Eversight, Inc.

David Moran

Michael Montero

Jamie Rapperport

Jamie Eldredge

Daniel Klatzko Gibson

Jacob Solotaroff

John Brockardt

Lynn Murphy

Mark Wilson

Methods and apparatus for generating intelligent offers with base prices are provided. In one embodiment, a promotion generator receives a current product base price, and also receives or calculates a remaining promotional program budget, a remaining promotional program duration, and a minimum discounted price for the product using the current product base price and any available previous base price data for the promoted product, creating or updating a predictive model of future product base prices.

Architecture and methods for generating intelligent offers with dynamic base prices

Systems and methods for the efficient generation and testing of promotions within a load to card environment are provided. A load-to-card abstraction layer collects store, user and offer data. The test promotions are then generated to span a design space of an offer. The user base is segmented and the test promotions are applied. The promotions include an offer, and the ability to load the offer for later redemption (load-to-card). Redemption and load rates are measured, and can be used individually, or in combination, to gauge consumer engagement with the promotion. Promotions with low consumer engagement may be discontinued, until only optimally performing promotions are remaining.

Systems and methods for efficient promotion experimentation for load to card

Methods and apparatus for conducting test promotions in a highly scalable and cost-effective manner using controlled experiment methodology are disclosed. Test promotions of interest are presented to visitors of a website in a controlled experiment manner wherein each page presents one test promotion of interest among other promotions. The other promotions presented in the same page are kept constant in the pages. The visitors' responses with respect to the test promotions of interest are then recorded and analyzed to determine the performance of each presented test promotion of interest.

Highly scalable internet-based controlled experiment methods and apparatus for obtaining insights from test promotion results

Systems and methods for optimizing base pricing of products within a physical retailer are provided. Such systems and methods include 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.

Systems and methods for price testing and optimization in brick and mortar retailers

Systems and methods for automated profile to promotion matching are provided. A plurality of segment variable value pairs that define a plurality of possible segments for a population of consumers are first identified, as are a plurality of promotion variable value pairs of a promotional design space. The variable value pairs are assembled into a plurality of test promotions by different permutations. The test promotions are administered, and results are collected. Trends between specific segment variable value pairs and promotion variable value pairs that result in statistically relevant shifts in the obtained responses are identified and used to define segments. The defined segments are then matched to promotion types (collected from a promotion repository) based upon likelihood of a positive result. The matches are then classified by a behavioral economic classification, which then may be outputted to a marketer to provide insights into consumer motivations.

Systems and methods for automated promotion to profile matching

Systems and methods for the generation of a promotional design matrix with recommendation overlay are provided. A product, discount, quantity and offer structure selection are received from a user, or are imputed based upon previous offers. Variants for the discount vale and quantity are generated by incrementing these values. The quantity and its variants are each assigned to a column of a design matrix, while the discount and variants are assigned to the design matrix rows. At each intersection of a row and column of the design matrix a plurality of offer structures based upon the offer structure selection is generated. The system applies recommendation logic to flag offers that are most likely to be successful based upon their performance, feasibility and diversity. This flagging may include placing an icon, such as a star, next to the recommended offers.

Systems and methods for generating and recommending promotions in a design matrix

Methods and apparatus for implementing automated online promotion testing in an efficient and platform-agnostic manner are disclosed. A test management module interacts with a concept generator module, a promotion analytics module, and agnostically interacts with promotion administering platforms to automatically generate, administer, and analyze a large number of test promotions to online consumers in a manner that minimizes labor-intensive changes to the promotion administering platforms.

Automatic mass scale online promotion testing

Methods and apparatus for conducting test promotions in a highly scalable and cost-effective manner using parallel experiment methodology are disclosed. Test promotions are presented to visitors of a website in a parallel experiment manner wherein each page presents one test promotion to isolate the test promotions from one another. The visitors' responses with respect to the test promotion are then recorded and analyzed to determine the performance of the test promotion.

Highly scalable internet-based parallel experiment methods and apparatus for obtaining insights from test promotion results

Methods and apparatus for implementing forward looking optimizing promotions by administering, in large numbers and iteratively, test promotions formulated using highly granular test variables on purposefully segmented subpopulations. The plurality of test promotions are automatically proposed. The responses from individuals in the subpopulations are received and analyzed. The analysis result is employed to subsequently formulate a general public promotion.

Automated promotion forecasting and methods therefor

Methods and apparatus for implementing forward looking optimizing promotions by administering, in large numbers and iteratively, test promotions automatically formulated using highly granular test variables on subpopulations. The responses from individuals in the subpopulations are received and analyzed. The analysis result is employed to subsequently formulate a general public promotion.

Adaptive experimentation and optimization in automated promotional testing

Methods and apparatus for implementing forward looking optimizing promotions by administering, in large numbers and iteratively, test promotions formulated using highly granular test variables on segmented subpopulations, whereby the test promotions are automatically generated. The responses from individuals in the subpopulations are received and analyzed. The analysis result is employed to subsequently formulate a general public promotion.

Automatic offer generation using concept generator apparatus and methods therefor

Methods and apparatus for conducting test promotions in a highly scalable and cost-effective manner using randomized experiment methodology are disclosed. Test promotions of interest are presented to visitors of a website in a randomized experiment manner wherein each page presents one test promotion of interest among other promotions. The other promotions presented in the same page may be randomized. The visitors' responses with respect to the test promotions of interest are then recorded and analyzed to determine the performance of each presented test promotion of interest.

Highly scalable internet-based randomized experiment methods and apparatus for obtaining insights from test promotion results

Methods and apparatus for implementing forward looking optimizing promotions by administering, in large numbers and iteratively, test promotions formulated using highly granular test variables on purposefully segmented subpopulations. The plurality of test promotions are associated with at least one behavioral economics principles. The responses from individuals in the subpopulations are received and analyzed. The analysis result is employed to subsequently formulate a general public promotion.

Automated behavioral economics patterns in promotion testing and methods therefor

Methods and apparatus for implementing forward looking optimizing promotions by administering, in large numbers and iteratively, test promotions formulated using highly granular test variables on purposefully segmented subpopulations. The responses from individuals in the subpopulations are received and analyzed. The analysis result is employed to subsequently formulate a general public promotion. The test promotions may be repeated continually and iteratively with different test promotions and/or different subpopulations to continue to uncover advantageous correlations between segmentation criteria, test promotion variables, and consumer response and/or to keep up with consumer changing taste.

Architecture and methods for promotion optimization

Methods and apparatus for implementing forward looking optimizing promotions by administering, in large numbers and iteratively, test promotions formulated using highly granular test variables and automatically incorporating constraints on segmented subpopulations. The responses from individuals in the subpopulations are received and analyzed. The analysis result is employed to subsequently formulate a general public promotion.

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Palo Alto, CA, United States of America

David Moran