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
Set of flashcards Details
Flashcards | 46 |
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Language | English |
Category | Marketing |
Level | University |
Created / Updated | 04.01.2017 / 27.12.2019 |
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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
- Cost based pricing: Product => Costs => Price => Value => Customers
- Value-based pricing: Customers => Value => Price => Costs => Product
- 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
- Acquire the preference scores of products
- Compute the average score for each attribute level (eg. 9+2+4=5 for 1GB, 1+4=2.5 for 10GB): \(AS_k= \frac {\sum U_i}{| M_K|}\)
- 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!
- 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
- Compute the relative importance for each attribute: \(PI_j=\frac {RI_j}{\sum RI_p}\cdot 100percent\)