How to analyse contributions

Contributions status

A contributions can have various status.

Anonymous

The person that left this comment was logged out and didn't input any email when writing their consultation. Since they weren't verified, they are considered as anonymous.

Anonymous data is still data that you can use in your analysis, however you can't be sure that this person was legitimate or real.

Pending

The person that left this comment was logged out and input their email when writing their consultation. The verification email was sent to them, however they didn't click on the verification link yet.

Pending contributions are better than anonymous in the sense that an email was collected to verify the person legitimacy, however no human clicked on it yet.

Confirmed

The person that left this comment was either logged in or clicked on the link that was sent to them via email, thus confirming that they are legitimate.

Confirmed contributions ensure that the data is 100% valid.

Respondents are allowed to confirm their contributions without limit of time, even when the project has ended. This is perfectly normal.

Surveyed

Surveyed refers to any comment that was gathered via survey mode.

The surveyed status is being sunset and will be replaced by a tag instead.

Best approach to analysing contributions

As any project is different, the best approach is to always dive in the data.

1. Identify trends

Go to the charts tab to browse through the questions asked.

In there, you will see visual representation of each question you asked:

- Smilie, text polls, image polls and others can be seen as pie, column or line charts

- Free text questions will show as weighted word clouds

- Ranking questions will show as horizontal stacked column charts

A sentiment question is a good place to start your analysis. The sentiment chart shows at the top of your contributions charts.

2. Apply filters

Once you find an interesting sub-group of replies, you can target this specific group by applying a filter to isolate them and dive into their behaviour.

Click here to read more about filters

In our example, maybe a lot of people had a negative sentiment, which can be interesting to understand better with a filter of this sub-group.