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Filters allow you to focus on specific segments of your business, like a store, product category, or customer group. Use component-level filters to apply different filters within the same dashboard, making it easy to isolate test and control groups and measure the impact of different initiatives in your business.

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Date filters

All dashboards have a date filter which is applied to all components. The default value in a new dashboard is ‘Last 7 days’. You can use date filter presets, like ‘Last 7 days’ or ‘Month to date’, or set a custom date range. Using a preset means the data will always stay up-to-date.
If you set a custom date range, the date period is fixed and the dashboard’s data will not update day-to-day
Date ranges are always inclusive.

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Comparison period

Dashboards also have an optional comparison period. Values for this period are compared to to the main date period to show increases/decreases over time. You can also select preset values: Comparison ranges are always inclusive.

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Non-date filters

You can filter dashboards by any dimension column, like product category, store, customer segment, or order ID.
  1. Press the ‘Add filter’ button next to the date filters
  2. Select the dimension you’d like to filter by
  3. Select the values you’d like to include. You may need to wait a few seconds for the possible values to load.
  4. Press apply and the filtered data will reload.

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Component filters

To add a component-level filter, hover over the component and press the edit button (pencil icon) to open the sidebar. In the sidebar filter section, you can select the dimensions you’d like to filter by just like you would for a dashboard filter. Component-level filters override dashboard filters. For example, if you filter the dashboard to Product Category ‘Shirts’, and add a component filter to Product Category ‘Jeans’, the component will only show data for Jeans. Other components will still show only Shirts data.

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Experiment tracking using filters

You can use this to track the performance of different experiments or initiatives. For example, you can track the launch of a new store versus other stores:
  1. Create a line chart of average net sales (or whatever metric you’d like to compare)
  2. Add a filter for Store, and select the previous stores
  3. Copy the component, but change the filter to the new store only
Now, you’ll see the charts side-by-side, making it easy to compare the sales performance of the new store against the previous stores. You can use this approach to track almost anything: changes in product descriptions or price, new marketing initiatives or discount codes, or any other dimension you’d like to track. Contact support@retail-q.com if you any expert advice on how to set up experiment tracking effectively.