{'fraction_non_alphanumeric': 0.011235955056179775, 'fraction_numerical': 0.0, 'mean_word_length': 5.541284403669724, '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}}
Identifying prospective customers

We describe data mining techniques designed to address the problem of selecting prospective customers from a large pool of candidates. These techniques cover a number of different scenarios, namely whether the marketing researchers have demographic information on the current customers, or the general market population, or people with propensity to become customers We also present a novel approach to the problem by exploiting the availability of a data sample from the general market population. Finally, we describe an on-line lead management and delivery system that uses the mining approach described in this paper for insurance agents to obtain qualified customer leads.