Lead Forecasting using LSTM based Deep Learning Architecture for Sentiment Analysis

Marketers adopt various strategies to improve the efficiency of lead conversion by understanding more about their prospective customers. Advancements in data mining and machine leaning techniques helped industry to adopt these techniques to the business benefit. Use of modern deep learning technology for marketing industry is limited. This paper proposes a methodology for identifying the probability of conversion of a lead by employing a structured and unstructured data analysis and modelling. A binary classification model is built using feed forward neural network for structured data classification. The unstructured user comments data is represented using a long short term memory (LSTM) network which has the ability to capture context information effectively. This method provide a comprehensive approach to consider both structured and unstructured data so as to utilize the data effectively to know the customer more and there by improve the probability of predicting the lead conversion. This methodology provide a strong basis for using LSTM based sentiment analysis forecasting of unstructured data along with the classification performed using structured data.