Survey Research

 

 

What Is Survey Research?

Survey research is a quantitative and qualitative method with two important characteristics. First, the variables of interest are measured using self-reports. In essence, survey researchers ask their participants (who are often called respondents in survey research) to report directly on their own thoughts, feelings, and behaviours. Second, considerable attention is paid to the issue of sampling. In particular, survey researchers have a strong preference for large random samples because they provide the most accurate estimates of what is true in the population.

Beyond these two characteristics, almost anything goes in survey research. Surveys can be long or short. They can be conducted in person, by telephone, through the mail, or over the Internet. They can be about voting intentions, consumer preferences, social attitudes, health, or anything else that it is possible to ask people about and receive meaningful answers.  Although survey data are often analyzed using statistics, there are many questions that lend themselves to more qualitative analysis.

 

Most survey research is nonexperimental. It is used to describe single variables (e.g., the percentage of voters who prefer one presidential candidate or another, the prevalence of schizophrenia in the general population) and also to assess statistical relationships between variables (e.g., the relationship between income and health). But surveys can also be experimental.   

There are three main survey research methods, divided based on the medium of conducting survey research:

• Online/ Email: Online survey research is one of the most popular survey research methods today. The cost involved in online survey research is extremely minimal, and the responses gathered are highly accurate.
• Phone: Survey research conducted over the telephone (CATI) can be useful in collecting data from a more extensive section of the target population. There are chances that the money invested in phone surveys will be higher than other mediums, and the time required will be higher.
• Face-to-face: Researchers conduct face-to-face in-depth interviews in situations where there is a complicated problem to solve. The response rate for this method is the highest, but it can be costly.

Further, based on the time taken, survey research can be classified into two methods:

·         Longitudinal survey research: Longitudinal survey research involves conducting survey research over a continuum of time and spread across years and decades. The data collected using this survey research method from one time period to another is qualitative or quantitative. Respondent behavior, preferences, attitudes are continuously observed over time to analyze reasons for a change in behavior or preferences. For example, suppose a researcher intends to learn about the eating habits of teenagers. In that case, he/she will follow a sample of teenagers over a considerable period to ensure that the collected information is reliable. Often, cross-sectional survey research follows a longitudinal study.

·         Cross-sectional survey research: Researchers conduct a cross-sectional survey to collect insights from a target audience at a particular time interval. This survey research method is implemented in various sectors such as retail, education, healthcare, SME businesses, etc. Cross-sectional survey research can either be descriptive or analytical. It is quick and helps researchers collected information in a brief period. Researchers rely on cross-sectional survey research method in situations where descriptive analysis of a subject is required.

 

 

Test Marketing

 

Test Marketing

 

What is Test Marketing? Definition

Test Marketing is a controlled experiment conducted by companies in a carefully selected market to test the viability of their new product and marketing strategy. It provides you an opportunity to learn, adapt and refine your product.

The objective of test marketing is to find the limitations and strengths of the product based on customers’ reactions. It also helps us to structure the marketing strategy of that product.

A test marketing campaign aims to predict the revenue model (sales, profit, pricing). It shows you the efficiency of the product and the promotional message.

What is the importance of Test Marketing?

Launching a product can be tricky, the level of acceptance of the product, customer satisfaction, sales, demand, marketing strategy everything needs to be taken care of. Test marketing helps you to get authentic results around every aspect listed above before spending all your resources on building the finished product and hoping it works.

Now that you’re aware of what test marketing is and what you can expect while conducting a test marketing campaign. Let us talk more about test marketing and its types.

Types of Test Marketing

 

 

There are two types of Test Marketing:

1. Consumer goods Market testing
2. Industrial goods Market testing

The Consumer goods market testing explains all the goods that are used by consumers directly, this test is conducted to know the consumer behavior towards the product. The companies aim customers to go through this entire flow:

Trial —> Repeat —>Adoption —-> Purchase

To attain all these stages following types of tests are conducted:

1. Sales-wave Research

This test is conducted to determine the potential of the product to be consumed/accepted every time it is offered to the customer. The samples are often distributed for free to analyze the willingness of consumers to accept the product. One major example is a free trial of new lipsticks, perfumes offered to the existing customers, and feedback.

2. Simulated Market test

This test is conducted to ascertain the preference of the customer and their product selection. A group of people are invited to the stores and are given some discount offers to motivate them to shop in the store. The New Product is placed with the old ones or the competitors and customers are observed closely, to know if the new product was picked up over other alternatives.

The products are also distributed for free to acquire more customers for the test and they are inquired about the product later on. The example is in departmental stores where new products are placed and consumers’ behavior is tracked.

3. Controlled Market test

In this test new products are introduced to the chain stores and the presentation of the product is controlled by the brand- their presentation, sales, pitch, introduction, etc. It is not distributed for free to make people try the product for the price they will get in the future. In cafes, you are pitched by the waiter for their new recipes and they even ask you to share the feedback multiple times.

4. Test Markets

Every brand has its market, they know the demographics and geographics of their customers. A test market is a set of particularly selected areas or segments of the audience used for a launch (product launch on a small scale), this small test market is representative of the entire market size of the company.

For example, many tech companies launch their new features in particularly selected areas on the basis of sales, customer loyalty, etc.

Industrial Goods

All the goods that are produced for further use to make the final good are considered industrial goods. They are divided into two major categories:

1. Alpha Testing

This is a cost-effective way to gather initial feedback about the product. The companies distribute their product increments inside the organization itself and ask for feedback from the users. 

Most of the telecommunication giants use it, such as Jio distributes most of the products to the employees to use it as they would ahave a better take on the product not as a consumer but as a business owner who will purchase the product.

2. Beta testing

The testing is done outside the firm with real customers, these tests are conducted often in events, shows, exhibitions where a set of the particular audience are gathered. These methods help you based on, customer’s gain immediate feedback from potential clients and see their reactions to the product.

Advantages:

• It removes the risk of large scale commercialization of a new product without having any iteration according to the customer feedback
• You can have a way better idea of your marketing strategy since you have been tracking customer’s reactions testing different marketing approaches.
• The authenticity of data is real, as you have gained feedback from real customers after they have used the product
• It helps you to retarget the existing customers with an updated new product

Disadvantages:

• Distributing products for free sometimes create problem in evaluating the revenue model.
• The test market selected might not be the right representative of your entire market size.
• There is a risk of competition tracking our new move and the product.

How is Test Marketing done?

Till now we know every theoretical knowledge about the topic but your study is never completed without practicals. Let us jump on to how to do test marketing? It has 5 major practical steps:

1. Select the Right Market

It is a very important step because if we select an audience that is biased due to any reason that will completely change the data that we will collect at the end. Always select a market that is representative of your entire market size. Generally, the cities/online platforms/demographics that give you the most business are the ones that fit best in this category.

2. Duration of Test

We need to select a set duration for the campaign. Based on the repurchasing period and state of competition select a time duration. The time should be at least 1 or 2 months more than your repurchasing period.

3. Cost of Test

The cost of marketing is directly proportional to the duration of the test campaign. Find out the cost per acquisition and the duration of the test to find the entire cost of Test Marketing.

4. Collecting Data⋆

This is the most crucial step in the test marketing process because all of this is done to collect authentic, credible data therefore we need to have a structured system in place to collect our data. Some of the data points you need to measure are customer persona, channels of distribution, consumer behavior, demand size, purchasing power, feedback from customers, etc.

Analyzing the Data

Next is the Product launch but before that let me put some emphasis on analyzing the data because it is not what data you have it is what you do with that data that decides your next big move.

 

 

Types of Research - Exploratory, Descriptive & Causal

 

Three Types of Research

Exploratory research

Exploratory research has the objective of giving a better understanding of the research problem. This includes helping to identify the variables which should be measured within the study. When we have little understanding of the topic we find it impossible to formulate hypotheses without some exploratory research. The techniques of exploratory research include reviews of secondary sources of data, informal interviews and focus group interviews.

 This is a good example of where insufficient is known to develop clear objectives since the problem cannot be articulated with any precision. Thus any research would be of an exploratory nature. Such research can take the form of literature searches, informal personal interviews with distributors and users/non-users of the product and/or focus group interviews with prospective customers and/or distributors. Exploratory research is intend to help in the task of formulating a researchable problem and testable hypotheses.

Descriptive research

As the name suggests, descriptive research is concerned with describing market characteristics and/or marketing mix characteristics. Typically, a descriptive study specifies the number and size of market segments, the alternative ways in which products are currently distributed, listing and comparison of the attributes and features of competitive products etc.

This type of study can involve the description of the extent of association between variables. For example, it may be observed that there is an association between the geographical location of consumers and their tendency to consume a product. Note that we are able to describe the relationship rather than explain it. Nonetheless if the relationship between the two is fairly stable this descriptive information may be sufficient for the purposes of prediction. We may, for example, be able to predict how fast the per capita consumption of red meat is likely to rise over a given time period.

The principal difference between exploratory and descriptive research is that, in the case of the latter, specific research question have been formulated before the research is undertaken. When descriptive research is conducted, a great deal is already known about the research problem -perhaps because of a prior exploratory study- and researchers are in a position to clearly define what they want to measure and how to do it.

Causal Research or Experimental Research

Causal research attempts to deal with the ‘why’ questions. This type of research is employed when there the objective is to understand to know why a change in one variable brings about a change in another variable. If we can understand the causes of the effects we observe then the ability to predict and control such events is increased.

Factor Analysis

 

Factor analysis is a statistical technique that reduces a set of variables by extracting all their commonalities into a smaller number of factors. It can also be called data reduction.

 

Factor analysis is a technique that is used to reduce a large number of variables into fewer numbers of factors.  This technique extracts maximum common variance from all variables and puts them into a common score. 

What is Factor Analysis? https://www.statisticshowto.com/factor-analysis/     Factor analysis is a powerful data reduction technique that enables researchers to investigate concepts that cannot easily be measured directly. By boiling down a large number of variables into a handful of comprehensible underlying factors, factor analysis results in easy-to-understand, actionable data.  Factor analysis is most commonly used to identify the relationship between all of the variables included in a given dataset.   The overall objective of factor analysis can be broken down into four smaller objectives:  To definitively understand how many factors are needed to explain common themes amongst a given set of variables. To determine the extent to which each variable in the dataset is associated with a common theme or factor. To provide an interpretation of the common factors in the dataset. To determine the degree to which each observed data point represents each theme or factor.

Cluster Analysis

 

Cluster Analysis

 

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields, including pattern recognition, image analysis, information retrieval, bioinformatics and machine learning.

Market research

Cluster analysis is widely used in market research when working with multivariate data from surveys and test panels. Market researchers use cluster analysis to partition the general population of consumers into market segments and to better understand the relationships between different groups of consumers/potential customers, and for use in market segmentation, product positioning, new product development and selecting test markets

Types of Clustering

1.            Centroid-based clustering

2.            Distribution-based clustering

3.            Density-based clustering

4.            Grid-based clustering

Popular notions of clusters include groups with small distances between cluster members, dense areas of the data space.

A "clustering" is essentially a set of such clusters, usually containing all objects in the data set. Additionally, it may specify the relationship of the clusters to each other, for example, a hierarchy of clusters embedded in each other.

Conjoint Analysis

 

https://www.surveyking.com/help/conjoint-analysis-explained

 

 

A Guide to Conjoint Analysis

Conjoint Analysis Explained: Examples + Survey Template

Definition: Conjoint analysis is a research technique used to quantify how people value the individual features of a product or service. A conjoint survey question shows respondents a set of concepts, asking them to choose or rank the most appealing ones. When the results are displayed, each feature is scored, giving you actionable data. This data can help determine optimal product features, price sensitivity, and even market share.

Why Is It Important? Conjoint analysis goes beyond a standard rating question. It forces respondents to pick what product concepts they like best, helping identify what your audience truly values.

Interactive Conjoint Analysis Example Question

Conjoint analysis is used by any company wanting to do product research; in this example, a restaurant chain. If the chain wanted to release a new ice cream slot on their dessert menu, conjoint analysis would help determine optimal flavor, size, and price, like in the example conjoint survey question below.

If we offered a new menu item for ice cream, which of the following options would be most appealing to you? Please make one choice per set. If no options look appealing, choose "None."

Set 1 / 2

	Option #1 Select	Option #2 Select	Option #3 Select	Select
Flavor	Vanilla	Strawberry	Vanilla	I would not choose any
Size	Large	Small	Large
Price	$2 USD	$5 USD	$5 USD
	Option #1 Select	Option #2 Select	Option #3 Select	Select
Flavor	Vanilla	Strawberry	Cookie Dough	I would not choose any
Size	Small	Large	Small
Price	$5 USD	$2 USD	$5 USD

This is an interactive example of choice based conjoint

When to Use Conjoint Analysis

Without conjoint analysis it would be impossible to ask about product prices along with flavor and size; a separate rating question for each flavor and size combination is needed. Conjoint analysis solves these problems with a straightforward survey question. When respondents evaluate this question, concept features are compared against one another, and a researcher can identify preferences.

Conjoint analysis is useful in two specific scenarios, marketing research and pricing analysis.

Marketing Research

Conjoint analysis is used in marketing research to identify what features of a product or service are most appealing to a customer base. This research can be conducted on existing products to improve advertising engagement or identify areas of improvement to increase sales. Conjoint analysis could also be used to conduct preliminary research for product feasibility.

A conjoint study will usually include demographic questions such as gender. A marketing executive can then segment the survey data by gender, revealing hidden insights used to bolster marketing strategy.

Pricing Research

Conjoint analysis is useful in pricing research because it forces customers to decide using trade-offs, helping to identify optimal prices for various levels. The ice cream example we use in this document has a $5 USD price with the highest utility, which is paired with a "medium" size. Without a conjoint study, it would have been logical to assume the "large" size should be sold for $5. Because of the trade-offs, the optimal size and price combination was found.

How to Conduct a Conjoint Analysis Study

Often, preliminary data needs to be collected before running your conjoint study. An initial survey would include a MaxDiff or a Van Westendorp question to determine important product features or an acceptable price range. The preliminary survey acts as a baseline to reduce the number of conjoint concepts. A smaller number of concepts reduces survey fatigue and increases the quality of responses.

You also want to organize any custom data that you can be used in the survey. Suppose you want to segment your research by country (USA vs European customers). In that case, you need to make sure that internal data is valid, complete, and accessible by your team before running the conjoint study. If custom data is unavailable, you can add additional questions to the survey before the conjoint question.

With the preliminary survey data in hand and custom data organized, you can now create your conjoint analysis study. You can upload the product attributes and levels, include custom data, and you can add follow-up questions to ensure a successful project.

Conjoint Analysis Terminology

Conjoint analysis is an advanced research technique that uses a variety of unique terminology. To help you get a complete understanding, here is a list of commonly used conjoint terminology:

Attribute

The high-level product features that respondents will evaluate are called attributes. Attributes are the first column in the above example question. That example has the following features: flavor, size, and price. If you studied a new car offering, you might have features such as color, make, model, MPG, and tire type. There is a limit of 20 attributes on the SurveyKing platform.

Levels

The items listed within an attribute are called levels. In the example, the "Flavor" attribute has levels of "Chocolate," "Vanilla," "Cookie Dough," and "Strawberry." When you create the conjoint survey, you define an attribute and the levels that go with each attribute. There is a limit of 15 levels on the SurveyKing platform.

Concept

Combining all your attributes and levels, which creates a hypothetical product, is called a concept. In the above example, concepts are the columns that respondents choose. Concepts are sometimes referred to as "cards" in statistical software. There is a limit of 7 concepts on the SurveyKing platform.

Set

Also referred to as a task, a set contains multiple concepts or product offerings. Respondents will choose one concept per set and then be shown a new set of concepts. There is a limit of 20 sets on the SurveyKing platform.

Part-Worths/Utilities

This term is the most crucial in conjoint analysis. It defines how a respondent values each attribute level. When all the utilities for all respondents are analyzed, a researcher can determine an overall product value. Utilities are the output of a regression equation.

Utilities have no scale compared to other conjoint projects you run. They only matter in the context of the current question you are looking at.

Sometimes utilities are called "part-worths" or "part-worth utilities." We use the term "utility."

Types of Conjoint Analysis

Choice-Based Conjoint

This is the most common form of conjoint. The example question above is a choice-based conjoint question. Respondents pick the most appealing concept for each set. Each set contains a random set of concepts that are evenly distributed. This type of conjoint best simulates buyer behavior since each set contains hypothetical products (concepts). When respondents choose a complete profile, a researcher can calculate preferences from the tradeoffs made. (e.g even though "Strawberry" isn't a preferred flavor, if the price were low enough, it would still provide consumer utility")

As with most conjoint studies, preliminary research is essential to reduce the number of attributes and levels to choose from. With fewer attributes and levels, the number of concepts is reduced, which lowers survey fatigue. A MaxDiff or ranking survey can be used to find the top four ice cream flavors.

Currently this is the only type of conjoint offered by SurveyKing.

Best/Worst Conjoint

Sometimes referred to as MaxDiff conjoint. Similar to choice-based conjoint, this method shows respondents a set of concepts. In each set, respondents are asked to pick the most/least (or best/worst) concepts. This approach is used when a product or service has features that cause both positive and negative reactions. An example could be studying how parents select daycare. The number of full-time faculty would draw a positive reaction. The percentage of fellow students that are economically disadvantaged could produce a negative reaction.

This is a future addition to the SurveyKing platform.

Adaptive Conjoint

This method is also similar to choice-based conjoint. Respondents pick the most appealing concept for each set, except with this method, the next set of concepts are not random but are tailored based on the previous answers. This method is more engaging to respondents and can help fine-tune the data.

This is a future addition to the SurveyKing platform.

Full-profile conjoint analysis

This method displays many concepts and asks respondents to rate each one based on the likelihood of purchase. This method is outdated and was primarily used prior to the introduction of survey tools that offer choice-based conjoint. Asking to rate lots of concepts at once is error-prone, quickly causes fatigue, and yields low-quality data.

Rating or Ranking Conjoint

Ranking and rating conjoint was the method used for full-profile conjoint analysis. As software has progressed, it is now possible to conduct rating or ranking conjoint similar to a choice-based conjoint. Respondents are shown a set of concepts and asked to rank or rate each concept. They could rank by entering a value for each concept, which sums to 100 for each set, or they could enter a number based on a scale. This method is also sometimes referred to as "Continuous Sum Conjoint".

Ranking conjoint is a future addition to the SurveyKing platform.

Menu-Based Conjoint

Menu-based conjoint is a new conjoint method. This method gives respondents the ability to pick multiple levels from a menu. For example, a car manufacturer could ask respondents to choose a base model and price, just like choice-based conjoint. But then they could also ask to check a box for each additional feature desired such as "Alloy Wheels for $1,500", "Sunroof for $1,000", or "Parking Assist for $1,500".

This method is much more advanced in terms of front-end programming and back-end statistics than choice-based conjoint. Often custom solutions need to be built for a company wishing to create this type of project.

Creating a Conjoint Survey

Any survey that contains a conjoint question is referred to as a conjoint survey. SurveyKing currently only offers choice-based conjoint. Here are the steps needed to create your own conjoint survey:

1. Navigate to the "Builder" page of your survey
2. Click on the "conjoint" element box, drag it into your questionnaire, or click the "Insert question" dropdown to add a conjoint question at the end of a specific page.
3. To add a new attribute, click "Add attribute" within the conjoint builder. The builder will show levels for the attribute to the right of each attribute.
4. Choose how many sets and concepts you want to display.
5. Select any options to customize the question further.

Conjoint Survey Options

• "None" choice - This option will add one additional card, or column, per set that says "None" This option is marked by default. This setting reflects the real world, where consumers can choose not to buy a product. You should exclude this setting from projects where customers are forced to pick an option, such as a government service.
• Reset choices - With this option, respondents can start back at the beginning. The respondent will clear all answers for the question, and the first set will be displayed when the "reset" button is clicked. We recommend reserving this option for specific circumstances, as it could lead to second-guessing and low-quality data.

How Many Attributes, Levels, Concepts, & Sets are Needed?

An ideal conjoint question will have roughly 5 attributes (rows), 4 concepts per set (columns), and approximately 5 - 10 sets. This will help ensure respondents are not fatigued. A detailed breakdown is below:

• Attributes - Roughly 5 attributes with no more than 10 total levels per attribute. Having fewer levels per attribute ensures the survey will show various concepts more often.
• Concepts - Roughly 4 concepts to show each set. Too many concepts per set, and you risk respondents not making effective choices. The total amount of concepts available is calculated by multiplying the number of levels in each attribute. In the example above, we had four flavors, three sizes, and two prices. Total concepts available would be equal to 4 * 3 * 2 = 24. Ideally, this number should be no larger than 50. The more total concepts, the harder it becomes to draw meaningful conclusions.
• Total Sets - Showing no more than 10 total sets to respondents to avoid survey fatigue. Generally, 3-5 are best.

How Many Responses are Needed?

We recommend collecting at least 100 responses for each segment being researched. For example, if you wanted to research both males and females, you would want to collect 100 responses for both.

Conjoint Analysis Scoring & Results

Conjoint analysis uses regression to calculate how different attributes and levels are valued.

Because conjoint uses categorical data (a name like ice cream flavor) instead of continuous data (a number like a temperature), a particular type of regression is used called logistic regression. Just like any regression equation, the result of this regression calculates coefficients. These coefficients are referred to as "utilities".

Utility is not a standard unit of measure. It can be thought of as "happiness". If a lot of respondents choose concepts containing "Cookie Dough" and only a few choose concepts with "Vanilla.", even without doing the math, you can imagine that the coefficient for "Cookie Dough" would be higher than the coefficient for "Vanilla."

To illustrate this concept, we ran the above ice cream example with 20 respondents. Below is the analysis of those responses. This analysis includes the utilities for each level in addition to the relative importance of each attribute.

Sample Survey Data - Summary Table

Attribute	Importance	Level	Utility
Flavor	61%	Chocolate Vanilla Cookie Dough Strawberry	.44 7.13 14.11 0
Size	17%	Small Medium Large	0 4.03 1.61
Price	22%	$2 USD $5 USD	0 5.06

Walking Through the Analysis

The utilities in the last column are the output of regression analysis. Next to each number is a small bar chart for visual representation.

Remember, utilities are not an actual unit of measurement and could be thought of as happiness. If we look at the above table, the "Cookie Dough" flavor has a utility of 14, and the "Vanilla" flavor has a utility value of 7. We could interpret this as "Cookie Dough has double the happiness of Vanilla."

The importance column is the weighted difference in utilities ranges for the product levels. You can see that flavor has the level with the largest difference of roughly 7. The larger the utility differences for an attribute, the more important they are to consumers. To get a significant difference, as we see with cookie dough, many respondents choose concepts with that flavor. We know the other levels are evenly distributed, meaning that cookie dough was a significant driving factor in decision-making regardless of size or price. Here's how you would calculate the importance:

Take the largest number for each level, and sum: 14.11+4.03+5.06 = 23.02

Divide each of the highest levels by this number. The calculation for flavor importance is 14.11 / 23.02 = 61%

Overall, it looks like "Cookie Dough" is the preferred flavor, "Medium" serving size, with a price of "$5 USD". "Vanilla" would also be an excellent addition to the menu as it brings a high amount of utility. You'll notice that the higher price has a higher utility value than the lower price. Respondents might have a perception that a higher cost has a better taste or better quality. The preference for "Medium" might also be tied to consumers being health conscious. This summarizes why conjoint is so essential. Not only did we find the two optimal flavors, but we also found the right size and correct price point. This data would be almost impossible to capture without conjoint analysis.

Statistical Details

SurveyKing uses ChoiceModelR, a package in the R statistical program to compute conjoint utilities. ChoiceModelR calculates a coefficient using logistic regression with the maximum likelihood for each attribute level by each respondent. When the analysis is complete, utilities for each level are averaged. The output of our example can be found in this Excel file.

We color-coded the Excel file for each attribute level. Row 22 has an average subtotal, which the average utility for a specific level. The regression equations use effects coding to ensure each attribute in total sums to 0. Because of this, you will notice the excel file contains negative utilities. We shift each number by a constant to eliminate negatives and put the baseline to 0. The dark blue flavor columns were adjusted by 5.43 before the results being loaded into our dashboard. Having a 0 baseline makes the data easier to interpret.

Data used to populate ChoiceModelR:

• Data Matrix - See this Excel file, which is the input for the ice cream example. The first row of each card set contains the card number chosen (column G). The first card selected was 4. This is because the "none" option was selected. When the "none" is the chosen option, the highest index + 1 is the card selection. This is the input required for ChoiceModelR. Other programs use an output similar to this file. You'll see it's the same setup, except column G has a "1" if the card is selected or "0" if not selected. An additional row is added for the none column.
• R - The total number of iterations of the Markov chain Monte Carlo (MCMC chain) to be performed. Default value: 4000.
• Use - The number of iterations to be used in parameter estimation. Default value: 2000.
• Keep - The thinning parameter defining the number of random draws to save. Default value: 5.
• wgt - the choice-set weight parameter; possible values are 1 to 10. This parameter only needs to be specified if estimating a model using a share dependent variable. Default value: 1.
• xcoding - A number that specifies the way in which each attribute will be coded. We code each attribute as categorical, which is the value 0. Prices could technically be labeled as continuous, but for ease of calculations and consistency, we code all variables are categorical.

Time Spent Per Set

The time spent on each conjoint set is also included in the results. This data is useful to eliminate low-quality responses. Responses that answered a set too fast (under 2 seconds) should generally be eliminated from the results.

Analyzing Concept Profiles

A powerful benefit of conjoint analysis is quantifying how each concept would fare in the market. We can easily see the product with the most utility would be Cookie Dough, Medium, for $5 USD. But what about the top three products? Or the bottom three products? In the ice cream example, there were 24 hypothetical products. Unique to the SurveyKing platform is the ability to scroll through each concept in ranked order, to see what profiles faired the best or worst (or offer the most utility). The reporting section will automatically include the table shown below:

Rank	Flavor	Size	Price	Total Utility
1	Cookie Dough	Medium	$5 USD	488.02
2	Cookie Dough	Large	$5 USD	436.91
3	Cookie Dough	Small	$5 USD	403.10
4	Cookie Dough	Medium	$2 USD	381.51
5	Vanilla	Medium	$5 USD	341.31
6	Cookie Dough	Large	$2 USD	330.40
7	Cookie Dough	Small	$2 USD	296.58
8	Vanilla	Large	$5 USD	290.20
9	Vanilla	Small	$5 USD	256.39
10	Vanilla	Medium	$2 USD	234.80
11	Chocolate	Medium	$5 USD	200.82
12	Strawberry	Medium	$5 USD	191.44
13	Vanilla	Large	$2 USD	183.68
14	Vanilla	Small	$2 USD	149.87
15	Chocolate	Large	$5 USD	149.71
16	Strawberry	Large	$5 USD	140.33
17	Chocolate	Small	$5 USD	115.90
18	Strawberry	Small	$5 USD	106.51
19	Chocolate	Medium	$2 USD	94.31
20	Strawberry	Medium	$2 USD	84.93
21	Chocolate	Large	$2 USD	43.20
22	Strawberry	Large	$2 USD	33.81
23	Chocolate	Small	$2 USD	9.38
24	Strawberry	Small	$2 USD	-

To get these figures from the Excel output file, you could create a table with all possible combinations, and use sumproduct to calculate to total utility. Here is an example.

Conjoint Analysis by Question Segments

Sometimes it's important to analyze different segments, such as gender. To do this, add a multiple-choice question to your survey for each segment you wish to study. In the reporting section, you can choose "Conjoint Segment Report." From here, select the appropriate question, and the report will output a data table for each answer. Using the ice cream example, you may notice "Males" prefer "Cookie Dough," while "Females" prefer "Vanilla." These are additional data points to fine-tune your marketing efforts.

Here is an interactive example of a conjoint comparison report unique to the SurveyKing platform. The first question asks for gender and the second question asks for a preferred ice cream concept. You can see males prefer "Cookie dough" with a utility of 23.06, while females prefer "Vanilla" with a utility of 25.63. Each gender segment lists flavor as the most important attribute. The report also includes a segmented ranking of concepts.

Conjoint Analysis Tips

• Keep descriptions simple - For both attributes and levels, keep the descriptions as short as possible. This will make picking choices easier and reduce survey fatigue.
• Images - Because of limited space, we recommend using images inside of each level sparingly. When images are used, we recommend that each image be custom-made for this project with a size no larger than 150px X 150px.
• Additional descriptions - Let's say you are researching a new phone. If you have a weight level of 7oz and 11oz, people won't be able to gauge that difference. You would want to say (ideally in the question text), "Use the iPhone 7 as a baseline weight, that weight would be considered average" Then the size product labels would be "Light," "Average," "Heavier."
• Be aware of incorrect conjoint content - There is a popular online video that explains conjoint analysis in Excel. The video uses "Dummy Variables" to compute the regression. This would be incorrect for two reasons. Excel cannot do logistic regression without any addons. Also, removing dummy variables is unnecessary if logistic regression is done correctly. The video codes a three-level attribute with 1's and 0's, which results in collinearity. Logistic regression assigns categorical data to a unique number. Like in our example, a four-level attribute would have the numbers 1, 2, 3, or 4, depending on what concepts were displayed.