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Defining a Metric

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All Analyses run in Sisu require a key metric on which to base the Analysis.


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Defining a New Metric

A Metric can be defined by either using an existing table in the data warehouse or by writing a custom SQL query in the Sisu Query Builder. Once a Metric has been defined, it can be used within multiple Sisu projects.

To create a new Metric:

  1. Select the desired Project, then select "Create new metric" in the Metric dropdown.
    The Add metric modal will be displayed.

    info_icon.png When creating a new Project, Sisu will navigate you to the Create Metric option automatically.


    tip_icon.png Note that you can also select “Add existing metric” to view a list of existing Metrics that you or your colleagues have previously created. These are Metrics that have been defined but have not been added to the current project.

    If the Metric that you need is in this list, you can select it and click Add metric instead of creating a new one.

    You can also edit or delete a Metric and edit the Metric’s underlying query from this modal.

  2. Give the new Metric a descriptive name in the New metric name field in the upper left corner.

    tip_icon_-_small.png Sisu recommends being specific and accurate with the name so that any teammate searching for that metric will easily be able to find it. Some users prefer to add a version to the Metric name (e.g., Average Order Value v1).
  3. Define the data source and table or query to use in the metric.


    Select data source

    In the “Select data source” dropdown, select where the data lives. CSVs can be used, but we recommend a live connection to a data warehouse to ensure every team member is always working with fresh data.

    Select table (or query)

    In the “Select table” dropdown, select the name of the table or the name of the custom query previously created for usage with this metric. For more information on this, please refer to Editing a Metric's Underlying Query.

    Note that if you select a query, Sisu provides a convenient link for you to view the query if you like.


  4. Define the Metric to be analyzed.


    Metric goal

    Specify whether your business goal is to increase or decrease this metric.

    Note:  This is an important step, because if your goal is to decrease a metric, every time we find a driver that increases that metric, Sisu will display it as a negative impact in red. Conversely, if your goal is to increase a metric, drivers that increase that metric will be displayed in green to indicate a positive impact.

    Select calculation type

    Specify what type of calculations should be used for this metric. You’ll be able to edit this for every analysis, but this will set the default value.


    Note that the calculation options are different for numeric and categorical Metric types. Refer to Understanding Metrics for more details.


    • Select “Average” for an average order value metric.
    • Select “Sum” for total sales related metrics.
    • Select “Rate” for a categorical metric such as churn rate, to analyze the percentage of users that have churned. 
    • Select “Count” for a categorical metric such as churn rate, to analyze the total number of users that have churned.

    Select metric column

    Select the column that represents the KPI (e.g., a revenue column or churn flag). The metric can be numerical or non-numerical (categorical).

    If a non-numeric metric (such as loyalty status or churn status) is selected, an additional field will be added, allowing you to define the category.


  5. Optional:  Select a time column.

    info_icon.png This field is optional but recommended to help perform more advanced analyses, such as a time comparison between two periods.

  6. Optional:  Click + Add filter to define any filters desired.
    For example, if your data set includes different order channels, you may be interested in analyzing digital mobile orders separately. In that case, add a filter, select the column (in example, “ORDER_CHANNEL”) equals the value “digital_mobile”.

    Or maybe you may want to look at user churn in the first 30 days. In that case, you may select a column like “days_since_signup”, select “<=” as the operator, and select “30” from the value column.

    The filter function supports multiple operators: =, <=, >=, <, >, !=. You can also add additional conditions with the AND / OR functions as well as filter by certain keywords within your data. Refer to Setting Custom Filters and Transforming a Column: Keyword Analysis for more information.

  7. In Section 2, you can preview the calculation that Sisu will use to complete your analysis as defined in Section 1.


  8. Section 3 allows you to control the default columns to be used in your analysis.


    info_icon.png The columns checked here will be selected by default in any Analysis using this Metric. However, you can still manage column selection within each Analysis. Refer to Managing Columns for an Analysis for more information.

    Here is an example of a completed screen:


  9. Click Create Metric to create the Metric and add it to the project.

    info_icon.png A Metric can only be added once to any project, but it can be added to multiple projects.