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.

Importance of Big Data and Hadoop in E-Servicing

Consumers are spending more and more time on the web to search information and receive e-services. E-commerce, e-government, e-business, e-learning, e-science, etc. reflect the growing importance of the web in all aspects of our lives. Along with the tremendous growth of online information, the use of big data has become a vital force in growing revenues. Consumers are today shopping multiple products across multiple channels online. This transformation is substantial and many of the e-commerce companies have now turned to big data analytics for focused customer group targeting using opinion mining for evaluating campaign strategies and maintaining a competitive advantage, especially during the festive shopping season. So, the role of intelligent techniques in e-servicing is massive. This chapter focuses on the importance of big data (since there is a large volume of data online) and big data analytics in the field of e-servicing and explains the various applications, platforms to implement the big data applications, and security issues in the era of big data and e-servicing.

Automation of Best-Fit Model Selection using a Bag of Machine Learning Libraries for Sales Forecasting

Sales forecasting became crucial for industries in past decades with rapid globalization, widespread adoption of information technology towards e-business, understanding market fluctuations, meeting business plans, and avoiding loss of sales. This research precisely predicts the automotive industry sales using a bag of multiple machine learning and time series algorithms coupled with historical sales and auxiliary features. Three-year historical sales data (from 2017 till 2020) were used for the model building or training, and one-year (2020-2021) predictions were computed for 900 unique SKU's (stock-keeping units). In the present study, the SKU is a combination of sales office, core business field, and material customer group. Various data cleaning and exploratory data analysis algorithms were implemented over raw datasets before use for modeling. Mean absolute percentage error (mape) were estimated for individual predictions from time series and machine learning models. The best model was selected for unique SKU's as per the most negligible mape value.

The Evolution of Classical Evaluation Standards in Competition Law: The Legal Assessment of Tying in View of Challenges Raised by Digital Markets

This paper provides a detailed review of evaluation standards for the legal assessment of tying. This practice, which constitutes an abuse of a dominant position, is a significant breach of competition law. The mechanism of this type of abuse is based on taking advantage of market power in the supply of one product to create packed offerings capable of precluding competition from superior rival solutions. Tying occurs when one product, the “tying product”, is sold only with another product, the “tied product”. In the prevailing number of cases, tying serves to consolidate the company’s dominant position on the tied product market, which usually aims to share the tying product’s large customer group with the less-desired product. However, tying is not illegal per se. In many cases, it does not lead to any anti-competitive concerns, and might be beneficial for consumers. This is why each assessment of this conduct must be carefully evaluated with special attention given to the effects, in accordance with the generally applied effect-based approach, and also potential efficiencies. An analysis of the case-law and literature reveals the basic mechanisms for conducting a legal assessment of tying. However, the use of these mechanisms will not be possible without their adaptation to the ongoing changes caused by technological development. Digital markets not only generate incremental revenues, but are also the sources of new or unusual legal arrangements. It will more frequently be the case that existing provisions will not be able to address every new practice accurately without new acts. The Digital Markets Act aims to adapt the existing legal framework to contemporary market realities and to become a modern tool for enforcing competition law rules on digital markets. The European Commission is seeking to broaden its powers to intervene at the earliest possible stage, before an undertaking affects the competition on a market.

Preliminarily Screening Geographical Hotspots for New Rooftop PV Installation: A Case Study in Thailand

As rooftop PV deployment accelerates around the world, forecasts of rooftop PV penetration by geographical region and customer group are essential to guide policy and decision-making by utilities. However, most state-of-the-art forecasting tools require detailed data that are often unavailable for developing countries. A simplified analytical tool with limited data is proposed to preliminarily identify the rooftop PV “hotspots”—that is, geographical areas where many new investments into rooftop PV investments are likely to occur. The tool combines the assessment of financial and technical indicator in form of the optimal PV-to-load ratio indicating the maximum penetration of solar PV, and the capital-to-expenditure ratio indicating the ease of such investment. Using Thailand as a case study, the results from this tool show that under the self-consumption and net-billing scheme, the Northern and Northeastern regions are marked as the potential hotspots where the utility’s impact will be realized early or strongly or both. The average LCOE and self-consumption level for all customer classes and regions are in the range of 0.084–0.112 USD/kWh and 41.33–73.13% of PV production, respectively.

The value in doing good: perspective from the Indian manufacturing sector

Purpose This study aims to identity the component of corporate social responsibility (CSR) and examine its role in industrial brand equity creation. This study examines the path via value proposition derived by industrial buyers through social performance of suppliers. Design/methodology/approach Drawing from the means-end chain paradigm, this study develops a conceptual model illustrating consequences of CSR perception. The model is empirically tested with 404 purchasing managers of manufacturing firms in India. Findings This study finds an empirical link between the CSR perception among industry buyers with consumption value and subsequent impact on industrial brand equity of suppliers. This study addresses the conundrum related to the role of social performance of supplier in purchase decision. Research limitations/implications The brand preference decision among industrial buyers has majorly focused on price quality conundrum. The supplier groups emphasis on providing the value to customer group across the price quality continuum. However, with growing importance of social accountability, the social performance of suppliers has become integral to decision-making for decision-makers. The supplier groups are expected to create offering with amalgamation of social responsiveness. Originality/value To the best of the author’s knowledge, this study is the first to demonstrate the impact of CSR perception on value proposition for industrial buyers. Multiple theories have advanced the importance of value in brand equity; the empirical study is probably first in the context.

A Distinct Type of Online Group for Customer Knowledge Innovation: The Virtual Product User Community

Among online communities of customers there are a number of different types of group that need to be distinguished. One interesting type are virtual product user communities, i.e. company sponsored online forums where product users share usage experience and collaboratively construct new knowledge to solve technical problems. The purpose of this paper is to show that these “virtual product user communities” are a distinct type of customer group with knowledge innovation capability. The research adopts a method combining observation and content analysis of discussion threads where technical problems are solved, complemented by thematic analysis of interviews with forum members to explore its character, especially its knowledge related attributes. The paper confirms empirically that the virtual product user community is a distinct type of virtual community and can be differentiated from other virtual communities of consumers. In addition, an enhanced classification framework, extending Porter’s (2004) classic 5Ps model, is proposed to highlight knowledge-related activities in virtual communities. Of particular interest is that the findings suggest that knowledge-related activities should be considered as an important attribute in defining and classifying virtual communities. In terms of practical implications, it is recommended that the virtual product user community should be given appropriate support from top management in order to fully exploit its knowledge innovation value. Moreover, tailored facilitation strategies to promote knowledge construction activities and community development can be developed in accordance with its unique attributes. The paper precisely distinguishes one specific type of innovative virtual community consisting of product users from other online customer communities. Moreover, it outlines a revised virtual community classification framework, which can be widely applied in analysing features of online groups. Its key attribute of knowledge-related activity redirects attention to virtual communities’ knowledge innovation capabilities.

The Digitally Associated Display Model For Convenience Stores: A Case Study

Commodity display is one of the important means to improve customer shopping experience and enhance competitiveness in the convenience. Previous research only based on the characteristics of the commodities, but did not consider the importance of customers to display of commodities. Therefore, this paper presents the digitally associated display model that combines customer group concepts and digital means to mine customer and commodities information. First, analyze the preliminary association rules of commodities by Apriori algorithms, to provide data support, and check whether the data is true and feasible. Second, apply RFM model to analyze customers to get the importance of customers. Finally, construct a digital display model that combines customer group concepts and digital means to mine customer and commodities information. The research showed that: (1) the digitally associated display model can provide a scientific basis for the community convenience store to optimize the way of commodity display; (2) the digitally associated display model can optimize the layout of commodities to bring great room for profit growth for convenience stores. (3) the digitally associated display model can provide a reference for optimizing customer management methods, which is conducive to enhancing customers' shopping experience and satisfaction.

Customer profile analysis of the mail-order pharmacy MedicineGo using the example of the German mail-order pharmacy MedicineGo

Pharmacies in transition - digitalisation measures, connection to the telematics infrastructure and the oligopoly position of leading mail-order pharmacies. Not only meeting the technical implementations in the digital transformation, but also satisfying customers are important issues for public pharmacies. While retail pharmacies have access to data on their regular customers, we know little about customers of mail-order pharmacies. As the trend to order medicines online grows, mail-order medicines will take on an increasingly important role. It is expected that the planned introduction of electronic prescriptions in Germany in mid-2021 will lead to a further increase in online sales. The research question is: What does the customer profile of a mail-order pharmacy look like? Thus, this study focuses on an economic customer profiling of a mail-order pharmacy using the example of the nationally operating mail-order pharmacy MedicineGo. Based on the assumption that younger people between 18 and 50 years of age prefer to use the internet as an ordering medium, due to a more savvy use of computers, a quantitative analysis of empirical data will present essential market research data. In order to interpret the results of the quantitative analysis and to establish causalities, additional expert interviews with the MedicineGo administrator and other employees of the mail-order pharmacy as well as a qualitative literature analysis were conducted by means of qualitative analysis. By means of quantitative analysis, it was empirically shown that with regard to the customer profile of the mail-order pharmacy MedicineGo, 72% of customers use the direct web shop as a sales channel for their orders, the proportion of women is about ten percentage points higher than that of men, and the largest purchasing groups by number of orders are persons between 51 and 80 years of age. The quantitative analysis showed that older people (over 50 years) prefer to use the mail-order pharmacy, so another result of the study is that public pharmacies compete with mail-order pharmacies for the same customer group.