Introduction to Marketing

Key learnings of the lecture «Introduction to Marketing», Fall 2016, Prof. Wangenheim @D-MTEC, ETH Zürich

Key learnings of the lecture «Introduction to Marketing», Fall 2016, Prof. Wangenheim @D-MTEC, ETH Zürich


Roland Schenkel
This flashcard set covers fundamental marketing concepts at a university level, focusing on customer segmentation, product pricing, and brand strategies. It delves into various pricing models, targeting methods, and the importance of customer equity, making it invaluable for marketing students and professionals aiming to understand core marketing principles and their practical applications.
Flashcards
46
Students
2
Language
English
Category
Marketing
Level
University
Created / Updated
04.01.2017 / 27.12.2019

Flashcards

Types of innovation

Incremental innovation (VW Golf 4 => Golf 5)

Market breakthrough (Selfie-sticks)

Technological break through (Seagway)

Radical innovation (www)

How can customers pay if it is not money?

• Data

• Attention

• Network value

• Co-production

• Word-of-mouth

• (future) transactions or revenues

3 approaches to pricing

  1. Cost based pricing: Product => Costs => Price => Value => Customers
  2. Value-based pricing: Customers => Value => Price => Costs => Product
  3. competition-base pricing

 

Skimming pricing <=> penetration pricing

Skimming: Price starts at high levels an then decreases (eg. iPhone)

Penetration: Price starts at low level an then increases (eg. Adobe)

Degrees of price discrimination

First degree: Individual price for each customer, Price equals each customer‘s willingness to pay, Perfect price discrimination with skimming the whole consumer‘s, surplus.

Second degree: Individual price for different customer segments, but customer decides in which segment he is (eg. SBB 1/2-Tax)

Third degree: Individual price for different customer segments, customer can not decide in which segment he is (Mensa)

Procedure for conjoint analysis

  1. Acquire the preference scores of products
  2. Compute the average score for each attribute level (eg. 9+2+4=5 for 1GB, 1+4=2.5 for 10GB): \(AS_k= \frac {\sum U_i}{| M_K|}\)
  3. Compute the part worh for each attribute level (normalize to [0,1]) \(PW_k=\frac{AS_k-minAS} {maxAS-minAS}\) max and min AS of ALL average scores!
  4. Compute raw importance for each attribute \(RI_j=maxPW-minPW\) = span of the attribute level (eg. for data volume we have max 0.57 and min 0.27 => RI = 0.3
  5. Compute the relative importance for each attribute: \(PI_j=\frac {RI_j}{\sum RI_p}\cdot 100percent\)