Galactic Air Improves Ancillary Revenues with Dynamic Personalized Pricing

Ancillaries are a rapidly growing source of revenue for airlines, yet their prices are currently statically determined using rules of thumb and are matched only to the average customer or to customer groups. Offering ancillaries at dynamic and personalized prices based on flight characteristics and customer needs could greatly improve airline revenue and customer satisfaction. Through a start-up (Deepair) that builds and deploys novel machine learning techniques to introduce such dynamically priced ancillaries to airlines, we partnered with a major European airline, Galactic Air (pseudonym), to build models and algorithms for improved pricing. These algorithms recommend dynamic personalized ancillary prices for a stream of features (called context) relating to each shopping session. Our recommended prices are restricted to be lower than the human-curated prices for each customer group. We designed and compared multiple machine learning models and deployed the best-performing ones live on the airline’s booking system in an online A/B testing framework. Over a six-month live implementation period, our dynamic pricing system increased the ancillary revenue per offer by 25% and conversion rate by 15% compared with the industry standard of human-curated rule-based prices.

Dynamic Pricing for New Products Using a Utility-Based Generalization of the Bass Diffusion Model

The Bass Model (BM) has an excellent track record in the realm of new product sales forecasting. However, its use for optimal dynamic pricing or advertising is relatively limited because the Generalized Bass Model (GBM), which extends the BM to handle marketing variables, uses only percentage changes in marketing variables, rather than their actual values. This restricts the GBM’s prescriptive use, for example, to derive the optimal price path for a new product, conditional on an assumed launch price, but not the launch price itself. In this paper, we employ a utility-based extension of the BM, which can yield normative prescriptions regarding both the introductory price and the price path after launch, for the new product. We offer two versions of this utility-based diffusion model, namely, the Bass-Gumbel Diffusion Model (BGDM) and the Bass-Logit Diffusion Model (BLDM), the latter of which has been previously used. We show that both the BGDM and BLDM handily outperform the GBM in forecasting new product sales using empirical data from four product categories. We discuss how to estimate the BGDM and BLDM in the absence of past sales data. We compare the optimal pricing policy of the BLDM with the GBM and derive optimal pricing policies that are implied by the BLDM under various ranges of model parameters. We illustrate a dynamic pricing approach that allows managers to derive optimal marketing policies in a computationally convenient manner and extend this approach to a competitive, multiproduct case. This paper was accepted by Gui Liberali for the Management Science Special Issue on Data-Driven Prescriptive Analytics.

Decisions and coordination of retailer-led fresh produce supply chains under two-period dynamic pricing and portfolio contracts

Fresh agricultural produce is almost the staple food and necessity of people's daily diet all over the world. However, natural perishability and freshness affect the demand for fresh agricultural produce. Due to the change of freshness, the retailer has to adopt a multi-period dynamic pricing strategy to deal with unsold products. The research object of this paper is the retailer's two-echelon supply chain of fresh agricultural produce, and the aim is to achieve the optimal two-period coordination and ordering through options and wholesale contracts in the supply chain. In the case of two-period pricing, we find that the optimal wholesale order quantity increases with the decline of the price in the first period and tends to be stable with the decline of the price in the second period. In contrast, the price change in the first period has a greater impact on the retailer's optimal order quantity. The profits of both the retailer and the supplier increase significantly with the increase of the price in the first period, while the impact of the change of the price in the second period is not obvious. Meanwhile, decentralized decision-making can only be coordinated in the supply chain through the original option contract at the first-period price. In the second period, the cost-sharing contract is introduced to coordinate the supply chain, increase orders, and increase the profits of both the retailer and the supplier. These findings are of great significance for both the retailer and the supplier in the multi-period dynamic pricing of fresh produce under the option contract.

A Demand-Response Scheme Using Multi-Agent System for Smart DC Microgrid

This article describes a framework for load shedding techniques using dynamic pricing and multi-agent system. The islanded microgrid uses solar panels and battery energy management system as a source of energy to serve remote communities who have no access to the grid with a randomized type of power in terms of individual load. The generated framework includes modeling of solar panels, battery storage and loads to optimize the energy usage and reduce the electricity bills. In this work, the loads are classified as critical and non-critical. The agents are designed in a decentralized manner, which includes solar agent, storage agent and load agent. The load shedding experiment of the framework is mapped with the manual operation done at Kisiju village, Pwani, Tanzania. Experiment results show that the use of pricing factor as a demand response makes the microgrid sustainable as it manages to control and monitor its supply and demand, hence, the load being capable of shedding its own appliances when the power supplied is not enough.