chris wiggins: "engagement & reality"
TRANSCRIPT
1851 1996
example:
millions of views per hour2015
data science: the web
is your “online presence”
data science: the web
is a microscope
data science: the web
is an experimental tool
data science: the web
is an optimization tool
news: 20th century
church state
news: 21st century
church state
engineering
news: 21st century
church state
engineering
supervised learning, e.g.,
“the funnel”innovation report, 2014
interpreting supervised learningsu
per
cool
stu
ff
collaboration w/b. chen
interpreting supervised learningsu
per
cool
stu
ff
optimization & learning, e.g.,
popular mechanics, 2015
getting to know the readers
daeil kim, cf. bit.ly/nyt-engagement
audiences matter
audiences matter
innovation report, 2014
R.I.P. good times
this talk: bit.ly/nyt-engagement
“a startup is a temporary organization in search of a repeatable and scalable business model” —Steve Blank
this talk: bit.ly/nyt-engagement
every publisher is now a startup
this talk: bit.ly/nyt-engagement
what else is there besides clicks?
what else is there besides clicks?
this talk: bit.ly/nyt-engagement
what else is there besides clicks?
this talk: bit.ly/nyt-engagement
“engagement”: examples
if your biz model is clicks,engagement=clicks
if your biz model is sharing,engagement=sharing
if your biz model is time on page,engagement=time on page
if your biz model is subscription…?
“engagement”: examples
if your biz model is clicks,engagement=clicks
if your biz model is sharing,engagement=sharing
if your biz model is time on page,engagement=time on page
if your biz model is subscription…?
“engagement”: examples
if your biz model is clicks,engagement=clicks
if your biz model is sharing,engagement=sharing
if your biz model is time on page,engagement=time on page
if your biz model is subscription…?
“engagement”: examples
if your biz model is clicks,engagement=clicks
if your biz model is sharing,engagement=sharing
if your biz model is time on page,engagement=time on page
if your biz model is subscription…?
WWND?
WWND?what would $NFLX do?
from “data scientists @ work”
-Caitlin Smallwood VP, Science and Algorithms at Netflix
this talk: bit.ly/nyt-engagement
from “data scientists @ work”
-Caitlin Smallwood VP, Science and Algorithms at Netflix
this talk: bit.ly/nyt-engagement
WWND?if your biz model is subscription,machine learning can help:
Balance predictive power for true KPI (retention) with
1.interpretability2.should be
• easy to measure, • quick to measure, • or both
ML can help!“engagement” is hard to define. you choose:
1.poetry 2.philosophy3.science
Wbinan of f2) which predicts 1)
ML can help!“engagement” is hard to define. you choose:
1.poetry 2.philosophy3.science
WE CHOSE SCIENCE:• find 1) reality: KPI, preferably units of USD• find 2) interpretable and observable features• learn combination of 2) which predicts 1)