for a more complete.  We use our proprietary data sets, from online retail data, consumer data that assists our machine learning and decision algorithms.  This data, combined with our proprietary product-focused natural language generation, has enabled us to develop a very different kind of natural language generation technology, one that actually listens and communicates with a level of personalization that has online retail data and  linguistic characteristics extracted from author text and brand content.  semi-autonomously “trained” with machine learning methods that  write in a unique brand persona (voice), specified range of subjects and topics.  machine learning that, by analyzing message performance and customer engagement signals, aides our decision algorithms to make improvements in message personalization.  Each product message delivered is monitored for performance in near-real-time, benchmarked against all prior and original product content, with respect to user engagement

and is safeguarded by autonomously edits content.  edit content based on message performance and customer engagement analytics.


analyzing customer engagement and system build is semi-autonomously trained to write in brand voice and writing style.  the voice of a specified brand or one or more human writers.

uniquely modeled after a specified brand voice or  that we deploy is unique,  semi-autonomously developed by using proprietary e-commerce data and


Second, our AI is fueled by proprietary e-commerce data that refines and accellerates , and decision algorithms are fueled by proprietary e-commerce data sets that help reinforce our machine learning and decision algorithms.  Third, and most crucial, our NLG “personas” interact with your shoppers, creating messages and evaluating customer engagement, in a continuous near-real-time cycle.  Finally, our AI utilizes customer engagement data to make improvements in our decision algorithms.  The NLG is encouraged to experiment with new message concepts as reward for positve edits; the NLG is imposed with greater editing restrictions when it makes poor editing decisions.  machine learning techniques that autonomously train our NLG to .   responses, with performance metrics and user data.  Narratives are created, distributed, and evaluated, in near-real-time, autonomously creating product content and analyzing customer engagement data.


Artificial Intelligence Powered Natural Language Generation
Product marketing teams that manage long assortments are often understaffed in the writing department. Online sellers must factor in the cost of writing copy for each new SKU. Additionally marketers are becoming overwhelmed with the amount of data science and systems knowhow required to remain competitive. Marketing teams are essentially a set of brains that have a finite amount of cognitive capability and creative production capacity. Automation to the rescue! Advancements in technology have enabled retailers and other marketers to efficiently run more experiments and quickly adjust based on easily interpreted results. The quality of shopping behavior and customer engagement data is fairly easy to access and manage, but all of this new and ever-changing tech is taking up a lot of time on the marketing floor. Running tests, crunching data, and analyzing results is certainly not something that writers typically want too involved in. Reluctantly, many are, but analyzing how content is received, by segment, is not like reading a chart. There are thousands of linguistic variables that must be accounted for. Fortunately, most skilled copywriters don’t tend to make huge edits. They have, through years of experience, learned what works and what doesn’t. Not that they repeat the same angles over and over, but they tweak and adjust and work within broader concepts of strategic messaging. For example, some writers use song lyrics as inspiration, while others connect with a particular emotional appeal and mood. It is simply not possible for a writer to understand all of the linguistic and audience features that computers can process. For this reason, with ever-improving insights garnered from online customers, Writesof’s natural language generation technology was developed. Your company has access to a trove of marketing data enriched with insights into your messages. Each time an edit was made, something changed with customer interactions. Our systems find correlations between changes in text and changes in customer behavior. It learns which changes created some measurable improvement in customer engagement and ultimately looks for improvement in conversions and margins.