arXiv research

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

Ancillaries have become a major source of revenue and profitability in the travel industry. Yet, conventional pricing strategies are based on business rules that are poorly optimized and do not respond to changing market conditions. This paper describes the dynamic pricing model developed by Deepair solutions, an AI te…

Analyzes retail trends from sales, search, and reviews.

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

Paper forecasts tax revenues in Bulgaria during pandemic.

Study optimizes crowdfunding platform offerings based on customer behavior.

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…

Simplifies complex pricing models for better interpretability and revenue.

Develops a Bayesian model to predict business revenue and demand.

A scalable model estimates revenue uncertainty for SMEs.

New loss functions optimize pricing policies using transaction data, ensuring expected revenue guarantees.

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…

New model optimizes assortment and pricing with dynamic customer arrivals.

New findings challenge the importance of forecast accuracy in battery storage optimization, highlighting the role of rank correlation instead.

Unfair pricing policies have been shown to be one of the most negative perceptions customers can have concerning pricing, and may result in long-term losses for a company. Despite the fact that dynamic pricing models help companies maximize revenue, fairness and equality should be taken into account in order to avoid u…

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

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

We study the dynamic assortment planning problem, where for each arriving customer, the seller offers an assortment of substitutable products and customer makes the purchase among offered products according to an uncapacitated multinomial logit (MNL) model. Since all the utility parameters of MNL are unknown, the selle…

Improved product recommendations using deep learning.

We consider the problem of multi-product dynamic pricing, in a contextual setting, for a seller of differentiated products. In this environment, the customers arrive over time and products are described by high-dimensional feature vectors. Each customer chooses a product according to the widely used Multinomial Logit (…

Ebay uses forecasting and simulation to decide when to disable a vendor.

Proposes robust assortment optimization from observational data.

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

Personalized pricing analytics is becoming an essential tool in retailing. Upon observing the personalized information of each arriving customer, the firm needs to set a price accordingly based on the covariates such as income, education background, past purchasing history to extract more revenue. For new entrants of t…

A scalable system detects price anomalies in online marketplaces to improve customer experience.

Custom loss functions improve accuracy of wildfire rate of spread forecasts.

Auto dealerships receive thousands of calls daily from customers who are interested in sales, service, vendors and jobseekers. With so many calls, it is very important for auto dealers to understand the intent of these calls to provide positive customer experiences that ensure customer satisfaction, deep customer engag…

Estimates price sensitivity from transaction data using a novel odds ratio method.

New framework forecasts both supply and demand in rental markets.

This paper monetizes customer load data to boost energy retailer profits.

The purpose of this study was to build a customer selection model based on 20 dimensions, including customer codes, total contribution, assets, deposit, profit, profit rate, trading volume, trading amount, turnover rate, order amount, withdraw amount, withdraw rate, process fee, process fee submitted, process fee retai…

Paper benchmarks and customizes energy forecasting methods.

Proposes a greedy algorithm for telecom offers to retain subscribers.

This paper addresses dynamic price discrimination with fairness constraints.

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

We study a stylized dynamic assortment planning problem during a selling season of finite length $T$. At each time period, the seller offers an arriving customer an assortment of substitutable products and the customer makes the purchase among offered products according to a discrete choice model. The goal of the selle…

We study the pricing problem faced by a firm that sells a large number of products, described via a wide range of features, to customers that arrive over time. Customers independently make purchasing decisions according to a general choice model that includes products features and customers' characteristics, encoded as…

We consider the problem of learning the preferences of a heterogeneous population by observing choices from an assortment of products, ads, or other offerings. Our observation model takes a form common in assortment planning applications: each arriving customer is offered an assortment consisting of a subset of all pos…

A new RL method improves revenue management with delayed feedback.

The study explores machine learning for predicting customer propensity-to-pay uncertainty.

Unfair stock trading strategies have been shown to be one of the most negative perceptions that customers can have concerning trading and may result in long-term losses for a company. Investment banks usually place trading orders for multiple clients with the same target assets but different order sizes and diverse req…

Dynamic assortment problem on two-sided platform with unknown parameters

A novel CAB-XDE framework predicts speculative stock prices with high accuracy.

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

In this study, the authors develop a structural model that combines a macro diffusion model with a micro choice model to control for the effect of social influence on the mobile app choices of customers over app stores. Social influence refers to the density of adopters within the proximity of other customers. Using a …

Firms miscount their customers who stop buying without saying goodbye.

Optimizes BESS for cross-market energy arbitrage to boost revenues.

Improved forecast accuracy for Knitwear by 20% using adaptive AI/ML model.