Create a Snapshot

Who this is for

This guide is for admins, RPO users, and report owners who need to create a new Engagement Signals snapshot for review, reporting, or discussion.

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Before you start

Engagement Signals works from submitted iMatter scorecards, so the quality of the report begins with the quality of the reporting window you choose. Before generating a snapshot, make sure the period contains enough submitted responses, the people you expect to analyse are included, and your region, department, and manager data is in good shape if you plan to use cohort filtering or comparisons.

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Step 1: Open the generator

Go to Signals > Engagement and select Generate snapshot. The generator opens as a guided workflow rather than a single form so you can check the cohort before starting the async generation job.

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Step 2: Choose the current reporting period

Start by choosing the current date window you want to analyse. It helps to begin with a clear reporting question in mind. You might be trying to understand how engagement looked over the last quarter, whether a recent organisational change affected sentiment, or how a particular group is feeling right now.

Good reporting questions are usually simple:

• What does engagement look like right now?
• Has this group improved or slipped back?
• Is the signal concentrated in one part of the organisation?

Engagement Signals uses the latest submitted iMatter response for each included person in the selected period, so the report is always grounded in a defined and repeatable slice of evidence.

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Step 3: Add an optional comparison period

If you want to understand change over time, you can enable a previous period. This is useful when your goal is not just to describe the current position, but to see what has improved, what has declined, and what looks largely stable.

The current and previous periods must not overlap. That keeps the comparison clean and makes the resulting deltas easier to trust.

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Step 4: Refine the cohort

Once Engagement Signals has found matching scorecards, you can refine what is included in the report. This is where the snapshot becomes much more than a date filter.

You can filter the cohort by Region, Department, and Manager. Those filters define the saved reporting boundary for the snapshot, which means the report and its comparisons work inside that selected population rather than the whole organisation by default.

You can also review the individual scorecards found in the chosen window and exclude people you do not want included in the final report. In practice, that is most useful when you are building a tightly focused stakeholder report, excluding a clear edge-case response, or narrowing the report to a defined operational group.

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Step 5: Add a title

You can optionally give the snapshot a title to make it easier to recognise in the snapshots list and when sharing links later. Good snapshot titles usually make the audience, the cohort, and the time period obvious at a glance.

For example, Q1 Operations Engagement Snapshot is much easier to reuse in conversation than a generic untitled report.

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Step 6: Generate the snapshot

When you start generation, Engagement Signals queues the snapshot in the background. The pipeline then builds the saved cohort, prepares the score and comment data, generates the report structure and charts, and produces narrative insight where enough comment evidence exists.

At a high level, generation moves through four stages:

• build the saved cohort
• prepare the score and comment data
• generate the report structure
• add narrative insight when the evidence supports it

You can return to the snapshot list to monitor progress while that work happens.

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Important behaviour to understand

A snapshot is a saved report, not a permanently live view. The cohort is saved with it so historical reports stay reproducible even if the live organisation changes later. Narrative insight also depends on the quality and quantity of written comments, which means a snapshot can still complete even if the AI narrative layer is unavailable.

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When generation does not go to plan

Most generation issues come back to one of a few causes: too few current responses, too few previous responses for comparison, overlapping date ranges, or a cohort that becomes too small once filters and exclusions have been applied. In some cases the structured report completes, but the narrative layer does not have enough comment evidence to support a strong result.

The most common causes are:

• too few current responses
• too few previous responses for comparison
• overlapping date ranges
• a cohort that becomes too small after refinement
• not enough written evidence for a strong narrative layer

If generation fails entirely, you can retry the snapshot from the snapshots list.