Publisher

UPIUPI

Journal Indonesian Journal of Science and TechnologyIndonesian Journal of Science and Technology Abstract

Consumer relationship management (CRM) can potentially influence business as it predicts changes in peoples perspectives, which could impact future sales. Accordingly, advancements in Information Technology are under investigation to see their capabilities to improve the work of CRM. Many prediction techniques, such as Data Mining, Machine Learning (ML), and Deep Learning (DL), were found to be utilized with CRM. ML methods were found to dominate other approaches in terms of the prediction of consumers intention to purchase. This review provides DL algorithms that are mostly used in the last five years, to support CRM to predict purchase intention for better product sales decisions. Prediction criteria related to online activities and behavior were found to be the most inputs of prediction models. DL approaches are slowly applied within purchase intention prediction due to their advanced capabilities in handling large and complicated datasets with minimum human supervision. DL models such as CNN and LSTM result in high accuracy in prediction intention with 98%. Future research uses the two algorithms (CNN, LSTM) compiled to make the best prediction consumption in CRM. Additionally, an effort is being made to create a framework for predicting purchases based on many DL algorithms and the most pertinent characteristics.

Conclusion

It can be concluded that consumer relationship management is considerably focused on peoples posts on social media because people reveal a lot regarding their perspectives towards products and purchase habits.Accordingly, prediction techniques such as Data Mining, Machine Learning, and Deep Learning have been utilized to predict purchase intention from peoples online words and activities.However, compared to Machine Learning methods, Deep Learning methods are found to be underrated in predicting consumers intention to purchase.Machine Learning methods have been established in the field a long time ago, while Deep Learning recently employed.However, Deep Learning is proceeding significantly in many fields including business, and replacing its Machine Learning methods due to its capability to improve accuracy while more training data is included, and it can improve accuracy while complicated datasets and need less human intervention.Moreover, this research studied and compared the existing Deep Learning models that predict consumption and concluded that CNN and LSTM as successful techniques for purchase prediction, as CNN separately yields the highest accuracy of 90% and above.Additionally, the review highlighted that features such as purchase under number view products, sessions, and others, are among the prominent factors.The findings support the current investigation of using Deep Learning models and picking up the most used data entry process such as segmentation that could improve the results for prediction.

Future Research

Based on the findings, future research should focus on combining CNN and LSTM algorithms to develop a more accurate purchase intention prediction model. Furthermore, exploring the use of deep learning to predict purchase intention in real-time, enabling instant discounts or rewards for potential buyers, presents a promising avenue for investigation. Finally, it is crucial to develop a streamlined feature selection process to reduce computational complexity and improve the efficiency of deep learning models in predicting consumer behavior, particularly in scenarios with large and incomplete datasets.

Metadata

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Pages	22
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