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Tracking Consumers Throughout Their Purchase Journey: Using Deep Learning Methods

Hayati, Danial | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 55093 (44)
  4. University: Sharif University of Technology
  5. Department: Management and Economics
  6. Advisor(s): Aslani, Shirin
  7. Abstract:
  8. Over the past two decades, the interest in exploring consumers' behavior has risen to popularity. Nowadays, it is necessary for marketers to reach out to the right consumers at the right time and with the right message. Even though the positive impact of tracking consumers throughout their purchase journey and its managerial implications have been emphasized several times by academics, There is still a lack of practical research on how to discern the consumers' stage in the purchase journey from a holistic point of view. In this study, we develop several machine learning models, including RNNs (GRU and LSTM), Transformers, and XGBoost, by utilizing historical data of Yektanet (the leading online advertising agency in Iran) to predict whether the consumers have entered the purchase journey and, if they are in the journey, at what stage they are currently in (Awareness, Evaluation, and Purchase). We show that all of our models beat the baseline accuracy signifying that machine learning methods can handle this problem. Moreover, the GRU-based models outperformed other models. Our study has several contributions to both academia and practice. First, our best model predicts whether the consumers have entered the purchase journey (which has not been investigated before) with accuracy and precision of 0.91 and 0.93. Second, this model outperforms the previous models in predicting the journey stage of consumers who are in the ``Evaluation'' and ``Purchase'' stages. Finally, such a model helps many online advertising agencies to revolutionize their advertising methods and enhance their efficiency and profitability. On the other hand, advertiser brands will experience more effective marketing campaigns.
  9. Keywords:
  10. Transformers ; Consumer Behavior ; Purchase ; Deep Learning ; Customer Relationship Management (CRM)Strategy ; Long Short Term Memory (LSTM) ; Consumer Analytics ; Extreme Gradient Boosting (XGBoost)

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