A Stochastic Optimization Model for Consecutive Promotion
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
AbstractNowadays in business environment, marketing competitiveness is as demanding as ever. To survive under keen competitions, industries must keep acquiring customers and make them loyal while maximizing profit from their service subscription or product purchasing. Intensive research works have been done in answering when and what kind of promotions should be used under limited marketing communication resources to maintain a perpetual generation of revenue. In this paper, we investigate the advantages in consecutive promotion based on the framework of the model proposed in Ching et al. [1]. The customers' behavior is modelled by using a Markov chain and we aim at maximizing the expected profit using stochastic dynamic programming. We find that a multi-period promotion strategy is better than the strategy of applying several single-period promotions in our tested examples.Keywords: Consecutive promotioncustomer behaviorMarkov processstochastic dynamic programming Additional informationNotes on contributorsHo-Yin LeungHo-Yin Leung M.Phil. student at the Department of Mathematics, the University of Hong Kong. He got his B.Sc. in Mathematics, the University of Hong Kong with first class honour. He was in the Dean's honour list for three years (2005-2007) and he got the following scholarships: CV Starr Scholarships (2007), HSBC Hong Kong Scholarship (2006) and the Koo Shui Ting Memorial Scholarship (2005-2007). He also obtained the following academic prizes: Alan John Ellis Prizes in Mathematics (2005), B.Sc. Class of 1971 Prize (2006), Ho Sin Hang Price in Science (2007) and Wong Yung Chow Prize in Mathematics (2006). His research interests are stochastic process and modelling with applications.Wai-Ki ChingWai-Ki Ching lecturer at the Department of Mathematics, the University of Hong Kong. He obtained his B.Sc. (1991) and M.Phil. (1994) degrees in Mathematics from the University of Hong Kong. He then obtained his Ph.D. degree in Systems Engineering and Engineering Management (1998) from the Chinese University of Hong Kong and was a visiting post-doc fellow at the Judge Business School of the Cambridge University (1999-2000). He has previously taught at the Hong Kong Polytechnic University and the University of Science and Technology. Before joining his Alma Mater, he was a lecturer at the University of Southampton (2000-2001). His research interests include data modelling, optimization algorithms, systems engineering and bioinformatics.Issic K.C. LeungIssic K.C. Leung lecturer at the University of Macau for two years (1990-1992) after he had obtained his Bachelor and Master degrees from the University of Manitoba, Canada (1990). He then studied further and graduated with his Ph.D. in Mathematics from the Flinders University of South Australia (1996) at there he was awarded the Flinders University Research Scholarship (1993-1996). He was a senior market analyst in PCCW, one of the giants in the telecom industry in Hong Kong, for many years before he became an assistant professor of mathematics education at the Hong Kong Institute of Education (2006-2008). His research interest is in mathematics in business modelling, quantitative methods in marketing analysis, mathematics education and teacher competence in mathematics.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle