{'fraction_non_alphanumeric': 0.015985790408525755, 'fraction_numerical': 0.0053285968028419185, 'mean_word_length': 5.403409090909091, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Predictive modeling of potential customers based on the customers clickstream data: A field study

With the development of e-commerce business and techniques, customer behaviors have been digitalized. In this research, we target on solving the challenge for most of the companies: finding the next group of paying customers. We conduct field studies at the telemarketing team of an online advertising classifieds website company. Dynamic models are developed to predict the probability of making purchases after each telemarketing contact. The real life data is collected from more than 1.2 million of the historical telemarketing campaign contacts. The model utilizing clickstream data successfully integrates the early indicators of purchase behaviors, and deliver comparable results (best AUC 0.867) with models on paid customers. We have also designed a practical decision support system which could generate and distribute candidate customers for the sales representatives of the telemarketing team. The decision support system has been launched and the effectiveness of the system has been tested in real life practices.