<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Facing the increasingly fierce market competition, enterprises need to make correct marketing decisions. Based on the decision support system, cloud server, and SOA framework, the article establishes an enterprise intelligent marketing decision-making system by combining the TCP/IP communication protocol and designing a data warehouse to store the marketing decision-making data. The Apriori algorithm in association rules is used to generate marketing rules to assist marketing decision-making, the marketing data is classified using the random forest algorithm, and the sparrow search algorithm optimizes the parameters of the random forest model. In practice, simulation experiments were designed to analyze the performance of the random forest model and the marketing decision-making system, and they were applied to the marketing decision-making example of the e-commerce platform. It was found that the AUC value obtained when the number of binary trees of the random forest model was 75 was 0.836, and the number of conflicts per second of the system was only 0.41 when the number of concurrent users was 5*104. The use of association rules resulted in eight marketing decisions, with the highest probability of user purchase prediction reaching 98.54%. Based on the intelligent marketing decision system, it can help decision-makers better analyze market changes and provide decision support for enterprises to develop scientific and reasonable marketing strategies.</p>
</abstract>

Facing the increasingly fierce market competition, enterprises need to make correct marketing decisions. Based on the decision support system, cloud server, and SOA framework, the article establishes an enterprise intelligent marketing decision-making system by combining the TCP/IP communication protocol and designing a data warehouse to store the marketing decision-making data. The Apriori algorithm in association rules is used to generate marketing rules to assist marketing decision-making, the marketing data is classified using the random forest algorithm, and the sparrow search algorithm optimizes the parameters of the random forest model. In practice, simulation experiments were designed to analyze the performance of the random forest model and the marketing decision-making system, and they were applied to the marketing decision-making example of the e-commerce platform. It was found that the AUC value obtained when the number of binary trees of the random forest model was 75 was 0.836, and the number of conflicts per second of the system was only 0.41 when the number of concurrent users was 5*104. The use of association rules resulted in eight marketing decisions, with the highest probability of user purchase prediction reaching 98.54%. Based on the intelligent marketing decision system, it can help decision-makers better analyze market changes and provide decision support for enterprises to develop scientific and reasonable marketing strategies.

From data to design: AI practice in design decisions

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"From data to design: AI practice in design decisions"}

Facing the increasingly fierce market competition, enterprises need to make correct marketing decisions. Based on the decision support system, cloud server,...

From data to design: AI practice in design decisions

Data publikacji: 07 cze 2024

Zakres stron: -

Otrzymano: 04 mar 2024

Przyjęty: 10 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1411

Słowa kluczowe
Decision support system, SOA framework, Apriori algorithm, Random forest, Sparrow search algorithm

© 2024 Wei Du, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

From data to design: AI practice in design decisions

Data publikacji: 07 cze 2024

Zakres stron: -

Otrzymano: 04 mar 2024

Przyjęty: 10 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1411

Słowa kluczoweDecision support system, SOA framework, Apriori algorithm, Random forest, Sparrow search algorithm

© 2024 Wei Du, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Decision support system, SOA framework, Apriori algorithm, Random forest, Sparrow search algorithm