Respondents & Panels

How Cashew reaches respondents through its proprietary panel and your own contact lists.

Two Audience Sources

Cashew gives you two main ways to reach respondents:

Cashew’s proprietary consumer panel — A built-in audience of opted-in participants
Your own contacts — Contact lists you upload or manage in Cashew

You can use one or both within the same study, depending on your targeting and sample needs.

Cashew’s Proprietary Panel

Cashew maintains a proprietary consumer database of people who have opted in to take surveys. Researchers can target this panel by:

Demographics — Age, gender, location, income, education, and more
Behaviors — Purchase habits, interests, lifestyle attributes
Custom attributes — Attributes you define for your research

Because the panel is owned and managed by Cashew, you avoid third-party panel sourcing, which can reduce cost and complexity. Targeting is configured directly in the study setup.

Your Own Contacts

You can also use your own contact lists for survey distribution:

CSV import — Upload a CSV file with email addresses and optional attributes
Contact lists — Create and manage lists in Cashew, then use them for distribution

Contacts are useful when you need to survey existing customers, email subscribers, event attendees, or any group you already have in your systems. Cashew supports custom attributes on contacts so you can segment and target within your lists.

Quota Management

Quotas let you control how many responses you collect in specific segments (e.g., 100 men, 100 women, 50 in each age band). Cashew supports:

Hard quotas — Stop collecting once a segment is full
Soft quotas — Continue collecting but deprioritize full segments

Quota management helps ensure balanced samples and prevents over-collection in easy-to-reach segments.

Response Quality Controls

To improve data quality, Cashew offers controls such as:

Speed checks — Flag or exclude respondents who complete too quickly
Attention checks — Questions that verify respondents are reading
Duplicate detection — Reduce repeat participation from the same person
Straightlining detection — Identify respondents who select the same option for every matrix question

These controls help you filter low-quality responses before or during analysis.