Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
Economic Aspects of Data Fusion and Separation
Alenka KrekResearch Assistant
Institute for GeoinformationTechnical University [email protected]
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
An Example: Movie Tickets
95
110
95
85
85
Product and Price Differentiation
Price of a movie ticket depends on the row one sits in.
One pays for the valuehe attaches to the product.
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
Motivation
How should Geoinformation products be designed in order to bring the most value to the user?
How to price the products?
Potential buyers differ in their• information needs• willingness to pay (income/revenue level)
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
Outline
The Model of Dataset Qualities
An Example: Street Network Dataset
Data Fusion and Separation
The Ideal Product
“Damaged products”
Economic Aspects of Data Fusion and Separation
Quality-Price Options
Self-selecting Principle
Conclusions
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
An Example: Street Network Dataset
Street network dataset – different categories
One-way streets attributes
Turn restrictions attributes
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
The Model of Dataset Qualities
Dataset quality is defined as a quantifiable property of a dataset which can be linked to the improvement of the decision-making process of the buyer.
A dataset is a composition of qualities.
Example: One-way streets, turn restrictions attributes etc. are dataset qualities for a particular decision-making process
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
Simulations
N9
Position: N1Orientation: N1-N4, N1-N2, N1-N3Destination: N5Objective: the shortest path to the
destination
N1
N2
N3
N4
N5
N6
N7
N8
the ideal case
the distance = 14.7
the distance = 11.4 allToBoth
Some results
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
The Value of a Quality
The buyer does not place the same valuation on all qualities of a dataset.
The valuation of a quality depends on the use of a dataset in a decision-making process.
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
Data Fusion and Separation
Different dataset quality composition is needed for different use.
Several versions of the Geoinformation product can be developed with multiple, different mixes of different qualities.
Different varieties are produced to cater to different types of consumers.
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
The Ideal Product
GI product (GIP) is represented as a point in a multidimensional quality product space.
GIP3 quality space
GIP 3
maximumversion
GIP 1
GIP 2
minimum version
d2
d3
GIP2 quality space
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
“Damaged Goods”
Manufacturers may intentionally damage a portion of their goods in order to price differentiate.
Deneckere and McAfee in their paper (published in the
Journal of Economics&Management Strategy) list many instances of this phenomenon.
Cost advantages.
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
Quality-Price Options
The seller offers a schedule of quality-price options of the same generic type of the product.
The same price is charged to all buyers of a given quality composition.
All buyers are offered the same quality-price schedule on the basis of self-selection principle.
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
Self-selecting Products
Quality-price options give the buyers an incentive to self select the product they are willing to pay and which satisfies their information needs.
The strategy for the seller who can not distinguish among buyers prior to an actual sale.
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
The Economic Aspects
New markets are served – The seller can sell to buyers who do not value the maximum product so much, without decreasing demand for the “ideal” product so much.
Additional revenue for the seller.
Better matching the user’s needs and his/her willingness to pay.
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
Revenue Gained by Selling One Price-Quality Option
Willingness to pay(WTP)
Quantity (q) Q1 Q2 Q3
P1
P2 P3 Di
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
Revenue gained by Selling Several Price-Quality Options
Willingness to pay(WTP)
Quantity (q) Q1 Q2 Q3
P1
P2 P3 Di
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
Conclusions
Data fusion and separation is important from the economic point of view.
Quality differentiated product on the basis of self selecting principle of quality-price options:
The buyer’s perspective
he satisfies his information needs
pays the amount he is willing to pay
The seller’s/producer’s perspective
new markets are served
higher revenue
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
Thank you for your attention!
Questions and Comments are welcome!!
Data Fusion and Separation Meeting A. KrekCarnuntum, Austria, June 2001
Product and Price Differentiation
Product differentiationproducts are similar and different at the same time, they are not complete substitutes
products designed in such a way that they bring the most value for the user/buyer
Price differentiation different price is charged to different users for the same product; -first, -second and -third price discrimination (Pigou 1929) non-linear pricing (Wilson 1995); price is not linear to the quantity purchased