background-image: url(https://raw.githubusercontent.com/jenitivecase/Kognito_DS_presentation/master/Klogo.png) background-size: 200px background-position: top 100px left 100px class: center, middle #Key Data-Driven Questions Jennifer Brussow | May 2019 ??? --- background-image: url(./space_bkg.png) class: inverse, center, middle # **A multitude of data points...** --- background-image: url(./data_bkg.png) class: center, middle # **How can data drive revenue?** --- class: inverse, left, middle .xxlarge[ * Marketing materials and targeting * User retention and UX * Productize user score data ] --- # Plan to get there: .xxlarge[ Step 1. What do we have? Step 2. How are people using it? ] --- # What do we have? -- .xlarge[ - What types of data are collected? ] -- .xlarge[ - Where are they stored? ] -- .xlarge[ - What scoring schema are used across sims? + What validity research could be used to support sims' use? ] --- # How are people using it? .xlarge[ - Basic usage statistics + User demographics + Attempts + Frequency of sim use ] --- # How are people using it? .xlarge[ - Score distributions + Overall scores + Subscores + Nodes selected ] --- # How are people using it? .xlarge[ - Use of key features + Coach feedback + Undos + Other interactive features ] --- class: inverse # Marketing - materials and targeting .xlarge[ - Validity research to support claims ] -- .xlarge[ - Information about feature use to support claims ] -- .xlarge[ - Predictive modeling to support targeting ] --- class: inverse # User retention and UX .xlarge[ - Information about feature use to drive UX decisions ] -- .xlarge[ - A/B testing to test UX features ] -- .xlarge[ - Predictive modeling for user retention ] --- class: inverse #Productize user score data .xlarge[ - Information about scoring to work toward standardization ] -- .xlarge[ - Outcomes research to drive validity claims ] -- .xlarge[ - Predictive modeling to predict future success ] --- class: center, middle background-image: url(./data_bkg.png) # Thanks! Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan).