Team E Day 11

Dhiraj Doley

14073

Conjoint Analysis:

Definition:
It is a statistical technique used in market research to determine how people value different features that make up an individual product or service.Conjoint analysis became popular because it was a far less expensive and more flexible way to address these issues than Concept testing.

The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making.

A controlled set of potential products or services is shown to respondents and by analyzing how they make preferences between these products.

Conjoint analysis must currently be run using syntax. Unlike most procedures in SPSS for Windows, conjoint analysis requires the user to invoke two files:

1.Plan File:The plan file contains the combinations that will be presented to the participants.

2.Data File:The data file contains the participants' responses.

Types of conjoint analysis:

Adaptive Conjoint Analysis - ACA:

Adaptive Conjoint Analysis (ACA) is one of two most common methods for carrying out conjoint analysis. The benefits of ACA are that it allows for a large number of attributes (up to 30) and levels (up to 7 per attribute) to be used.

Choice Based Conjoint Analysis- CBC:

The most common alternative to ACA is Choice-based conjoint (CBC). Although this uses the same over-arching principles as ACA, in design, implementation and calculation it is completely different.

Discrete Choice Analysis

A more advanced form of choice-based conjoint is Discrete Choice Analysis (also known as "stated preference research"). DCA studies are particularly popular for transportation studies looking at modal choice - the preference between a train, car and airline for instance. 

Full profile Conjoint Analysis

An additional option that dates back a long time but that is still used is full profile conjoint analysis. Full-profile is the original form of conjoint and is still in use; though predominantly in the US it would appear. Like choice-based conjoint this uses a more limited number of attributes to describe the product or service, but sufficient cards or treatments are shown to one respondent to enable individual level utilities to be calculated.

Other forms and formats

Recent developments in conjoint include the Adaptive Choice Based Conjoint method from Saw tooth. Which combines elements of a configurator, an adaptive element and choices? In addition we have our own dobney.com conjoint designer that allows for a range of other more bespoke research areas where traditional forms of conjoint analysis are lacking or where current designs can seem too difficult from a respondent point-of-view 

Steps in generating conjoint analysis:

Step 1: Generating the Plan file:
Open SPSS ->Data > Orthogonal Design-> Generate

Define factors :
Step 2:Data -> Orthogonal Design->Display

This will give you profiles (Multiple combination of factors).Go to your subject and find out their ranking for the given profiles.

Step 3: Generate the data file
The file is generated on basis of ranking provided to different preferences.
Step 4:Run a conjoint Analysis: CONJOINT PLAN='C:\Documents and Settings\Administrator\Desktop\VXLPLAN.SAV'
/DATA=*
/SUBJECT=ID
/FACTORS=LAPTOP_CARRY INT_CONNECT_VIDEO_DEMAND
VIDEO_CONF_VOIP PRICE_PREMIUM
/RANK=PREF1 TO PREF9
/UTILITY='C:\Documents and Settings\Administrator\Desktop\OUTPUT.SAV'
/PLOT=SUMMARY
/PRINT=SUMMARYONLY.

Step 5: Analyse the output

Team C day 11 (Akash Singh)

Akash Singh

14065

Conjoint Analysis

Conjoint analysis is a popular marketing research technique that marketers use to determine what features a new product should have and how it should be priced.  Conjoint analysis became popular because it was a far less expensive and more flexible way to address these issues than concept testing. 

Conjoint analysis has been successfully applied in many industries, such as Air Travel, Smart Phones, Computers, Financial Services, Health Care, Real Estate, and Electronics. If a job includes configuring a defined set of features for a product or service and the consumer’s purchase decision will be “rational,” conjoint analysis can help. If, on the other hand, one has to decide if a consumer’s purchase decision will be “impulse” or “image,” conjoint is not the right tool.

Respondents usually complete between 12 to 30 conjoint questions. The questions are designed carefully, using experimental design principles of independence and balance of the features. By independently varying the features that are shown to the respondents and observing the responses to the product profiles, the analyst can statistically deduce what product features are most desired and which attributes have the most impact on choice. In contrast to simpler survey research methods that directly ask respondents what they prefer or the important of each attribute, these preferences are derived from these relatively realistic tradeoff situations.

Because conjoint analysis helps one understand market’s preferences, it can be applied to a variety of difficult aspects of the job, including product development, competitive positioning, pricing, product line analysis, segmentation and resource allocation. “How should we price our new product to maximize adoption?” “What features should we include in our next release to take market share from our competition?” “If we expand our product line, will overall revenue grow, or will we suffer too much cannibalization?” “For which value-added features is the market willing to pay?”

Conjoint (trade-off) analysis has become one of the most widely-used quantitative methods in Marketing Research. It is used to measure the perceived values of specific product features, to learn how demand for a particular product or service is related to price, and to forecast what the likely acceptance of a product would be if brought to market.

Rather than directly ask survey respondents what they prefer in a product, or what attributes they find most important, conjoint analysis employs the more realistic context of respondents evaluating potential product profiles by giving them a variety of product details and arriving at a score.

There are different ways to show product profiles. The original version of conjoint analysis developed in the early 1970s showed products one-at-a-time, as in the above example. Sawtooth Software’s CVA System may be used for this traditional form of conjoint analysis. Later forms of conjoint analysis showed products in pairs (CVA system orACA System for Adaptive Conjoint Analysis), or sets at a time (CBC System for Choice-Based Conjoint).

Day 11- Team F( Pixy)

Conjoint analysis in product management  

                                                                                                                                                 

Conjoint analysis

It is a statistical technique used in market research to determine how people value different features that make up an individual product or service.

The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making. A controlled set of potential products or services is shown to respondents and by analyzing how they make preferences between these products, the implicit valuation of the individual elements making up the product or service can be determined. These implicit valuations (utilities or part-worths) can be used to create market models that estimate market share, revenue and even profitability of new designs.

Today it is used many of the social sciences and applied sciences including marketing, product management, and operations research. It is used frequently in testing customer acceptance of new product designs, in assessing the appeal of advertisements and in service design. It has been used in product positioning, but there are some who raise problems with this application of conjoint analysis.

Conjoint analysis techniques may also be referred to as multiattribute compositional modeling, discrete choice modeling, or stated preference research, and is part of a broader set of trade-off analysis tools used for systematic analysis of decisions. These tools include Brand-Price Trade-Off, Simalto, and mathematical approaches such as evolutionary algorithms or Rule Developing Experimentation.

Conjoint / Discrete Choice Analysis

Conjoint or Discrete Choice Analysis elicits your market’s preferences for the components that make up your product, including product features, service levels, brands, prices, etc.  Although there are differences among the various conjoint and discrete choice techniques, for our purposes here we will consider them as a group and refer to them as conjoint.  As a product manager, you can leverage conjoint to:

·         Understand your market’s requirements

·         Determine your business case and the market feasibility of your product

·         Scope and define your product offering

·         Create a differentiated product

·         Price your product

·         Plan your product and extensions to optimize your objectives

·         Assess possible responses  to competitive actions

Anytime you need to make tradeoffs among different aspects of an offering, you should consider using conjoint analysis. A conjoint study forces respondents to make tradeoffs and, therefore, uncovers their hot buttons. With conjoint you can understand which features, services, and brands are most important to your target market, the level of price elasticity, how likely your market is to purchase your product, how preferred your product is versus the competition, whether there are attractive preference-based segments for your product, how you can drive an increase in preference for your product, whether you should consider product line extensions and the extent to which multiple product offerings might cannibalize one another.

In short, conjoint helps you uncover a wealth of information. You will be better equipped to make the tradeoffs you need to as a product manager by getting respondents to make the tradeoffs for you.

Perceptual Mapping

Perceptual mapping uncovers how the market perceives your product in relation to your competition. It gives you a visual representation of the market, helps you determine what is most important to your market, and helps to uncover opportunities for your brand by seeing gaps along those important factors. As a product manager, you can leverage perceptual mapping to help you.

·         Differentiate your product offering

·         Position your product or brand

Anytime you’re trying to understand how the market perceives your position in the market place, you should consider perceptual mapping. To position your product effectively, you need to know what’s important to your market, how your customers perceive your performance in those areas and how they perceive your competition’s performance. Perceptual mapping helps you uncover those perceptions so that your product’s strengths, weaknesses, opportunities and threats become evident through the proverbial picture that’s worth a thousand words. The end result is a winning, sustainable positioning for your brand.

 Submitted by                                         

 Pixy Rain

Team F

DAY11_Team G

CONJOINT ANALYSIS: TEAM G

DEFINITION & INTRODUCTION:

Conjoint analysis is a popular marketing research technique that marketers use to determine what Features a new product should have and how it should be priced. 

Conjoint analysis became popular because it was a far less expensive and more flexible way to address these issues than Concept testing

Conjoint analysis is a statistical technique used in market research to determine how people value different features that make up an individual product or service.

The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making.

A controlled set of potential products or services is shown to respondents and by analyzing how they make preferences between these products.

Conjoint analysis helps organizations understand which factors drive decisions. Analysts determine factor preferences by presenting multiple combinations of factors and asking respondents to rank them.

Conjoint analysis must currently be run using syntax. Unlike most procedures in SPSS for Windows, conjoint analysis requires the user to invoke two files:

1.       Plan File:The plan file contains the combinations that will be presented to the participants.

2.       Data File:The data file contains the participants' responses.

The syntax includes the full location of the plan file, but uses an asterisk to alert SPSS to use the file in the data editor as the data file.

Types of conjoint analysis

Most conjoint analysis studies carried out professionally use either Adaptive Conjoint Analysis (ACA) which relies on computer based interviewing, or Choice-based Conjoint. However, there are actually many types of conjoint analysis depending on the task at hand.

Choice-Based Conjoint (CBC) is favored academically and widely used for pricing and brand value studies, ACA for larger more marketing focused work. Other forms of conjoint incorporate Buy-your-own tasks or take the basic principles and extend them to create tailored conjoint designs for specific markets.

Adaptive Conjoint Analysis - ACA

Adaptive Conjoint Analysis (ACA) is one of two most common methods for carrying out conjoint analysis. The benefits of ACA are that it allows for a large number of attributes (up to 30) and levels (up to 7 per attribute) to be used. However, ACA does require a computer-based interview and the large number of attributes means that it is common for an ACA interview to last 45 minutes or more. In addition, some of the methods it uses to simplify the task of working out utilities mean that some care is needed in choosing and designing the attributes in order to get reliable results.Technically ACA is known as a hybrid technique as it contains elements of 'self-explication' followed by the trade-off tasks themselves. ACA itself is produced by Sawtooth Software and can be conducted face-to-face or on-line. Telephone use of ACA is difficult and paper-base questionnaires are not possible.

Choice Based Conjoint Analysis- CBC

The most common alternative to ACA is Choice-based conjoint (CBC). Although this uses the same over-arching principles as ACA, in design, implementation and calculation it is completely different.Whereas ACA has respondents selecting from products described with two or three attributes, CBC shows full descriptions using all the attributes available. In addition, CBC can show more than just two "products" at the same time, together with a none-of-these option enabling more realistic choice decisions to be evaluated.The limitation on the amount a respondent can absorb at a time, combined with the rapidly increasing number of "full-profile" combinations that are possible means that choice-based conjoint is typically limited to 5-7 attributes (in contrast to 25-30 for ACA).An additional twist is that utilities and importances in CBC are calculated across a sample as a whole, whereas for ACA you get utilities and importances for each individual in the sample. To get at individual level estimations relies on techniques such as Hierarchical Bayes analysis to infer back individual values. However, combined with the lower number of attributes, choice-based studies require far shorter questionnaires (15-20 mins) and can be designed to be purely paper-based.The advantages choice-based conjoint gives you are greater robustness of results - particularly for pricing work (although there are ways of getting around ACA's pricing limitations), combined with shorter and therefore less costly fieldwork. It is also favoured for its rigour academically. In applications such as pharmaceutical work it also enables comparisons with fixed products or fixed tasks enabling you to test new formulations against an existing gold standard.The disadvantages are the lower number of attributes that are possible unless you move to more complex bespoke designs using partial profiles, and the lack of directly valued individual level utilities - although techniques such as hierarchical bayesian analysis seeks to remedy this by post-hoc simulation of individual level values. However, if you are looking to use conjoint analysis for clustering or segmentation you will need to be aware of the trade-offs needed to get individual level utility scores.

Discrete Choice Analysis

A more advanced form of choice-based conjoint is Discrete Choice Analysis (also known as "stated preference research"). DCA studies are particularly popular for transportation studies looking at modal choice - the preference between a train, car and airline for instance. The main difference from CBC is the inclusion of continuous variables such as price and time. This allows the ability to examine the varying costs of the ticket with varying times taken to travel and so to establish the value of time for the journey. This enables transport economists to make statements like "2cm extra leg room is worth 10 minutes longer journey time or £40 extra fare" or "an extra train every 15 minutes would encourage x% of car drivers to switch to the train".

Full profile Conjoint Analysis

An additional option that dates back a long time but that is still used is full profile conjoint analysis. Full-profile is the original form of conjoint and is still in use; though predominantly in the US it would appear. Like choice-based conjoint this uses a more limited number of attributes to describe the product or service, but sufficient cards or treatments are shown to one respondent to enable individual level utilities to be calculated. A fractional factorial design is used to specify a fixed set of profiles that need to be shown for analysis. The difficulty is that this does limit the number of attributes quite severely. However these old school studies are still popular for simple, non-computer-based conjoint projects and are most common for students learning about conjoint for the first time.

Other forms and formats

Recent developments in conjoint include the Adaptive Choice Based Conjoint method from Saw tooth. Which combines elements of a configurator, an adaptive element and choices? In addition we have our own dobney.com conjoint designer that allows for a range of other more bespoke research areas where traditional forms of conjoint analysis are lacking or where current designs can seem too difficult from a respondent point-of-view These include emotional association tasks, and repertoire purchasing (where someone is buying a bundle of products across a range of uses), volumetric measurement and improved choice displays such as using sliders or more interactive elements to encourage a fuller participation in the decision making process. Similarly, for international research the presentation of prices in different formats and currencies requires careful consideration.

Deciding which format of conjoint analysis to use

We often find that the choice of the type of conjoint analysis to use depends on a number factors and it's often difficult for someone new to conjoint analysis to visualize the options and in cases where there are 6-8 attributes, there can be several options and approaches. We often produce several versions and presentations for illustration to help clients see and understand which form and presentation version of conjoint to use, without compromising the final statistical quality of the survey.

STEPS:-

Step 1: Generating the Plan file:
Open SPSS ->Data > Orthogonal Design-> Generate
Define factors :

Step 2: Data -> Orthogonal Design->Display
This will give you profiles (Multiple combination of factors).Go to your subject and find out their ranking for the given profiles.

Step 3: Generate the data file
the file is generated on basis of ranking provided to different preferences.

Step 4: Run a conjoint Analysis: CONJOINT PLAN='C:\Documents and Settings\Administrator\Desktop\VXLPLAN.SAV'

/DATA=*
/SUBJECT=ID
/FACTORS=LAPTOP_CARRY INT_CONNECT_VIDEO_DEMAND
VIDEO_CONF_VOIP PRICE_PREMIUM
/RANK=PREF1 TO PREF9
/UTILITY='C:\Documents and Settings\Administrator\Desktop\OUTPUT.SAV'
/PLOT=SUMMARY
/PRINT=SUMMARYONLY.

Step 5: Analyze the output

Author

ANKIT MAHESHWARI

TEAM G

CONJOINT ANALYSIS -Day: 11 Team:D

                                                                       Team: D 

Conjoint analysis is a popular marketing research technique that marketers use to determine what Features a new product should have and how it should be priced.  Conjoint analysis became popular because it was a far less expensive and more flexible way to address these issues than Concept testing.  It is used to measure the perceived values of specific product features, to learn how demand for a particular product or service is related to price, and to forecast what the likely acceptance of a product would be if brought to market. Rather than directly ask survey respondents what they prefer in a product, or what attributes they find most important, conjoint analysis employs the more realistic context of respondents evaluating potential product profiles. Each profile includes multiple conjoined product features (hence, conjoint analysis).

Later forms of conjoint analysis showed products in pairs as in the example below:

Respondents usually complete between 12 to 30 conjoint questions. The questions are designed carefully, using experimental design principles of independence and balance of the features. By independently varying the features that are shown to the respondents and observing the responses to the product profiles, the analyst can statistically deduce what product features are most desired and which attributes have the most impact on choice. In contrast to simpler survey research methods that directly ask respondents what they prefer or the important of each attribute, these preferences are derived from these relatively realistic tradeoffs situations.

Conjoint analysis questionnaires ask respondents to evaluate realistic product profiles (described by multiple features) and to choose which they would buy. Such surveys are more realistic than traditional questionnaires that simply ask respondents which features they prefer or regarding the generic importance of attributes.

 Common business applications include:

• Designing new products
• Re-designing existing products
• Product line extension research
• Estimating brand equity
• Measuring price sensitivity (elasticity)
• Optimizing employee compensation packages and workplace conditions
• Branding and packaging.

Conjoint analysis must currently be run using syntax. Unlike most procedures in SPSS for Windows, conjoint analysis requires the user to invoke two files:

1.Plan File:The plan file contains the combinations that will be presented to the participants.

2. Data File:The data file contains the participants' responses.

The syntax includes the full location of the plan file, but uses an asterisk to alert SPSS to use the file in the data editor as the data file. 

       STEPS:-

Step 1: Generating the Plan file:
Open SPSS ->Data > Orthogonal Design-> Generate

Define factors :

Step 2:Data -> Orthogonal Design->Display

This will give you profiles (Multiple combination of factors).Go to your subject and find out their ranking for the given profiles.

Step 3: Generate the data file
The file is generated on basis of ranking provided to different preferences.

Step 4:Run a conjoint Analysis: CONJOINT PLAN='C:\Documents and Settings\Administrator\Desktop\VXLPLAN.SAV'
/DATA=*
/SUBJECT=ID
/FACTORS=LAPTOP_CARRY INT_CONNECT_VIDEO_DEMAND
VIDEO_CONF_VOIP PRICE_PREMIUM
/RANK=PREF1 TO PREF9
/UTILITY='C:\Documents and Settings\Administrator\Desktop\OUTPUT.SAV'
/PLOT=SUMMARY
/PRINT=SUMMARYONLY.

Step 5: Analyse the output

Author: VINOD JOSHI

Day 11 - Team I

Conjoint Analysis

Conjoint analysis is a statistical technique used in market research to determine how people value different features that make up an individual product or service. The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making. A controlled set of potential products or services is shown to respondents and by analyzing how they make preferences between these products

 Types of conjoint analysis

Most conjoint analysis studies carried out professionally use either Adaptive Conjoint Analysis (ACA) which relies on computer based interviewing, or Choice-based Conjoint. However, there are actually many "flavours" of conjoint analysis depending on the task at hand. 

Choice-Based Conjoint (CBC) is favoured academically and widely used for pricing and brand value studies, ACA for larger more marketing focused work. Other forms of conjoint incorporate Buy-your-own tasks or take the basic principles and extend them to create tailored conjoint designs for specific markets.

There are three key design elements for conjoint analysis. Firstly, breaking down a product or service into component attributes and levels. Then, the choice of how to present a series of descriptions (product profiles) and what method should be used to find out which are most preferred. And finally the method for the calculation of the value of each attribute and level to the market - the importance and utility scores, or part-worths.

The design decision of which to use depends on the number of attributes you have, the contact method and time available for the interview - for instance, on-line, face-to-face CAPI, or postal or paper-based interviewing. In addition, the different flavours of conjoint analysis each have differing characteristics in analysis. If data is available at an individual level as with Adaptive Conjoint Analysis, then it is simpler to develop market segmentation schemes. If the data needs to be more robust, perhaps because of the inclusion of attributes that may interact with each other, then Choice Based Conjoint analysis may be more relevant. If you run into conjoint analysis as a student, then it is most probably through the use of full-profile conjoint analysis. There are other options available depending on the task at hand.

Adaptive Conjoint Analysis - ACA

Adaptive Conjoint Analysis (ACA) is one of two most common methods for carrying out conjoint analysis. The benefits of ACA are that it allows for a large number of attributes (up to 30) and levels (up to 7 per attribute) to be used. However, ACA does require a computer-based interview and the large number of attributes means that it is common for an ACA interview to last 45 minutes or more. In addition, some of the methods it uses to simplify the task of working out utilities mean that some care is needed in choosing and designing the attributes in order to get reliable results.

Technically ACA is known as a hybrid technique as it contains elements of 'self-explication' followed by the trade-off tasks themselves. ACA itself is produced by Sawtooth Software and can be conducted face-to-face or on-line. Telephone use of ACA is difficult and paper-base questionnaires are not possible.

Choice Based Conjoint Analysis- CBC

The most common alternative to ACA is Choice-based conjoint (CBC). Although this uses the same over-arching principles as ACA, in design, implementation and calculation it is completely different.

Whereas ACA has respondents selecting from products described with two or three attributes, CBC shows full descriptions using all the attributes available. In addition, CBC can show more than just two "products" at the same time, together with a none-of-these option enabling more realistic choice decisions to be evaluated.

The limitation on the amount a respondent can absorb at a time, combined with the rapidly increasing number of "full-profile" combinations that are possible means that choice-based conjoint is typically limited to 5-7 attributes (in contrast to 25-30 for ACA).

An additional twist is that utilities and importances in CBC are calculated across a sample as a whole, whereas for ACA you get utilities and importances for each individual in the sample. To get at individual level estimations relies on techniques such as Hierarchical Bayes analysis to infer back individual values. However, combined with the lower number of attributes, choice-based studies require far shorter questionnaires (15-20 mins) and can be designed to be purely paper-based.

The advantages choice-based conjoint gives you are greater robustness of results - particularly for pricing work (although there are ways of getting around ACA's pricing limitations), combined with shorter and therefore less costly fieldwork. It is also favoured for its rigour academically. In applications such as pharmaceutical work it also enables comparisons with fixed products or fixed tasks enabling you to test new formulations against an existing gold standard.

The disadvantages are the lower number of attributes that are possible unless you move to more complex bespoke designs using partial profiles, and the lack of directly valued individual level utilities - although techniques such as hierarchical bayesian analysis seeks to remedy this by post-hoc simulation of individual level values. However, if you are looking to use conjoint analysis for clustering or segmentation you will need to be aware of the trade-offs needed to get individual level utility scores.

Discrete Choice Analysis

A more advanced form of choice-based conjoint is Discrete Choice Analysis (also known as "stated preference research"). DCA studies are particularly popular for transportation studies looking at modal choice - the preference between a train, car and airline for instance. The main difference from CBC is the inclusion of continuous variables such as price and time. This allows the ability to examine the varying costs of the ticket with varying times taken to travel and so to establish the value of time for the journey. This enables transport economists to make statements like "2cm extra leg room is worth 10 minutes longer journey time or £40 extra fare" or "an extra train every 15 minutes would encourage x% of car drivers to switch to the train".

Full profile Conjoint Analysis

An additional option that dates back a long time but that is still used is full profile conjoint analysis. Full-profile is the original form of conjoint and is still in use, though predominantly in the US it would appear. Like choice-based conjoint this uses a more limited number of attributes to describe the product or service, but sufficient cards or treatments are shown to one respondent to enable individual level utilities to be calculated. A fractional factorial design is used to specify a fixed set of profiles that need to be shown for analysis. The difficulty is that this does limit the number of attributes quite severely. However these old school studies are still popular for simple, non-computer-based conjoint projects and are most common for students learning about conjoint for the first time.

Other forms and formats

Recent developments in conjoint include the Adaptive Choice Based Conjoint method from Sawtooth. Which combines elements of a configurator, an adaptive element and choices. In addition we have our own dobney.com conjoint designer that allows for a range of other more bespoke research areas where traditional forms of conjoint analysis are lacking or where current designs can seem too difficult from a respondent point-of-view These include emotional association tasks, and repertoire purchasing (where someone is buying a bundle of products across a range of uses), volumetric measurement and improved choice displays such as using sliders or more interactive elements to encourage a fuller participation in the decision making process. Similarly, for international research the presentation of prices in different formats and currencies requires careful consideration.

Deciding which format of conjoint analysis to use

We often find that the choice of the type of conjoint analysis to use depends on a number factors and it's often difficult for someone new to conjoint analysis to visualise the options and in cases where there are 6-8 attributes, there can be several options and approaches. We often produce several versions and presentations for illustration to help clients see and understand which form and presentation version of conjoint to use, without compromising the final statistical quality of the survey.

Author

Pulkit Kabra

Team I

Day 11 - Team H

CONJOINT ANALYSIS

Conjoint analysis helps organizations understand which factors drive decisions. Analysts determine factor preferences by presenting multiple combinations of factors and asking respondents to rank them. Market researchers often use conjoint analysis to determine which product features are most critical to purchase decisions.

Conjoint analysis must currently be run using syntax. Unlike most procedures in SPSS for Windows, conjoint analysis requires the user to invoke two files:

1.Plan File:The plan file contains the combinations that will be presented to the participants.

2.Data File:The data file contains the participants' responses.

The syntax includes the full location of the plan file, but uses an asterisk to alert SPSS to use the file in the data editor as the data file. 

You can also save utilities—the values assigned to each factor from the conjoint—using the utility command. Make sure to include the utility command at the end of the syntax run and indicate where to save the file. You can use this to segment customers based on their preference patterns.

Step 1: Generating the Plan file:
Open SPSS ->Data > Orthogonal Design-> Generate 

Define factors :

Example : In a Hi-Tech hotel survey, IMNU students defined LAPTOP_CARRY, INT_CONNECT_VIDEO_DEMAND,VIDEO_CONF_VOIP, PRICE_PREMIUM as different factors.
They got 9 profiles by orthogonal design.

Step 2:Data -> Orthogonal Design->Display 

This will give you profiles (Multiple combination of factors).Go to your subject and find out their ranking for the given profiles.

Step 3:Generate the data file
This file is generated on basis of ranking provided to different preferences.

Step 4:Run a conjoint Analysis:CONJOINT PLAN='C:\Documents and Settings\Administrator\Desktop\VXLPLAN.SAV'
/DATA=*
/SUBJECT=ID
/FACTORS=LAPTOP_CARRY INT_CONNECT_VIDEO_DEMAND 
VIDEO_CONF_VOIP PRICE_PREMIUM
/RANK=PREF1 TO PREF9
/UTILITY='C:\Documents and Settings\Administrator\Desktop\OUTPUT.SAV'
/PLOT=SUMMARY
/PRINT=SUMMARYONLY.

Step 5: Analyse the output

Author-

Manish Lath (14145)

TEAM A DAY 10: Avinash Pandey

CONJOINT ANALYSIS

Conjoint analysis is a statistical technique used in market research to determine how people value different features that make up an individual product or service.

The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making. A controlled set of potential products or services is shown to respondents and by analyzing how they make preferences between these products, the implicit valuation of the individual elements making up the product or service can be determined. These implicit valuations (utilities or part-worths) can be used to create market models that estimate market share, revenue and even profitability of new designs.

Conjoint analysis requires research participants to make a series of trade-offs. Analysis of these trade-offs will reveal the relative importance of component attributes. To improve the predictive ability of this analysis, research participants should be grouped into similar segments based on objectives, values and/or other factors.

First we listed attributes and asked respondents to give score and then some of all was given

the difference between the attributes is calculated and higher difference shows higher importance.

Coming to Generating orthogonal design

here we filled in all attributes when it came to a large number and cannot be done manually on excel, this takes in all attributes and finally gives us best 16 chosen lists which we can analyse on excel and calculate differences. It represents the best combinations.

Generate Orthogonal Design generates a data file containing an orthogonal main-effects design that permits the statistical testing of several factors without testing every combination of factor levels. This design can be displayed with the Display Design procedure, and the data file can be used by other procedures, such as Conjoint.

Example. A low-fare airline startup is interested in determining the relative importance to potential customers of the various factors that comprise its product offering. Price is clearly a primary factor, but how important are other factors, such as seat size, number of layovers, and whether or not a beverage/snack service is included? A survey asking respondents to rank product profiles representing all possible factor combinations is unreasonable given the large number of profiles. The Generate Orthogonal Design procedure creates a reduced set of product profiles that is small enough to include in a survey but large enough to assess the relative importance of each factor.

Day 11- Team F( Sidharth )

 We started off the 9 ‘o’ clock session with a very interesting topic ‘Conjoint Analysis’. It is one of the very important tools of Business Analytics and performs the analysis in a much better and concise manner. So what exactly is Conjoint Analysis? It is one of the most widely-used quantitative methods in Marketing Research. It is used to measure the perceived values of specific product features, to learn how demand for a particular product or service is related to price, and to forecast what the likely acceptance of a product would be if brought to market.

           

Respondents usually complete between 12 to 30 conjoint questions. The questions are designed carefully, using experimental design principles of independence and balance of the features. By independently varying the features that are shown to the respondents and observing the responses to the product profiles, the analyst can statistically deduce what product features are most desired and which attributes have the most impact on choice. In contrast to simpler survey research methods that directly ask respondents what they prefer or the important of each attribute, these preferences are derived from these relatively realistic tradeoff situations.

But the direct survey question "how much would you pay for xyz?" is unreliable and misleading. So instead, we ask the consumer's opinion on a series of products with differing features over a range of prices. Our techniques then use regression analysis to compute mathematical values that explain consumer behavior -  how much value is placed on price, or location, or features, etc. and then correlate this data to demographic, lifestyle, or other consumer profiles.

Decoding of the Conjoint Analysis Results:

Here’s an example of a survey regarding the consumer tastes in ice creams. The data is collated as per the consumer preferences.

Given the consumers' ratings of all 16 diverse combinations, the software package computes a mathematical regression to tell us how important each of the five factors is to the individual responding consumer, and to the group of responding consumers as a whole.

According to the results shown to the left (actual output from the online survey), we'd know that consumer X bases 47% of his decision on price, 23% on the flavor, 19% on the freshness, and is less concerned about the container or healthiness. We also learn get a relative ranking of the different flavors, as shown in the lower graph.

Maybe older customers who eat ice cream regularly are more concerned about healthiness.   Maybe younger consumers don't really care about the cone after all.  Perhaps those who work in a nearby office building and pass by for a snack really appreciate the homemade fresh ingredients.  All of these facts will be mathematically predicted using conjoint analysis. The end result is a quantitative, robust analysis of what consumers really want, with each attribute evaluated in the context of the others, incorporating the trade-offs that ultimately project the greatest influence on consumer behavior.

- By Sidharth 

Team F

Team I - day 10 -Rajdeep (Marketing)

Understanding consumer behavior using conjoint analysis using example

(note the responses generated for results are as per a study paper on the internet)

Conjoint Method

First, select what attributes of the product you would like to test, and what the possibilities are for each attribute. To demonstrate, let's use the example of an ice cream shop, which might want to know consumer attitudes about:

• preferred flavor (vanilla, chocolate, strawberry, or black raspberry)
• price ($1.50, $2.00, $2.50, $3.00)
• container (cone, cup)
• freshness (homemade & fresh, factory-produced)
• healthiness (reduced fat, regular)

Is there a preferred flavor, or do customers like a variety? How much "extra" would someone be willing to pay more for a reduced-fat option? Do kids really prefer cones? How much do consumers value a neighborhood shop using fresh local ingredients?

The scientific way to answer these questions is to test each of the 5 attributes in the context of the others. To do that, we take each of these descriptors and create a series of "hypothetical" products, each with 5 attributes. The software creates templates for 16 (or 18 - depending upon the number of variables) of these, and we portray the description of the proposed product visually, on a "card", as shown to the right.

"Cards" can describe the product using words only - but can also use logos, pictures, or even smells or sounds. In any case, respondents will be asked to read each of the 16 "cards", and then assign a ranking of some kind (using numbers 1-x, or using adjectives like favorable, unfavorable, ideal, etc.)

Perhaps Card #1 is a factory-produced low-fat cheap vanilla cone. Maybe #2 is a homemade non-low-fat chocolate cup at a medium price point. The process goes on with 16 mathematically designed cards that offer all the relevant combinations of choices.

 

Conjoint Results

Given the consumers' ratings of all 16 diverse combinations, the software package computes a mathematical regression to tell us how important each of the five factors is to the individual responding consumer, and to the group of responding consumers as a whole.

According to the results shown to the left (actual output from the online survey), we'd know that consumer X bases 47% of his decision on price, 23% on the flavor, 19% on the freshness, and is less concerned about the container or healthiness. We also learn get a relative ranking of the different flavors, as shown in the lower graph.

In addition, each consumer will be asked a number of informational questions to create a demographic profile, so that we can compare the results and analyze them based upon income, age, location, and other variables that may affect consumer behavior towards a particular product.

Maybe older customers who eat ice cream regularly are more concerned about healthiness. Maybe younger consumers don't really care about the cone after all. Perhaps those who work in a nearby office building and pass by for a snack really appreciate the homemade fresh ingredients. All of these facts will be mathematically predicted using conjoint analysis.

The end result is a quantitative, robust analysis of what consumers really want, with each attribute evaluated in the context of the others, incorporating the trade-offs that ultimately project the greatest influence on consumer behavior.

 

 

DAY 10 Group I: Procedure to use of Conjoint in SPSS

Open SPSS Processor

Create plan file

Create data in excel for analysis (convert excel file as per SPSS version. i.e SPSS version 15 can use only that excel file which are made in office 93-2003 format)

Open excel file into SPSS processor

Save and close the excel file.

If the excel file is open during SPSS processing it will show error report.

     

Select sheet from excel file

Save the current file, e.g  Data.sav

Open SPSS syntax file

Get syntax file from the location where  it is saved.

   

You have to code syntax file as per given instructions.

While coding syntax file you need path of the every file created (i.e Data.sav, plan.sav,Data.exl etc)

Take curser to the file and open properties. Copy the path from the property box. Paste in the syntax box in front of respective instructions. Write name of files with their path. 

Save syntax

Run Syntax file  (Run All)

Processor will generate the output

BY

Sushilkumar Balvir