Surya Teja Tunuguntla
ABSTRACT
Short Message Service (SMS) headers are alpha-numeric codes which identify the message sender without divulging the contents of the message. The code by itself may be uninformative of the intent of the message or the interest of the recipient. We show the application of embedding techniques in learning representations of the SMS headers for commercial communication. We use these embeddings to 1) discover insightful header cohorts and, 2) create customer embeddings which are then applied as features in supervised modeling tasks such as lookalike modelling and gender prediction. The experimental results show the customer embeddings help in improving performance of these models and also emerge as top features. This derived intelligence improves customer experience, product offerings and advertisement yield. To the best of our knowledge, this is the first application of representation learning for SMS headers.
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Index Terms
- Customer Informatics by Embedding SMS Headers
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