Welcome to the future of education!<\/p>\n
At Embibe, we have just one mission – to truly personalise education. Because every child deserves it. This has led us to embark on this noblest of journeys to deliver life and learning outcomes for every student! Rooted in consumer behavior, we are leveraging AI to deliver personalised achievement journeys for every student.<\/p>\n
Embibe has traversed a long journey from a data-centric product to an AI platform. On this journey, we have realised that the most powerful teams are: 1. Vision Led in understanding student context and obsessed with success; 2. Self-Organising in defining their own agenda; 3. Intellectually Fierce and Globally Conscious in their choices, and 4. Consistently Excellent in their execution.<\/p>\n
After exploring a deeply functional organisational structure in engineering, we are now evolving towards a problem-solving team structure that manifests at the platform and backend level as an agile team supporting a unified front-end and augmented by a strong Architect + Principal Engineer + Advisory Group for technical mentoring. This document outlines the problem statement and other aspects of Gamification, A\/B Testing, Growth Hacking and Notifications.<\/p>\n
Learning science is nothing without homogenised control testing. A\/B experiments have yielded great dividends in user time spent and stickiness. Further, gamification can be implemented at many levels including but not limited to correctly challenging adaptive algorithms, CTA optimisation and more. Exciting and timely notifications have been known to build strong addiction and return visitors to a lot of digital products. This team aims to use these interdisciplinary paradigms to drive up retention and product ROI for stakeholders.<\/p>\n
We are inspired by:<\/p>\n
| METRIC NAME<\/th>\n | UNIT<\/th>\n | FREQUENCY<\/th>\n<\/tr>\n |
|---|---|---|
| Engagement<\/td>\n | Percentage<\/td>\n | Daily<\/td>\n<\/tr>\n |
| CTR<\/td>\n | Ratio<\/td>\n | Daily<\/td>\n<\/tr>\n |
| Engagement<\/td>\n | Minutes<\/td>\n | Daily, Weekly<\/td>\n<\/tr>\n |
| Session Time<\/td>\n | Minutes<\/td>\n | Daily, Weekly, Monthly<\/td>\n<\/tr>\n |
| Total Virtual Currency<\/td>\n | Embiums<\/td>\n | <\/td>\n<\/tr>\n |
| Total Redemptions<\/td>\n | Percentage<\/td>\n | Weekly, Monthly<\/td>\n<\/tr>\n |
| Notification Led Sessions<\/td>\n | Number<\/td>\n | Daily<\/td>\n<\/tr>\n |
| Retention<\/td>\n | Percentage<\/td>\n | Daily, Weekly, Monthly<\/td>\n<\/tr>\n |
| Average Session Duration<\/td>\n | Minutes<\/td>\n | Daily<\/td>\n<\/tr>\n |
| Pages per Session<\/td>\n | Number<\/td>\n | <\/td>\n<\/tr>\n |
| AU to EU to PU to UPU Conversion<\/td>\n | Percentage<\/td>\n | Weekly<\/td>\n<\/tr>\n |
| Trial to Paid Auto Conversion<\/td>\n | Percentage<\/td>\n | Weekly<\/td>\n<\/tr>\n |
| Bounce Rate<\/td>\n | Percentage<\/td>\n | Daily<\/td>\n<\/tr>\n |
| Content Error Rate<\/td>\n | Percentage<\/td>\n | Daily<\/td>\n<\/tr>\n |
| Algo Success Rate (NQE: >10 attempts, Search Result CTR near 100%, etc.)<\/td>\n | Percentage<\/td>\n | Daily<\/td>\n<\/tr>\n |
| Content Load Time (Media, Question Types- esp FIB, Solve With Us)<\/td>\n | Milliseconds<\/td>\n | Daily<\/td>\n<\/tr>\n |
| Content Completion<\/td>\n | Percentage<\/td>\n | Daily<\/td>\n<\/tr>\n |
| Number of Experiments<\/td>\n | Number<\/td>\n | Daily<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nL2 PROBLEMS OWNED<\/h4>\nWe believe in building an organisation at the intersection of domain modelling and problem intuition. While the L1 teams give us the flexibility to have a multi-faceted view of the problem and cluster similar problems together, the L2 structure ensures independent and focused problem-solving. The following L2 teams have been suggested for the L1 problem stated above:<\/p>\n
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