Case-Based Reasoning in E-Commerce

Joe SoutoCSE 435

What is E-Commerce?

“The exchange of information, goods, or services through electronic networks”1

How can CBR help?

How many times have you seen this?

How can CBR help?

Or this?

What’s wrong?

Demand is either over-specified or under-specified

It is up to the user to find what they want

There is no intelligent sales support

We have a problem

Buyer has limited knowledge of product base

Seller has limited knowledge of buyer’s requirements”Knowledge Gap”

We have a problem

Knowledge gap is solved in real-life by a human sales agent as a mediator. We don’t have this luxury online.

Solution: CBR approach product knowledge is stored as experience in a case base.

Sales agent makes recommendations based on the stored experience.

Some Preliminary Info

We need a way to define user requirements

Customers buy items in order to satisfy their desires

Define a customer’s desire as a “Wish” Wishes have various properties

Individual Wish Properties

Importance Hard: MUST be met (ie: “vacation for <$2000”) Soft: not essential, but helpful (ie: “red” car)

Agent must satisfy ALL hard req’s and as many soft as possible

Precision Precisely Determined (specific, ie: “>3GHz P4”) Undetermined (vague, ie: “fast processor”)

Individual Wish Properties

Certainty Certain Uncertain

Sales agent must try to increase certainty of wishes and make recommendations based on them

Overall Wish Properties

Redundancy Wishes can be redundant

Ex: Computer that’s “fast” and can play Half-Life 2 Agent must recognize and avoid redundant

inquiries

Consistency Wishes can be contradictory

Ex: new Ferrari, and under $1000 Agent must either ask user to clarify, or suggest

products that satisfy one of the two wishes

Product Classifications

How Do These Properties Help?

1. Customers want a product to satisfy a wish

2. Products have various properties

3. Therefore, product properties can be mapped to the satisfaction of a customer’s wishWith all that in mind, now we

can look at the transaction process

Transaction Model

Single transaction can be modeled with three phases

Pre-Sales

Buyer wants a product, Seller provides information

3 Phases Supplier Search

Client determines which supplier can satisfy their wishes

Product Search Mapping of customer criteria to products

Negotiation1. Price and way of payment 2. Details of delivery 3. Regulations about cost and delivery

Pre-Sales

Recall the Google Example

No “intelligent sales support”

Burden of knowledge is in hands of the customers

Example

Due to Knowledge Gap, Analog Devices added a CBR system to assist Pre-Sales

Analog Devices:http://www.analog.com

How Does It Work?

Similarity Metrics! Similarity function

for single attribute OK to be under, less

similar if over desired value

The overall similarity is computed weighted average of local similarities.

Remember the “priority” box

Sales

Product has been chosen, must be configured and paid for

Customer and Sales Agent negotiate about product attributes and costs

Intelligent Support is needed for negotiation

Negotiation “A process where two parties bargain

resources for an intended gain”1

In Sales phase, customers navigate through products to satisfy their wish.

Some wishes known, others discovered in the process. Hard wishes must be fulfilled, soft wishes can be negotiated. Agent finds out these demands with the customer and finds a product which fulfills them. Agent can be “Active” or “Passive”

Sales

CBR Model must be modified Standard Model:

2. Reuse3. Revise

4. Retain

Case Library

1. RetrieveBackground Knowledge

Sales

New Model No Retain phase: sale

does not add another product to the product base

Add Refine phase: user demands refined based on the evaluations given by the customer.

Example

CBR approach to negotiating a BMW sale

Agent here is passive

Buttons for “sportier”, “more comfortable”, “cheaper”, etc.

After-Sales

Customer has already bought a product and needs support during its usage

To assist the customer, they are supported with a case base of possible product problems, a query interface, and similarity measures which should help to find a similar problem and solution

Many companies have online CBR customer-support websites (Dell, 3Com, etc) Help Desk Systems

Example

Dell Support site:http://support.dell.com

Summary

E-commerce is a growing field with lots of potential revenue

Standard search technology is too limited

CBR can be applied in all 3 transaction phases

Key is to provide intelligent sales support agent guides customer through each phase of transaction

References

1. “Intelligent Sales Support with CBR”Wilke, Lenz, Wess

2. “Experience Management for Electronic Commerce”Bergmann

3. Wikipedia: http://en.wikipedia.org/wiki/E-commerce