Audience.ID was developed out of a need to better understand Instagram communities and measure an influencers reach beyond likes and follows. While working at MLLNNL, I created a platform with a set of powerful tools which allow brand managers to track, measure, and engage with niche online communities.
From mid 2016 to 2018, I designed and led a team of remote developers building a content aggregation, audience analysis, and direct engagement platform to help brands better understand and engage with their online communities.

Aside from designing the product interface and developing a comprehensive feature roadmap, I helped architect our API to create a 1 to 1 relationship between design and development. Additionally, I worked with IBM Watson to integrate their machine learning offerings into our influencer analysis model.


While leading the creative team at MLLNNL, I was looking for ways to target highly niche communities for our micro-influencer campaigns. Unfortunately, Instagram's API does not support multi query searching. This meant that searching for niche influencers in a specific category and location was nearly impossible to achieve. In addition to this, as a performance driven agency, we weren't keen on hiring influencers based on their likes and follows - a metric which felt indirect to what we were trying to achieve. I set out to build a platform to solve these two issues.

Early wireframes for Audience.ID
To achieve these goals we needed to scrape Instagram and collect as much data as possible. Using this data, we could parse influencers based on multiple criteria and identify their audience interests as a better metric for campaign success. With that in mind, our engineer and I began designing a backend system and a minimal interface for an online tool our community managers could use to scrape the content they needed to identify key influencers.

Our first iteration of the product used by our internal team to scrape, analyze, and engage with Instagram content
While our goal at first was to achieve specific campaign goals, we ultimately decided that we were better off turning this project into a complete platform that would enable community managers on multiple teams to collaborate on building micro communities and campaigns.
Our first launch of the Audience.ID platform
After building the first iteration, we began to learn and listen from our initial clients and continue to build and test new features. This lead us to cleaning up the UI, adding a machine learning component, and reorganizing the community filtering aspect of the product. As we began building more code, we adapted our internal process to be more organized and agile. This included writing and documenting robust API and developing a organized scrum workflow.
Audience.ID today. Completely redesigned and integrated with IBM Watson
In addition to my role as user experience and product designer, I was also responsible for hiring our developer team and managing our product and feature workloads. Using Toptal, I hired several software engineers, helped design our robust API, tracked features and workload, and managed our remote team of engineers. 
Below are a few screenshots of the inner working of Audience.ID
Back to Top