Big Data & Pricing

Michel Bruley
Michel BruleyMarketing Director à Michel Bruley
www.decideo.fr/bruley 
PPrriicciinngg 
DDiissccrriimmiinnaattiioonn 
DDyynnaammiicc 
PPeerrssoonnaalliizzaattiioonn 
RReeaall TTiimmee 
September 2013 
michel.bruley@teradata.com
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
www.decideo.fr/bruley 
IItt IIss DDoonnee EEvveerryywwhheerree 
(Especially to Foreigners)
www.decideo.fr/bruley 
PPrriiccee  PPrrooffiitt iinnccrreeaassee
www.decideo.fr/bruley 
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)
www.decideo.fr/bruley 
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
www.decideo.fr/bruley 
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
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…)
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
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
www.decideo.fr/bruley 
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
www.decideo.fr/bruley 
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
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
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
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
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
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
www.decideo.fr/bruley 
AAsstteerr DDiissccoovveerryy PPllaattffoorrmm
1 
www.decideo.fr/bruley 
AAsstteerr DDiissccoovveerryy PPllaattffoorrmm 
New business insights from all kinds of data with 
all types of analytics for all types of enterprise 
users with rapid exploration 
 Large Volumes 
 Interaction Data 
 Structured 
 Unstructured 
 Multi-structured 
 Hadoop 
2 
 Relational/SQL 
 MapReduce 
 Graph 
 Statistics, R 
 Pathing 
3 
 Business Users 
 Analysts 
 Data Scientists 
4 
 Fast 
 Iterative 
 Investigative 
 Easy
www.decideo.fr/bruley 
TTeeaamm PPoowweerr
1 sur 20

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Big Data & 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)
  • 4. www.decideo.fr/bruley PPrriiccee PPrrooffiitt iinnccrreeaassee
  • 5. www.decideo.fr/bruley 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)
  • 6. www.decideo.fr/bruley 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
  • 7. www.decideo.fr/bruley 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
  • 11. www.decideo.fr/bruley 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
  • 12. www.decideo.fr/bruley 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
  • 19. 1 www.decideo.fr/bruley AAsstteerr DDiissccoovveerryy PPllaattffoorrmm New business insights from all kinds of data with all types of analytics for all types of enterprise users with rapid exploration  Large Volumes  Interaction Data  Structured  Unstructured  Multi-structured  Hadoop 2  Relational/SQL  MapReduce  Graph  Statistics, R  Pathing 3  Business Users  Analysts  Data Scientists 4  Fast  Iterative  Investigative  Easy

Notes de l'éditeur

  1. Aster Discovery Platform provides new business insights: from all kinds of data – that means not only large volumes, but also different types of data such as structured and unstructured and from various data sources with all types of analytics – from SQL to MapReduce to Graph to Statistics and R….to more specialized types of analytics like Pathing and Pattern Analysis for all types of enterprise users – whether it’s a business user or a SQL user or a developer or data scientist. and finally makes it rapid, fast, iterative. I want to emphasize analytics as it is key. With the 5.10 release of Aster Discovery Platform, we have expanded the analytic capabilities. To understand this, we need to discuss a new concept which is Multi-Genre Analytics. Multi-Genre Analytics is the practice within big data discovery that says not only can you use a lot of different analytic techniques like SQL, MapReduce, statistical, graph. But it also has the thesis that there are many big data problems that require more than one analytic technique to be applied at the same time to produce the right insight to solve the problem properly. Lets take a look at some examples of Multi-Genre Analytics.
  2. Aster Discovery Platform provides new business insights: from all kinds of data – that means not only large volumes, but also different types of data such as structured and unstructured and from various data sources with all types of analytics – from SQL to MapReduce to Graph to Statistics and R….to more specialized types of analytics like Pathing and Pattern Analysis for all types of enterprise users – whether it’s a business user or a SQL user or a developer or data scientist. and finally makes it rapid, fast, iterative. I want to emphasize analytics as it is key. With the 5.10 release of Aster Discovery Platform, we have expanded the analytic capabilities. To understand this, we need to discuss a new concept which is Multi-Genre Analytics. Multi-Genre Analytics is the practice within big data discovery that says not only can you use a lot of different analytic techniques like SQL, MapReduce, statistical, graph. But it also has the thesis that there are many big data problems that require more than one analytic technique to be applied at the same time to produce the right insight to solve the problem properly. Lets take a look at some examples of Multi-Genre Analytics.