Over the years, I’ve held many titles.
I’ve been the editor-in-chief of a tech news platform I built covering diverse founders. I was the founder and CEO of Silicon Valley’s first accelerator advancing diverse founders. In my last venture, which was acquired last year, I wore the title of ecosystem builder, running a multi-city accelerator boot camp in emerging markets.
I’ve developed a unique blend of helping nascent consumer technology companies grow and produce content, in many cases, for a market that wasn’t, and to this day, has still yet to realize it’s full potential. Our world has changed from producing content purely based on inspiration to what will get the most clicks or shares or streams or whatever arbitrary metric we equate with successful viewership.
I won’t be the one to tell you that we should only produce content based on what a producer might be inspired by. We’re in a time where the future of content needs both inspiration and data.
A large part of what we are working on at Streamlytics is focused on this problem. We want to empower content producers to thoroughly understand a genre and emerging trends based on what users are consuming across platforms. Simultaneously, it is our job to apply targeted insights to help balance what is currently in production and help to fuel new ideas and opportunities.
Views, streams, likes, shares, and engagement as we know it, are only the tip of the iceberg. In fact, there are plenty of other tools that exist that can help measure popularity. We’re focused on bringing value to the user in a way that truly reflects who they are.
When an API call or input request is made from either the Streamlytics Insights Pro dashboard or by direct access to our API, we have the ability to display and measure correlations between those inputs. A single input or combination of input requests can be made based on various data points that we store like an artist, actress, genre, streaming platform, user activity, etc. For instance, if a request is made that passes along 1 music artists (A), 1 actress (B), a genre (C) and a streaming platform (D), Streamlytics will incorporate these anonymized vertices into our machine learning models in order to draw associations between A, B, C, D and further refine Streamlytics Outputs.
In simpler terms, we will not only display what people are streaming most across genres by location, age, and gender; we can also display correlations on the most popular genre a person is watching based on what they are listening to. Or, the most-watched actress of a group, let’s say millennials, based on their viewing patterns across Netflix, Hulu, and Amazon. To take it one step further, we will also be able to make accurate recommendations across platforms on what users should watch. For example, a user might get a recommendation for Billions if they are an avid viewer of Power.
For content producers, whether you’re at a studio, network, brand, or agency, having this kind of data can be transformative, not just to the creative process, but also to the future of producing content.
For more information about Streamlytics or to schedule a demo, contact us here.