- Introduction to question types available
- Intro text: No respondent input
- Multiple choice
- Dropdown menu
- Ranking
- Short text
- Email address
- Number
- Image heatmap
- Video response
- Positive/negative open-ended feedback
- Likert scale
- Dual negative-positive scale
- Net Promoter Score
- Star rating grid
- Constant sum
- Slider
- Single swipe card
- Set of swipe cards
- Regular expression
- Text highlighter
- Paragraph input
- Matrix grid
- Calculated variable
- Simple block
- Randomisation block
- Survey flow controls
- What is conjoint analysis?
- Classification of conjoint
- Alternatives to conjoint
- Technical notes on conjoint
- Setting up a conjoint analysis study
- Setting up a Brand-Price Trade-Off study
- Tips for setting up conjoint studies
- Display logic for conjoint experiments
- Partworth utilities
- Marginal willingness to pay
- Manual calculation of partworth utilities
- Price Elasticity of Demand
- Covariates in conjoint
- Introduction to preference share simulations
- Top simulator usability tips
- Calculating volume, revenue, and profit
- Scale factor adjustments
- Models for calculating preference shares
- Margin of error in simulations
- Availability adjustments in the simulator
- The segregate function
- The source of business function
- Correlation matrix for simulations
- Sensitivity to adding or removing concepts
- Adding groups of concepts
- Using adcepts in simulations
- Interactive Excel simulator
- Comparison against the TURF simulator
- AI-based survey and question creation
- Setting up a conversational survey
- Multiple languages in one study
- GET variables
- External variables
- Display logic for survey questions
- Survey flow of a monadic block
- Setting up quotas
- Passing on options from previous questions
- Piping in previous answers or other data
- Displaying modal windows to respondents
- Anonymising responses and hiding PII
- Integrating with a third-party tool
- Review survey participants
Should you include the “None of the above” option?
For your conjoint experiment, you can choose one of three options that characterise your product. This will affect how the “None of the above” option is presented to respondents and how analytics is run on the data we collect.
| 1. Normal product that customers routinely buy | 2. Product that customers are forced to buy | 3. New product that customers are not used to buying | |
|---|---|---|---|
| Examples | FMCG/CPG, education, personal services. The vast majority of products fall into this category. | Critical medication or government services. | Start-ups with new business models. |
| When to use | This option should be in most marketing studies should because it reflects actual market behaviour of not buying a product. | This option is suitable to medical studies should because it reflects doctors’ and patients’ lack of “opt-out” options. | When consumers are not used to buying this product 😊 |
| How it works | Respondents will be able to select a “None of the above” option, which will be part of the choice set. | When such products are studied, respondents will not be able to select a “None of the above” option. This is technically known as a “forced choice response”. | With such products, respondents will first be forced to choose an alternative, and then they will confirm if they would buy it at all. This is technically known as a “two-stage response”. |
| In analysis | In addition to relative preference scores for all levels tested, a utility value will be computed for "None" that will measure respondents' tendency to select none of the alternatives. All simulations will include a preference for "None of the above". | No utility value is computed for None, and simulations do not include a "None of the above". | Despite the different user experience, this method is analytically identical to the normal none. In any choice task where the respondent indicates they would not choose the product in the dual-response, they are recorded as selecting none. A utility value for "None" is computed, and all simulations will include a "None of the above". |
Why do these options depend on product types?
Consumers tend to think twice when it comes to buying new products. Therefore the chance of not buying any product in new categories is higher. This needs to be reflected in conjoint analysis as well.
The table below shows the percentage of “none of the above” responses for a sample of 709 experiments with two-stage response and 4863 experiments with the standard “none of the above” option (including both generic and brand-specific conjoint studies). It is split by type of response and by country (listing only top 12 countries of respondents):
| Country | Standard response (normal products) | Two-stage response (new products) | ||
| % of “None of the above” | Number of choice sets | % of “No” | Number of choice sets | |
| United States | 14% | 1,500,371 | 18% | 269,830 |
| United Kingdom | 9% | 392,662 | 25% | 16,303 |
| Mexico | 16% | 248,873 | Insufficient data | |
| Germany | 14% | 206,879 | Insufficient data | |
| Canada | 18% | 129,778 | 18% | 21,593 |
| China | 6% | 136,844 | Insufficient data | |
| Netherlands | 15% | 130,807 | Insufficient data | |
| France | 13% | 124,528 | Insufficient data | |
| Australia | 14% | 111,837 | 27% | 20,188 |
| Brazil | 8% | 89,825 | 32% | 13,123 |
| India | 8% | 89,034 | Insufficient data | |
| Russia | 18% | 85,293 | Insufficient data | |
| All (including those not listed above) | 13% | 4,013,278 | 19% | 476,427 |