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Essentials: Expected Lifetime Value (LTV) Analysis

Forecasting the lifetime value of customers as they place more orders is one of the most important aspects of any business of any size.

Here are the steps to create analyses to understand your current customers' lifetime value, and forecast how lifetime value will increase with more orders:

expected_ltv_720.png

The first step will be to construct a new metric with the following steps:
    • Navigate to Manage Data > Metrics
    • View the existing Avg lifetime revenue Note the table this metric is constructed on (probably `customer_entity` or `sales_order` depending on your store’s ability to accept guest checkout!).
    • Click Create New Metric and select the table from above.
    • This metric performs a Median on the Customer’s lifetime revenue column, ordered by created_at.
      • FILTERS:
        • Add the Customers we count (Saved Filter Set) (or Registered accounts we count)
    • Give the metric a name, such as Median lifetime revenue.
Once the metric has been created, you can create a dashboard by doing this:
  • Navigate to Dashboards > Dashboard Options > Create New Dashboard.
  • Give the dashboard a name such as Expected LTV.
    • This will be where we create and add all the reports.
If you haven’t already, check out this video about using the Visual Report Builder to build charts, tables, and scalar values.
  • Note on Time Period: The time period for each report is listed as “All-time”. Please feel free to alter this to suit your analysis needs. We recommend all reports on this dashboard cover the same time period, such as “All time”, “Year-to-date”, or “Last 365 days”.

 

  • Average LTV (all)
    • Metric: Avg lifetime revenue
    • Time period: All time
    • Interval: None
    • Chart Type: Number (scalar)
  • Average LTV (customers / non-guest checkout)
    • Metric: Avg lifetime revenue
      • Add filters:
        • [A] `Customer’s group code` Not Equal To `Not Logged In`
        • [B] `Customer’s lifetime number of orders` Greater Than `0`
    • Time period: All time
    • Interval: None
    • Chart Type: Number (scalar)
  • Average and Median LTV
    • Metric 1: Avg lifetime revenue
    • Metric 2: Median lifetime revenue
    • Time period: All time
    • Interval: By Month
    • Chart Type: Line
    • Uncheck Multiple Y-Axes
  • LTV by lifetime number of orders
    • Metric 1: Avg lifetime revenue
    • Metric 2: New customers
    • Time period: All time
    • Interval: None
    • Group by: Customer’s lifetime number of orders
    • Chart Type: Line
    • Note:
      • Do not add all of the values for “Customer’s lifetime number of orders”, instead look at a point where the number of New Customers reaches a small number, and manually add each Customer’s lifetime number of order value to that point. For instance, if there are 200 customers at one order, 75 at two, 15 at three, and 3 at four, add 1, 2, and 3.
  • Add the existing ‘Avg customer lifetime revenue by cohort’ report

After building the reports, you can organize them on the dashboard as you desire, we recommend the layout of the image at the top of this article!

If you run into any questions while building this analysis, or simply want to engage our professional services team to build the analysis for you, simply open a support ticket here.

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