61,961 research outputs found
Optimal duration of magazine promotions
The planning of promotions and other marketing events frequently requires manufacturers to make decisions about the optimal duration of these activities. Yet manufacturers often lack the support tools for decision making. We assume that customer decisions at the aggregated level follow a state-dependent Markov process. On the basis of the expected economic return associated with dynamic response to stimuli, we determine the ideal length of marketing events using dynamic programming optimization and apply the model to a complex promotion event. Results suggest that this methodology could help managers in the publishing industry to plan the optimal duration of promotion event
OPTIMAL DURATION OF MAGAZINE PROMOTIONS
The planning of promotions and other marketing events frequently requires manufacturers to make decisions about the optimal duration of these activities. Yet manufacturers often lack the support tools for decision making. We assume that customer decisions at the aggregated level follow a state-dependent Markov process. On the basis of the expected economic return associated with dynamic response to stimuli, we determine the ideal length of marketing events using dynamic programming optimization and apply the model to a complex promotion event. Results suggest that this methodology could help managers in the publishing industry to plan the optimal duration of promotion events.
Optimal duration of magazine promotions.
The planning of promotions and other marketing events frequently requires manufacturers to make decisions about the optimal duration of these activities. Yet manufacturers often lack the support tools for decision making. We assume that customer decisions at the aggregated level follow a state-dependent Markov process. On the basis of the expected economic return associated with dynamic response to stimuli, we determine the ideal length of marketing events using dynamic programming optimization and apply the model to a complex promotion event. Results suggest that this methodology could help managers in the publishing industry to plan the optimal duration of promotion events.Optimal duration of promotion events; Markovian process; Dynamic programming;
Methods of NLP in arts management
The boost of digital archives and libraries in art, literature, and music; the shift of cultural marketing and cultural criticism to social media and online platforms; and the emergence of new digital art and cultural products lead to an enormous increase in digital data, creating challenges as well as opportunities for arts management practitioners and researchers. For arts practitioners, NLP can be used to improve marketing and communication for target group analysis, event evaluation, (social) media analysis, pricing, social media optimization, advertisement targeting, or search engine optimization. In the field of archives, collections, and libraries, NLP can contribute to the improvement of indexing, consistency, and quality of databases as well as the development of suitable search algorithms. In the distribution of cultural products, online platforms can be improved and the markets analyzed
Content marketing model for leading web content management
This paper is envisaged to provide the Ukrainian businesses with suggestions for a content marketing model for the effective management of website content in order to ensure its leading position on the European and world markets. Our study employed qualitative data collection with semi-structured interviews, survey, observation methods, quantitative and qualitative methods of content analysis of regional B2B companies, as well as the comparative analysis. The following essential stages of the content marketing process as preliminary search and analysis, website content creation, promotion and distribution, and content marketing progress assessment were identified and classified in detail. The strategic decisions and activities at each stage of the process showed how a company’s on-site and off-site content can be used as a tool to establish the relationship between the brand and its target audience and increase brand visibility online. This study offered several useful insights into how website content, social media and various optimization techniques work together in engaging with the target audience and driving website traffic and sales leads. We constructed and described the content marketing model elaborated for effective web content management that can be useful for those companies that start to consider employing content marketing strategy for achieving business goals and increasing a leadership position
Forecasting Player Behavioral Data and Simulating in-Game Events
Understanding player behavior is fundamental in game data science. Video
games evolve as players interact with the game, so being able to foresee player
experience would help to ensure a successful game development. In particular,
game developers need to evaluate beforehand the impact of in-game events.
Simulation optimization of these events is crucial to increase player
engagement and maximize monetization. We present an experimental analysis of
several methods to forecast game-related variables, with two main aims: to
obtain accurate predictions of in-app purchases and playtime in an operational
production environment, and to perform simulations of in-game events in order
to maximize sales and playtime. Our ultimate purpose is to take a step towards
the data-driven development of games. The results suggest that, even though the
performance of traditional approaches such as ARIMA is still better, the
outcomes of state-of-the-art techniques like deep learning are promising. Deep
learning comes up as a well-suited general model that could be used to forecast
a variety of time series with different dynamic behaviors
Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue
Motivated by the observation that overexposure to unwanted marketing
activities leads to customer dissatisfaction, we consider a setting where a
platform offers a sequence of messages to its users and is penalized when users
abandon the platform due to marketing fatigue. We propose a novel sequential
choice model to capture multiple interactions taking place between the platform
and its user: Upon receiving a message, a user decides on one of the three
actions: accept the message, skip and receive the next message, or abandon the
platform. Based on user feedback, the platform dynamically learns users'
abandonment distribution and their valuations of messages to determine the
length of the sequence and the order of the messages, while maximizing the
cumulative payoff over a horizon of length T. We refer to this online learning
task as the sequential choice bandit problem. For the offline combinatorial
optimization problem, we show that an efficient polynomial-time algorithm
exists. For the online problem, we propose an algorithm that balances
exploration and exploitation, and characterize its regret bound. Lastly, we
demonstrate how to extend the model with user contexts to incorporate
personalization
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