1.
engagement & reality
chris.wiggins@columbia.edu
chris.wiggins@nytimes.com
@chrishwiggins
this talk: bit.ly/nyt-engagement

2.
1851 1996

3.
example:
millions of views per hour2015

4.
data science: the web
is your “online presence”

5.
data science: the web
is a microscope

6.
data science: the web
is an experimental tool

7.
data science: the web
is an optimization tool

8.
news: 20th century
church state

9.
news: 21st century
church state
engineering

10.
news: 21st century
church state
engineering

11.
supervised learning, e.g.,
“the funnel”
innovation report, 2014

12.
interpreting supervised learningsupercoolstuff
collaboration w/b. chen

13.
interpreting supervised learningsupercoolstuff

14.
optimization & learning, e.g.,
popular mechanics, 2015

15.
getting to know the readers
daeil kim, cf. bit.ly/nyt-engagement

16.
audiences matter

17.
audiences matter
innovation report, 2014

18.
R.I.P. good times
this talk: bit.ly/nyt-engagement

19.
“a startup is a temporary organization in search of a
repeatable and scalable business model” —Steve Blank
this talk: bit.ly/nyt-engagement

20.
every publisher is now a startup
this talk: bit.ly/nyt-engagement

21.
what else is there besides clicks?

22.
what else is there besides clicks?
this talk: bit.ly/nyt-engagement

23.
what else is there besides clicks?
this talk: bit.ly/nyt-engagement

24.
“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…?

25.
“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…?

26.
“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…?

27.
“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…?

28.
WWND?

29.
WWND?
what would $NFLX do?

30.
from “data scientists @ work”
-Caitlin Smallwood
VP, Science and Algorithms at Netflix
this talk: bit.ly/nyt-engagement

31.
from “data scientists @ work”
-Caitlin Smallwood
VP, Science and Algorithms at Netflix
this talk: bit.ly/nyt-engagement

32.
WWND?
if your biz model is subscription,
machine learning can help:
Balance predictive power for true KPI
(retention) with
1. interpretability
2. should be
• easy to measure,
• quick to measure,
• or both

33.
ML can help!
“engagement” is hard to define. you choose:
1. poetry
2. philosophy
3. science
Wbinan of f2) which predicts 1)

34.
ML can help!
“engagement” is hard to define. you choose:
1. poetry
2. philosophy
3. 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)

35.
chris.wiggins@columbia.edu
chris.wiggins@nytimes.com
@chrishwiggins
this talk: bit.ly/nyt-engagement
ghost of science past

36.
chris.wiggins@columbia.edu
chris.wiggins@nytimes.com
@chrishwiggins
this talk: bit.ly/nyt-engagement
we’re hiring!