how to use conjoint and maxdiff to put your best product forward

Survey Analytics Webinar

December 4th, 2013

Wednesday, December 4, 13

Agenda ...Esther LaVielle

VP of Client Services

“HI FOLKS!”

Agenda ...Esther LaVielle

VP of Client Services

“HI FOLKS!”

4 Years with Survey Analytics7 Years Market Research Experience

Qualitative & Quantitive

“When I’m not working and assisting clients with conjoint analysis projects, I enjoy being in nature, traveling, cooking

new recipes, eating and trying out new restaurants and food trucks around Portland,OR and hanging out with my dog,

Cooper.”

How to Use Conjoint & MaxDiff To Put Your Best Product Forward

AGENDA• Conjoint Definitions

• How to set a Conjoint Question in SA

• Conjoint Analysis 101

• MaxDiff Definitions

• MaxDiff vs. Rating Scales

• New Enhancements on MaxDiff

• Live Demo of both Conjoint & Maxdiff

• Q & A

Conjoint Analysis Made Easy ... With Doughnuts

CHOICE BASED CONJOINT ANALYSISConjoint is like is display case of

doughnuts . . .

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

The objective: Determine what combination of a limited number of attributes is most influential on respondent choice or decision making

Survey Analytics ONLY supports the Choice Based / Discrete Choice Conjoint Analysis methodology

CBC empowers the shopper to choose what they want to buy from you. You, the survey creator, are figuratively placing the survey taker in front of the display (of doughnuts)

Price, flavors, types of ingredients, texture, time of day etc. will influence which doughnuts they will buy

WHY CONJOINT ANALYSIS?If you are trying to determine the best

combination of products/services to sell that will make you the most money . . .

Choice Based Conjoint mimics the decision making process without actually needing to physically create & sell these products/services. Selling ideas through surveying your target consumer.

Will allow you to review estimated market share and run simulations of concepts before deciding what’s going to give you the biggest ROI.

It’s easy to add on to your current online/mobile research strategy. Survey Analytics has the easiest conjoint set up in the business.

*Cool New Stuff*CONJOINT SET UP DEFINITIONS• Features: A prominent or distinctive aspect,

quality, or characteristic that is an important part of a package of goods.

• Levels / Attributes: An object or characteristic that is directly related to the defined features.

• The features and levels have to be set in stone PRIOR to running a conjoint project. Critical to success of a project.

• Rule of thumb: Generally aim for no more than 5-7 features and no more than 5-7 levels.

• Task Count: Number of conjoint cycles you want to show the respondents.

• Concepts per task: Number of concepts or packages you want to display on each cycle (page). N/A Option is possible if desired.

*Cool New Stuff*CONJOINT SET UP DEFINITIONS• Prohibited Pairs: Prevents specific pairs

of levels from showing up as part of a concept.

• *Only on a 1-to-1 level*

• Fixed Tasks: Allows you to set up a controlled concept and test against random or d-optimal generated concepts.

• Tips for Respondents: Add Additional Info/Pictures to Conjoint features/Attributes

What does Bacon topping look like? Add picture and description

• Where is validation for conjoint?:

Located under Survey Overview

*Cool New Stuff*CONJOINT DESIGN OPTIONS• Random: This design is a purely

random sample of the possible attribute levels. For the number of tasks per respondent SurveyAnalytics produces a unique set of attribute configurations to be presented to the respondent.

• D-Optimal: It is a design algorithm that will produce an optimal design for the specified number of tasks per respondent and sample size. It uses a desired set of experiments to optimize or investigate a studied object.

• Import: For statistically savvy clients who want to design your own conjoint project and have self-determined through your own calculations whether their design will yield significant output. *NOT just for anyone...*

WHAT SAMPLE SIZE SHOULD I USE? Sample size is a question that comes up very frequently. Richard Johnson, one of the inventors of conjoint analysis, has presented the following rule of thumb for sample size in choice based conjoint:

(nta/C) > 1000

n = the number of respondents x t= the number of tasks x a=the number of alternatives per task / C= the largest number of level for any one attribute.

Example: 500 respondents, 3 tasks per respondent, 2 alternatives per task and the maximum number of levels on an attribute is 3 you get:

(500 x 3 x 2) / 3 = 1000

Generally speaking sample sizes tend to be around 200 – 1200 respondents,

300 comes up most often for a single homogeneous group of subjects.

However, if you know you will be working with a smaller homogenous group of respondents, then use D-Optimal design

ARE YOU READY FOR CONJOINT? Do the Features and Attributes/Levels appear to be concrete?

Features and Attributes should be set in stone, otherwise more research is required (Qual/Quant) to help define the features and attribute list.

Do the options appear to be clear and simple to understand?

If no, you may want to use the presentation text or the pop up hyperlinks to define the features/levels. Another option: Redefine features and attributes using vocabulary familiar to target audience

What is the estimated sample size?

Based on number of completes needed, you should decide ahead of time which CBC design to run. Random, DOptimal, Import designs.

Additional Tips:

-No more than 20 trade-off exercises -No more than 5-6 attributes-Keep the ranges simple

CONJOINT DATA REVIEW

*Cool New Stuff*CONJOINT RELATIVE IMPORTANCE CALCULATIONS

Relative Importance Calculates the percentage of which a particular feature and it’s levels influenced the concept choice made by the respondent.

For each Attribute, the difference between the highest and the lowest Part-Worths is calculated. This value divided by the total across all the attributes is the relative importance.

https://www.surveyanalytics.com/help/158.html

*Cool New Stuff*CONJOINT PARTS WORTH CALCULATIONS

Parts worth calculation determines which levels within a feature are more valued than others.

Survey Analytics uses a maximum likelihood calculation coupled with a Nelder-Mead Simplex algorithm to get the parts worth calculation

http://www.scholarpedia.org/article/Nelder-Mead_algorithm

*Cool New Stuff*CONJOINT PARTS WORTH CALCULATIONS

Parts worth calculation determines which levels within a feature are more valued than others.

Survey Analytics uses a maximum likelihood calculation coupled with a Nelder-Mead Simplex algorithm to get the parts worth calculation

http://www.scholarpedia.org/article/Nelder-Mead_algorithm

*Cool New Stuff*BEST & WORST PROFILE

The Best & Worst Profile highlights highest parts worth values and lowest parts worth values.

*Cool New Stuff*MARKET SEGMENTATION SIMULATOR

Market Segment Simulator gives you the ability to "predict" the market share of new products and concepts that may not exist today.

Ability to measure the "Gain" or "Loss" in market share based on changes to existing products in the given market.

Important steps in Conjoint Simulation:

1- Describe/Identify the different products or concepts that you want to investigate. We call "Profiles".

Example: Vacation Package with On-site wedding, zoo visit, swimming with dolphins, spa.

2- Find out all the existing products that are available in that market segment and simulate the market share of the products to establish a baseline.

3-Try out new services and ideas and see how the market share shifts based on new products and configurations.

SEGMENT SIMULATOR SET UP1) Click on Online tools >>Name Simulator Profile>>change profiles

2) Click on to see results!

ANALYZING SIMULATOR DATA

MAX DIFFERENCE SCALING

MAX DIFFERENCE SCALINGMaxDiff assumes that respondents evaluate all possible pairs of items within the displayed set and choose the pair that reflects the maximum difference in preference or importance

Comparative judgments that can be easily performed, even when the number of attributes is not small (up to 30).

The task presents respondents with a set of items, usually 3 to 6, and simply asks them to select the most preferred and the least preferred in the set.

Another name for it : Best/Worst Scaling

Respondents can typically handle a number of these evaluations, and may be asked to respond to a series of maxdiff questions to gather more preference data.

MAX DIFFERENCE SCALINGMaxDiff assumes that respondents evaluate all possible pairs of items within the displayed set and choose the pair that reflects the maximum difference in preference or importance

Comparative judgments that can be easily performed, even when the number of attributes is not small (up to 30).

The task presents respondents with a set of items, usually 3 to 6, and simply asks them to select the most preferred and the least preferred in the set.

Another name for it : Best/Worst Scaling

Respondents can typically handle a number of these evaluations, and may be asked to respond to a series of maxdiff questions to gather more preference data.

*Cool New Stuff*MAXDIFF VS. RATING SCALES

MaxDiff has less much less respondent fatigue compared to rating scale questions.

Survey fatigue is so low that you can run even up to 15-20 different MaxDiff questions if needed.

Most/Least or Love/Hate combos.

The MaxDiff Share of Preference Data is either in percentages up to a 100%, or 100 point count

23% preference to Russell Wilson vs. 20% Marshawn Lynch = analyze and conclude there is a 3% difference in preference between the Seattle Seahawks players.

In rating scales, it’s hard to measure what a 4/5 vs. 2/5 scale really means.

Rating scales are more difficult to quantify than MaxDiff data output

*Cool New Stuff*MAXDIFF FAQS

Can I setup and experimental design - where the total items are larger than the items presented in a single screen? YES! You can use a large list of attributes and specify the number of attributes per page if desired.

What kind of a utility estimation model does the system use to calculate the share of preference? Multinomial Logit model

Co-created with VP & Director of Analytics, Chris Robson, of ORC International

What kind of a utility estimation model does the system use to calculate the share of preference? Multinomial Logit model

Co-created with VP & Director of Analytics, Chris Robson, of ORC International

*Cool New Stuff* MAXDIFF SET UP

Attribute Settings:

Maximum Attributes: Define how many attributes out of the list you created that will be tested for each respondent.

Example:

All = All attributes tested6/9 = Respondent will only see 6 out of list of 9

Attributes per task: How many of the attributes you will see on each page.

Example:

3 Attributes per Task + All from the list = 3 cycles

Attribute Settings:

Example:

Images for Maxdiff are now supported!

Attribute Settings:

Example:

Attribute Settings:

Example:

Attribute Settings:

Example:

Attribute Settings:

Example:

Attribute Settings:

Example:

Attribute Settings:

Example:

Attribute Settings:

Example:

*Cool New Stuff*MAXDIFF ANALYSIS

Preference Analysis Overview:

MaxDiff data output is usually represented either in a 100 point system or in percentage calculation that must add up to 100%.

Survey Analytics uses the percentage calculation mode.

Breakdown is Most or Least Preferred:

Each end of the spectrum is calculated separately in most and least preferred table.

Overall Percentage Pie Chart:

Gives the overall preference calculation

LIVE DEMO

MAKE MORE MONEY WITH CONJOINT & MAXDIFF

Both conjoint and Maxdiff can allow your company to uncover additional insights on consumer preferences on products/services.

Use on both new & existing products/services.

Do you need to create new products/services or repackage existing, or a combo of both?

Will give you a strong and compelling argument to recommendations for your company or clients.

As long as your Conjoint and MaxDiff are clear and easy for people to answer, you can add and create a comprehensive online or mobile survey and review past with new data.

DIY conjoint and maxdiff: No consultants required!

MAKE MORE MONEY WITH CONJOINT & MAXDIFF

Both conjoint and Maxdiff can allow your company to uncover additional insights on consumer preferences on products/services.

Use on both new & existing products/services.

Do you need to create new products/services or repackage existing, or a combo of both?

Will give you a strong and compelling argument to recommendations for your company or clients.

As long as your Conjoint and MaxDiff are clear and easy for people to answer, you can add and create a comprehensive online or mobile survey and review past with new data.

DIY conjoint and maxdiff: No consultants required!

If you have any further questions about MaxDiff or Conjoint that have not been addressed or would like me to assist with an upcoming project in 2014, Cooper and I are standing by waiting by the computer!

Contact a Survey Analytics Sales Representative to get started:[email protected]

My e-mail address:[email protected]

THANKS EVERYONE! Q&A

how to use conjoint and maxdiff to put your best product forward

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