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Predicting lead close rates is one of the most problematic tasks in the leadgeneration industry. In most cases, the only available data on the prospect isthe self-reported information inputted by the user on the lead form and a fewother data points publicly available through social media and search engineusage. All the major market niches for lead generation [1], such as insurance,health & medical and real estate, deal with life-altering decision making thatno amount of data will be ever be able to describe or predict. This paperillustrates how character-level, deep long short-term memory networks can beapplied to raw user inputs to help predict close rates. The output of the modelis then used as an additional, highly predictive feature to significantly boostperformance of lead scoring models.

Variable-sized input, character-level recurrent neural networks in lead generation: predicting close rates from raw user inputs