Eversight, Inc.

Maplebear Inc.

Michael Montero

David Moran

Jamie Rapperport

Tony Ventrice

Jamie Eldredge

Daniel Klatzko Gibson

Jacob Solotaroff

Brian Glover

John Brockardt

Lynn Murphy

Systems and methods for scoring promotions are provided. A set of training offers are received, which include combinations of variable values. These combinations of variable values are converted into a vector value. The offers are paired and the vectors subtracted from one another, resulting in a pair vector. Metrics for the success of offers is collected, and are subtracted from one another for the paired offers to generate a raw score. This raw score is then normalized using the pair vector. The normalized scores are utilized to generate a model for the impact any variable value has on offer success, which may then be applied, using linear regression, to new offers to generate an expected level of success. The new scored offers are ranked and the top-ranked offers are selected for inclusion in a promotional campaign.

Systems and methods for intelligent promotion design with promotion scoring

Systems and methods for a collaborative offer portal is provided. A proposed offer is received from a manufacturer, including an offer structure and a number of consumers they wish to target. Transaction logs of a retailer are accessed to determine an audience for the offer by calculating a return on investment (ROI) for the customer base using the retailer's records given the offer type. The consumers are then grouped by their ROI distribution, and the ROI for the deal is calculated based upon the offer size in light of this distribution. From the offer ROI a discount percentage to be paid by the retailer versus the merchant can be created. The retailer may then choose to accept the offer for deployment.

Systems and methods for collaborative offer generation

Systems and methods for the selection of the promotions are provided. A set of possible offers are initially received, each including a set of variables, with each variable having a set of possible values. These form a combination of variable values for each offer. A heuristic is applied to all possible offers to reduce the number of offers being considered. The combination of variable values for these reduced number offers is converted into a vector value, which is then scored, ranked and the top ranked offers are selected for inclusion in a promotional campaign. The remaining offers are then analyzed to select additional offers to include into the promotional campaign which maximizing a determinant for the selected offers using their vectors. All selected offers are administer in a promotional test campaign across many consumer segments. Feedback from the campaign may be collected to generate a 'general' offer.

Systems and methods for intelligent promotion design with promotion selection

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

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 offer redemption are provided. In some embodiments, payment data for a product is received via a payment gateway, along with an identifier unique to the payer. This payer identifier is correlated to a database associated with household IDs that have been collected from loyalty programs, pharmacy data and payment collected at a retailer. When the identifier has been associated with a household ID the system may identify applicable promotions that are associated with that household. The details of the offer/promotion are sent back to the point of sale where they are applied to the transaction. Once this is confirmed the adjusted payment is processed and the promotion is cleared from the database.

Systems and methods for democratized coupon redemption

Methods and apparatus for improving the result of online promotion testing are disclosed. In one or more embodiments, base-lining is employed to improve the mapping between the online test promotion responses and projected in-store result. Alternatively or additionally, the test population responses may be adjusted to account for differences between the test population and the in store customer population.

Linkage to reduce errors in online promotion testing

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.

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Palo Alto, CA, United States of America

Michael Montero