A Music industry learning launch

Challenge

I am an aspiring music producer and a software engineer that provides software-based services to  companies in the music industry.

While attending several music industry networking events I noted record label artist & repertoire  managers (A&R) have several common challenges. One of those challenges is appropriately handling  the influx of artists in seek of their big breakthrough. Listening to an artist’s music sent via email is a  time demanding chore involving lots of manual operations. However, it is almost mandatory to listen  to the submissions as nobody wants to miss out on the next big thing in music. I noted this challenge  and set out to improve their workflow with a software solution.

Selection

Six record label A&R managers I know personally showed interest in the idea I pitched. If the idea  would work, they were willing to pay for the service. Prototypes were made to validate insights and  assumptions on the desired workflow for the software. There were several assumptions that needed  to be addressed with real-life data which can be acquired through a learning launch:

-  Artists will accept the software over sending regular email;

-  The software will reduce the amount of manual operations required for a record label to  communicate with an artist;

-  The automation of the communication process between the artist and record label shall  result in time savings for the record label.

Application

The first learning launch was set up to validate all three aforementioned assumptions. The software  was tested on a single record label website for two weeks. There was only one A&R manager to test  the service in order to reduce software development cost for the learning launch.

Insights from the first learning launch uncovered new ideas and assumptions about the true  customer need. A second and third learning launch took place in order to validate new assumptions.  The second learning launch took place with two record labels. The third learning launch was held  with all 6 record labels participating.

Additionally, for all learning launches qualitative feedback and quantitative feedback was collected in  the form of feedback from users (A&R managers), end-users (artists) as well as software usage  metrics.

Insight

After each learning launch data was analyzed. The first learning launch got artists really excited! With  the introduction of the new software the record label offered a guaranteed response. The record  label was indifferent to the result. Overall the A&R noted a slight decline in manual operations and in  time spent on communicating with artists. Software metrics showed no significant change compared  to the prior situation without the software. The A&R’s feedback surfaced the need for an additional  feature to further simplify communication with the artist. This feedback warranted a second learning  launch.

The second learning launch took place at two record label websites. Artists using the service from the  website of the second record label where indifferent to the introduction of the software, seemingly

because there was no guaranteed response being offered. The additional feature which was  implemented changed software metrics drastically. The time A&R’s spent on communicating with  artists dropped by 60%. Both A&R’s were very much impressed. A needle mover.

For the third learning launch several features were added. Most importantly – the feature to provide  a guaranteed response to every artist, improving overall satisfaction of the artists and pleasing all 6  A&R managers.

Approach

The way the A&R manager advertised my service drastically altered the perceived value for the end- customer (artists). A clear and concise working agreement could have prevented dirty data during  the first learning launch. Running the first learning launch with 2 companies could have uncovered  this data discrepancy earlier in the process. Alternatively, if the record label would not have  advertised with a guaranteed response, getting a guaranteed response likely would not have existed  as a feature today.

Real-life data is critical in a design-thinking approach. Design thinking lends itself to software  development in that it allows you to quickly iterate over ideas and validating assumptions. The  iterative approach containing three consecutive learning launches allowed for quick alterations,  validating newly identified customer needs and assumptions.


A Music industry learning launch Challenge I am an aspiring music producer and a software engineer that provides software - based services to companies in the music industry . While attending several music industry networking events I noted record label artist & repertoire managers ( A&R ) have several common challenges . One of those challenges is appropriately handling the influx of artists in seek of their big breakthrough . Listening to an artist ’s music sent via email is a time demanding chore involving lots of manual operations . However , it is almost mandatory to listen to the submissions as nobody wants to miss out on the next big thing in music . I noted this challenge and set out to improve their workflow with a software solution . Selection Six record label A&R managers I know personally showed interest in the idea I pitched . If the idea would work , they were willing to pay for the service . Prototypes were made to validate insights and assumptions on the desired workflow for the software . There were several assumptions that needed to be addressed with real - life data which can be acquired through a learning launch : - Artists will accept the software over sending regular email ; - The software will reduce the amount of manual operations required for a record label to communicate with an artist ; - The automation of the communication process between the artist and record label shall result in time savings for the record label . Application The first learning launch was set up to validate all three aforementioned assumptions . The software was tested on a single record label website for two weeks . There was only one A&R manager to test the service in order to reduce software development cost for the learning launch . Insights from the first learning launch uncovered new ideas and assumptions about the true customer need . A second and third learning launch took place in order to validate new assumptions . The second learning launch took place with two record labels . The third learning launch was held with all 6 record labels participating . Additionally , for all learning launches qualitative feedback and quantitative feedback was collected in the form of feedback from users ( A&R managers ) , end - users ( artists ) as well as software usage metrics . Insight After each learning launch data was analyzed . The first learning launch got artists really excited ! With the introduction of the new software the record label offered a guaranteed response . The record label was indifferent to the result . Overall the A&R noted a slight decline in manual operations and in time spent on communicating with artists . Software metrics showed no significant change compared to the prior situation without the software . The A&R ’s feedback surfaced the need for an additional feature to further simplify communication with the artist . This feedback warranted a second learning launch . The second learning launch took place at two record label websites . Artists using the service from the website of the second record label where indifferent to the introduction of the software , seemingly because there was no guaranteed response being offered . The additional feature which was implemented changed software metrics drastically . The time A&R ’s spent on communicating with artists dropped by 60 % . Both A&R ’s were very much impressed . A needle mover . For the third learning launch several features were added . Most importantly – the feature to provide a guaranteed response to every artist , improving overall satisfaction of the artists and pleasing all 6 A&R managers . Approach The way the A&R manager advertised my service drastically altered the perceived value for the end- customer ( artists ) . A clear and concise working agreement could have prevented dirty data during the first learning launch . Running the first learning launch with 2 companies could have uncovered this data discrepancy earlier in the process . Alternatively , if the record label would not have advertised with a guaranteed response , getting a guaranteed response likely would not have existed as a feature today . Real - life data is critical in a design - thinking approach . Design thinking lends itself to software development in that it allows you to quickly iterate over ideas and validating assumptions . The iterative approach containing three consecutive learning launches allowed for quick alterations , validating newly identified customer needs and assumptions .