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Conjoint Analysis - 328053-M-6 - samenvatting
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  • Conjoint Analysis- 328053-M-6
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  • Tilburg University (UVT)

Complete summary of all lectures & slides from module 1 - 11. For Master students in Marketing Management & Marketing Analytics at Tilburg University - Conjoint Analysis - -M-6

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Document informatie

  • 8 december 2020
  • 47
  • 2020/2021
  • Samenvatting

Onderwerpen

  • conjoint analysis
  • 328053 m 6
  • master marketing management
  • master marketing analytics
  • tilburg university

Geschreven voor

  • Tilburg University (UVT)
  • Marketing Management Or Marketing Analytics
  • Conjoint Analysis- 328053-M-6
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Voorbeeld van de inhoud

Conjoint analysis – Tilburg university – blok 2 – 328053-M-6

,MODULE 1: INTRODUCTION TO CONJOINT ANALYTICS
Conjoint analytics
 combining all or both people or things involved  join together
 Products are represented as bundles of attributes
 Levels of each attribute define the product e.g. coca cola (brand, packaging, volume & taste)

What it is used for?
Conjoint analysis is a survey-based technique that allows the analyst to understand people’s preferences for a
[product / service / brand / medical treatment / job / course] and especially the trade-offs they make in
making choices  Trade-off between product attributes

Why conjoint analysis?
In contrast to conjoint analysis: direct surveys  respondents might say they consider all attributes important
 not informative because it is not realistic
 Conjoint enforces tradeoffs between attributes as in real purchase occasion
- All attributes evaluated at once
- Respondents evaluate “complete” products with both strong and weak attributes
 Conjoint reduces problem of socially desirable answers (in reallife they also choice just what they like)
 Conjoint adds realism
- In real-life consumers evaluate products, not isolated attributes (do they consciously know which
attributes matter?)
 Conjoint analysis is straightforward
- Suitable software is available (Sawtooth)

Conjoint in the age of big data?
 Why should we run hypothetical choice experiments if firms can increasingly make use of large amounts of
transactional purchase data?
1. Lack of experimental price variations required to learn consumers' preferences
2. Conjoint allows to measure consumer preferences for products or attribute levels not yet introduced in the
marketplace
3. Especially relevant for e.g.,pricing of new product innovations

 Field experiments are prominent alternatives for the goal of learning consumer preferences. However, ...
1. Often, field experiments are difficult to conduct and not feasible in high-ticket product categories (like cars,
laptops, etc.)
2. Field experiments are limited to products already existing in the marketplace

Why is that relevant?
- Every year, many new products are introduced
- But very few succeed
- That is a major issue for companies

Why do we need a course for this?
- Product design decisions are highly complex
- A product is characterized by many, many attributes
- Some of these attributes are very complex to understand

Course objectives:
- Which new product to launch?
- How to position existing products better?
- How to price existing and new products?
- How to manage product portfolios / product line designs?

More than just product design (examples of application)
 Consumer complains on social media  how to respond:
- React on complaints

,- We should react fast to complains
 Customers who received any kind of response to their tweet were willing to pay more  e.g. $9
more for a ticket on that airline in the future & $8 more on average for a monthly wireless plan from
that carrier

 Conjoint is in sectors like health care, economics, law, human resources

Course overview
1. Conjoint design
2. Conjoint analysis
3. Choice simulator

PART 1: Conjoint design
 Ranking-based conjoint:
Choose the most-preferred product, then the second most-preferred product, …
until the least-preferred product

 Rating-based conjoint:
Give a score to each product in turn

Problems of Rating-Based conjoint:
- Notrealistic
 In real-life, we buy products rather than rating them
- Not clear whether spread in ratings is due to real preferences or due to response style
 E.g.,small spread in example above, weak preferences or cautious answers?
- Implications for sales levels and market shares are not clear
 Sales and shares result from consumer choices, notratings
 What would be the rating threshold?

“Why don’t we ask the respondents to choose a product directly, rather than asking them to rate
products?”

 Choice-based conjoint:
Choice between different variants, choose the most-preffered product only between choice sets
(most used in practice because it is most closely to what consumers will do in reality)

- We record the choices made by every customer during the n tasks (i.e. choice sets).
- Because, in every choice set, a different combination of attribute levels is used, we can derive the effect of
different combinations of attribute levels on choice.
 preferences = attribute part-worths
- As we repeat the conjoint exercise across many customers, we can also detect whether different
customers have different preferences.
 Customer-specific preferences
* You have to test at least 1000 respondents so you can really see & catch the different segments in the market. Although it is not good to
target 1000 respondents of one segment (e.g. only apple users if you want to know laptop preferences) = not representative

Advantages of choice-based conjoint:
- Tradeoffs are enforced even more
- Realistic: the choice-setting mimics real-life
- Accommodates no-choice option (”none of the offered alternatives is attractive”,“I would stick to my
current product”), which is also in reality  salesproxy
- Avoids the need of ad-hoc rules to predict market shares
- No subjective scaling
- Choice is cognitively less demanding than ratings (Louviere1994) because you simply choose the one which
attracts you the most, while by rating you have to think about the differences between teh attributes.
Disadvantages of choice-based conjoint:
- Hypothetical bias:

, Respondents' product choices (potentially at very large prices) might be influenced by the
experimental setting, i.e. with no consequences for actual purchase behavior in the real world

- Small individual level data:
Respondents become fatigue if exposed to a large number of choice tasks that are actually required if
the experiment includes large numbers of attribute levels

Which is similar to choice overload: they have to much choices which is too much information for the
respondents. which will biased your estimation results.  To avoid a biase of the choice process ==>
small individual level data

- Bayesian methods and prior specifications:
Bayesian statistical methods help as they efficiently pool information across respondents (shrinkage).
However, some analysts regard the inclusion and specification of priors as subjective because the
attributes are chosen by the analyst




Rating-based Conjoint Choice-based Conjoint

Disadvantages Benefits
· Some tradeoffs are made · Tradeoffs are enforced even more (have to
· Rating products is not choose between products or the no choice
common in daily life option)
· All products are considered · Realistic: the choice-setting mimics real-life
· Implications for sales levels · Accommodates no-choice option (“none of
and market shares are not the offered alternatives is attractive”, “I
clear would stick to my current product”) sales
· Not clear whether spread in proxy
ratings is due to real · Avoids the need of ad-hoc rules to predict
preferences or due to market shares (it’s about choosing rather
response style than an attitude towards)
· No subjective scaling (every choice has the
same meaning)
· Choice is cognitively less demanding than
ratings (Louviere 1994)



Benefits Disadvantages
· Ratings are informative of the · Observations are less informative (no extent
intensities of preference to which one product is preferred over
· Easy to implement and another)
estimate · Implementation more complicated



PART 2: Conjoint Analysis
 Logistic Regression
DV = product chosen ornot
IV = productattributes

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