Asaii Uses AI to Discover Musical Artists
Asaii uses artificial intelligence and machine learning software to identify what the next big hits will be in the music industry. The company offers a SaaS product that they sell to record labels, Fortune 500 companies, and artist managers.
I sat down with the Christopher Zhang, one of three co-founders at Asaii, to hear how they are using technology to look for the next Justin Timberlake.
The music industry has undergone some dramatic changes in the last three decades. From a boom in the late 90s to a bust in the 2000s, it is the prime example of the importance of adapting to shifting market environments (especially through digitization). As revenues grow through newer players in the music industry like Spotify, Apple Music, and YouTube, the industry is seeing a resurgence in both revenue and profit.
From an artist’s perspective, the market has been democratized as barriers to releasing music have diminished. These trends have led to a rapid increase in the number of weekly releases as well as an increase in data availability. In order to use these changes to their advantage, record labels are making efforts to adapt their decision making processes to a more data-driven approach. This challenges A&R (Artist & Repertoire) Managers, who are responsible for finding new talents, as well as Product Managers, who are trying to optimize their spendings and campaigns. A successful adaption is absolutely crucial for developing hit singles and for building sustainable artist careers.
But how does Asaii fit into all of this?
How Asaii Got Started
In 2016, three UC-Berkeley undergraduates, Christopher Zhang, Sony Theakanath, and Austin Chen, got together to explore their shared passion for music and technology.
They would frequently compare notes on what the best new music artists were. However, given the ever-increasing number of artists releasing hits, it was difficult to parse through all of the options.
Given that all three of them were Cal-trained engineers, they created a script that would identify artists who were currently trending on Soundcloud and Spotify, but who were not household names. As they kept improving the algorithm, they started wondering if they were onto something bigger. Perhaps this wasn’t just for their own discovery of great music. What if record labels could use something like this too?
Co-Founders of Asaii
The trio quickly built a pitch deck and sent it out to ten record labels. To their own surprise, eight of the ten stated interest within two weeks. A couple of weeks later, they met with was Mom + Pop Music, an NYC-based record label best known for Flume and Poliça. Soon, Mom + Pop turned into a mentor for Asaii, providing knowledge and insights about the needs of the customer in the music industry. Based on the feedback they received, Asaii went on to develop two core products:
Discover is the original product and directly based on the initial idea described before. It provides AI curated lists of talent that fit with the label’s strategy. Asaii’s puts all of this data on a leaderboard dashboard, giving it an Asaii score to help users make more data-informed decisions on whether to engage an artist. Or if users want deeper data insights, they can use Asaii’s data insights tool, which provides more detailed numbers for making decisions.
Due to the multitude of channels (TV, offline, radio, streaming, press, social media, etc.) by which music is consumed and discovered, it has been a continuously growing challenge for labels and artist managers to figure out what makes an artist successful. To tackle the problem, Asaii went on to build out a tracking tool that analyzes the effects of so-called trigger-events on three distinct buckets: social, sales, and media. What happens to G-Eazy’s Instagram followers after he performs on SNL. What happens to Flume’s Spotify streams when an influencer mentions one of their songs on YouTube?
Asaii Discover Platform
A Competitive Landscape
Data-driven talent discovery is certainly one of the most important challenges the music industry is facing. Both music labels and startups like Asaii are searching for the “holy grail algorithm” to tackle this problem. Music XRay, a startup using AI and crowdsourcing to predict the next big pop superstars, raised its last round at a valuation of $100 million.
However, Asaii wants to offer more than just finding talent. They hope to offer a solution that identifies the optimal mix of marketing and promotions as well. As Asaii co-founder Chris Zhang puts it, “we don’t want to become just another dashboard company.”
“We don’t want to become just another dashboard company.” Chris Zhang, Co-Founder of Asaii
Asaii’s goal is not to compete with record labels, but to help them draw the best insights and conclusions from the masses of data that have become available within the past few years through the rise of streaming, social media, and marketing analytics.
So far, Asaii has signed four artists amongst their 20 paying customers and is exploring partnerships with several other labels. However, the founders highlight that their focus is on honing the product with feedback from their existing customers before expanding too quickly.
Asaii’s official launch was this week at SXSW (South by Southwest). We are excited to see what’s next for the Berkeley trio.