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

TL;DR

This paper demonstrates that character-level LSTM networks applied to raw user input data can significantly improve lead scoring models by better predicting close rates in lead generation, despite limited available data.

Contribution

It introduces a novel application of character-level RNNs to raw user inputs for predicting lead close rates, enhancing lead scoring accuracy.

Findings

Character-level RNNs improve close rate prediction.

Enhanced lead scoring performance with raw input features.

Applicable across various lead generation industries.

Abstract

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.