Exporting check-in data for reporting

Check-in data is most valuable when you can use it beyond the Dailybot dashboard. Whether you’re preparing a performance review, building a slide deck for leadership, or creating an audit trail for compliance, exporting your check-in responses puts the data exactly where you need it.

Dailybot lets you export check-in data in three formats and filter by date range, so you can extract precisely the information you need for any reporting scenario.

How to export your data

The export process is straightforward. Navigate to the Check-ins tab in Dailybot and select the check-in whose data you want to download. Click the three-dot dropdown menu next to the check-in name, and you’ll see the export option.

From there, you choose your format, adjust the date range if needed, and download the file. The whole process takes about thirty seconds.

Choosing the right format

Dailybot supports three export formats, and each one fits a different use case.

CSV is the most versatile option. Each response becomes a row in a spreadsheet, making it easy to filter, sort, build pivot tables, and run formulas. If you’re importing data into a BI tool like Tableau, Power BI, or Looker, CSV is usually the best choice. It’s also the lightest file format, so it downloads quickly even for large datasets.

XLSX gives you a native Excel file that opens directly in Microsoft Excel or Google Sheets without conversion. The formatting is preserved, so columns are properly labeled and the data is ready to work with immediately. This is a good choice when you’re sharing data with someone who works primarily in Excel and doesn’t want to deal with CSV import settings.

PDF produces a formatted, read-only report. It’s designed for sharing with stakeholders who need to review the information but don’t need to manipulate the raw data. PDFs work well for retrospective summaries, quarterly reviews, and any situation where you want a polished document rather than a raw dataset.

Filtering by date range

By default, the export includes all responses ever collected for that check-in. For most reporting scenarios, you’ll want to narrow this down. Click the date field in the export dialog to set a custom timeframe.

Common date ranges include the last sprint (one or two weeks), the current month, the current quarter, or a custom window that matches your review cycle. This flexibility lets you pull exactly the data you need for the task at hand, whether that’s a weekly team review or an annual performance assessment.

What you can do with exported data

Once you have your export file, the possibilities open up. Here are some of the most common ways teams use their check-in data outside of Dailybot.

Performance reviews become much easier when you have a written record of what each person worked on, the blockers they faced, and the progress they made over a quarter. Instead of relying on memory, you can reference actual check-in responses to give specific, evidence-based feedback.

Retrospectives benefit from concrete data. Importing a month of check-in responses into a spreadsheet lets you identify recurring blockers, track how quickly issues get resolved, and spot patterns in team productivity. This turns a subjective “how did the sprint go?” conversation into a data-informed discussion.

Compliance and auditing requirements in some industries demand documentation of team processes and communication. Exported check-in snapshots provide a timestamped record of regular team updates that can satisfy audit requirements.

Trend analysis across quarters helps you see whether process changes are making a real difference. If you introduced a new workflow in Q2, comparing check-in data from Q1 and Q2 shows you whether blocker frequency decreased, response quality improved, or team sentiment shifted.

Dailybot keeps all your check-in data organized and accessible in the dashboard, and the export feature ensures you can take that data wherever it needs to go. Three clicks, your preferred format, and the date range that matters. Your check-in data is ready for whatever comes next.