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A Storyline insight brings related evidence into a finding you can investigate. Its history connects the finding across reporting waves, so you can distinguish a new development from a recurring pattern. Open Insights, choose the period you want to examine, and open a finding. Accounts with Highlights and Explore can also reach insight details from findings shown in those views.

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How insights are generated

Conveo analyzes the wave’s data to identify signals: patterns in answers, groups, or changes over time. Related evidence is synthesized into insights. Conveo then compares findings with earlier waves to maintain their histories. An insight can include a summary, supporting signals and participant evidence, and follow-up actions. Availability depends on the data and completed analysis. A missing finding does not prove that an issue is absent from the population.

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Why Storyline insights are different

Lifecycle labels describe how a finding relates to its history: Related histories can merge or split. Those relationships are recorded in the history rather than appearing as separate lifecycle labels. Treat these labels as AI-supported interpretations of the observed evidence. Ongoing does not establish a universal truth, and Ended does not guarantee that a topic will never recur.

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Reading an insight’s thread

Open Insight over time on an insight to examine how the finding developed. Historical wave views show the finding for that period, which can differ from the current interpretation. Actions are paused when viewing a historical snapshot. Return to the current finding to create an output or act on a suggested next step. Compare the period and audience behind each observation. Changes in who participated, the questions asked, or the way answers were coded can affect a trend. The insight board may surface warnings about sample-composition changes or a measurement reset; read those caveats before comparing waves.

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Signals: the evidence underneath

Inspect the supporting signals and participant evidence before relying on a summary. Ask:
  • Which answers or groups support the finding?
  • How much data is available, and is a subgroup small?
  • Does the evidence describe a relationship, or support the stronger conclusion being proposed?
  • Could changed recruitment or measurement explain the difference?
AI-generated explanations can be incomplete or mistaken. Participant quotes help establish what someone said; they do not by themselves establish how common a view is. Use the dashboard to inspect counts and coverage, and Talk to your data for follow-up analysis.

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Organize recurring interests

Use insight themes to save a topic and find its matching insights across chapters. Themes classify findings; they are separate from filtering the participant population.

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Suggested next steps

Current insight details offer Create report, Create slide deck, and an action to include the insight in the next chapter report. Creating a report or deck opens a Talk to your Data conversation with a prepared request; review the generated output before sharing it. Additional suggestions may ask a follow-up question or add a measure to the dashboard. Suggestions depend on the finding and may be absent. Flagging an insight for the next report does not rewrite an already generated report. See Reports and Slides for working with those outputs.