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Purchase Regret is considered as the negative variable which can affect the consumer’s purchasing behavior and impacts the return on investment of the seller negatively too. This research topic, thus, focused on how to predict consumer’s regret on a purchase and the reasons for the same by using deep learning methods. The study focused on finding out which method and model of research and data analysis provided the best conclusion in order to come up with the solutions to sort these issues out.

Suggested Solutions Concluded To Reduce Purchase Regret

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  Recommendation System

  These systems are more or less used by all the e-commerce platforms which is why it was easier to collect and evaluate data through the help of them. These systems are not just for the traditional existing consumers but also for the new ones. Through deep learning modelling methods and by using PISA (Purchase Intent System-bAsed) Algorithm. Along with deep learning methods PISA techniques helped in managing the imbalance dataset well to see the purchasing behavior of the consumers.

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  Behavioural Purchase

  Understanding and evaluating the purchasing behavior is another method opted to recognize the reasons which can lead to regret and if that regret is because of pricing of the product or other reasons. For this, Long Short Term Memory(LSTM) and Quantile Regression(QR) data are collected and evaluated through deep learning methods.

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  Time Series Data Stability

  To understand the regret behavior, it is also necessary to see the history of purchase. However, for this an algorithm which provides time series data stability is important. Using the traditional ML approaches and vanilla DNN models of deep learning provided better results unlike the hybrid and newer version of deep neural networks like TreNet.

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  Dynamic Pricing Result

  Adaptive pricing and customized products usually offered less post-purchase regret ratio. This point, thus, highlights that regret is more likely affected by the durability and need of the product rather than the high price. However, with the adaptive price and steadily discount options can bring about a change in the post-purchase regret prediction too.