Seminar Fuzzy Marketing Methods (Customer...

Seminar Fuzzy Marketing Methods (FMsquare), Kickoff-Meeting, HS09 Departement für Betriebswirtschaftslehre, Prof. Dr. Silke Bambauer-Sachse, Sabrina Mangold Departement für Informatik, Prof. Dr. Andreas Meier, Darius Zumstein

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Kickoff-Meeting, 23rd of September 2009

Seminar Fuzzy Marketing Methods (Customer Segmentation)

Information Systems Research Group Department of Informatics

Chair of Marketing Department of Management

Prof. Dr. Bambauer-Sachse Sabrina Mangold

Prof. Dr. Andreas Meier Darius Zumstein

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  Administration   Evaluation   Requirements   Research Topics   Literature   Questions

Overview


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  Teachers Prof. Dr. Bambauer-Sachse Prof. Dr. Andreas Meier

  Contact / [email protected] Assistants [email protected]

  Information http://diuf.unifr.ch/is/fm2_hs09 http://www.unifr.ch/marketing2/lehre/ Customer%20Segmentation.php

  Languages English // Deutsch // Français   Level Master   Faculty SES / Sciences   ECTS 4.5 (4.5 x 30 = 135 working hours)

Administration I

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  Date: Friday, 11th of December, 8.15 to 17.00 Place: Room C230

  Compulsory attendance   Active participation & collaboration   Maximal number of students: 24   Number of students for each of the topics: 1 or 2

(if >2 students are interested in one topic: 2 groups per topic are possible on agreement with assistants)

  Single presentations: 1 person   Presentations in groups: 2 persons

Administration II


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Course Description The seminar FMsquare (Fuzzy Marketing Methods) deals with fuzzy classification, which is an innovative approach to classify and segment objects. In contrast to a conventional, sharp classification, in a fuzzy classification objects can belong to several classes at the same time to a certain percentage. This, and other advantages discussed in the seminar, can be used for individual marketing (e.g. mass customization or personalization), market & customer segmentation, price management (e.g. individual prices, discounts or interests), online marketing (e.g. CRM or web analytics) and for performance measurement (e.g. classification of metrics, ratings or valuations). The seminar is therefore interesting for management students and for students of information management or informatics, since the fuzzy approach can be applied also to information retrievel (e.g. weblog extraction), data bases, data mining and data warehousing.

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Evaluation

  Single person or two persons (same mark)   Präsentation (English // German // Français)

  2 persons: 30 minutes; 1 Person: 20 min.   +10 minutes discussion

  Report (Term paper)   English // German // Français   20-40 pages   like a Seminararbeit // Travail de seminaire


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  Independent research into a topic, i.e.:   Searching/reading books of libraries

& stores (Uni FR, BE, BA, SG, ZH)   Searching/reading of scientific papers

http://rzblx1.uni-regensburg.de/ezeit/index.phtml?bibid=KUBFR&colors=7&lang=de http://www.jstor.org/action/showBasicSearch?cookieSet=1 http://www.unifr.ch/bp2/b_elecFull.htm Informatics: http://ieeexplore.ieee.org/Xplore/dynhome.jsp, http://www.acm.org

  Internet research http://scholar.google.ch

Requirements I

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Requirements II

1)   Concept (theory) 2)   a) Marketing-oriented approach:

Empirical study b) IS-/IT-oriented research approach: Case study or prototype

  Critical discussion of the topic & literature   Own ideas, elaborations & scientific

contribution


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Seminar/Research Topics 1)   Fuzzy Web Analytics Darius Zumstein 2)   Fuzzy Customer Performance Measurement Darius Zumstein 3)   Perf. Measurement of NPOs & NGOs with FC Darius Zumstein 4)   Hierarchical Decomposition of Cu. Portfolios Darius Zumstein 5)   Database Queries with the fCQL-Toolkit Daniel Fasel 6)   Fuzzy Data Warehousing Daniel Fasel 7)   Inductive Fuzzy Classification Michael Kaufmann 8)   Fuzzy Recommender Systems Luis Teran 9)   Fuzzy Weblog Extraction Edy Portmann 10)   Community Building Process with FC Edy Portmann 11)   Fuzzy Customer Segmentation Zoltan Horvath 12)   Customer Segmentation with regard to

a)   Age Sabrina Mangold b)  Skiing/Snowboarding experience Sabrina Mangold c)   Family Status Sabrina Mangold d)  Gender Sabrina Mangold

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  the measurement, collection, analysis and reporting of Internet data for the purposes of understanding and optimizing Web usage. Web Analytics Association (2009)

  the monitoring and reporting of website usage so that enterprises can better understand the complex interactions between website visitor actions and website offers, as well as leverage insight to optimise the site for increased customer loyalty and sales. Phippen et al. (2004)

1. Web Analytics is ...


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1. Web Analytics

Click stream & click path Depth of visit Length of visit (duration) Visit frequency

Reach

Web pages

Stickiness

Number of page views Number of visitors

Number of visits Ø time on page

Usa

ge b

ehav

iour

of

vis

itors

Legend:

Basket

Click-to-basket rate

Number of (returning or new) online customers

Display click rate

Product page

Bounce Rate

Exit page

Basket-to-buy rate

Order

Ad

clic

k ra

te

Online advertising

Search engines

Ad conversion rate

Metric-ratio (rate) Key Performance Indicators (KPIs) Web metric

External links

Beh

avio

r of

onlin

e cu

stom

ers

Purchase frequency & recency

Conversion rate Online revenue (total or per visit, visitor, order,…)

Order rate

Bookmark & URL

Clic

k ra

te

Landing page

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1. Web Analytics: Useful for the Optimization of...

  website quality: navigation & information structure, content, design, functionality & usability

  online CRM: customer orientation, customer acquisition & retention, customer development (e.g. cross-/up-selling)

  internal processes and communication: contacts, interactions, & relations with (potential) online customers

  search engine optimization: reach & visibility of corporate website, search engines rankings, PageRank

  online marketing: awareness & image of the website & company, campaigns, banner & keyword advertising

  segmentation of the traffic, visitors and online customers   traffic: page views, visits, visitors, etc.   e-business profitability (or achievement of other objectives):

efficiency & effectiveness of the web presence


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1. Sharp Classification of Age & Page Views (/any other metric)

0 10 20 30 40 50 60 70 Age /

Page view

1

µ

0 25

Young Middle-aged

55

Old

Few page views

↓ Low user interest

Medium page views

↓ Medium user

interest

Many Page views

↓ High user

interest

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1. Fuzzy Classification of Age & Page Views (/any other metric)

Middle-aged

Medium page views

0 10 20 30 40 50 60 70 Age /

Page view

1

µ

0

Young

Few page views

Old

Many page views


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1. Definition of Fuzzy Constructs

0 Tim

e (h

ours

)

12 1 2 3 4 5 6 7 8 9 10 11 12

µ 1

Evening Night Afternoon Fuzzy Time Constructs   Morning   Afternoon   Evening   Night

Time (month)

µ 1

0 May June July August September

High season (summer)   Weekend, etc.

  High season (e.g. summer or winter)

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1. Fuzzy Web Analytics

  Analysis & fc of web metrics & KPIs (website traffic, visitors & online customers)

  Keywords:   Web analytics, web controlling   Web metrics, Key Performance Indicators (KPIs)   Website optimization

  Literature:   Books: [Hassler 2008, Kaushik 2009]   Monographs: [Hukemann 2004, Stolz 2007]   Ongoing Research [Zumstein & Meier 2009]


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  Keywords:   Performance Measurement/Management (Systems)   Customer Relationship Management (CRM)   Marketing Performance Measurement   Marketing & Customer Controlling

  Literature:   [Bauer et al. 2006]   [Neckel und Knobloch 2006]   [Reinecke 2004, Reinecke und Tomczak 2006]   [Zumstein 2007]

2. Fuzzy Customer Performance Measurement

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  Keywords:   Performance Measurement/Management   Non Profit/Govermental Organisations   NPO Websites, Online Fundraising

  Literature:   Book: [Meier 2007]   Ongoing research: [Kaufmann et al. 2007]   See library VMI (www.vmi.ch => Service)

3. Performance Measurement of NPOs & NGOs with fc


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  Keywords:   Customer portfolios   Portfolio technique, analysis & management   Fuzzy Marketing Methods

  Literature:   Book: [Storbacka 2004]   Monograph: [Werro 2007]   Ongoing research: [Zumstein 2007, Zumstein et al.

2007]

4. Hierarchical Decomposition of Customer Portfolios

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From a Sharp Classified Customer Portfolio...

C2

C4 C3

Ford

Cu. Attractiveness 8

Competitive Position very bad

Class Renunciation customers (C4)

Smith

48

insufficient

Brown

56

sufficient

Star customers (C1)

C2

Development customers (to invest)

C4

Renunciation customers

(not to invest)

C3

Absorption customers (to skim)

100

50

prom

isin

g un

prom

isin

g

49

0

C1

Star customers (to maintain)

Com

petit

ive

posi

tion

Customer attractiveness

Millerm

92

excellent

weak very bad bad insufficient

strong sufficient good excellent

Smith .

Brown .

Miller .

Ford .

C2

C4 C3

C2 Question marks

C4 Poor dogs

C3 Cash cows

C1 Stars

R

elat

ive

mar

ket s

hare

Real market growth Analogically

Customer turnover

Boston Consulting Group (BCG) Portfolio


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... to a Fuzzy Classified Customer Portfolio

C3

Absorption customers (to skim)

C1

Star customer (to maintain)

C2

Development customers (to invest)

C4

Renunciation Customers

(not to invest)

Smith

Customer attractiveness

weak

unpr

omis

ing

Com

petit

ive

posi

tion

100

50

strong

prom

isin

g 49

1 µ un

prom

isin

g

0 1

µ p

rom

isin

g

0.65 0.35

C2

C4 C3

very bad bad insufficient sufficient good excellent

Brown

31.5% Star customer 25.1% Absorption customer

24.5% Development customer

Miller .

µ weak µ strong

0

1

0.4 0.6

0.2

0.8

Ford .

.

18.9% Renunciation customer

.

22.9% Development

customer

17% Star customer

33.7% Renunciation customer

26.4% Absorption customer

C1 100% Star customer

100% Renunciation customer

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  Keywords:   Databases, database technologies   SQL, fCQL (fuzzy Classification Query Language)   Applications, Testing of Prototype

  Literature:   Monographs: [Schindler 1997, Werro 2007]   Ongoing research: [Meier and Werro 2007, Hofer

2009]

5. Database Queries with the fCQL-Toolkit


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Architecture of the fCQL-Toolkit

Generated SQL queries

Relational Database Management System (RDBMS)

Ser

ver

Data

SQL queries

Case 1 fCQL

queries

Case 2

fCQL- toolkit

User / application

Case 3 Graphical interaction

Data architect

Meta-Tables

No migration of data

necessary

attractiv. promising

c. position strong strong weak all

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  Keywords:   Data Warehouse, DWH Tools & Technologies   fc/aggregation of Dimensions Facts   Slicing, Dicing, Drill-down & Roll-up

  Literature:   Books: [Bauer 2004, Kimball 2007, Inmon et al.

2008]   Ongoing research: [Fasel 2009, Fasel und Zumstein

2009]

6. Fuzzy Data Warehousing


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  Keywords:   Inductive Fuzzy Classification   Fuzzy logic/sets   Data Mining

  Literature:   Books: [Witten 2007]   Ongoing research: [Kaufmann und Meier 2009]

7. Inductive Fuzzy Classification

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  Keywords:   Recommender Systems   Personalization, Individualisation   Mass Customization

  Literature:   Books: [Piller 2006]   Monographs: [Werro 2008, Stormer 2008]

8. Fuzzy Recommender Systems


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  Keywords:   Weblogs, Blogs   Information Retrieval   Web Search Engines   Web 2.0, Web 3.0

  Literature:   Books: n.A.   Ongoing research: [Portmann 2009]

9. Fuzzy Weblogs Extraction

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  Keywords:   Virtual Communities   Web 2.0, Social Software, Social Networks

  Literature:   Books: n.A.   Ongoing research: n.A.

10. Community Building Process with Fuzzy Classification

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