arXiv research

Research analyzes Georgia's tax system and suggests improvements.

Study on tax administration issues and their impact on Georgia's budget revenues.

'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 …

Study estimates long-term effects of online advertising mechanisms on user behavior and revenue.

Uplift models support decision-making in marketing campaign planning. Estimating the causal effect of a marketing treatment, an uplift model facilitates targeting communication to responsive customers and efficient allocation of marketing budgets. Research into uplift models focuses on conversion models to maximize inc…

This paper optimizes multi-channel sequential advertising to maximize cumulative revenue.

Online advertising in E-commerce platforms provides sellers an opportunity to achieve potential audiences with different target goals. Ad serving systems (like display and search advertising systems) that assign ads to pages should satisfy objectives such as plenty of audience for branding advertisers, clicks or conver…

Georgia needs a new budget code to manage fiscal policies effectively.

This paper proposes a new AED framework for multi-metric experiments with fixed budget.

Proposes a greedy algorithm for telecom offers to retain subscribers.

This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributio…

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. …

Study shows local governments smooth fiscal shocks from property tax revenues.

This paper optimizes revenue and resource balance in network revenue management.

Paper forecasts tax revenues in Bulgaria during pandemic.

A scalable model estimates revenue uncertainty for SMEs.

Germany's tax admin costs likely exceed 20% of total revenue, requiring system improvement.

Study finds tax avoidance and IT issues hinder revenue in Gombe state.

The paper proposes a new method for product recommendation that considers revenue contributions and user similarity.

Model for dynamic pricing across multiple RE groups to maximize revenue.

Improved algorithms solve multi-period multi-class packing problems with bandit feedback.

A blockchain protocol uses bandit algorithms to dynamically price transactions.

This study improves valuation of post-revenue biopharmaceutical assets using Pfizer's data.

Proposes balancing revenue and environmental impact in assortment planning.

The study uses historical revenue data to forecast music catalog cashflows and multipliers.

The paper explores fairness, welfare, and equity in personalized pricing across various applications.

Optimizes web publisher revenues from RTB auctions.

For any business, planning is a continuous process, and typically business-owners focus on making both long-term planning aligned with a particular strategy as well as short-term planning that accommodates the dynamic market situations. An ability to perform an accurate financial forecast is crucial for effective plann…

Proposes a revenue function to evaluate dendrograms from comparisons.

Study learns optimal auctions from corrupted or perturbed bidder valuation samples.

This paper improves bidding price prediction for ancillary services markets, boosting revenues.

In markets for online advertising, some advertisers pay only when users respond to ads. So publishers estimate ad response rates and multiply by advertiser bids to estimate expected revenue for showing ads. Since these estimates may be inaccurate, the publisher risks not selecting the ad for each ad call that would max…

This work studies learning curves for revenue maximization algorithms.

Online advertisement is the main source of revenue for Internet business. Advertisers are typically ranked according to a score that takes into account their bids and potential click-through rates(eCTR). Generally, the likelihood that a user clicks on an ad is often modeled by optimizing for the click through rates rat…

A new RL approach optimizes reserve prices in multi-phase auctions, reducing revenue regret.

In order for an e-commerce platform to maximize its revenue, it must recommend customers items they are most likely to purchase. However, the company often has business constraints on these items, such as the number of each item in stock. In this work, our goal is to recommend items to users as they arrive on a webpage…

Paper shows re-solving heuristics have constant regret for price-based revenue management.

Simplifies complex pricing models for better interpretability and revenue.

DeepCausalMMM models marketing impacts using deep learning and causal inference.

The paper tackles revenue management with time-varying demand using posterior sampling.

New method for probabilistic modeling of integer submodular functions.

OTT services are replacing traditional telecom services, affecting revenue streams.

Contextual bandit framework improves revenue optimization in securities lending market.

A new RL method improves revenue management with delayed feedback.

In recent years, RTB(Real Time Bidding) becomes a popular online advertisement trading method. During the auction, each DSP(Demand Side Platform) is supposed to evaluate current opportunity and respond with an ad and corresponding bid price. It's essential for DSP to find an optimal ad selection and bid price determina…

Triangle fees adjust fees based on trade size and price movement, improving price accuracy and revenue.

Proposes a variational autoencoder for long-term customer revenue forecasting.

A new mechanism optimizes data marketplace pricing efficiently.