arXiv research

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

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

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

This paper examines and proposes several attribution modeling methods that quantify how revenue should be attributed to online advertising inputs. We adopt and further develop relative importance method, which is based on regression models that have been extensively studied and utilized to investigate the relationship …

Study optimizes crowdfunding platform offerings based on customer behavior.

Study examines revenue from scam tokens on Ethereum, revealing key characteristics and market factors.

Paper uses DRL for dynamic pricing on e-commerce platforms.

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.

Improved algorithm reduces regret in NRM with unknown demand.

Paper uses 2-step Gradient Boosting to predict VAT tax gap.

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…

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.

Proposes robust assortment optimization from observational data.

Research analyzes Georgia's tax system and suggests improvements.

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

'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 …

Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc for matching restaurants to customers. In these online to offline service problems, individuals who are respo…

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

A new model predicts fashion demand 6-12 months ahead, boosting retailer profits.

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

Proposes balancing revenue and environmental impact in assortment planning.

Online advertising is an important and huge industry. Having knowledge of the website attributes can contribute greatly to business strategies for ad-targeting, content display, inventory purchase or revenue prediction. Classical inferences on users and sites impose challenge, because the data is voluminous, sparse, hi…

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

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.

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…

Sponsored search in E-commerce platforms such as Amazon, Taobao and Tmall provides sellers an effective way to reach potential buyers with most relevant purpose. In this paper, we study the auction mechanism optimization problem in sponsored search on Alibaba's mobile E-commerce platform. Besides generating revenue, we…

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

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

Simplifies complex pricing models for better interpretability and revenue.

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

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.

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

We have created a framework for analyzing subscription based businesses in terms of a unified metric which we call SCV (single customer value). The major advance in this paper is to model customer churn as an exponential decay variable, which directly follows from experimental data relating to subscription based busine…

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

A new mechanism optimizes data marketplace pricing efficiently.

Study examines how governance, corruption, and R&D affect economic development.