New safeguards against bots and professional respondents
New safeguards against bots and "professional" respondents
Published on
20 May 2025

Conjointly added two new quality checks i.e. extra hard exclusion rules and detection of anomalous network activities to protect your research from fraudulent respondents.


Conjointly already has numerous automated quality checks to make sure your survey data is as clean as possible, especially when it comes from online panels.

Now, two more quality checks have been added: "extra hard exclusion rules" and "detection of anomalous network activities".

Extra hard exclusion rules

Extra hard exclusion rules are a new set of proprietary rules that identify behaviours strongly linked to fraudulent responses from online panels. These criteria are deliberately undisclosed and continuously updated to protect your data from evolving fraud tactics.

These new rules have been used for several months now when you source respondents through Conjointly. They are now available for you to switch on studies where you bring your own sample, at no cost.

Filter low-quality responses

Detection of suspicious network activities

Conjointly has also introduced new protective measures against respondents who conceal their connection details via VPNs, browser spoofing, and other tricks.

In your participant list, you may now notice "Anomalous network activity detected" and "Avoidance of network detection" as reasons for automatic exclusion.

Exclusion rules

This detection of anomalous network activities intelligently flags suspicious network patterns without disrupting the survey experience. Problematic responses are automatically excluded from your analysis.

Again, these new rules have been used for several months now when you source respondents through Conjointly. They are now available for you to switch on studies where you bring your own sample, at USD 0.01 per survey entry attempt (not per complete).

Detection of suspicious network activities

Read these articles next:

Comparing different approaches to claims' diagnostics

Comparing different approaches to claims' diagnostics

You can perform diagnostic assessment on claims either through an individual claim approach or a combined concept approach, but which approach is more suitable?

View article
Methodology Behind Claims Test

Methodology Behind Claims Test

The methodology behind our Claims Test tool is based on a proven choice-based technique & was refined through multiple projects for FMCG brands. Learn more here!

View article
A better alternative to simultaneous feature selection and pricing optimisation testing

Pricing and feature selection: One or multi-stage study?

While performing feature selection and pricing optimisation in one experiment may seem like a good idea, it may result in increased complexity and costs compared to multi-stage research.

View article