The Machine Learning team at Hootsuite released the Suggested Reply to a small number of our customers (~200) on April 8th, 2020. The following details our design process for the system, as well as the final system architecture and preliminary usage metrics.

The Suggested Reply feature is used in Inbox in Hootsuite. When our customers receive messages from a social network they can respond to them in Inbox. Often, they use the same responses for many messages, and a major pain point that they had was storing and accessing these responses. Customers would use an external tool like Google Docs to accomplish these tasks. This workflow is very awkward so our Engage team build a feature that allows them to save and reuse those messages. But what happens if an organization has 100s of these saved messages? …


Hootsuite’s Premier Machine Learning Feature

In 2018 Hootsuite took steps to increase our machine learning capabilities for our product by forming a team to deliver the Suggested Tag Service. In addition to this feature, our team, along with teams from New York and Bucharest, was tasked with creating a GDPR compliant data lake for machine learning data, and a deployment pipeline template for future ML services. The following is an account of the final system design for this project.

Motivation

Hootsuite, as part of its offering, amalgamates all the messages directed through social channels such as Twitter, Facebook and Instagram into one platform. All manners of comment types (reply, posts and private messages) can be seen and responded to, and in addition, Hootsuite also offers an additional functionality: the ability to tag the messages. Tags are used for a variety of purposes by our customers. For example, reports are generated on the semantics of incoming messages (complaints, praise, enquiries). Our team thought it would be possible to suggest these tags given the message content, type, and social network. …

About

Tyler Lanigan

Senior Developer working on machine learning at Hootsuite.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store