Optimal Pricing with Recommender Systems

We study optimal pricing in the presence of recommender systems. A recommender system affects the market in two ways: (i) it creates value by reducing product uncertainty for the customers and hence (ii) its recommendations can be offered as add-ons which generate informational externalities. The quality of the recommendation add-on is endogenously determined by sales. We investigate the impact of these factors on the optimal pricing by a seller with a recommender system against a competitive fringe without such a system. If the recommender system is sufficiently effective in reducing uncertainty, then the seller prices otherwise symmetric products differently to have some products experienced more aggressively. Moreover, the seller segments the market so that customers with more inflexible tastes pay higher prices to get better recommendations.Recommender system, Collaborative filtering, Add-ons, Pricing, Information externality

Bundling and pricing for information brokerage: customer satisfaction as a means to profit optimization

Traditionally, the study of on-line dynamic pricing and bundling strategies for information goods is motivated by the value-extracting or profit-generating potential of these strategies. In this paper we discuss the relatively overlooked potential of these strategies to on-line learn more about customers' preferences. Based on this enhanced customer knowledge an information broker can-- by tailoring the brokerage services more to the demand of the various customer groups-- persuade customers to engage in repeated transactions (i.e., generate customer lock-in). To illustrate the discussion, we show by means of a basic consumer model how, with the use of on-line dynamic bundling and pricing algorithms, customer lock-in can occur. The lock-in occurs because the algorithms can both find appropriate prices and (from the customers' perspective) the most interesting bundles. In the conducted computer experiments we use an advanced genetic algorithm with a niching method to learn the most interesting bundles efficiently and effectively

Towards personalized data-driven bundle design with QoS constraint

Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiativ

Designing an AI-enabled Bundling Generator in an Automotive Case Study

Procurement and marketing are the main boundary-spanning functions of an organization. Some studies highlight that procurement is less likely to benefit from artificial intelligence emphasizing its potential in other functions, i.e., in marketing. A case study in the automotive industry of the bundling problem utilizing the design science approach is conducted from the perspective of the buying organization contributing to theory and practice. We rely on information processing theory to create a practical tool that is augmenting the skills of expert buyers through a recommendation engine to make better decisions in a novel way to further save costs. Thereby, we are adding to the literature on spend analysis that has mainly been looking backward using historical data of purchasing orders and invoices to infer saving potentials in the future – our study supplements this approach with forward-looking planning data with inherent challenges of precision and information-richness

A framework for personalized dynamic cross-selling in e-commerce retailing

Cross-selling and product bundling are prevalent strategies in the retail sector. Instead of static bundling offers, i.e. giving the same offer to everyone, personalized dynamic cross-selling generates targeted bundle offers and can help maximize revenues and profits. In resolving the two basic problems of dynamic cross-selling, which involves selecting the right complementary products and optimizing the discount, the issue of computational complexity becomes central as the customer base and length of the product list grows. Traditional recommender systems are built upon simple collaborative filtering techniques, which exploit the informational cues gained from users in the form of product ratings and rating differences across users. The retail setting differs in that there are only records of transactions (in period X, customer Y purchased product Z). Instead of a range of explicit rating scores, transactions form binary datasets; 1-purchased and 0-not-purchased. This makes it a one-class collaborative filtering (OCCF) problem. Notwithstanding the existence of wider application domains of such an OCCF problem, very little work has been done in the retail setting. This research addresses this gap by developing an effective framework for dynamic cross-selling for online retailing. In the first part of the research, we propose an effective yet intuitive approach to integrate temporal information regarding a product\u27s lifecycle (i.e., the non-stationary nature of the sales history) in the form of a weight component into latent-factor-based OCCF models, improving the quality of personalized product recommendations. To improve the scalability of large product catalogs with transaction sparsity typical in online retailing, the approach relies on product catalog hierarchy and segments (rather than individual SKUs) for collaborative filtering. In the second part of the work, we propose effective bundle discount policies, which estimate a specific customer\u27s interest in potential cross-selling products (identified using the proposed OCCF methods) and calibrate the discount to strike an effective balance between the probability of the offer acceptance and the size of the discount. We also developed a highly effective simulation platform for generation of e-retailer transactions under various settings and test and validate the proposed methods. To the best of our knowledge, this is the first study to address the topic of real-time personalized dynamic cross-selling with discounting. The proposed techniques are applicable to cross-selling, up-selling, and personalized and targeted selling within the e-retail business domain. Through extensive analysis of various market scenario setups, we also provide a number of managerial insights on the performance of cross-selling strategies

Paying for News: Opportunities for a New Business Model through Personalized News Aggregators (PNAs)

News consumption has been evolving from offline newspapers to online news. However, while offline newspapers sales are decreasing, online news business models have never been entrenched. Meanwhile, the new technology of social recommender systems enable automated news aggregation. Personalized news aggregators (PNAs) rely on this technology, and provide personalized news in visually appealing ways that might deliver the potential for a new business model. However, there is no research on PNA configuration or users’ willingness to pay (WTP). An empirical investigation with 116 participants examined usage features influencing PNA users’ adoption and their WTP for a paid-based service. First, we showed that perceived usefulness, usage comfort, awareness, and (social) personalization significantly influence intention to use a PNA. Users are also considering price. Second, we found an optimal price point of 1.88€ and a price range up to 6.83€ for monthly use

EvoRecSys: Evolutionary framework for health and well-being recommender systems

Hugo Alcaraz-Herrera's PhD is supported by The Mexican Council of Science and Technology (Consejo Nacional de Ciencia y Tecnologia - CONACyT).In recent years, recommender systems have been employed in domains like ecommerce, tourism, and multimedia streaming, where personalising users’ experience based on their interactions is a fundamental aspect to consider. Recent recommender system developments have also focused on well-being, yet existing solutions have been entirely designed considering one single well-being aspect in isolation, such as a healthy diet or an active lifestyle. This research introduces EvoRecSys, a novel recommendation framework that proposes evolutionary algorithms as the main recommendation engine, thereby modelling the problem of generating personalised well-being recommendations as a multi-objective optimisation problem. EvoRecSys captures the interrelation between multiple aspects of well-being by constructing configurable recommendations in the form of bundled items with dynamic properties. The preferences and a predefined well-being goal by the user are jointly considered. By instantiating the framework into an implemented model, we illustrate the use of a genetic algorithm as the recommendation engine. Finally, this implementation has been deployed as a Web application in order to conduct a users’ study.Consejo Nacional de Ciencia y Tecnologia (CONACyT