How Big Data could help for pricing

1. www.decideo.fr/bruley
PPrriicciinngg
DDiissccrriimmiinnaattiioonn
DDyynnaammiicc
PPeerrssoonnaalliizzaattiioonn
RReeaall TTiimmee
September 2013
michel.bruley@teradata.com

2. www.decideo.fr/bruley
PPrriiccee DDiissccrriimmiinnaattiioonn
The law of demand tells us that demanders are different,
and so are willing to pay different amounts (elasticity of
demand differs and values are different—so different
willingness to pay)
What it means:
– Charge different prices to different consumers in an
effort to increase market and profits

3. www.decideo.fr/bruley
IItt IIss DDoonnee EEvveerryywwhheerree
(Especially to Foreigners)

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PPrriiccee PPrrooffiitt iinnccrreeaassee

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WWhheerree DDooeess PPrriicciinngg FFiitt??
Price is rarely the headline
Pricing is never about the number, it’s about the model
Part of the business model
– How do we make money? How much?
– Revenue/profit/shipment forecasts
Supports core value proposition
– “Our product/service saves you $$$$…”
– …and we want 20% of the savings
Often an obstacle to buying
– Too complex
– Much too high (sticker shock)
– Much too low (desperate, unprofitable)
– Free (no reason to trade up)

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PPrriicciinngg OObbjjeeccttiivveess
FIRST…
– Don’t make price the primary issue
– Don’t over-complicate the sale
– Don’t require customers to be smart
– Don’t change prices too often
THEN…
– Support the business model/plan
– Reinforce benefit of products/services
– Pick natural units
– Make correct ordering easier

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PPrriiccee MMooddeellss
Fixed pricing
– one national price, everywhere, for everyone
Dynamic pricing
– the price of a product is based a merchant’s understanding of
how much value the customer attaches to the product and their
own desire to make a sale – supply and demand
Trigger pricing
– used in m-commerce applications, adjusts prices based on the
location of the consumer
Utilization pricing
– adjusts prices based on the utilization of the product
Personalization pricing
– adjusts prices based on the merchant’s estimate of how much
the customer truly values the product

8. www.decideo.fr/bruley
PPrriiccee DDiissccrriimmiinnaattiioonn
First degree: willingness to pay (rare)
Second degree: artificial hurdles but open
Third degree: based on external factors
 Geography (neighborhood, state)
 Gender (women's clothing)
 Age (senior/student discounts)
 Profession/affiliation (small/large business,
educational, medical…)

9. Dynamic PPrriicciinngg:: IIss TThhiiss PPrriiccee
www.decideo.fr/bruley
RRiigghhtt??
New forms of dynamic pricing include:
 Time-based dynamic pricing: Adjusts price to different points
in the product life cycle
 Peak-load dynamic pricing: Adjusts prices to times of day
 Clearance dynamic pricing: Used when products lose value
over time (plus, “perishables”): Produce, Airplane seats,
Hotel Rooms, “old” technology
Dynamic pricing is opposed by some consumer groups and
individuals
 Amazon example
 Some forms appear to be more acceptable than others

10. This gives them a competitive
edge
www.decideo.fr/bruley
IInntteerrnneett PPrriicciinngg MMooddeellss
Through the use of real-time
pricing technology, e-tailers can
change and post prices instantly at
very little cost
Real-time pricing the
ability to change prices
instantly to keep up
with changes in the
marketplace
Secion 14-2

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PPeerrssoonnaalliizzaattiioonn
Definition:
Use knowledge about a customer to merchandise, present, modify
and deliver products and services most appropriate to that
individual at that time
Real Life Example
• Giving a personal gift, comforting words to people in
suffering,...
• Specific offer, pricing, …
It must have
• Knowledge about the customer
• Knowledge about supply choices
• Business rules about what to offer
• Dynamic web delivery mechanism

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PPeerrssoonnaalliizzaattiioonn ffoorr BB22CC
Analyze who
The User is
1. User Profiling 2. Content Management
Deliver
Personalized
Service
Analyze what
We have
Match
making
3. Marketing control
4. Dynamic
Delivery
Satisfied
Customer

13. www.decideo.fr/bruley
TThhee MMaattcchh MMaakkiinngg EEnnggiinnee
Behavior of site
navigation
content
services
user
navigates
site
next action
on site
marketing
control
User
needs
wishes
interests
matchmaking engine
user
profile
content
model
matches
pleasant surfing experience
feel special
site loyalty

14. www.decideo.fr/bruley
AA PPeerrssoonnaalliizzeedd SShhoopp PPaaggee
Search links to
a dynamic,
native search
(within group)
Nav bar is
consistent
throughout site -
has group identifier
(sized to fit)
Includes Group
product hierarchy
and shopping links
Welcome
message by
group
Show the math

15. Co Personalization Commppoonneennttss TTeecchhnnoollooggyy
User
match me
www.decideo.fr/bruley
Behavior of site
navigation
content
services
matchmaking engine
User
needs
wishes
interests
algorithm selection
Business Manager
matches
feedback loop
open
adapter
current
activity
3rd party
source
past
history
legacy
data
Q A
registration
data
personalized
web content
personalized
e-mail
personalized
push channel
product
recommendation
targeted
promotion
targeted
advertisement
business
control
user
profile
content
model
neural
net
collaborative
filtering
rule
based
memory
agent
open
adapter
site
analysis
usage
analysis
user
analysis
commerce
analysis
data
mining
delivery
building tools operations tools
tool set
content
tagging
catalog
builder
user profile
customizer
dynamic content
authoring
rule
editor
rule
management
direct
mailer
site analysis
reporting
file
system
relational
database
document
mgmt system
open
adapter
Example

16. AAsstteerr uussee ccaassee eexxaammppllee
• Analyzing item price movements and its impact on:
• Basket size over a long duration (6-10yrs) will
provide key insights into halo impact and
affinity contribution for items
• Basket composition over a long duration (6-
10yrs) will provide key insights into price bands
for items.
• Analyzing Affinity of items over a long duration (6-10
yrs) will provide key insights into running better
promotions, planogram and price planning of around
affinity items.
• Analyzing Affinity of items impact on basket
composition
www.decideo.fr/bruley

17. PPrriicciinngg AAffffiinniittyy
Business Questions:
•Analyzing item price movement and its impact on basket size and affinity
of items over a long duration (6 yrs).
•Data Set (6 years): Transaction Data, Price data
Aster Steps:
1.Use Aster Collaborative Filter function to create resulting correlation
coefficient.
 Query runtime: 48 minutes
Use Aster Correlation Stats function to discover relationship between
items.
Query runtime: 48 minutes
Use BI tool like a Tableau to visualize results and drill into individual
categories.
www.decideo.fr/bruley

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AAsstteerr DDiissccoovveerryy PPllaattffoorrmm

19. 1
www.decideo.fr/bruley
AAsstteerr DDiissccoovveerryy PPllaattffoorrmm
New business insights from all kinds of data with
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 Large Volumes
 Interaction Data
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TTeeaamm PPoowweerr