This document provides an overview of predictive analytics. It discusses that predictive analytics uses techniques like data mining, statistics, modeling, machine learning and AI to analyze current data and make predictions about the future. It also explains some key elements of predictive analytics like the importance of data quality, the various statistical tools and techniques used, and how assumptions are critical but must be carefully monitored for validity over time.

1. A PREDICTIVE
ANALYTICS
PRIMER
A N A L Y S I S B Y S H L O K A

2. B Y T H O M A S H .
D A V E N P O R T
A N A L Y S I S B Y S H L O K A

3. P r e d i c t i v e a n a l y t i c s i s t h e
b r a n c h o f t h e a d v a n c e d
a n a l y t i c s w h i c h i s u s e d t o
m a k e p r e d i c t i o n s a b o u t
u n k n o w n f u t u r e e v e n t s .
P R E D I C T I V E
A N A L Y T I C S

4. P r e d i c t i v e a n a l y t i c s u s e s m a n y
t e c h n i q u e s f r o m d a t a m i n i n g ,
s t a t i s t i c s , m o d e l i n g , m a c h i n e
l e a r n i n g , a n d a r t i f i c i a l
i n t e l l i g e n c e t o a n a l y z e c u r r e n t
d a t a t o m a k e p r e d i c t i o n s a b o u t
f u t u r e .

5. 2
1
K n o w w h e r e
y o u ’ r e l i k e l y t o
b e i n t h e f u t u r e .
I d e n t i f y d i f f e r e n t
g r o u p s o f c u s t o m e r s
f o r t a r g e t e d a n a l y s i s
a n d p r e c i s i o n
m a r k e t i n g .
W H Y D O W E N E E D
P R E D I C T I V E A N A L Y T I C S ?

6. 4
3
A n a l y z e y o u r d a t a t o
p r e d i c t i n d i v i d u a l o r
g r o u p b e h a v i o r .
Q u a n t i f y t h e r i s k
a s s o c i a t e d w i t h
c u s t o m e r s o r
a c q u i s i t i o n s .
W H Y D O W E N E E D
P R E D I C T I V E A N A L Y T I C S ?

7. Q u a n t i t a t i v e
a n a l y s i s i s n ’ t
m a g i c — b u t i t i s
n o r m a l l y d o n e
w i t h a l o t o f
p a s t d a t a , a
l i t t l e s t a t i s t i c a l
w i z a r d r y , a n d
s o m e i m p o r t a n t
a s s u m p t i o n s .
ITS NOT
MAGIC!

8. T H A T C A N
M A K E L I F E
E A S I E R
O R
T O U G H !
E L E M E N T S

9. I T S T A R T S W I T H D A T A
It is imperative that any advanced analytics are
based on stable and accurate information.
Therefore, there's huge focus on sound data
management to ensure all data is properly
scrubbed and validated prior to analysis.
01

10. I T S T A R T S W I T H D A T A
Lack of good data is the most common barrier to
organisations seeking to employ predictive
analytics.
If you have multiple channels, it is imperative
that they capture data on customer purchases in
the same way your previous channels did.
01

11. I T S T A R T S W I T H D A T A
it’s a fairly tough job to create a single customer
data warehouse with unique customer IDs on
everyone, and all past purchases customers have
made through all channels. If you’ve already done
that, you’ve got an incredible asset for predictive
customer analytics.
01

12. P r e d i c t i v e
a n a l y t i c s
e n c o m p a s s e s a
h o s t o f t o o l s a n d
t e c h n i q u e s t o
a c h i e v e y o u r
s p e c i f i c g o a l s a n d
i n c r e a s e y o u r
k n o w l e d g e .
02
T H E S T A T I S T I C S

13. P R E D I C T I V E A N A L Y T I C S
T O O L S A N D T E C H N I Q U E S

14. R E G R E S S I O N A N A L Y S I S I N I T S
V A R I O U S F O R M S I S T H E P R I M A R Y
T O O L
An analyst hypothesizes
that a set of independent
variables are statistically
correlated with the
purchase of a product for
a sample of customers.
The analyst performs a
regression analysis to
see just how correlated
each variable is
and finds that each
variable in the model is
important.
The analyst can then use
the regression
coefficients to create a
score predicting the
likelihood of the
purchase.
T H E S T A T I S T I C S

15. E v e r y m o d e l
h a s t h e m , a n d
i t ’ s i m p o r t a n t
t o k n o w w h a t
t h e y a r e a n d
m o n i t o r
w h e t h e r t h e y
a r e s t i l l t r u e .
04
A S S U M P T I O N

16. S i n c e f a u l t y o r
o b s o l e t e
a s s u m p t i o n s c a n
c l e a r l y b r i n g
d o w n w h o l e
b a n k s a n d e v e n
( n e a r l y ! ) w h o l e
e c o n o m i e s , i t ’ s
p r e t t y i m p o r t a n t
t h a t t h e y b e
c a r e f u l l y
e x a m i n e d .
A S S U M P T I O N

17. M a n a g e r s s h o u l d a l w a y s a s k a n a l y s t s
w h a t t h e k e y a s s u m p t i o n s a r e , a n d
w h a t w o u l d h a v e t o h a p p e n f o r t h e m
t o n o l o n g e r b e v a l i d . A n d b o t h
m a n a g e r s a n d a n a l y s t s s h o u l d
c o n t i n u a l l y m o n i t o r t h e w o r l d t o s e e
i f k e y f a c t o r s i n v o l v e d i n a s s u m p t i o n s
m i g h t h a v e c h a n g e d o v e r t i m e .
A S S U M P T I O N

18. I f y o u r m o d e l w a s
c r e a t e d s e v e r a l
y e a r s a g o , i t m a y
n o l o n g e r
a c c u r a t e l y p r e d i c t
c u r r e n t b e h a v i o u r .
T h e g r e a t e r t h e
e l a p s e d t i m e , t h e
m o r e l i k e l y
c u s t o m e r b e h a v i o u r
h a s c h a n g e d .
A
R E A S O N S F O R I N V A L I D
A S S U M P T I O N S
A: TIME

19. N e t f l i x p r e d i c t i v e
m o d e l s t h a t w e r e
c r e a t e d o n e a r l y
I n t e r n e t
u s e r s ( t e c h n i c a l l y -
f o c u s e d a n d y o u n g )
h a d t o b e r e t i r e d
b e c a u s e l a t e r
I n t e r n e t u s e r s
( e s s e n t i a l l y
e v e r y o n e ) w e r e
d i f f e r e n t . A
R E A S O N S F O R I N V A L I D
A S S U M P T I O N S
A: TIME

20. P r e d i c t i v e m o d e l ’ s
a s s u m p t i o n s m a y n o
l o n g e r b e v a l i d i s i f
t h e a n a l y s t d i d n ’ t
i n c l u d e a k e y
v a r i a b l e i n t h e
m o d e l , a n d t h a t
v a r i a b l e h a s
c h a n g e d
s u b s t a n t i a l l y o v e r
t i m e .
A
R E A S O N S F O R I N V A L I D
A S S U M P T I O N S
B: MISSING
KEY
VARIABLE

21. A
R E A S O N S F O R I N V A L I D
A S S U M P T I O N S
B: MISSING
KEY
VARIABLE
T h e f i n a n c i a l c r i s i s o f
2 0 0 8 - 9 , c a u s e d l a r g e l y
b y i n v a l i d m o d e l s
p r e d i c t i n g h o w l i k e l y
m o r t g a g e c u s t o m e r s w e r e
t o r e p a y t h e i r l o a n s . T h e
m o d e l s d i d n ’ t i n c l u d e
t h e p o s s i b i l i t y t h a t
h o u s i n g p r i c e s m i g h t
s t o p r i s i n g o r t h e y m i g h t
f a l l . I n e s s e n c e , t h e f a c t
t h a t h o u s i n g p r i c e s
w o u l d a l w a y s r i s e w a s a
h i d d e n a s s u m p t i o n .

22. A S K Q U E S T I O N S
C a n y o u t e l l m e s o m e t h i n g a b o u t t h e
s o u r c e o f d a t a y o u u s e d i n y o u r
a n a l y s i s ?
A r e y o u s u r e t h e s a m p l e d a t a a r e
r e p r e s e n t a t i v e o f t h e p o p u l a t i o n ?
A r e t h e r e a n y o u t l i e r s i n y o u r d a t a
d i s t r i b u t i o n ? H o w d i d t h e y a f f e c t t h e
r e s u l t s ?
W h a t a s s u m p t i o n s a r e b e h i n d y o u r
a n a l y s i s ?
A r e t h e r e a n y c o n d i t i o n s t h a t w o u l d
m a k e y o u r a s s u m p t i o n s i n v a l i d ?
W H A T S H O U L D
M A N A G E R S D O ?