Nerdy SPAC Presentation Deck
2.
Matching Layer
Selecting the best expert
We optimize across a high dimensional set of
features to identify the learner-to-expert matches
with the best projected probability of a successful
interaction
100+
Features per learner
and instructor¹
>800K
Learner and expert
matches²
Sample Data set (Experts)
● Sessions per student motivation cohorts
● Sessions per requested subject
● Standardized test math score
Standardized test science score
(+ more data points)
●
Rating Match Quality - considering over 100 factors
Sample Data set
(Learners)
Student grade
• Current grade
performance
● Disposition towards.
learning
Desired subject
● Schedule availability
Previous test score
Motivation
●
●
● Communication
preferences
● (+more data points)
James
Pankaj
Kumiko
Defined as data points generated from student attributes, instructor attributes, past matching, learning interactions from online platform, website and marketing event interactions, and self study interaction. Amounts exclude Legacy Businesses.
2012 thru December 2020. Amounts exclude Legacy Businesses.
Jason
Pij
Pij
Pij
Priya
Pij
Pij
Pij
Sarah
Pij
Pij
Pij
Match
Score
28
TPG nerdy
PACE
TECH OPPORTUNITIES
Ⓒ Nerdy / TPG Pace Tech Opportunities Corp. 2021View entire presentation