This slide deck was intended to be presented to Spotify executives to encourage Generation Z Employee retention through 3 sturdy recommendations grounded in evidence.

As more and more companies grasp the importance of not just attracting Generation Z but also maintaining them, I urged Spotify to act with haste, abiding by the following actions:

1. Mentorship and Rotation Programs for Career Growth and Self Actualization: Within 2 months, create and publish reports of salaries, including analyses of salaries across different squads and tribes, and hold feedback sessions to evaluate employee feelings.

2. New Bonus Package to Reward a “Job Well Done”: Within 5 months, HR should develop program specifics, working closely with the engineering department to create an algorithm that matches mentees with higher-level mentors.

3. Pay Transparency to Emphasize Ethics and Fairness: By March of 2021, upper management should decide benchmarks for each level of bonus pay and assign an accountant to determine financial allocation for the bonus compensation.

Ultimately, by implementing these 3 strategies, Spotify could achieve Generation Z retention, which will lead to greater innovation and cohesion within the company through increased idea flow between lower and upper levels. This unity will augment profits and warrant the company to continue dominating the music streaming industry for decades to come. 

All research and graphics are curated by Neha Gundavarapu unless other specified in the report.

This brief report summarizes methods for Spotify to streamline a greater competitive advantage through developing a more social platform.

In my research for this project, I determined the company’s core competencies, the industry landscape, and strategic position. I decided what problem I was looking to help them solve and why.

Based on the biggest parts of that problem, I came up with a recommendation and differentiated why this recommendation does not apply to all companies in the space. I looked into whether the company could afford the recommendation and what success looks like if adopted?

Lastly, I utilized frameworks from my strategy class to help guide my thinking and lead the reader through the narratives. The frameworks included virtuous cycle, RBV (resource-based view), Porter’s 5 Forces, and financial ratio analysis.

4. “Hit Predictor”: Machine Learning Python App

Song Search is a tinder-style playlist builder using Spotify’s reccomendation code, which is embedded in their open-source API. The user inputs an artist that they like, and will recieve song recommendations one at a time. They can choose to listen to the song through a 30 second preview, and if they like it— they add it to they current playlist. If they don’t like it, they are able to simply skip. After the user is satisfied, they are able to create a playlist out of all the songs they liked, which then creates a custom playlist right in their Spotify from the app itself.

The video to the left demonstrates my thinking behind the code.

2 teammates and I used machine learning in Python (Pandas) and data mining in SQL to construct a “Hit Predictor” for a hypothetical music label. We extracted a data frame of 50,000 songs, using a set of 9 features to built and run a logistic regression model. We then built a test/train program that predicts with 83% accuracy whether any song on Spotify will be a hit or a flop in any given decade. We also used a k-nearest-neighbors function to find similar songs. Lastly, I used my Tableau knowledge to visualize this data in an easily digestible format for my audience.