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Case Study for Product Analytics Candidates

Instructions

This case study is based on actual data and business problems from Babbel. It is confidential and should not be shared.

This is your opportunity to show off both your data analysis and visualization skills and your ability to communicate your findings to an audience of typical stakeholders in a compelling way.

Please work through the below case study and prepare a presentation (Powerpoint, Keynote, or comparable) to walk a mixed audience through your results during your case study interview. You’ll have 20-25 minutes to present. Afterwards, there will be about 20 minutes of time for questions and discussion. In light of the COVID-19 pandemic, please be prepared to present your slides remotely using Google Meet (formerly Hangouts) and ensure that your browser is set up for screen sharing.

Imagine your audience will be your typical stakeholder group (as well as potential team mates): likes straight to the point, likes actionable, likes easy to read.

We expect the entire challenge to take you 4-5 hours, but you may dedicate as much time as you like. Please note that we expect you to submit your presentation within one week of receiving this case study. Feel free to choose whatever tool you like to analyze and visualize the data attached, may it be Excel, Python or anything else. In case you do not manage to work through all questions, just let us know what you’d like to do.

Learning Engagement in the Babbel Ecosystem

Babbel is a quality leader when it comes to learning a language via digital self-study. We build our product for those who are aiming to become conversational in a language and are willing to pay for achieving this goal.

Our products offer a portfolio of Learning Experiences – different features offering learning activities with a stand-alone learning value proposition. Our main learning experiences are:

  • Lessons – short units that introduce vocabulary and grammar via contextual content, including exercises

  • Review – vocabulary review to practice previously introduced words and phrases

  • Audio experiences such as Podcasts

  • Games – fun and casual ways to practice vocabulary

  • Live Classes – Digital live classroom (~60 min) with a teacher and a small group of students

The respective learning content for these is designed by our team of linguistic experts and language teachers, and specifically tailored but not necessarily available in each learning language.

Task

Your product team wants to explore new ways of activating learners in their weeks of learning with Babbel. You are tasked with analysing how Babbel learners engage with our learning experiences in their first two months after gaining full access and identify possible avenues for improvement.

Please use the attached dataset and the following guiding questions to analyse the early learning engagement journey, and point out optimisation potential:

  1. Given the data you have available, how would you operationalise user behavior that marks successful activation?

  2. How does the early engagement journey differ across learner segments?

  3. How are live classes being used in conjunction with other learning experiences? Do Live subscribers behave differently than other subscribers?

  4. Assume that the team is planning to build an experimental learning path aimed at activating new learners in their first three weeks after subscribing. The team wants to A/B test and iterate on the feature every 2 weeks so they cannot optimize based on three week lagged behavior. Based on your experience and the dataset provided, what would you advise the team to do? Are there any risks they should be aware of?

Data

The dataset “learning_sessions.csv” contains learning session data of subscribers in their eight weeks with Babbel, starting with the time of subscription. Each row represents a unique learning session.1

Column name Definitions
uuid String, user identifier
subscription_started_at Timestamp of the start of the subscription (in UTC)
age String, age bracket of the user
motivation String, answer to question during registration: "What is the main reason you want to learn a new language?"
subscription_type String, type of the user’s subscription
● Core (Standard access to all experiences except Live)
● Live (Access to all experiences + Live)
geo_area String, indicates the geographic areas that the subscription was purchased from
● ESM (English speaking other than U.S.A)
● GSM (German speaking)
● FIM (French speaking)
● SPM (Spanish speaking)
● USA
learning_activity String, indicates the learning activity used in the session
learning_os String, indicates the operating system of the user’s device
learning_language String, indicates the language the user is learning
session_ended_as String, state in which the session was ended (completed, aborted, missing)
session_started_at Timestamp of learning session start (in UTC)
session_local_started_at Timestamp of learning session start in user’s local timezone
time_spent Float, minutes spent learning

Footnotes

  1. This dataset originates from actual user behavior but we sampled, anonymised it, and changed some details here and there so as not to expose any sensitive information. ↩