Choice-Based Dynamic Pricing for Vacation Rentals

2021 ◽  
Author(s):  
Yaping Wang ◽  
Kelly McGuire ◽  
Jeremy Terbush ◽  
Michael Towns ◽  
Chris K. Anderson
Keyword(s):  
Revenue Management ◽  
Dynamic Pricing ◽  
Conditional Logistic Regression ◽  
Model Performance ◽  
Price Sensitivity ◽  
Management Problem ◽  
Rolling Window ◽  
Time Availability ◽  
Optimization Routine ◽  
The Impact

In this paper, we propose a new dynamic pricing approach for the vacation rental revenue management problem. The proposed approach is based on a conditional logistic regression that predicts the purchasing probability for rental units as a function of various factors, such as lead time, availability, property features, and market selling prices. In order to estimate the price sensitivity throughout the booking horizon, a rolling window technique is provided to smooth the impact over time and build a consistent estimation. We apply a nonlinear optimization algorithm to determine optimal prices to maximize the revenue, considering current demand, availability from both the rental company and its competitors, and the price sensitivity of the rental guest. A booking curve heuristic is used to align the booking pace with business targets and feed the adjustments back into the optimization routine. We illustrate the proposed approach by successfully applying it to the revenue management problem of Wyndham Destinations vacation rentals. Model performance is evaluated by pricing two regions within the Wyndham network for part of the 2018 vacation season, indicating revenue per unit growth of 3.5% and 5.2% (for the two regions) through model use.

scholarly journals Online Network Revenue Management Using Thompson Sampling

2018 ◽  
Vol 66 (6) ◽  
pp. 1586-1602 ◽  
Author(s):  
Kris Johnson Ferreira ◽  
David Simchi-Levi ◽  
He Wang
Keyword(s):  
Revenue Management ◽  
Dynamic Pricing ◽  
Domain Knowledge ◽  
Poor Performance ◽  
Management Problem ◽  
Network Revenue Management ◽  
Thompson Sampling ◽  
Main Challenge ◽  
Inventory Constraints ◽  
Network Revenue

Thompson sampling is a randomized Bayesian machine learning method, whose original motivation was to sequentially evaluate treatments in clinical trials. In recent years, this method has drawn wide attention, as Internet companies have successfully implemented it for online ad display. In “Online network revenue management using Thompson sampling,” K. Ferreira, D. Simchi-Levi, and H. Wang propose using Thompson sampling for a revenue management problem where the demand function is unknown. A main challenge to adopt Thompson sampling for revenue management is that the original method does not incorporate inventory constraints. However, the authors show that Thompson sampling can be naturally combined with a linear program formulation to include inventory constraints. The result is a dynamic pricing algorithm that incorporates domain knowledge and has strong theoretical performance guarantees as well as promising numerical performance results. Interestingly, the authors demonstrate that Thompson sampling achieves poor performance when it does not take into account domain knowledge. Finally, the proposed dynamic pricing algorithm is highly flexible and is applicable in a range of industries, from airlines and internet advertising all the way to online retailing.

An Empirical Analysis of Delhi - Mumbai Sector Flight Fares

2017 ◽  
Vol 4 (4) ◽  
pp. 60-78
Author(s):  
T. Godwin
Keyword(s):  
Revenue Management ◽  
Dynamic Pricing ◽  
Time Windows ◽  
Analysis Framework ◽  
Pricing Decisions ◽  
Departure Time ◽  
Departure Date ◽  
Reservation System ◽  
The Right ◽  
The Impact

Revenue management is the art and science of making the right product or service available to the right customer at the right time through the right channel at right price. Dynamic pricing plays a crucial role in the implementation of revenue management in passenger airline reservation system. The liberalization of domestic aviation sector in countries such as India has seen many new market entrants resulting in higher competition while setting the flight fares. The variation in flight fares of Delhi – Mumbai passenger airline sector is studied for a departure date based on the number of days in advance the booking is made. Descriptive and inferential statistical analyses of the fares reveal the impact of airlines, booking channels and departure time windows on the pricing decisions of flight fares. The analysis framework of this study could be used as a basis for a continuous tracking study of flight fares by airline revenue managers to help them arrive at the right fare for each fare class of a flight.

An Empirical Analysis of Delhi - Mumbai Sector Flight Fares

2021 ◽  
pp. 1477-1495
Author(s):  
T. Godwin
Keyword(s):  
Revenue Management ◽  
Dynamic Pricing ◽  
Time Windows ◽  
Statistical Analyses ◽  
Pricing Decisions ◽  
Departure Time ◽  
Departure Date ◽  
Reservation System ◽  
The Right ◽  
The Impact

Revenue management is the art and science of making the right product or service available to the right customer at the right time through the right channel at right price. Dynamic pricing plays a crucial role in the implementation of revenue management in passenger airline reservation system. The liberalization of domestic aviation sector in countries such as India has seen many new market entrants resulting in higher competition while setting the flight fares. The variation in flight fares of Delhi – Mumbai passenger airline sector is studied for a departure date based on the number of days in advance the booking is made. Descriptive and inferential statistical analyses of the fares reveal the impact of airlines, booking channels and departure time windows on the pricing decisions of flight fares. The analysis framework of this study could be used as a basis for a continuous tracking study of flight fares by airline revenue managers to help them arrive at the right fare for each fare class of a flight.

Data Processing and Modeling of Dynamic Pricing of Cruise Transportation Based on Revenue Management

2014 ◽  
Vol 1030-1032 ◽  
pp. 1984-1987
Author(s):  
Ya Ru Dong ◽  
Bin Jia ◽  
Dong Fan Xie
Keyword(s):  
Data Processing ◽  
Revenue Management ◽  
Dynamic Pricing ◽  
Arrival Rate ◽  
Actual Situation ◽  
Cruise Industry ◽  
Fierce Competition ◽  
The Impact

With increasingly fierce competition making price becomes important question relating to the cruise industry. Focusing primarily on three factors, customer arrival rate, ticketing remaining cycles and the remaining tickets, we further improve dynamic pricing method to the market in which we consider random factors and calculate by MATLAB to reflect the impact of three factors on pricing and revenue. It shows that the customer’s arrival rate and the price have the same monotonicity and the more the ticketing remaining cycles are, the lower the price is and the higher the revenue is, and so does the remaining tickets, which is accordance with the actual situation.

scholarly journals The Impact of Probability Density Functions Assessment on Model Performance for Slope Stability Analysis

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 322
Author(s):  
Evelina Volpe ◽  
Luca Ciabatta ◽  
Diana Salciarini ◽  
Stefania Camici ◽  
Elisabetta Cattoni ◽  
...  
Keyword(s):  
Probability Density ◽  
Central Italy ◽  
Model Performance ◽  
Probability Density Functions ◽  
Slope Instability ◽  
Complex Nature ◽  
Density Functions ◽  
Umbria Region ◽  
Input Variables ◽  
The Impact

The development of forecasting models for the evaluation of potential slope instability after rainfall events represents an important issue for the scientific community. This topic has received considerable impetus due to the climate change effect on territories, as several studies demonstrate that an increase in global warming can significantly influence the landslide activity and stability conditions of natural and artificial slopes. A consolidated approach in evaluating rainfall-induced landslide hazard is based on the integration of rainfall forecasts and physically based (PB) predictive models through deterministic laws. However, considering the complex nature of the processes and the high variability of the random quantities involved, probabilistic approaches are recommended in order to obtain reliable predictions. A crucial aspect of the stochastic approach is represented by the definition of appropriate probability density functions (pdfs) to model the uncertainty of the input variables as this may have an important effect on the evaluation of the probability of failure (PoF). The role of the pdf definition on reliability analysis is discussed through a comparison of PoF maps generated using Monte Carlo (MC) simulations performed over a study area located in the Umbria region of central Italy. The study revealed that the use of uniform pdfs for the random input variables, often considered when a detailed geotechnical characterization for the soil is not available, could be inappropriate.

scholarly journals Relation of severe COVID-19 in Scotland to transmission-related factors and risk conditions eligible for shielding support: REACT-SCOT case-control study

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Paul M. McKeigue ◽  
David A. McAllister ◽  
David Caldwell ◽  
Ciara Gribben ◽  
Jen Bishop ◽  
...  
Keyword(s):  
General Population ◽  
Rate Ratio ◽  
Conditional Logistic Regression ◽  
Population Attributable Risk ◽  
Case Control ◽  
Care Practice ◽  
Solid Organ ◽  
Related Factors ◽  
Recent Exposure ◽  
The Impact

Abstract Background Clinically vulnerable individuals have been advised to shield themselves during the COVID-19 epidemic. The objectives of this study were to investigate (1) the rate ratio of severe COVID-19 associated with eligibility for the shielding programme in Scotland across the first and second waves of the epidemic and (2) the relation of severe COVID-19 to transmission-related factors in those in shielding and the general population. Methods In a matched case-control design, all 178,578 diagnosed cases of COVID-19 in Scotland from 1 March 2020 to 18 February 2021 were matched for age, sex and primary care practice to 1,744,283 controls from the general population. This dataset (REACT-SCOT) was linked to the list of 212,702 individuals identified as eligible for shielding. Severe COVID-19 was defined as cases that entered critical care or were fatal. Rate ratios were estimated by conditional logistic regression. Results With those without risk conditions as reference category, the univariate rate ratio for severe COVID-19 was 3.21 (95% CI 3.01 to 3.41) in those with moderate risk conditions and 6.3 (95% CI 5.8 to 6.8) in those eligible for shielding. The highest rate was in solid organ transplant recipients: rate ratio 13.4 (95% CI 9.6 to 18.8). Risk of severe COVID-19 increased with the number of adults but decreased with the number of school-age children in the household. Severe COVID-19 was strongly associated with recent exposure to hospital (defined as 5 to 14 days before presentation date): rate ratio 12.3 (95% CI 11.5 to 13.2) overall. The population attributable risk fraction for recent exposure to hospital peaked at 50% in May 2020 and again at 65% in December 2020. Conclusions The effectiveness of shielding vulnerable individuals was limited by the inability to control transmission in hospital and from other adults in the household. Mitigating the impact of the epidemic requires control of nosocomial transmission.

scholarly journals The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact

scholarly journals When does the use of individual patient data in network meta-analysis make a difference? A simulation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Steve Kanters ◽  
Mohammad Ehsanul Karim ◽  
Kristian Thorlund ◽  
Aslam Anis ◽  
Nick Bansback
Keyword(s):  
Simulation Study ◽  
Treatment Effect ◽  
Mean Squared Error ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Model Performance ◽  
Patient Data ◽  
Meta Regression ◽  
Treatment Comparisons ◽  
The Impact

Abstract Background The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only. Methods Three analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision. Results Overall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates. Conclusions Our simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used.

scholarly journals Qualitative Recognition of Primary Taste Sensation Based on Surface Electromyography

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4994
Author(s):  
You Wang ◽  
Hengyang Wang ◽  
Huiyan Li ◽  
Asif Ullah ◽  
Ming Zhang ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
Model Performance ◽  
Recognition Task ◽  
Taste Sensation ◽  
Pass Filter ◽  
Power Frequency ◽  
Coffee Powder ◽  
Adaptive Power ◽  
Feature Dimension ◽  
The Impact

Based on surface electromyography (sEMG), a novel recognition method to distinguish six types of human primary taste sensations was developed, and the recognition accuracy was 74.46%. The sEMG signals were acquired under the stimuli of no taste substance, distilled vinegar, white granulated sugar, instant coffee powder, refined salt, and Ajinomoto. Then, signals were preprocessed with the following steps: sample augments, removal of trend items, high-pass filter, and adaptive power frequency notch. Signals were classified with random forest and the classifier gave a five-fold cross-validation accuracy of 74.46%, which manifested the feasibility of the recognition task. To further improve the model performance, we explored the impact of feature dimension, electrode distribution, and subject diversity. Accordingly, we provided an optimized feature combination that reduced the number of feature types from 21 to 4, a preferable selection of electrode positions that reduced the number of channels from 6 to 4, and an analysis of the relation between subject diversity and model performance. This study provides guidance for further research on taste sensation recognition with sEMG.

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