Nice Ltd

Nice Systems Technologies UK Limited

Loopme, Ltd.

Nice Technologies UK Limited

Leonard Michael Newnham

Natalia Beatriz Piaggio

Jason Derek McFall

David Silver

David J Barker

Lampros Stavrogiannis

Piyush Paliwal

Oleksii Renov

Tomer Ram

Marco Van De Bergh

Stephen Upstone

Facundo Bellosi

A hybrid, optimized exchange is provided, the hybrid, optimized exchange operably connected to a demand-side platform (DSP), the hybrid, optimized exchange further operably connected to a supply-side platform (SSP), the hybrid, optimized exchange configured to receive an advertising request, the hybrid, optimized exchange further configured to conduct, using a bid floor, a hybrid, optimized DSP auction of the advertising request among a plurality of DSPs, thereby generating a winning DSP that makes a winning DSP bid in an automated advertising auction system after the SSP initiates an SSP auction of the advertising request, the hybrid, optimized exchange optimizing the bid floor provided to the plurality of DSPs in the hybrid, optimized DSP auction and simultaneously optimizing a shading factor used by the hybrid, optimized exchange to place a hybrid, optimized bid on behalf of the winning DSP in the SSP auction.

Automated hybrid, optimized advertising auction system and method

The demand-side platform (DSP) is a technological ingredient that fits into the larger real-time-bidding (RTB) ecosystem. DSPs enable advertisers to purchase ad impressions from a wide range of ad slots, generally via a second-price auction mechanism. In this aspect, predicting the auction winning price notably enhances the decision for placing the right bid value to win the auction and helps with the advertiser's campaign planning and traffic reallocation between campaigns. This is a difficult task because the observed winning price distribution is biased due to censorship; the DSP only observes the win price in the case of winning the auction. For losing bids, the win price remains censored. In this invention, we generalize the winning price model to incorporate a gradient boosting framework adapted to learn from both observed and censored data. This yields a boost in predictive performance in comparison to classic linear censored regression.

Real-time bidding

Systems and methods of managing customer journeys are implemented using one or more processors in a computing system. Each journey may comprise a succession of interactions at interaction points such as telephone conversations, responses to an interactive voice response 'IVR' system and viewing a web page. Customer journey scores are determined for customers at one or more interaction points along the customer journey and the customer journey score is used to determine whether and when an intervention should take place. Models for determining customer journey scores may be created for a set of customers based on one or both of subjective and objective data relating to a subset of the set of customers that have made some or part of the journey, e.g. customers that have responded to polls. An intervention may take place during the journey or after completion of the journey.

Customer journey management

A system for collecting brand awareness and advertising campaign performance results in real-time. Embodiments allow the system to adapt (e.g., machine learning) to target advertisements to users that are most likely to be influenced by exposure to a brand awareness advertising campaign, and present results, in real-time, via a data exchange for an advertiser to monitor performance and benchmark performance against similar campaigns across the industry.

System and method for real-time optimization and industry benchmarking for campaign management

Methods and systems for calculating a current response rate to an offer are disclosed. Examples of offers include but are not limited to design options for a web page and scripts used by operators in a call center. An offer may be served multiple times to one or more users and the current response rate may be determined for a point in time. The method may comprise obtaining estimates of response rate in successive iterations of response rate estimation. Each estimate may be determined over a period of time ending at the point in time, and each estimate may be determined over a larger period of time than the previous estimate. A range of uncertainty may be defined by upper and lower bounds for the estimates of response rate, with at least the first estimate being within the range of uncertainty. Each estimate may be compared to one or both of the upper and lower bounds and the result of the comparison may be used in the selection of one of the estimates as the calculated current response rate.

Systems and methods for response rate determination and offer selection

Methods and systems predict a reward resulting from serving one of a set of offers to one or more potential respondents, for example via a webpage, using one or more processors to calculate a predicted reward according to a model. The model may include one or more variables characterizing the respondent, a set of offer-specific coefficients comprising at least one coefficient for each of said one or more variables for each offer, and a set of common coefficients comprising a separate coefficient for each of said one or more variables, each coefficient of said set of common coefficients being common to all of said offers. The model may be trained in a two stage update process including a first update operation updating at least one common coefficient and a second update operation updating at least one offer-specific coefficient.

Growing community of inventors

Calvert, United Kingdom

Leonard Michael Newnham