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PricingPricingDiscriminationDiscriminationDynamicDynamicPersonalizationPersonalizationReal TimeReal Time
September 2013
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Price DiscriminationPrice Discrimination
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
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It Is Done Everywhere It Is Done Everywhere (Especially to Foreigners)
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Price & Profit increasePrice & Profit increase
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Where Does Pricing Fit?Where Does Pricing Fit?
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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Pricing ObjectivesPricing Objectives
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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Price ModelsPrice Models
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
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Price DiscriminationPrice Discrimination
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…)
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Dynamic Pricing: Is This Price Dynamic Pricing: Is This Price Right?Right?
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
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Internet Pricing ModelsInternet Pricing Models
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
This gives them a competitive edge
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PersonalizationPersonalization
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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Personalization for B2C Personalization for B2C
Analyze whoThe User is
DeliverPersonalized
Service
Analyze whatWe have
Match making
1. User Profiling 2. Content Management
3. Marketing control
4. DynamicDelivery
SatisfiedCustomer
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The Match Making EngineThe Match Making Engine
Behavior of sitenavigationcontentservices
user navigates site
next action on site
marketingcontrol
user profile
content model
matchmaking engine
Userneedswishesinterests
matches
pleasant surfing experiencefeel specialsite loyalty
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A Personalized Shop PageA Personalized Shop Page
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
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Personalization Components & Technology Personalization Components & Technology
User match me
Behavior of sitenavigationcontentservices
matchmaking engine
Userneedswishesinterests
algorithm selection
Business Manager
matches
feedback loop
openadapter
currentactivity
3rd partysource
pasthistory
legacydata
Q & A
registrationdata
personalizedweb content
personalizede-mail
personalizedpush channel
productrecommendation
targetedpromotion
targetedadvertisement
businesscontrol
user
pro
file
contentmodel
neuralnet
collaborativefiltering
rulebased
memoryagent
openadapter
siteanalysis
usageanalysis
useranalysis
commerceanalysis
datamining
delivery
tool set
building tools operations tools
contenttagging
catalogbuilder
user profilecustomizer
dynamic contentauthoring
ruleeditor
rulemanagement
directmailer
site analysis& reporting
filesystem
relationaldatabase
documentmgmt system
openadapter
Example
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• 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
Aster use case exampleAster use case example
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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.
Pricing AffinityPricing Affinity
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Aster Discovery PlatformAster Discovery Platform
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Aster Discovery PlatformAster Discovery Platform
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
1
Relational/SQL MapReduce
Graph Statistics, R
Pathing
2
Business Users Analysts
Data Scientists
3
Fast Iterative
Investigative Easy
4
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Team PowerTeam Power