SkillShare: Think Like a PM

Your
data
is
telling
you
something.

$1B

Who
cares?

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Listen
to
it.

$1B

Kevin
Systrom
realizes
customers
only
use

their
product
for
one
thing:
photos.
Burbn

dies,
Instagram
is
born.

Who
cares?

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Agenda

•  Background

•  IdenEfying
opportuniEes

•  Using
metrics
to
prioriEze

•  TesEng
hypothesis
with
experiments

•  Running
“post-‐mortem”
analysis

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Goals
of
“Think
Like
a
PM”

•  Introduce
the
idea
of
data
driven
PM’ing

–  Focus
on
an
example
using
user
data

•  Review
the
“end-‐to-‐end”
process
of
a
data

driven
feature

–  Use
Foursquare
as
an
illustraEve
example

•  Provide
you
with
another
tool
for
approaching

product
development

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

How
do
PMs
decide
what
features
to
build?

•  Data

•  Talking
to
customers

•  Vision
about
the
future
of
the
product

•  Beliefs

•  Wild-‐ass
guesses

•  Looking
at
the
compeEEon

•  DirecEon
from
managers
/
execs

•  They
don’t
(indecision
strikes!)

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

What
types
of
data
do
PMs
focus
on?

•  Market
data

–  “CompeEtors
that
have
focused
on
Z
approach
have
out-‐
performed
and
we
should
consider
that…”

•  Anecdotal
data

–  Eg,
“When
we
talk
to
customers,
they
always
complain

about
Y
taking
too
long…”

•  User
data

–  We
know
25%
of
users
take
X
acEon
in
the
game…”

–  Some
famous
examples:
Instagram’s
pivot,
Facebook’s

localizaEon
eﬀorts,
Zynga’s
dominance
of
FB
channels

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Agenda

•  Background

•  Iden4fying
opportuni4es

•  Using
metrics
to
prioriEze

•  TesEng
hypothesis
with
experiments

•  Running
analysis

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Why
study
Foursquare?

•  Everyone
can
use
it
(it’s
free)

•  People
are
familiar
with
it
(25M
users)

•  It’s
an
evolving
product
–
you
can
observe
the

Foursquare
team
making
changes
to
the
product

•  Clear
deﬁned
user
ﬂows
&
acEons
to
study

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Foursquare
top
level
metrics:
the
“Vanity”

metrics

•  2B
“check-‐ins”

•  25M
registered
users

•  7.2M+
daily
acEve
users
(DAU)

•  20%
of
searches
result
in
a
check-‐in

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

NOTE:
Stats
from
TechCrunch,
“Foursquare
looks
into
a
4th
round”,
Nov.
2,
2012

Two
things
to
remember
when
working
with

data

What
is
this?

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

NOTE:
Stats
from
TechCrunch,
“Foursquare
looks
into
a
4th
round”,
Nov.
2,
2012

Start
with
the
full
picture,
peel
back
layers
of

the
onion

Zoom
out
so
you
can

And
then
you
can
work
on

see
the
whole
picture…
peeling
back
the
layers…

It’s
a
bridge!

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Key
steps
to
idenEfying
opportuniEes

1)  Define
a
clear,
measurable
goal

–  Eg,
“We
want
to
increase
Foursquare
check-‐ins
/
day”

2)  Define
the
relevant
data
set

–  Eg
“What
drives
daily
check-‐ins?”

3)  Determine
the
status
quo

–  Eg,
“What
does
the
current
data
show
about
daily
check-‐
ins?”

4)  IdenEfy
opportuniEes
to
improve
the
goal

–  Eg,
“What
are
the
inflecEon
points?”

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Deﬁning
a
clear,
measurable
goal

Foursquare
derives
value
from

loca4on
data

•  Check-‐ins
are
a
criEcal
piece

(eg
build
the
database
of

locaEon
data)

•  They
have
viral
value
(eg

“Kenton
checked
in
here…)

•  Check-‐in
rates
indicate
the

health
of
the
app
/
user

base
(eg,
Check-‐ins
/
day
is
a

good
indicator
of
user

acEvity)

•  Result:
Check-‐ins
could
be
a

great
piece
of
data
to

understand
beCer

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

What’s
the
anatomy
of
a
check-‐in?

iPhone
home
screen
Foursquare
home
Loca4on
picker
Check
in
details

•  How
many
•  How
many
users
•  How
many
users
•  How
many
users

users?
reach
it
daily?
reach
it
daily?
reach
it
daily?

•  How
many
•  How
many
•  How
many
•  How
many
share
on

decide
to
login
decide
to
click
to
decide
to
select
social
media?
On

on
any
given
iniEate
a
check
an
actual
twiper?
On

day?
in?
locaEon?
facebook?

•  How
many
include
a

photo?

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Foursquare
data:
the
top
level
funnel
of
user

acEvity

Funnel
Step
Users
hiGng
that
step
%
proceeding
from
previous

Total
registered
users
25,000,000

Daily
acEve
users
7,200,000
28.8%

Click
“Check-‐in”
1,800,000
25%

Select
locaEon
900,000
50%

Complete
check-‐in
630,000
70%

Social
Media
sharing
189,000
30%

Share
photo
126,000
20%

No
meta
data
315,000
50%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

NOTE:
Total
registered
users,
DAU
stats
from
TechCrunch,
“Foursquare
looks
into
a
4th
round”,
Nov.
2,
2012.
All
other
numbers
are
SWAG
at
Foursquare
core
funnel

IdenEfy
opportuniEes
by
understanding
what

the
data
suggests
about
user
behavior

•  QuesEons
to
consider:

–  What’s
going
on
at
the
top
of
the
funnel?

–  At
the
bopom
of
the
funnel?

–  Which
acEons
are
we
most
concerned
with?

–  Where
do
we
“lose”
the
most
users?

–  What’s
working
well?
Why?

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Opportunity
#1:
Increase
daily
logins

Funnel
Step
Users
hiGng
that
step
%
proceeding
from
previous

Total
registered
users
25,000,000

Daily
acEve
users
7,200,000
28.8%

Click
“Check-‐in”
1,800,000
25%

1

Select
locaEon
900,000
50%

Only
~29%
of
the
user
base
logs
into
the
app
each

Complete
check-‐in
630,000

day.
One
opportunity
would
be
to
apract
more
70%

users
to
the
app
each
day.
This
would
“widen
the

Social
Media
sharing
189,000
f
the
funnel”

top
o 30%

Share
photo
126,000
20%

No
meta
data
315,000
50%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

NOTE:
Total
registered
users,
DAU
stats
from
TechCrunch,
“Foursquare
looks
into
a
4th
round”,
Nov.
2,
2012.
All
other
numbers
are
SWAG
at
Foursquare
core
funnel

Opportunity
#2:
Increase
the
daily
check-‐ins

Funnel
Step
Users
hiGng
that
step
%
proceeding
from
previous

Total
registered
users
25,000,000

Daily
acEve
users
7,200,000
28.8%

Click
“Check-‐in”
1,800,000
25%

Select
locaEon
900,000
50%

2

Complete
check-‐in
630,000
70%

Only
~25%
of
the
user
base
starts
the
“check-‐in”

Social
Media
sharing
process
each
189,000
is
opportunity
to
increase

day.
There
30%

the
number
of
“check-‐ins”
simply
by
gewng
the

Share
photo
126,000
20%

apenEon
of
our
logged
in
users

No
meta
data
315,000
50%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

NOTE:
Total
registered
users,
DAU
stats
from
TechCrunch,
“Foursquare
looks
into
a
4th
round”,
Nov.
2,
2012.
All
other
numbers
are
SWAG
at
Foursquare
core
funnel

Opportunity
#3:
Increase
the
%
of
users

selecEng
locaEon

Funnel
Step
Users
hiGng
that
step
%
proceeding
from
previous

Total
registered
users
25,000,000

Daily
acEve
users
7,200,000
28.8%

Click
“Check-‐in”
1,800,000
25%

Select
locaEon
900,000
50%

Complete
check-‐in
630,000
70%

3

Social
Media
sharing
189,000
30%

Only
~50%
of
the
users
that
start
a
“check-‐in”

Share
photo
126,000
20%

actually
select
their
locaEon.
There
is
room
to

opEmize
this
step
of
the
funnel
and
minimize
the

No
meta
data
315,000
drop-‐oﬀ
50%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

NOTE:
Total
registered
users,
DAU
stats
from
TechCrunch,
“Foursquare
looks
into
a
4th
round”,
Nov.
2,
2012.
All
other
numbers
are
SWAG
at
Foursquare
core
funnel

Opportunity
#4:
Increase
the
number
of
users

compleEng
the
ﬁnal
check-‐in
step

Funnel
Step
Users
hiGng
that
step
%
proceeding
from
previous

Total
registered
4
sers

u 25,000,000

Daily
acEve
users
7,200,000
28.8%

We
lose
another
30%
of
users
on
the
ﬁnal
step
of

Click
“Check-‐in”
the
“check-‐in.”
Is
there
anyway
to
prevent
that?

1,800,000
25%

Select
locaEon
900,000
50%

Complete
check-‐in
630,000
70%

Social
Media
sharing
189,000
30%

Share
photo
126,000
20%

No
meta
data
315,000
50%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

NOTE:
Total
registered
users,
DAU
stats
from
TechCrunch,
“Foursquare
looks
into
a
4th
round”,
Nov.
2,
2012.
All
other
numbers
are
SWAG
at
Foursquare
core
funnel

Summary:
4
key
steps
to
idenEfying
product

opportuniEes
with
data

Key
things
to
remember:

1)  Deﬁne
a
clear,
measurable
goal:
“Increasing

check-‐ins”

2)  Collect
the
relevant
data
set
&
assemble
it

3)  Determine
the
status
quo

4)  IdenEfy
opportuniEes
to
improve
the
goal

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Dennis
says:
“I’ve
just
realized
that
…
”

…
for
every
photo
that
gets
shared
on

Twiper
via
Foursquare,
we
acquire
2

new
users.
If
we
could
double
the

amount
of
photos
shared,
we’d
double

our
user
base.
How
many
more
photos

can
we
get
users
sharing
on
Twiper?”

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Which
of
the
4
opportuniEes
does
Dennis

want
to
take
advantage
of?

Eeeny
…
meeny
…
miny
…
moe
….

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Opportunity
#5:
Increase
the
top
of
the

funnel
by
increasing
the
bopom!

Funnel
Step
Users
hiGng
that
step
%
proceeding
from
previous

Total
registered
users
25,000,000

Daily
acEve
users
7,200,000
28.8%

Click
“Check-‐in”
5
1,800,000
25%

Select
locaEon
900,000
50%

Dennis’
insight:
If
we
increase
those
sharing
photos,

We
lose
another
30%
of
users
on
the
ﬁnal
step
of

we
will
get
more
users
which
will
increase
the
top
of

Complete
check-‐in
the
“check-‐in.”
Is
there
anyway
t70%

630,000
o
prevent
that?

the
funnel

Social
Media
sharing
189,000
30%

Share
photo
126,000
20%

No
meta
data
315,000
50%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

NOTE:
Total
registered
users,
DAU
stats
from
TechCrunch,
“Foursquare
looks
into
a
4th
round”,
Nov.
2,
2012.
All
other
numbers
are
SWAG
at
Foursquare
core
funnel

Agenda

•  Background

•  IdenEfying
opportuniEes

•  Using
metrics
to
priori4ze

•  TesEng
hypothesis
with
experiments

•  Running
analysis

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Given
Dennis’
goals
of
increasing
photo
shares,

we
need
to
beper
understand
that
data

•  QuesEons
to
consider

–  What
does
the
photo
sharing
funnel
look
like?

–  What
drives
photo
sharing?

–  How
do
photos
get
shared
today?

–  How
can
we
encourage/discourage
that
behavior

to
achieve
our
goals?

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Zoom
in
on
the
social
media
and
photo

sharing
aspect
of
the
funnel

Funnel
Step
Users
hiGng
that
step
%
proceeding
from
previous

Compete
check-‐ins
630,000

Social
media
shared
189,000
30%

Shared
to
Twiper
37,800
20%

Shared
to
Twiper
w/
photo
34,020
90%

Shared
to
FB
151,200
80%

Shared
to
FB
w/
photo
15,120
10%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

#1:
Increase
the
%
of
users
who
share
a

photo
ayer
they’ve
decided
to
tweet

Funnel
Step
Users
hiGng
that
step
%
proceeding
from
previous

Compete
check-‐ins

1
630,000

If
we
increase
the
%
of
users
who
share
a
photo

Social
media
shared
when
they
tweet,
w189,000
that
do
to
our

hat
would
30%

numbers?

Shared
to
Twiper
37,800
20%

Shared
to
Twiper
w/
photo
34,020
90%

Shared
to
FB
151,200
80%

Shared
to
FB
w/
photo
15,120
10%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

By
increasing
Twiper
sharing,
gain
10%+

photo
shares

Funnel
Step
Users
hiGng
that
step
%
proceeding
from
previous

Compete
check-‐ins
630,000

Social
media
shared
189,000
30%

Shared
to
Twiper
37,800
20%

Shared
to
Twiper
w/
photo
37,800
(+10%)
100%

Shared
to
FB
151,200
80%

Shared
to
FB
w/
photo
15,120
10%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

#2:
Increase
the
%
of
people
sharing
via
social

media
channels

Funnel
Step
Users
hiGng
that
step
%
proceeding
from
previous

Compete
check-‐ins
630,000

Social
media
shared
189,000
30%

Shared
to
Twiper
37,800
20%

2

Shared
to
Twiper
w/
photo
90%

What
happens
if
we
increase
the
%
of
people

Shared
to
FB
151,200

sharing
via
social
media
from
30%
to
50%?
80%

Shared
to
FB
w/
photo
15,120
10%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

By
increasing
%
of
people
sharing
via
social,

gain
66.6%+
more
photo
shares!

Funnel
Step
Users
hiGng
that
step
%
proceeding
from
previous

Compete
check-‐ins
630,000

Social
media
shared
315,000
50%

Shared
to
Twiper
63,000
20%

Shared
to
Twiper
w/
photo
56,700
(+66.6%)
90%

Shared
to
FB
252,000
80%

Shared
to
FB
w/
photo
25,200
10%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

#3:
Increase
the
%
of
users
sharing
via
Twiper

vs.
Facebook

Funnel
Step
Users
hiGng
that
step
%
proceeding
from
previous

Compete
check-‐ins
630,000

Social
media
shared
189,000
30%

Shared
to
Twiper
37,800
20%

Shared
to
Twiper
w/
photo
34,020
90%

3

Shared
to
FB
151,200
80%

What
happens
if
we
increase
the
%
of
users
who

Shared
to
FB
w/
photo
15,120

share
via
Twiper
from
20%
to
50%?
10%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

By
increasing
mix
of
social
shares
to
Twiper,

gain
125%+
photo
–
holy
cow!!

Funnel
Step
Users
hiGng
that
step
%
proceeding
from
previous

Compete
check-‐ins
630,000

Social
media
shared
189,000
30%

Shared
to
Twiper
94,500
50%

Shared
to
Twiper
w/
photo
85,050
(+125%)
90%

Shared
to
FB
94,500
50%

Shared
to
FB
w/
photo
9,450
10%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

If
you
were
forced
to
only
make
1
change,

which
would
it
be?

•  Increase
the
%
of
users
who
share
a
photo
when
TweeEng
their

check-‐in

–  Expected
impact:
+10%
increase
in
Tweets
w/
photo

•  Increase
the
%
of
users
who
decide
to
share
his/her
check-‐in
on

social
media

–  Expected
impact:
+66%
increase
in
Tweets
w/
photo

•  Increase
%
of
users
who
share
his/her
check-‐in
on
Twiper
vs.

Facebook

–  Expected
impact:
+125%
increase
in
Tweets
w/
photo

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

How
could
you
increase
%
of
users
sharing
via

Twiper
vs.
Facebook?

Op4ons
to
increase
%
of
TwiCer

shares

•  Remove
FB
as
an
opEon

•  Make
Twiper
“Opt-‐out”

•  Provide
incenEve
to
“Tweet”
(eg,

“Extra
Foursquare
points”

•  Make
it
mandatory
for
any
user
w/

a
linked
Twiper
account

•  Move
it
“up”
in
the
funnel

•  Move
it
“down”
in
the
funnel
and

make
it
“opt-‐out”

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

AddiEonal
consideraEons
when
prioriEzing

•  What
if
we
did
mul4ple
features
together?

–  Sure!
That
could
increase
the
expected
impacts
even
further

–  NOTE:
Must
be
careful
w/
experiment
design
here
so
results
aren’t
muddled

•  What
is
the
maximum
%
of
social
media
shares
that
TwiCer
could
get?

–  Data
needed:
What
%
of
users
have
linked
Twiper
accounts?

•  What
if
20%
is
the
maximum
share
percentage
(because
only
20%
of
users
have

TwiCer
linked)

–  You
need
to
apack
a
diﬀerent
part
of
the
funnel

–  Build
a
feature
that
encourages
users
to
link
Twiper
accounts

•  But
there
must
be
more!?

–  Could
be
even
*more*
aggressive
by
puwng
social
media
and
photo
sharing
higher

in
the
funnel

–  Or
could
make
social
media
sharing
“opt-‐out”
vs.
“opt-‐in”
SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Agenda

•  Background

•  IdenEfying
opportuniEes

•  Using
metrics
to
prioriEze

•  Tes4ng
hypothesis
with
experiments

•  Running
analysis

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

A
good
experiment
begins
with
a
clear
hypothesis

•  Our
hypothesis:

–  We
can
increase
the
%
of
users
sharing
to
Twiper

vs.
Facebook
to
50%
by
making
Twiper
“opt-‐out”

–  This
will,
in
turn,
drive
the
number
of
Tweeted

photos
up
125%+

–  For
every
addiEonal
Tweeted
photo,
Foursquare

will
gain
2
new
users
/
day

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

The
goal:
Prove
the
criEcal
aspects
of
our

hypothesis

•  CriEcal
aspects:

–  Get
50%
of
social
media
sharers
to
use
Twiper

–  Drive
up
Tweeted
photos
+125%

–  Acquire
2
new
users
for
each
addiEonal
photo

•  To
prove:

–  Run
a
controlled
A/B
test

–  Setup
a
test
where
50%
of
users
get
status
quo
flow

–  The
other
50%
get
the
new
Twiper
“opt-‐out”
flow

–  Make
sure
you
have
staEsEcally
significant
sample

sizes
(eg
here
were
using
50%,
~300K
check-‐ins)

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Key
steps
to
assembling
the
post-‐mortem

analysis

1)  Collect
&
assemble
data
from
test
vs.
control

–  Eg,
“What
is
the
core
data
from
the
experiment”

2)  Compare
test
results
vs.
expected
results

–  Eg
“What
exceeded
or
missed
expectaEons?”

3)  What
are
the
next
steps

–  Eg,
“Should
we
invest
more
Eme/eﬀort?
If
so,
on

what?
What
will
be
the
impact?”

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Test
shows
40%
sharing
on
Twiper,
resulEng

in
+78%
in
tweeted
photos

Funnel
Step
Control
(50%
of
Test
(50%
of
users)

users)

Compete
check-‐ins
315,000
315,000

Social
media
shared
94,500
30%
94,500
30%

Shared
to
Twiper
18,900
20%
37,800
40%

Shared
to
Twiper
w/
17,000
90%
30,240
(+78%)
80%

photo

Shared
to
FB
75,600
80%
56,700
60%

Shared
to
FB
w/
photo
7,560
10%
5,670
10%

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

NOTE:
Stats
from
TechCrunch,
“Foursquare
looks
into
a
4th
round”,
Nov.
2,
2012

How
does
this
compare
to
expectaEons?
Why

did
this
happen?

Funnel
Step
Expecta4ons
%
proceeding
Test
(50%
of
users)
%
proceeding
Delta

Compete
check-‐ins
315,000
315,000

Social
media
shared
94,500
30%
94,500
30%
~

Shared
to
Twiper
47,250
50%
37,800
40%
-‐10%

Shared
to
Twiper
w/
42,525
(+125%)
90%
30,240
(+78%)
80%
-‐10%

photo

Shared
to
FB
47,250
50%
56,700
60%
+10%

Shared
to
FB
w/
photo
4,725
10%
5,670
10%
~

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Key
steps
to
assembling
the
post-‐mortem

analysis

1)  Collect
&
assemble
data
from
test
vs.
control

–  Eg,
“What
is
the
core
data
from
the
experiment”

2)  Compare
test
results
vs.
expected
results

–  Eg
“What
exceeded
or
missed
expectaEons?”

3)  What
are
take
aways
&
next
steps

–  Eg,
“Should
we
invest
more
Eme/eﬀort?
If
so,
on

what?
What
will
be
the
impact?”

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Key
quesEons
&
take-‐aways

Key
ques4on
Result
Why?
Next
steps

Did
we
get
50%
of
users
No.
We
got
40%
•  Maybe
hit
a
natural
limit
(%
of
•  Determine
natural
limit

to
share
on
Twiper?
users
w/
Twiper
accounts)
•  Consider
encouraging

account
linking

Did
we
get
+125%
No.
We
got
78%
•  Photo
sharing
%
dropped
to
•  Can
we
increase
photo

increase
in
photo
80%
sharing
%?

sharing?
•  We
only
got
40%
sharing
via

Twiper
(vs.
expected
50%)

What’s
the
upside
ley?
12,525
photo
•  If
we
can
tweak
to
hit
goals
of
•  What
%
of
that
upside
is

shares
/
day
50%
and
90%
*truly*
achievable
given

our
results?

Was
the
test
a
success?
Yes!
•  Proved
that
tweaking
Twiper
•  Evaluate
above
opEons,

opEon
can
drive
photo
shares
determine
prioriEes
&

repeat!

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Conclusions

•  OrganizaEon
is
key

–  Start
with
the
big
picture,
peel
back
the
layers

•  Deﬁne
clear
goals,
hypothesis

–  You
won’t
know
if
your
tests
or
features
worked
if
you

don’t
pre-‐deﬁne
a
good
goal
and
hypothesis

•  Data
driven
PM’ing
is
applicable
to
all
aspects

–  We
focused
on
internal
data
but
you
could
use
it
on

market
data,
with
surveys,
with
organizaEonal
issues,

almost
anything…

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

Thanks
&
ﬁnal
notes

•  Slides
will
be
sent
out

•  Contact
info:

–  @kivestu

–  kivestu@gmail.com

–  kentonkivestu.com
(thoughts
on
product

development,
mobile)

SkillShare:
Think
Like
a
PM,
Kenton
Kivestu

SkillShare: Think Like a PM

Recomendados

Recomendados

Más contenido relacionado

Similar a SkillShare: Think Like a PM

Similar a SkillShare: Think Like a PM (20)

SkillShare: Think Like a PM