 l02e is shown testing four test promotion variables (X, Y, Z, and T).
• test promotion l02a involves test variable XI (representing a given value or ahribute for test variable X) while test promotion l02b involves test variable X2 (representing a different value or ahribute 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 l02a and l02b) or many of the test promotion variables (as can be seen in the comparison between test promotions l02a and l02d).
• test promotions l02a and l02e 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 l02a and l02e) 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 l02a and l02b).
• test promotions may be generated using automated test promotion generation software 110, 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.
• test promotion generation software 110 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 l04e 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 l04c 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 l04a has four individuals (P1-P4) whereas purposefully segmented subpopulation l04e 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 l04a and purposefully segmented subpopulation l04c wherein the attribute A changes from Al to A2) or in the values of many segmentation criteria simultaneously (as can be seen in the comparison between purposefully segmented subpopulation l04a and purposefully segmented subpopulation l04d wherein 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 l04a and
• subpopulations must be segmented using the same number of segmentation criteria (as can be seen in the comparison between purposefully segmented subpopulation l04a and l04e wherein purposefully segmented subpopulation l04e is segmented using five criteria and purposefully segmented subpopulation 104a is segmented using only four criteria) although for the purpose of validating the effect of a single criterion, it may be useful to keep the number and values of other segmentation criteria (e.g., the control criteria) relatively constant from purposefully segmented subpopulation to purposefully segmented subpopulation.
• other segmentation criteria e.g., the control criteria
• the purposefully segmented subpopulations may be generated using automated segmentation software 112, 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 l04a. If the user (such as user Pl) 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 Pl '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
• a "winning" test promotion value (e.g., 20% off listed price) from one test promotion may be combined with another "winning” test promotion value (e.g., packaged in plain brown paper bags) from another test promotion to generate yet another test promotion.
• testing 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 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.
• 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. In turn, these more
• Figure 2B shows, in accordance with an embodiment of the invention, the steps for generating a general public promotion.
• each, some, or all the steps of Figure 2B may be automated via software to automate the forward-looking promotion optimization process.
• step 202 the plurality of test promotions are generated.
• test promotions have been discussed in connection with test promotions l02a-l02e 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
• 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).
• 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.
• 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 promotional information pertaining to the product, place of sale, time of sale, etc.), in a computer-implemented account (such as a loyalty program account) associated with the user that is a member of the segmented subpopulation to be tested, via one or more social media sites, or displayed on electronic pricing tags within a retailer’s physical store.
• a computer-implemented account such as a loyalty program account
• the test promotion is presented to the user.
• 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 and 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).
• 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. For example, 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, and 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, l2-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, value, economy
• size e.g., 32 oz., 16 oz., 8
• 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.
• FIG. 9 shows, in accordance with some embodiments, a block diagram 900 of a brick and mortar retailer 920 A-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 920A-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. 11A shows a possible use case where the electronic display follows the user 1180, by coupling directly with the shopping cart 1110 as a heads up display, mobile display monitor, tablet style device, projector, 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.
• the displays 1130 and 1140 are illustrated as being mounted in different places on the shopping cart 1110.
• 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.
• 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 9-11D.
• 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.
• Promotions are designed typically to make the most profit possible. While overall profitability is advantageous, it does not necessarily equate to the best long term strategy for a product. For example, many times profitability
• maximization squeezes margins in an unsustainable manner. Small disruptions in supply or demand can result in catastrophic losses, and it can be a risky operating condition. Thus, most retailers wish to set their products’ base price according to a desired margin rather than to optimize profit (or other metric). For the process of setting the base price, the retailer must first provide this target margin (at 1310) to the system. The system then sets a deviation from the current price (typically up to a maximum of a 10% swing) to ascertain the impact on profitability (at 1320). Since a fixed margin goal equates to a set price of the goods, varying the price too much is determined disadvantageous. Modulating prices around a margin goal however, may identify local profitability maxima that may be fine-tuned.
• 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 11 :59pm, and the price changed to $4.99 and $9.50 for the ice cream and pizza, respectively, at l2:0lam, if the consumer purchases the items before fOOam the prices charged would be $3.99 and $9.50 respectively.
• 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. Thus, for lower volume products, it may be more advantageous to set a statistically meaningful number of transactions (say 400 for example) and only modify the price once this this number of transactions has been met. Additionally, for long lasting products, it may be advantageous to also have prolonged testing periods (commiserate with the lifetime of the product) in order to ascertain demand. For example, a Glade Plug In cartridge is intended to last 30 days. If promoted on one day, and most consumers are not in need of the item since their last cartridge is still operating, the short promotional testing may not adequately capture the impact of the promotion.
• 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. Overall sales of this item may be monitored, and should indicate an increase in overall profitability for the base priced item.
• 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.
• different promotion types e.g., percent off, buy-one-get-one, reduced price, etc.
• the electronic tags allow, the testing of different images, color schemes, sounds, smells, and videos may all be tested for impact.
• the altering of 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.
• 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.
• Graph 1 [00162] In this example graph, the price of a product is shown on the x-axis, and sales volume is on the y-axis. For this product, the cost per item for the retailer is approximately $1, resulting in the following profitability curve, as shown at graph 2.
• 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. Otherwise, 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 may have very different historical transaction records, but may have similar volume lifts based upon the altering of particular promotional variables for 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).
• 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 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 promotions that are more effective may be retained and reused for shoppers with similar movements throughout the retail space.
• 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.
• the disclosed systems and methods address these concerns by ensuring the fidelity of testing data, enhanced testing deployment across many retailers in a retail chain, and advanced analytics to minimize the chance that external factors unduly impact the base price testing results. Pricing is tested incrementally, and optimizations adopted while continued verification occurs. As such, the results of the disclosed testing are far more accurate than prior methods of base price optimization techniques. The incremental changes in tested price (and the conditioning imposed by online retailers) minimizes consumer aversion to such testing. Lastly, the efficient test design and early adoption of optimized results reduces costs to a retailer significantly. In fact, when paired with the electronic signage disclosed previously, the cost may be negligible even at the testing outset, and will result in a net gain before the testing is even completed (before any global adoption). This allows physical retailers to more effectively compete in the marketplace in a manner that has never before been possible.
• FIG. 18 provides a block diagram illustrating the system 1800 for base price optimization, in accordance with some embodiments.
• block diagram data 1810 is employed for analysis.
• This data 1810 is typically a collection of historical transaction information (t-logs). These transaction data sets may be aggregated by individual stores within the retailer chain, and by day. In some advanced embodiments, t-log data may even be aggregated on a more granular level, say on an hourly basis, to provide for more detailed analysis of purchasing habits.
• t-logs historical transaction information
• t-logs may even be aggregated on a more granular level, say on an hourly basis, to provide for more detailed analysis of purchasing habits.
• a physical retailer will not wish to alter pricing in the store more than once a day (even though such capability may be possible using electronic tags) due to the confusion it may cause the customers in the store.
• more granular aggregation may provide interesting insights into price impacts on behaviors, this degree of analysis may be merely academic as it will be impractical to take action
• Retailers even when fitted with electronic tags and automated pricing rollout software, are notoriously inconsistent in making pricing changes with fidelity. This is particularly true when the price change decision is made by a third party rather than a corporate headquarters. This is particularly pertinent in that the disclosed systems and methods for price testing and base price optimization may be employed by a retailer as an in- house pricing solution, or may alternatively be provided by a consultant company to maximize the retailer’s profits. Most retailers are not data analytics companies, and lack the infrastructure, IT expertise and knowhow to deploy this kind of testing internally. As such, for most retailers, it may be more efficient and economical to have this process performed by a third party.
• the price auditors 1820 may make the comparisons between the rollout plan for price testing, by store and day, against the actual data collected in the transaction logs.
• a series of adjusters 1830 may modify the data to reduce the impact of external variables, and normalize the data.
• a store and day adjuster 1833 may modify data by day and store. For example, in many places lift is much higher generally on weekend days as opposed to weekdays. The day adjuster may globally modify the t-log data to account for such day-to-day variations. Additionally, certain days tend to generate greater lift for particular goods or classes of goods. For example, eggs may sell at much higher rates before Easter, and grilled foods on Saturdays during the summer and especially before the 4 th of July.
• the day adjustments may code each day of the year numerically, and have an associated set of adjustments that apply to that day. By applying a separate set of adjustments that are tied to each day, the impacts of seasonality and the like are accounted for. Additionally, known events that occur on different days each year, such as Chanukah or the Chinese New Year, may likewise be accounted for and the adjustments for these events may be applied to the correct numerical day.
• the system may also consume external data feeds that may be correlated to sales volume shifts, and these may be used to adjust the t-log data accordingly.
• external information may include weather feeds. On very hot periods the sales of frozen confections may experience an unusual volume lift, and hot beverages like coffee may experience a depression of sales, for example.
• each store may cater to different customer segments, and this may influence the volumes of products sold. From t-log data, if it is seen that a particular store always sells more widgets than another store, the impact of price should be tempered by this innate lift advantage of the store.
• the t-log data may be normalized by store level attributes.
• category sales by store maybe a function of percent category sales of the store, average basket size of the store, total store transactions, etc.
• These performance store attributes can be directly applied to category sales as coefficient adjustments or by normalizing the sales by a modeled value dependent on these attributes via GLM or OLS methods.
• promotional adjustment methods may be employed by the promo adjuster 1835. These promotional adjustment methods may include, for example, regressive methods or relative pair-wise methods. Accounting for promotional activity within a category is important given how products interact relative to one another from a consumer’s buying preference.
• price elasticity measurement for non-promoted products are estimated by ensuring that promotional factors or variables are considered in, for example, a regression based model that looks to extract such elasticity coefficients while also accounting for promotional effects. Another approach looks to estimate these elasticity coefficients only when promotional activity on promoted line groups within a category is homogeneous across stores that have different test price points for non- promoted product line groups. Pair-wise comparisons of these particular types of stores will ensure that the cross-elastic promotional effect is experienced equally for the non-promoted tested product line groups.
• an increment calculator 1840 may undergo ongoing pricing calculations for the sales prices from the control price determined by degree of price change magnitude and statistical differentiation, as well as historically tested prices. For example, magnitude changes may be limited to a 10% change, and the system may have determined that there is no statistically measurable differentiation between prices that are less than three cents different from one another. If the control price is $1.99, the initial test prices may be $1.79 and $2.19 (within the 10% change limit). It may be determined that volume and margin results in larger profits at $1.79 versus the control price. Next iteration the test prices may be $1.65 and $1.89 due to the percent change limitation, and the fact that $1.99 has already been tested.
• the modeler 1850 consumes the adjusted t-log data and calculates elasticity between the estimations between the various products found within the retailer. In addition to the adjusted data, the modeler 1850 may also consume constraints from the rule engine 1870, which will be discussed in greater detail below. Elasticity calculations are known in the art, and any suitable techniques or calculations for elasticity may employed.
• the modeler may calculate an objective function.
• a general linear model may be constructed for estimating product self-elasticity and cross- product elasticities. Spurious elastic effects may be filtered out, and overfitting to errors may by avoided by reducing the number of individually estimated elasticities by simple aggregation techniques, by also adjusting the statistical level of significance for assessing statistical effects (e.g., Bonferroni adjustment, etc.) and finally by cross-validating models and their elasticity estimates through sampling techniques.
• the objective model may be built in a manner that is easily consumed by a variety of solvers.
• Output from the modeler 1850 may be utilized by the optimizer 1860 to solve the objective function, under the constraints from the constraint engine 1870 and elasticity estimations.
• the category objective function may be solved for a generalized maximization of the following function:
• e is a matrix of price elasticities
• T is the transposition of the elasticity matrix
• Ax p is a vector of product line group price changes (or deltas) within a given category where x is a price and p is a product line group number.
• the multidimensional representation of elasticity multiplied by price change will yield change in quantity (or sales). The general maximization of this function is subject to:
• A is a matrix of margin percentages constraining product line group prices, x p , to be above or equal to a cumulative vector margin, in set by the category manager and c is a vector of price constraints by which product line group prices must remain under.
• Price constraint definitions or rules maybe more complex than simple price thresholds but also encompass price relationships amongst other product line groups (i.e. Xi - 0.5x 2 ⁇ 0 or xi ⁇ 0.5X 2 ).
• Methods that may be employed in this general maximization may include linear programming solvers (Simplex and Interior Point), sequential least squares programming, gradient ascent for analytic solve, generalized linear model solvers (such as Gauss-Newton method) and generalized linear model with recommendations.
• linear programming solvers Simplex and Interior Point
• sequential least squares programming gradient ascent for analytic solve
• generalized linear model solvers such as Gauss-Newton method
• generalized linear model with recommendations may include linear programming solvers (Simplex and Interior Point), sequential least squares programming, gradient ascent for analytic solve, generalized linear model solvers (such as Gauss-Newton method) and generalized linear model with recommendations.
• the nearest neighbor of test price point may be selected using algorithmic methods, such as maximum objective value.
• algorithmic methods such as maximum objective value.
• the best of the three test price points, the optimal price, and a new test price within the price movement constraints are then recommended.
• These recommendations are used by the test designer 1880, again subject to the constraints from the constraint engine 1870, to generate a test design within the available physical retailer stores.
• the constraint engine 1870 may include rules associated with brands, pack sizes, maximum and minimum allowed prices, ending digit of the price, competitive gap between a price and another retailer, store execution rules, and store to store maximum price changes. This listing of rules is intended to be merely illustrative, and additional rules may be employed based upon retailer demands, or manufacture requirements. A rule conversion occurs to change these rules into a canonical set of constraints that is, as discussed previously, consumed by the modeler 1850 and test designer 1880.
• test designer 1880 employs algorithms for experimental designs for concurrent multiple price changes for multiple products under constraints. Below a series of examples are provided that will more fully explain the methods employed for test design. Generally, however, the test design will include randomized store allocation for price deployment, //-optimal designs via exchange algorithm, and Box-Behnken design. The results of any tests are then recorded in the transaction logs, which become part of the ever expanding data 1810 corpus.
• Figures 19A and 19B show, in accordance with some embodiments, flow diagrams illustrating the method for base pricing optimization.
• this example process 1900 is shown with the initial aggregation of transaction data by day and store (at 1910) as discussed previously. This may include aggregation of many years of historical pricing and transaction data, when available, and the collection of all future transactions that provide results of the price testing.
• the data may be validated (at 1920) for accuracy against the assigned price testing since, as discussed, retailers often are not good at deploying the prices as directed.
• the t-log data is then adjusted (at 1930).
• This adjustment process is shown in greater detail at Figure 19B, where corrupt data that has been identified by the price auditors is filtered out (at 1931).
• the prices may be adjusted by day (at 1933), by store (at 1935) and by any external factors as described previously in considerable detail.
• the transactions may be normalized (at 1937) and the promotions adjusted by regression method and relative pair-wise method (at 1939).
• test prices are incrementally calculated (at 1940) by solving for an objective function and using what known elasticity between products that is known, subject to constraints. These test prices are experimented (at 1950), and the results are collected. This allows for better elasticity models to be generated (at 1960). Again the optimization is solved for (at 1970) and this refined set of test prices may be tested (at 1970). This allows for a repetitive set of transaction data to be collected, verified, adjusted and used to update the elasticity models. Each testing iteration allows for prices to be tested that are closer to the optimal price point for each product. Once the optimal price has been identified, it may be deployed to the majority of retailers with minimal ongoing validation occurring (at 1990).
• Figure 20 shows an illustration of an example rollout of a base price optimization test, shown generally at 2000.
• the 66 stores are divided evenly into a three groups. Each group is assigned either a current (historical) price for each stock keeping unit (SKU) of butter (shown in light grey), a lower test price (shown in a medium grey), and a higher test price (shown in the darkest grey).
• SKU stock keeping unit
• the lower test price has been incremented ten cents lower than the current price
• the higher price is incremented ten cents above the current price.
• Which store group receives the lower, current or higher price may be randomized, as may which of the stores are placed into each group of stores.
• the prices are then rotated on a weekly basis between the groups of stores.
• Transaction data from each store is collected from this rollout enabling an elasticity matrix 2100 to be generated, as seen in Figure 21.
• each product is listed on the column and row header. The diagonal intersection is thus the self-elasticity of the product (light grey), and the cross elasticity between each given product will be found for each other portion of the matrix (darker grey).
• the degree of elasticity for each of these product pairs may be calculated.
• all products in the store may be included in this cross elasticity matrix, but due to the low degree of cross elasticity between entirely disparate items, this may not be desirable, particularly give the rather significant processing demands in calculating cross elasticities for such a large group of items. For example, the price of and given brand of butter likely has nearly no impact on the sales of cereal. Calculating a cross elasticity between these items would be basically valueless, but consumes considerable processing resources. As such, it may be desirable to calculate cross elasticities only between products in the same category, and some well-established associated products (such as gram crackers, large marshmallows and Hershey’s chocolate bars). Likewise, the costs of testing and large degree of data processing needed may make the analysis of all products within a product category unnecessary and undesirable.
• Rules and constraints may be applied in the setting of the prices, in these example the constraints may include that the final digit must be a“9” or a“4”, and there may be a maximum price restriction.
• the objectives may be set for the optimization.
• the objective for base pricing is the maximized profitability subject to constraints, but other objectives may include margin or volume growth goals.
• Figure 22 shows an illustration of a sales graph 2200 for the example rollout of the base price optimization test.
• This graph is an elasticity curve for the entire category of the tested items where the sales (darker grey) and margin (lighter grey) are plotted versus the category group prices.
• the maxima of these two metrics (margin and sales) are not in alignment, and one of the objectives needs to designated as a primary objective (here margin growth).
• a category goal is then determined based upon a weighted average of the maxima for the primary goal versus the secondary goal.
• the primary objective is being very heavily weighted, so the category goal is near the maxima for this curve.
• the category goal may exist anywhere between the two curve maximum values.
• the current pricing structure may also be plotted on the graph, and the difference between the current pricing architecture and the goal is the optimization opportunity for this category of products. These curves are dependent upon accurate elasticity measures, which relies upon thorough testing of prices.
• the process may begin honing in on an optimal price structure.
• the store groups are reshuffled into four store groupings.
• Figure 23 shows an illustration of an example refinement of the base price optimization test, shown generally at 2300.
• a control group of stores is defined which is smaller in size than the three test scores.
• Store assignment to any of these groups is done through randomization.
• the control group of stores is maintained at the original“control” price (lightest grey).
• the remaining stores are assigned what is estimated as being the optimal price (light-medium grey), a lower than optimal test price (dark-medium grey), and higher than optimal test price (dark grey).
• the optimal price estimate may be continually refined, and new lower and higher prices may be generated, all subject to the constraints. This results over time to a refinement of the elasticity curve, as seen at Figure 24 at plot 2400.
• the pricing structure also moves closer over time to the optimal category goal.
• Figure 25 shows an illustration of an example of the completed base price optimization test that has entered this validation, as seen at 2500.
• the optimal price there still is four categories of stores, but now nearly half the stores are assigned the optimal price (light-medium grey).
• the remaining stores are then split nearly equally between the control price stores (light grey), and two test store groups that receive either a lower than optimal test price (dark-medium grey) or higher than medium price (dark grey).
• the system may operate in this mode in perpetuity, or upon reaching some second, higher level of confidence that the optimal price is correct switch again to the deployment of the optimal price to more, or even all, the retailers. In such cases, the system may periodically reenter a testing phase to ensure the optimal price has not migrated over time.
• Figures 26A and 26B illustrate a Computer System 2600, which is suitable for implementing embodiments of the present invention.
• Figure 26A shows one possible physical form of the Computer System 2600.
• the Computer System 2600 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 2600 may include a Monitor 2602, a Display 2604, a Housing 2606, a Disk Drive 2608, a Keyboard 2610, and a Mouse 2612.
• Disk 2614 is a computer-readable medium used to transfer data to and from Computer System 2600.
• Figure 26B is an example of a block diagram for Computer System 2600. Attached to System Bus 2620 are a wide variety of subsystems.
• Processor(s) 2622 also referred to as central processing units, or CPUs
• Memory 2624 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 2626 may also be coupled bi directionally to the Processor 2622; it provides additional data storage capacity and may also include any of the computer-readable media described below.
• Fixed Disk 2626 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 2626 may, in appropriate cases, be incorporated in standard fashion as virtual memory in Memory 2624.
• Removable Disk 2614 may take the form of any of the computer-readable media described below.
• Processor 2622 is also coupled to a variety of input/output devices, such as Display 2604, Keyboard 2610, Mouse 2612 and Speakers 2630.
• 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 2622 optionally may be coupled to another computer or telecommunications network using Network Interface 2640.
• the Processor 2622 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 2622 or may execute over a network such as the Internet 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 2600 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.
• 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 management systems.
• Windows® is the family of operating systems known as Windows® from Microsoft Corporation of Redmond, Washington, and their associated file management systems.
• Windows® from Microsoft Corporation of Redmond, Washington
• Windows® Windows® from Microsoft Corporation of Redmond, Washington
• Linux operating system is the Linux operating system and its associated file management system.
• 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.
• 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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