81% of SMBs use email campaigns to nurture their audiences.
Email is the most important tool for any business communication. The key to great e-mail communication is crafting content that people actually want in their inboxes and that speaks to each customer separately, instead of a single e-mail sent to one and all. A badly written email, rather than speeding up communication, proposal or instruction slows down business processes. However generating a proper email content for any business discussion is a difficult task. It needs lots of experience and practice with multiple iteration. The future of e-mail is real-time, behavior-based personalization.
With advancement of AI related tools and methodologies, there are efforts to assist in email content generation. Can AI help or fully automate content generation? I would be covering couple of my experience on this subject.
Artificial intelligence (AI) and machine learning (ML) are fascinating in that they benefit every facet of content marketing. Specifically, through advanced machine learning technology, the process of creating content marketing strategies becomes easier.
Developing such systems have two types, one is static type of check typos, grammar and syntax and second type of a complete AI solution, an adaptive system to generate content based on a set of questionnaire.
There are tools, like browser plugin Grammarly to highlight any grammatical mistakes, tone of your email as you compose your emails. There is a javascript based SDK https://www.inboxsdk.com/ to be used for developing such mail plugins. You can use gmail plugin development framework for developing such mail plugins too.
Its AI everywhere and its time to use the power of AI and machine learning to assist in content generation for business. The very first step of a successful AI system depends upon the quality of training data set and methodology used.
Content of an email would be different based on the context – domain. Likewise, training data is also context dependent. Email training data set for finance domain is different than retail domain. There need to have a general training set and then domain specific training sets. User will be prompted with set of questionnaire to aid in text generation. Or user can enter a set of keywords – domain specific. Based on the user feedback, appropriate training set would be used, or a hybrid of multiple training set would be used to generate email content.
Recently google has introduced text suggestions in their gmail platform, when user types any sentence, the AI engine would prompt auto suggestion. It helps commonly used to words to complete the sentence.
There are some neural network based libraries to help in character level prediction like char-rnn – https://github.com/karpathy/char-rnn the model takes one text file as input and trains a Recurrent Neural Network that learns to predict the next character in a sequence. The RNN can then be used to generate text character by character that will look like the original training data.
https://github.com/jcjohnson/torch-rnn torch-rnn provides high-performance, reusable RNN and LSTM modules for torch7, and uses these modules for character-level language modeling similar to char-rnn.
There are some RNN – Recurrent neural network based text generation libarries like textgenrnn – https://github.com/minimaxir/textgenrnn – a library developed on top of Keras/TensorFlow , easy to train and can be integrated quickly. Then we come across OpenAI’s text generation framework https://openai.com/blog/gpt-2-1-5b-release/ , Gpt-2 from OpenAI is the most powerful text generation framework. OpenAI unveiled its new AI language system, GPT-2, and TalkToTransformer is a slimmed-down, accessible version of that same technology, which has been made accessible only to select scientists and journalists in the pastIn the beginning, it was not released completely with fear that it might be used for anti-social activities.
In a nutshell, AI based email text generation is a long way to go and there are some good libraries to help you. GPT-2 language modeling system from OpenAI would be the most innovative text generation system.
GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text
Generating title of any email, especially business email is a tricky task. The very first thing User sees is the subject of email. There is a Neural Network based library NLU Email Title to help in email subject generation.