The paper introduces revenue uplift modeling to maximize marketing profits.
problem Maximizing incremental sales versus maximizing incremental revenue in marketing campaigns.
method Response transformation and two-stage models to decompose campaign profit.
result Revenue uplift modeling can improve campaign profit substantially.
A new model predicts fashion demand 6-12 months ahead, boosting retailer profits.
problem Accurate demand forecasting for fashion retailers with short product life cycles.
method Product age-based forecast model, incorporating unique feature engineering.
result Significant revenue uplift of 41% compared to retailer's plan.
Dynamic promotion optimization for e-commerce platforms within financial constraints.
problem Balancing promotional costs with incremental revenue for sustainable growth.
method Knapsack Problem formulation for dynamic optimization, Retrospective Estimation, online-dynamic calibration.
result Significant increase in target outcome while staying within financial constraints.
A Qini-based uplift model improves retention marketing campaign performance.
problem Isolating the marketing effect of a campaign and identifying responsive customers.
method Qini-based uplift regression model using logistic regression.
result Qini-optimized uplift models improve performance and provide interpretable models.
Proposes a resampling method to compare uplift models with uncertainty.
problem Uncertainty in estimating uplift curves when full population data is unavailable.
method Two-step sampling procedure and resampling-based approach.
result Validates the proposed method through simulations and real data applications.
A new method for uplift modeling using learning-to-rank techniques.
problem Improving customer targeting in marketing and retention.
method Unified formalization of uplift measures, learning-to-rank with PCG metric, LambdaMART optimization.
result Improved results compared to standard learning-to-rank metrics and state-of-the-art uplift modeling.
Uplift modeling is an area of machine learning which aims at predicting the causal effect of some action on a given individual. The action may be a medical procedure, marketing campaign, or any other circumstance controlled by the experimenter. Building an uplift model requires two training sets: the treatment group, w…
Graph neural networks integrate causal knowledge for more accurate uplift modeling.
problem Identifying the most effective treatments and clients for marketing interventions.
method Combining graph neural networks with causal knowledge to estimate uplift values.
result The proposed method outperforms traditional approaches in predicting uplift values with minimal errors.
Our paper improves uplift model evaluation on randomized controlled trials (RCT) data.
problem Variance in uplift evaluation metrics makes their signals arbitrary and unreliable.
method Theoretical analysis and statistical adjustment of the outcome to reduce variance.
result Variance reduction methods improve uplift evaluation metrics on RCT data.
New neural model improves uplift modeling accuracy.
problem Improving accuracy in uplift modeling for specific treatments.
method Developed a twin neural network architecture with a new loss function.
result Proposed model outperforms existing methods in simulations and real data.
New neural network method improves uplift modeling accuracy.
problem Overfitting in uplift modeling.
method Jointly optimizing difference in conditional means and transformed outcome losses using neural networks.
result Improves state-of-the-art uplift modeling on synthetic and real data.
New feature selection methods improve uplift modeling accuracy.
problem Overfitting and poor interpretability in feature selection for uplift models.
method Explicitly designed feature selection methods inspired by statistics and information theory.
result Proposed methods outperform traditional feature selection methods in uplift modeling.
Supervised randomization makes randomized experiments more cost-effective for uplift modeling.
problem Costly randomized experiments for uplift modeling.
method Integrates existing scoring models into randomized trials to target relevant customers while correcting for selection bias.
result Cost-efficient data collection under supervised randomization with competitive uplift model performance.
This paper enhances uplift modeling for multi-treatment marketing campaigns.
problem Optimizing marketing strategies by selecting individuals likely to respond to different treatments.
method Leveraging score ranking and calibration techniques.
result Improves overall performance of marketing campaigns.
A framework for causal classification using uplift and causal heterogeneity methods.
problem Predicting the effect of interventions on different individuals from data.
method Causal classification framework using off-the-shelf supervised methods.
result Framework works for causal classification and uplift modelling, competitive with other methods.
Heteroskedasticity biases uplift model rankings, leading to inefficient treatment allocation.
problem Bias in uplift model rankings due to heteroskedasticity.
method Theoretical analysis and simulation on real-world data.
result Heteroskedasticity can cause individuals with high treatment effects to be ranked at the bottom, leading to inefficient treatment allocation.
Uplift modeling is an emerging machine learning approach for estimating the treatment effect at an individual or subgroup level. It can be used for optimizing the performance of interventions such as marketing campaigns and product designs. Uplift modeling can be used to estimate which users are likely to benefit from …
Before A/B testing online a new version of a recommender system, it is usual to perform some offline evaluations on historical data. We focus on evaluation methods that compute an estimator of the potential uplift in revenue that could generate this new technology. It helps to iterate faster and to avoid losing money b…
UCB algorithms estimate uplifts in multi-variable reward systems.
problem Marketing campaigns and recommender systems with multiple outcome variables.
method UCB-style algorithms for estimating uplifts over a baseline.
result Sublinear regret bounds for multiple problem variants.
Uplift modeling is aimed at estimating the incremental impact of an action on an individual's behavior, which is useful in various application domains such as targeted marketing (advertisement campaigns) and personalized medicine (medical treatments). Conventional methods of uplift modeling require every instance to be…
Randomized experiments have been critical tools of decision making for decades. However, subjects can show significant heterogeneity in response to treatments in many important applications. Therefore it is not enough to simply know which treatment is optimal for the entire population. What we need is a model that corr…
FairUDT uses uplift decision trees to detect and mitigate discrimination in training data.
problem Bias in machine learning classifiers due to historical discrimination or underrepresentation of minority groups.
method Integrates uplift modeling with decision trees and introduces a modified leaf relabeling approach for fairness.
result Achieves an acceptable accuracy-discrimination tradeoff while maintaining interpretability.
A new uplift modeling approach uses binary treatment indicators more efficiently.
problem Lack of full value utilization in binary uplift modeling.
method Design a novel transformed outcome for binary target variables.
result Our new approach outperforms traditional methods in synthetic and real-world datasets.
A new method flips class values to address class and treatment imbalance in uplift modeling and HTE.
problem Class and treatment imbalance in imbalanced RCT data.
method Class flipping approach to address imbalance without distorting predictions.
result The method does not distort predicted effects and does not require calibration.
Uplift modeling aims to directly model the incremental impact of a treatment on an individual response. In this work, we address the problem from a new angle and reformulate it as a Markov Decision Process (MDP). We conducted extensive experiments on both a synthetic dataset and real-world scenarios, and showed that ou…
Proposes a generalized causal tree for handling multiple treatments in uplift modeling.
problem Handling multiple treatments in uplift modeling.
method Generalizes causal tree algorithm to handle multiple discrete and continuous-valued treatments.
result Demonstrates improved performance over existing methods in experiments and real data examples.
Unified survey of treatment effect heterogeneity and uplift modeling methods.
problem Estimating heterogeneous treatment effects and uplift modeling.
method Unified survey of treatment effect heterogeneity and uplift modeling approaches.
result Unified notations for comparing methods and applications in personalized marketing, medicine, and social studies.
Bridges uplift modeling and sequential decision-making with online budget allocation.
problem Treatment allocation under budget constraints in digital advertising.
method Budget-Constrained Causal Bandits (BCCB) integrates learning, exploration, and budget pacing.
result Data-efficiency crossover: BCCB operates effectively from the first user, 3-5x lower performance variance.
We analyze annual revenues and earnings data for the 500 largest-revenue U.S. companies during the period 1954-2007. We find that mean year profits are proportional to mean year revenues, exception made for few anomalous years, from which we postulate a linear relation between company expected mean profit and revenue. …
A new method optimizes individual treatment effects in dynamic settings.
problem Optimizing individual treatment effects in changing environments.
method Uplifted contextual multi-armed bandit (U-CMAB) approach.
result U-CMAB outperforms state-of-the-art methods in dynamic environments.
The paper compares methods for estimating individual treatment effects.
problem Estimating the optimal treatment effect for each individual.
method Comparison of machine learning methods for individual treatment effect estimation.
result Combination of Logistic Regression and Difference Score method, as well as Uplift Random Forest method, provides the best prediction accuracy.
Study shows local governments smooth fiscal shocks from property tax revenues.
problem Impact of revenue shocks on local fiscal policy.
method Causal machine learning strategies and post-double-selection LASSO estimator.
result Local policymakers predominantly smooth fiscal shocks, but react differently to positive and negative shocks.
Large dataset released for ITE and UM research.
problem Estimating causal impact of actions in various sectors.
method Release of a large dataset, formalization of UM, synthetic response surfaces, heterogeneous treatment assignment.
result Validation of ITE prediction and UM methods with high statistical significance.
This paper optimizes revenue and resource balance in network revenue management.
problem Maximizing revenue while ensuring fair resource consumption across different suppliers.
method Introduces a regularized revenue objective and a primal-dual UCB algorithm for continuous prices and balancing.
result Achieves a worst-case regret of O ~ ( N 5 / 2 T ) \widetilde O(N^{5/2}\sqrt{T}) O ( N 5/2 T ) for revenue maximization and balancing. Ethereum's Pectra upgrade introduces 0x02 compounding validators, offering higher stake and potential APR uplifts.
problem Staking rewards on Ethereum
method Analyzing compounding effects on consensus-layer rewards
result 0x02 validators achieve modestly higher median CL APR
Paper forecasts tax revenues in Bulgaria during pandemic.
problem Forecasting tax revenues during pandemic.
method Model based on IMF recommendations, using 1995-2019 data.
result Pandemic negatively impacts tax revenues, but econometrics can still produce forecasts.
A scalable model estimates revenue uncertainty for SMEs.
problem Estimating revenue uncertainty for SMEs to manage credit limits.
method Scalable Natural Gradient Boosting Machines.
result The method distinguishes accurate from inaccurate revenue forecasts.
GCF estimates heterogeneous treatment effects for continuous treatments in online marketplaces.
problem Estimating heterogeneous treatment effects for continuous treatments in online marketplaces.
method Kernel-based doubly robust estimator and distance-based splitting criterion.
result GCF estimates heterogeneous treatment effects for continuous treatments effectively.
Germany's tax admin costs likely exceed 20% of total revenue, requiring system improvement.
problem High tax administrative costs in Germany and other jurisdictions.
method Statistical data, surveys, and a novel approach to measure total administrative cost as a percentage of total tax revenue.
result Germany's 2021 tax administrative costs likely exceeded 20% of total tax revenue.
Study finds tax avoidance and IT issues hinder revenue in Gombe state.
problem Problems of personal income tax on revenue generation in Gombe state.
method Survey with primary and secondary data, chi square test.
result Tax avoidance and IT issues are major problems.
The paper proposes a new method for product recommendation that considers revenue contributions and user similarity.
problem High dimensionality and sparsity in user-item data, especially in terms of revenue contributions.
method The approach encodes revenue contributions in the user-item matrix and computes customer similarity using suitable distance measures.
result The method segments users based on revenue-based similarity and supports recommendations aligned with profitability objectives.
Research analyzes Georgia's tax system and suggests improvements.
problem Improving tax revenues in Georgia's state budget.
method Analysis of past and present tax systems, review of influencing factors, comparison of foreign models.
result Stimulating measures can increase tax revenues for Georgia's state budget.
This work explores the idea of a causal contextual multi-armed bandit approach to automated marketing, where we estimate and optimize the causal (incremental) effects. Focusing on causal effect leads to better return on investment (ROI) by targeting only the persuadable customers who wouldn't have taken the action orga…
Model for dynamic pricing across multiple RE groups to maximize revenue.
problem Maximizing revenue from multiple RE pricing groups.
method Mathematical model incorporating multiple pricing groups, revenue goals, and time value of money.
result Algorithm for constructing a pricing policy for multiple RE groups.
'There is no terror in the bang, only is the anticipation of it' - Alfred Hitchcock. Yet there is everything in correctly anticipating the bang a movie would make in the box-office. Movies make a high profile, billion dollar industry and prediction of movie revenue can be very lucrative. Predicted revenues can be used …
Paper introduces RTT2Vec for real-time grocery recommendations, achieving 9.4% uplift over baselines.
problem Personalized grocery recommendations to improve user experience and sales.
method RTT2Vec deep architecture for real-time recommendations, approximate inference technique.
result 9.4% uplift in prediction metrics over baseline models.
Study on tax administration issues and their impact on Georgia's budget revenues.
problem Problems in revenue administration and tax rates in Georgia.
method Analyzed foreign experience and proposed a progressive tax system.
result A progressive tax system would benefit Georgia's business and economy.
This paper optimizes multi-channel sequential advertising to maximize cumulative revenue.
problem Maximizing cumulative revenue in multi-channel sequential advertising under a budget constraint.
method Formulated as a dynamic knapsack problem, proposed a bilevel optimization framework with action space reduction.
result Significantly improved cumulative revenue compared to state-of-the-art baselines.