Skip to main content

Engagement Reporting

This guide shows how to prepare Sesimi engagement data for reporting by removing internal activity that is not relevant to your analysis.

Sesimi Editorial avatar
Written by Sesimi Editorial
Updated this week

To report on Sesimi engagement accurately, we recommend cleansing the data of users that may not be relevant to your reports, such as Sesimi staff, head office or regional staff, and agency staff.

How Cleansed Reports Work

Cleansing reports helps remove noise from your exports by excluding irrelevant activity such as:

  • Views rather than downloads

  • Internal users (Sesimi staff, head office, regional marketing, or agencies)

  • Template or admin-only categories

  • Optional: Location-based activity (e.g. head office locations)

Utility: Cleansed reports focus only on true user engagement, giving you accurate insights into how your brand is being used across your network.

How to Pull the Data

  1. Go to your Reports tab in the left-hand navigation of Sesimi.
    ​

  2. Use the filters on the left-hand side of reports to select your date range.
    ​

  3. In the top-right corner, select Download CSV Data.

  4. Open the CSV file and save it as an Excel file.

💬 Note: Only users with admin access can download raw report data. If you do not see the Reports tab, contact Sesimi Support or your internal asset admin.

How to Clean the Data

You can clean the data manually in Excel, or by using a LLM like ChatGPT to help automate the process.

Step 1. Focus on downloads

  • Remove rows where Event Type = viewed.

  • Retain downloaded activity only.

💡 Pro Tip: You can use ‘viewed’ data instead of ‘downloads’. We recommend focusing on downloads, as they better represent true user engagement.

Step 2. Exclude internal teams

Remove rows where the Teams column contains:

  • Asset admins

  • Sesimi

  • Head office or Headoffice

  • Regional marketing

Step 3. Remove internal categories

Remove rows where the Categories column contains:

  • Template materials

  • Internal admin

Step 4. Optional – filter locations

If your data includes location information, filter out any location names that you do not want to include (e.g. Head office).

💬 Note: Always check the cleansed file to confirm results. If using an LLM, review the output and, if needed, clarify what was done incorrectly before re-running.

Using an LLM tool for cleansing

If you prefer automation, upload your Excel file to an LLM and ask it to apply these rules:

  1. Remove rows where Event Type is "viewed" (case-insensitive).
    ​

  2. Remove rows where Teams matches your internal groups (see list above).
    ​

  3. Remove rows where Categories contain internal-only content (see list above).
    ​

  4. Optionally, filter on Location data to exclude internal offices.

💡Pro Tip: Always review the output. If the data looks incorrect, tell ChatGPT what went wrong and ask it to re-run.

How to Analyse the Data

Once cleansed:

  1. Highlight your dataset in Excel.
    ​

  2. Insert a simple pivot table to summarise downloads by user, team, or location.
    ​

  3. Use this pivoted data for engagement reporting.

FAQ

Can I use “viewed” data instead of downloads?

Yes, you can use ‘viewed’ data instead of ‘downloads’. We recommend focusing on downloads, as they better represent true user engagement.

What if my internal team names are different?

If your internal team names are different update the filter list to match the way your organisation has set up team names.

Can I automate this?

Yes you can automate this. You can use an LLM with the provided prompts to speed up cleansing.

What if I don’t have admin access to Reports?

If you don’t have admin access to reports you’ll need to request admin access from your internal asset admin or Sesimi Support.

Related Topics 📚

Did this answer your question?