chapter 10 customer modelling. - 2 - (c) 2000 dr. ralph bergmann and prof. dr. michael m. richter,...

Chapter 10

Customer Modelling

- 2 - (c) 2000 Dr. Ralph Bergmann and Prof. Dr. Michael M. Richter, Universität Kaiserslautern

Recommended References

• Mehlmann, O.; Landvogt, J.; Jameson, A.; Rist, Th.; Schäfer, R.

(1998): Einsatz Bayes’scher Netze zur Identifikation von

Kundenwünschen im Internet. In: KI-Themenheft “Intelligente

Informationsagenten”, Nr. 3, 1998, S. 43-48.

• Richter,M. M.,Schmitt, S.: Kundenmodellierung und

Dialogführung: Eine Herausforderung für eCRM. In: Festschrift

für F. Bliemel, to appear.


Customer Classes

• Customer classes have been dealt with implicitly in many ways.

• Such treatments have been reflected mostly intuitively insights and have lead to enormous insights.

• On the other hand, a formal treatment is missing.• Why are formal methods in this context important ?

– The computer support in general needs a formal representation.

– Tool support requires a formal representation because tools need a formalized input.

– Further consequences of tool results can only be processed if they are represent formally.


The Task (1)

• The customer in electronic commerce is not seen directly. The customer may leave and reenter the shop without being observed by the supplier.

• The information about customers is coming from – his own utterances: they contain explicit and implicit

information about the customer; the latter has to be made explicit;

– the customers history, if available; the consequence is that past behavior has to be recorded;

– general market analysis: This is a task for data mining.

• These information types are not in all situations available or useful.


The Task (2)

• It is important to model the customer in order to treat him properly

• This model has to be built from the available information• For the supplier it is important that classes of customers

are large: Then many customers can be treated in the same way.

• Customer classes do not have a sharp definition and they may overlap: A customer may belong more or less to one or more classes.


Customer Classes and Market Segments

• Each customer class corresponds to a market segment• Large segments are too general small segments are to

specific.

C

Appropriate classes Ccorrespond to intersectionsof large segments whichare• simple to describe• easy to test

Different purposes may require different intersectionsof large segments


Types of Customer Classes (1)

• We distinguish three basic situations:(A) All customers have the same characterization

(B) The customers split into classes of uniform charaterization; it is advisable that there are few such classes

(C) Characterization of customers is individual

• In reality there is always a mix of these situations. The description of customers should therefore split into three disjoint parts:– PartA: Refers to situation A– PartB: Refers to situation B– PartC: Refers to situation C


Situation A: Uniform characterization

Class 1 Class 2 Class n.....

Inheritance ofpartA

Inheritance ofpartB

Individual customers

Types of Customer Classes (2)


Customer Classes, Storage and Compilation (1)

• The three parts of the customer description are

brought into the system at different times:

– PartA is compiled at an early compile time

– PartB be is usually compiled at later times.

Because new customer classes may arise this

compilation is done incrementally

– PartC is filled at run time when a customer shows

up. There is usually no time for compilation so

storage takes place. Partially PartC can be filled if

a customer history exists.


Customer Classes, Storage and Compilation (3)

• Simplest situation: Only partA exists

– usually in company-company business

• More difficult: PartB and partC exist; then the customer class has to be determined at run time

– In company-company business usually easy to determine

• All three parts exist: PartA and partB allow some preprocessing; customer class and individual aspects are determined at run time (if no history is available)

– e.g. individual travel, private real estate


General knowledge,informationand data

Actual information

Actual data

knowledge & information

base of the system

compiled

stored

• Three levels of the customer characterization:

Characterization of Customers


Storage Revisited

Information aboutcustomer

Easy to express

Difficult to express

initial information

Dialogue

SystemCustomer

• Initial information: • about customer• Initial formulation of customer query

• Dialogue: • Completes information about customer• Makes query precise


Three Aspects of Customer Classes

• The first aspect: For which purpose is the supplier interested in the class of a customer? If the class is known, which further consequences does this have?

• The second aspect: How to obtain knowledge about useful classes and their descriptions ?

• The third aspect: How determine one or more classes to which a customer belongs and how to use this knowledge in the sales process ?

• All three aspects include general learning aspects dealt with at compile time while the third one enters the scenario at run time (unless there is only one class) and is a specific learning aspect.


The First Aspect (1)

• The description of the customer class should contain knowledge about the customer. We distinguish two types of knowledge:

• Utility Knowledge:– Which products are useful for the customer?– Which level of quality, price etc. is wanted?

• Treatment knowledge:– Which knowledge does the customer have?– Which terminology is understandable?– Which expression style is appreciated/resented?– Which presentation (text, graphics,...) is preferred?



• The main purpose of the customer classes is that the supplier would like to treat customers of the same class in the same way.

• The consequence is that strategies for customer treatment should be associated with the class.

• The actions of further customer treatment are of great variety and will be discussed below in the action model.

• The actions have costs/gains. The profit depends very much on the accuracy of the customer classes and of the usefulness of the classes.



• Major types of actions are:– Advertising: Where (TV, special journals etc.) and at which

time (summer, winter, before holidays etc.). It is based e.g. on general or specific buying behavior and influenced by the marketing strategy;

– Layouts of catalogues and forms– Reactions to questions in the dialogue with the customer– Changes of the dialogue strategy;– Selection of indiviual product offers and of general product

types;– Pricing and special offers;– etc.


The Second Aspect

• There are two phases for obtaining knowledge about customer classes.

• The first phase takes place before one actually decides to sell certain products. Each company has at least some rough customer model before a business is started. This model is, however, often implicit and should be made explicit.

• The second phase is the data mining phase. Here the rough model should be completed, refined and corrected. It requires tasks like basket analysis, marketing analysis etc.; see chapter 13.


The Third Aspect (1)

• There may many difficulties in determining customer classes. They depend on the specific type of the customer and the kind of products which are sold.

• In principle this is a vague classification problem with incomplete information where approximate reasoning is used.

• The vague classification is guarded by the utility aspect: If the membership to the class discovered so far is high enough such that the corresponding actions are useful then the classification process should terminate (see the analogy to the diagnostic situation).


The Third Aspect (2)

• There are two phases (if they apply) for determining the class– The compilation phase: Use customer histories– The run time phase: Use the dialogue

• The dialogue component plays a twofold role:– It should complete the information in order to determine the

customer class,– its strategy and further details (e.g. products offered) is

influenced by the knowledge about the class available so far.

(See chapter 11).


Data Mining and Learning (1)• There are different things which have to be learned in the

different phases:• Determining the right ontology:

– There is an infinity of expressions about customers, which should be chosen?

– Which virtual predicates, attributes, and relations are important ?– Which values of attributes are landmarks, i.e. separate domains

qualitatively ?

• There are two aspects involved here:– The ontology should be adequate from the suppliers point of view– The ontology should attract customers

• Both aspects deal mainly with the catch phase.


Data Mining and Learning (2)• Learning the behavior of customers: How do they react

on– advertising – questions– offers– details of products, prices– after sales support– other services

• These refer to the catch phase as well as to the keep phase. Both need adequate knowledge.

• The knowledge for the catch phase is usually more of short time character while the knowledge for the keep phase has more a long term character


Data Mining and Learning (3)

• For the long term aspects the concept of the potential value is important: What business will we make with the customer if he stays lifetime with us (e.g. in insurance business)? Related is the potential loss: Which business will be lost if the customer leaves us now ?

• These concepts require computations on– aspected long term gains– probabilities of losing the customer

• Both computations must be be based on statistical evidence based on data mining activities.


Three Models• The three aspects of customer classes the supplier

require three models :– One used for general aspects of the company: The

strategic model– One used for the classification purpose: The diagnostic

model– One for further actions after the classification is done: The

action model• All models may have formal as well as informal descriptions. In

E-C it is desirable that the diagnostic and the action model are formally described such that the corresponding activities can be carried out automatically. This implies that they are simple and not too involved.


Representation of the Models

• The representation of the models can potentially use all methods described in chapters 4 and 5.

• The content of the corresponding knowledge bases is maintained by the knowledge manager (see chapter 15)

• Of particular interest are the actions, they usually describe the core of the knowledge (see chapter 4).

• A particular type of knowledge (the ECA-rules) is discussed in chapter 15.

• It is important to point out that the knowledge is not only of logical (0-1) - character but will contain knowledge of fuzzy-type (approximative knowledge).


Interaction between the Models (1)

• These models are not independent but also not identical.

• The strategic model has a major impact on the two other models.

• After the diagnostic model has been determined it is used to chose the action model and to determine its parameters.

• The diagnostic model is also used to interprete the customer, i.e. in the action model to transform the customers wishes into demands on the basis of which products – can be selected – can be customized.


Interaction between the Models (2)

• The interaction between the knowledge is twofold:• Knowledge: One model can contain knowledge needed

by another model– at compile time– at run time

• Action call: One model may call an action of another model e.g. the diagnostic model may call a query to the customer.

• The interaction between the models has to be organized by the knowledge manager (see chapter 15)


The Strategic Model (1)

• It contains all major aspects of the company:– the products to sell– the questions of manufacturing products or buying them from

other producers– the main types of customers– etc.

• The strategic model will be to some an informal one but it may use data mining methods extensively.

• The strategic model also influences the activities of the knowledge manager. In particular, activities of the knoweldge manager may be part of the strategic model.

• The strategic model is mainly stable and changes only very slowly.


The Strategic Model (2)

• The strategic model contains a first indication of customer classes: – If a company wants to sell a product it has a rough indication

who possible customers are (everybody, men, women, old/young people, students, ...).

• The initial knowledge about customer classes may be inefficient and need to be improved.

• Three data mining tasks:– Find out wether customer classes are desribed suffciently

well– Make customer classes more precise– Connect customer classes with subsequent actions


The Diagnostic Model

• The diagnostic model has two parts:– the description of the customer class– the strategy in the classification process

• The choice of which customer classes the model contains is a strategic question and determined by the strategic model.

• The general aspects of classification are modeled: this is built after models of fault diagnosis

• The realization of the details of the classification process is established by the dialogue with the customer: see chapter 11.


Analogy:Customer Classification - Diagnosis

Identical top level-algorithms:

Initial information

classificationpossible ?

The choice of queries is determined by the actual hypothesis for the class (which can vary)

Realiziation:

Dialogue ControlComponent

yes

no

Get additional information


Comparison: Customer Classification - Diagnosis (1)

• The diagnostic process has to find out facts of the real world (blood ressure, temperature,...). Such facts have to be discovered and cannot be changed in the process.

• The process is guided by utility aspects as costs of queries and in particular maximal information gain (entropie considerations).

• The process terminates if the classification can be made sufficiently detailed for the therapy (repair).

• Diagnosis and therapy are usually two subsequent processes.



• The customer dialogue has to determine – demands of the customer

– a match between the demands and the facts of the reality (i.e. the available products).

• While the available products cannot be changed during the dialogue demands can possibly be changed because they contain weak constraints.

• The target of the dialogue process is to perform a successful sale but possibly with changed demands.

• The two processes have in common that the classification is only a means for the ultimate goal (therapy, sale).



• Because the dialogue is the only direct contact to the customer (except subsequent processes like payment etc.) it has to incorporate– customer classification– making the demand precise– changing the demand if no succesful match with the product

base is possible.

• For this reason the diagnostic model cannot be separated from the models of focusing and adapting the demands. The diagnostic process is not an isolated one. This influences the dialogues to a large degree.


Difficulties

• Sometimes it is difficult or impossible to determine a customer class. Such situations should be detected and the classification should be given up.

• Examples:– A customer who buys a book on Madagaskar may not want to

buy further books of this type.– Before Christmas many people buy gifts for other persons.

• Here individual customer treatment based on customer classes is not advisable.

• However, statistics and subsequent data mining activities may still be very useful.


Classification and Similarity

• There are two ways of using similarity for the classification:– Comparing the customer description and the class description.

Here the meaning of similarity between the two descriptions is “customer belongs to the class”.

– Selecting one or more prototypes of the class and comparing actual customer and prototype customer. Here similarity is interpreted in the usual intuitive sense.

• Although conceptually different the main work is in both cases to identify the most relevant properties of the class.

• The second approach may be preferred if the class is heterogeniously described.


Customer Classes as Fuzzy Sets (1)

• Because customer classes are vaguely described it is justified to regard them as fuzzy sets. The degree of membership determines the degree with which they are treated as if they were full members of the class. This is exactly the way in which fuzzy control proceeds.

• Prototypes of a class are persons (institutions) that have a behavior and properties which is typical for the class.

• A class can also have several prototypes if one is not sufficiently representative.


Customer Classes as Fuzzy Sets (2)

• An arbitrary customer has a (fuzzy-) degree of membership to the class.

• The degree of membership of some y is determined by the similarity (given a measure) to the nearest prototype of the class:

µK (y), = sim(y, x)

where y is the nearest of the prototypes to x and K is the class of x.

(See chapter 6).


General Properties of Customer Demands (1)

• The query (demand) of the customer has two main aspects:

Getting to some place

Renting a specific car

Expressing anintended functionality

Demanding acertain product



• Intended functionality:– Has correctness conditions: A technical problem (mainly:

what has to be achieved?)– Has quality: problem of preferences and utility– Utility aspects are defined by customers and have to be

determined

• The relation between functionalities and products has been described in chapter 9.

• The way how functionalities are described usually contain customer information.



• Demand for a specific product : The demanded product is not necessarily a product which exists or can be obtained by adaptation or configuration.

• The product is usually incompletely and partially incorrectly described.

• It is often only a placeholder for describing an intended functionality.

• Properties of the product and style of the description contain again implicit information about customer characteristics


Aspects of Diagnostic Models

• There is an infinity of possible customer classes.• There is, however, a selection of not too many classes

which are simple to describe. The intersection of such classes can lead already to useful results. Examples:– expert versus layman– {male, female}, age– financial situation– location in the business or social hierarchy– etc.


Example: Layman - Expert

• There are two classes: Layman class, expert class with a super class “customer class”

• The customer class has two instances, layman and expert.

• We consider two instances which are prototypes:– non-expert is the prototype of layman– expert is the instance of expert

• The customer class refers to two other classes, product properties and functional properties and the way the customer describes them. How this is done in detail corresponds to the special situation.


Object class Customer ClassName: StringExpression style: {naive, expert}Product aspects: Product propertiesFunctional aspects: Functional properties

Object class Product propertiesDescription: {detailed, partial, not} ....Property n: Symbol n

Object class Functional propertiesDescription: {detailed, partial, not}Quality aspects : Symbol1...Property m: Symbolm

Description of „Customer Class“


Prototype Non-Expert (PC-Domain)

Object instance non-expertName: „non_expert_business“Experession style: naiveProduct aspects: nonexp_prodFunctional aspects: nonexp_func

Object instance nonexp_prodDescription: partial...Property n: value_n

Object instance nonexp_funcDescription: detailedQuality apects : user friendly...Property m: value_m


Example: Travel (1)

• The purpose of this example is mainly to illustrate certain aspects.

• Depending on the types of travels offered, individual aspects may play a role.

• The distinction layman-expert is of no interest.• Aspects of importance are:

– age– financial situation – standard holidays versus special interests


Example: Travel (2)

vacation type area activities duration priceNo.of persons

summervacations

medita-ranian

sports,discos

2 weeks < 2500 2

Weightsfilter

8 83 5 strict strict

Technical data

The weights are provided by the customer. They are indicators of utilities.The customer can also give strict commands.

Instance:some customer


Example: Travel (3)

• The activity attribute gives a hint on the age: “young” (high importance of sports, disco).

• This is supported by the attribute values of vacation type and area.

• The values for price and duration give some indication on the financial situation: “middle class”.

• Therefore the customer class is the intersection of young and middle class.

• Observe: Both classes may have non-identical prototypes. If the prototype technique is used one needs an additional prototype for the intersection class.

• A third hint is on the class “not single” (bcause two persons will travel).


Example: Travel (4)

• Additional information about the customer may give further hints for the customer class:– Time of the year: family status (school vacations)– First name (age)– Hotel category (financial situation).

• The customer class may be used for– offering a vacation if the demanded one is not available– offering extensions– offering additional products (music events, extra tours,...)– grouping people together– presenting special catalogues– ....


Example: Customer Class from History (1)

• Supermarket: Customers buy regularly and are recorded

• One can e.g. deduce the following classes:– customers who buy goods from famous brands– customers who buy no name goods– customers who buy ecological goods

• The classes can then be attached to individual customers.

• The shop can then present specific offers to the customers.


Example: Customer Class from History (2)

• In this example we have to observe that for the mentioned goods we have a split into three classes:– famous brands, no name goods, does not apply (e.g. vegetables)– ecological goods, non-ecological goods, does not apply (e.g. a

switch).

• The customer classes should be restricted to the class regular customers.

• The vague classification has to be defuzzified because to send a special order is 0-1. This can e.g. be done by using thresholds for the degree of membership to the class.


Similarities and Classes of Customers (1)

• All customers have the same concept of similarity:

Similarity is based on technological or generaleconomical or social principles:

• Two PC´s may have a comparable capacity (technology)• If nothing else is changed then cheaper is better

(economy)• Hospitals with traffic noise are not desired (general)

This refers to situation A.



• Similarities depend on classes of customers:

Such classes have to be modelled with respect to their behavior:

• Professional classes (PC for office or for CAD?)• Hierarchical classes (PC for the boss or the secretary?)• Income classes (economy home or luxury home ?)

• This refers to situation B.



• Similarities are individual:

There has to be a goal direct dialogue in order to

determine the relevant aspects:

• Do you insist on St. Moritz? („we always go there“)

• Will you buy a book with a horror story?

(„no, it is a gift for my grandmother“)

• Do you want a fitness room in your house

(„yes, but not in the basement“)

This refers to situation C.


The Action Model (1)

• The action model is based on and interwoven with the strategic model and on the diagnostic model.

• Both are mainly influenced by the customer class.• The action model contains rules of the form

IF condition THEN action

(but compare also the ECA-rules,chapter 15)• These rules can be interpreted in the sense of classical

logic only if the conditions are fully satisfied. • The action model describes all actions concerned with

customer treatment (see “First Aspect” from above)



• The product offer is directly influenced by the customer class. In particular, it has to refer to the concept of similarity of the customer, i.e. when customers find products similar and could be exchanged if necessary.

• More basically it has to refer to the utility function of the customer. The utility is reflected by the weights of the measure and they depend on the customer class.

• This is of particular importance if the demand is changed: The change is performed by adaptation rules but– not the product is adapted

but – the demand is adapted!



• Actions concerning product offers depend on the class:– Members of a class C by certain products A and B together:

Offer either none of them or both.– Customers of class C prefer certain quantities (singles do not

buy family packages).

• There are certain channels preferable for advertizing:– The class of teenagers reads certain journals and watches

certain TV-shows– Hunting equipment is best offered in a hunter’s journal.

• These actions affect the strategic model and are usually performed by the knowledge manager. It is important to determine the customer class precisely.

•


Rules in the Action Model (1)

• The conditions of the rules are often of the form– IF customer belongs to customer class C

• or– IF products p1 and p2 are sufficiently similar

• These conditions are fully satisfied only if the customer is a prototype of the class or if the products are the identical. Otherwise the partial fulfillment is given by– the degree of membership of the customer to the class or

the degree of similarity to the prototype;– the degree of similarity between the products.

• The actions should reflect this partial degrees.


Rules in the Action Model (2)• The actions are therefore described in such a way that

they contain parameters which refer to the similarity notions in the conditions, i.e. they are fuzzy rules. Examples:

• Real Estate: – IF customer is rich THEN house price can be maximal

– Customer C is rich to degree 0.8, maximal price is 1 Mio., then one can offer houses with price up to 0.8 Mio.

• Travel:– IF customer C demands region X and region Y is similar to X

THEN region Y can be offered to C.

– If region A is demanded, sim(A,B) = 0.8. Region B is offered before any other region with a lower similarity to A is offered.


Rules in the Action Model (3)• Adaptation rules for demands are often of the form

IF members of class C have typical behavior

AND IF member of class C demands product P which requires some untypical behavior

THEN change demand appropriately.

Example:

- Computer laymen have difficulties with advanced tools for experts

- Customer is layman to a high degree

- Demand asks for a package including Microsoft Photo Tool

- Microsoft Photo Tool requires expert knowledge

- Actions:

- Inform the customer about the difficulties with this tool

- Offer a simpler tool for dealing with graphics


Summary• In order to treat customers adequately and at the same

time efficiently customers are aggregated into groups called customer classes (corresponding to market segments).

• Customer classes are vaguely described (fuzzy sets) which are described by prototypes and similarities.

• There are three models, the strategic, the diagnostic and the action model.

• The purpose of the diagnostic model is to classify customers.

• The purpose of the action model is to treat the customer properly (understanding the demands, starting a dialogue etc.)

• All models are interleaved.

chapter 10 customer modelling. - 2 - (c) 2000 dr. ralph bergmann and prof. dr. michael m. richter,...

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