IAB Canada Roadshow 2015: Turning Machine Learning Into Human Insights

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This document discusses the strengths and weaknesses of machines versus humans in making insights from machine learning. It notes that while machines can perform cold calculations quickly and adjust models, they cannot understand outside context problems or human emotions. Humans are better at solving outside context problems, testing hunches, communicating insights, and providing customer service. The document argues that combining the strengths of machines and humans through data-informed decisions, not just data-driven ones, provides the best results.

Marketing

Turning
Machine

Learning
into

Human
Insights

A
D
V
E
R
T
I
S
E
R
S
P
U
B
L
I
S
H
E
R
S
A
U
D
I
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N
C
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Massive

CTR!

Geo-‐target
Demographics
CreaCve

Huge

Sales!

Low

CPA!

Algorithm

8
Reasons
computers
need
humans
3 Sure-fire
tactics for
mind-
blowing,
KPIs!
MagazineOctober,
2015

Is
local
better?

Outside
context
problem:

When
the
context
that
created

results
is
invisible
to
the

machine

-‐
Paul
Goad

Results

Time

CompeEtor
big
sale

Outside
Context
Problem

Overall
campaign
CTR:

0.18%

Geo-‐fence
tacCc
CTR:

0.39%

Machines

•  Make
cold,
hard

calculaEons
with
no

emoEon

•  Sound,
staEsEcal
models

with
no
fuzzy
hunches

•  Make
quick
adjustments

•  MulEple
variable

analysis

•  Never
goes
to
sleep
and

doesn’t
take
Eme
oﬀ

Humans

•  Are
emoEonal
and

understand
emoEon

•  BeXer
at
solving
the

outside
context
problem

•  Test
hunches

•  Sweeping
changes

•  Communicate

•  Are
creaEve

•  Bring
history

•  Provide
customer
service

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