Historical Data Comparison on Onsite Analytics
  • 23 Feb 2024
  • 1 Minute to read

    Historical Data Comparison on Onsite Analytics


      Article Summary

      Suggested Readings: Executive Summary Dashboard: Metric Definitions, Historical Data Comparison on ESD

      Historical data comparison helps you compare your data between a selected date range and the previous range of the same duration, and provides insights about the historical performance of your onsite campaigns.

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      Data comparison is off on your dashboard by default. To enable it, click the Compare button.

      How does it work?

      The comparison mechanism takes the selected date range and the previous date range of the same duration into account. For example, if you select the 12 January 2023 - 20 January 2023 range (9 days), the system will compare the data to that in the 3 January 2023 - 11 January 2023 range (9 days).

      You can interpret the data comparison as per the following indicators:

      • Green label: Represents the increase in the respective figure(s). E.g. ↑3.2%.
      • Red label: Represents the decrease in the respective figure(s). E.g. ↓1.6%.
      • -0.0%: Represents no significant change within the selected time period.
      • N/A: Indicates that comparison for the selected date ranges is not applicable. This might happen in a case where the data for one of the date ranges is 0.

      To turn it off, click the close button next to the Comparison Range picker.

      What are the capabilities?

      • The maximum date range you can compare data for is 90 days. This means that you can compare the data of a 90 day-range to that of the previous 90 day-range. Any date range selected longer than 90 days will not display any comparison, and the Compare button will be disabled.
      • For both selected periods, you can see the compared campaign count above the data table.
      • Exporting data is not supported in the comparison mode. You can export your OnSite Campaign Analytics as usual.
      • While comparing your data, you can exclude variants, and apply filters. 

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