This guide shows you the range of options we offer to get responses for your experiments.
We have partnered with leading panel partners, such as Cint and ResearchNow, to deliver quality respondents straight into your experiment. When building your experiment:
Cost per respondent varies by country, age, and other characteristics. You will be quoted in real-time. Once you launch your experiment, we will confirm feasibility and immediately start data collection, which normally takes less than two days. Conjoint.ly allows you to target respondents by country, region, age, gender and deep profiling characteristics. Specifically, you can filter respondents by questions they have previously answered to our panel provider. For example, to survey people in a certain income bracket, choose a question about income and select all the answers that correspond to the people you’d like to survey. These filtering questions vary by country.
Our panel partners will manage the incentives provided to the panelists. Incentives may include monetary payouts, coupons, points, vouchers, charity donations, and lottery draws. Respondents’ participation is voluntary. Their recruitment includes a double opt-in procedure and confirmation of their personal information. As with other respondents, we check for the quality of responses (e.g., how much time they spend per questions). With us, you only pay for quality complete responses.
By default, you are provided with a link which you can share with your own respondents. This link starts to work when you launch the experiment.
Tip: Conjoint.ly works with other survey platforms. Learn how to use URLs to integrate Conjoint.ly with a survey platform such as Decipher, SurveyGizmo, SurveyMonkey, or Qualtrics.
If you have a list of customer emails, you can upload it on Conjoint.ly and assign it to your experiment. Conjoint.ly will send out invitations on your behalf, track responses, and also automatically remind those who have not responded in a couple of days.
The system will automatically provide a recommended minimum sample size based on the settings of your experiment. This minimum sample size is acceptable for exploratory research. It is recommended to aim for a higher sample size if:
If you oversample, but keep the proportions of your groups (for example, if you are aiming at 50%/50% female/male split and your sample is still 50%/50%), there is no issue (in fact, the more the better for statistical robustness). But if your quotas are skewed (say 70%/30% female/male split instead of 50%/50%), you can re-weight using the Excel file (see the weighting columns in the “Individual preferences” sheet).