big data & pricing

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PricingPricingDiscriminationDiscriminationDynamicDynamicPersonalizationPersonalizationReal TimeReal Time

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September 2013


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


It Is Done Everywhere It Is Done Everywhere (Especially to Foreigners)


Price & Profit increasePrice & Profit increase


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)


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


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


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…)


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


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


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


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


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


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


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


• 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


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


Aster Discovery PlatformAster Discovery Platform


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


Team PowerTeam Power