Openstage is a platform that enables artists and fans to better find each other and gives artists in-depth insights into fan behaviour, location and demand with one simple dashboard. Fans place markers for their favourite artists to tell the artists where they want to see them and connect with them so that fans can be ahead of the pack for all announcements and offers such as tickets, special pop up events or street team activations. Artists then have the ability to super-serve their most engaged fans and gain impactful results by making data-driven decisions.
Business Type: Software, Online Platform
Openstage has a subscription plan for artists that gives artists insights about their fans and how to engage with their fans. In order to attract more artists to join the Openstage’s platform, pay the subscription plan and remain with Openstage, Openstage needs to continuously create more added value for their customers. With the existing AI platform that we developed, Openstage is now sure what other data they could use to generate useful values to the artists and artists managers.
Apart from artists and fans, Openstage had an idea to involve a third party group, create a subscription plan to provide insights for this third party group, which could increase their revenues. Venue managers were the first logical choice because the venue managers have a relationship with most artists and fans: the venue managers invite artists to their venue to perform and fans would come to the venues as customers. However, Openstage was not sure if/how their existing data could generate meaningful insights and if there is any other external data that could be used.
We first conducted several brainstorming sessions to come up with potential features that could be added to the existing tools and their added values to artists and venue managers, the most cost-effective solution would be the one that works for both artists and venue managers.
Based on the results of the business assessment and some potential features, we identified several social network data sources that could generate useful insights for both venues and the artists. We found the best source to be related to events since events connect all three relevant entities artists, fans, and venues. With these, we assessed the technical feasibility of extracting relevant insights, built web crawlers, API connectors and conducted exploratory analysis on how these data would perform and whether the perceived added functions could be achieved.
The first step of generating added value related to venues was to create an aggregated database of more than 500,000 music venues around the world. The ETL solution allows the database to be automatically populated to capture the venues used for the artist’s events. One of the main technical challenges that were successfully managed was the removal of duplicates and matching venues found from multiple sources.
From a database of 500,000 venues, 100,000 artists, and almost 3,000,000 events, we developed predictive models to recommend new opportunities for artists and venues based on past experiences. For artists and venues, we created venue and artist genre profile models, and analysed past events using sentiment, popularity, attendees and revenues to develop a recommendation model to suggest the next best artist to invite to perform in a venue, and a list of venues an artist should visit in order to maximise the success rate of an event and their own fame progression. The recommendation system is able to suggest based on three levels, venues that an artist has already visited in the past, new venues that were not visited by an artist but was visited by their peers, so an artist can tap into the peer fan base for new insight.
This tool enables artists to track geographical ticket sales opportunity, identify unknown regions of high demand, and eventually make data-driven decisions to own, discover and fulfil their fan demand and increase live performance output. The data relates directly to those fans who are willing to spend money to see you live, allowing you to reduce the risk of gigging. Moreover, the venue recommendation system uses past events and peer events to suggest what venues to visit with the lowest risk.
Venue manager can know who to invite next and could get more happy customers and more well-attended events tailored to local demand, will be able to identify and engage with potential customers whilst measuring the appetite for events in the area before booking them, and also be able to spot emerging trends via genre and buzz that brings the opportunity to beat the competition.
Note: Openstage and the related IP and technologies are proprietary to Openstageit Limited, if you are interested in knowing more about Openstage and their services and products, please feel free to email: firstname.lastname@example.org
We help OpenStage build a tour planning AI tool for artists and venue managers to minimise risk and drive best results through data warehousing for data gathered from the web and developing predictive models to predict new opportunities, so artists can plan their next tour easier with less risk through data-driven decision and venue managers are empowered to plan events that increase profit while reducing risk.