  Predicting lead close rates is one of the most problematic tasks in the lead
generation industry. In most cases, the only available data on the prospect is
the self-reported information inputted by the user on the lead form and a few
other data points publicly available through social media and search engine
usage. All the major market niches for lead generation [1], such as insurance,
health & medical and real estate, deal with life-altering decision making that
no amount of data will be ever be able to describe or predict. This paper
illustrates how character-level, deep long short-term memory networks can be
applied to raw user inputs to help predict close rates. The output of the model
is then used as an additional, highly predictive feature to significantly boost
performance of lead scoring models.
