Case-Based Reasoning in E-Commerce Joe Souto CSE 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 wish With 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 Negotiation 1. 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:

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. Reuse 3. Revise 4. Retain Case Library 1. Retrieve Background 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:

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: commerce