Dashboard: Customer Lifetime Value (CLV)

Estimate contribution profit for the coming year using our predictive customer lifetime value metrics.

The CLV dashboard is included with SoundCommerce Profit when used with SoundCommerce Campaign or SoundCommerce Customer.

Topics:

• Where to find it

• Dashboard description

• Reports and metrics

• Source Datasets

Where to find it

1.  Navigate to Sigma and sign in.
2. Click "Shared with me".
   
3. Click your organization's production folder.
4. Click "Customer Lifetime Value".

Dashboard description

CLV represents the profit value a customer generates during the lifetime of their engagement with a company (between first purchase and churn). CLV metrics are averaged across all customers and averaged across specific customer segments. Because CLV accounts for cost data, it's a key indicator of profitability and how customers impact the contribution margin.

 

CLV segments

These visualizations categorize customers by Low, Medium, and High CLV. Segments make predictive CLV models easier to interpret and are highly actionable for marketers and operations managers who want to improve strategies and processes by learning from, or appealing to, specific segments. They also help to visually demonstrate larger trends within your customer base.

To generate these segments from your data, the SoundCommerce CLV model first predicts lifetime value for each customer record, and then leverages k-means clustering to create three groups of customers based on their CLV. For details, see How customer segments are built through k-means clustering.

 

Predicted Profit segments

Here, customers are categorized according to predicted profit, which is the contribution profit they are expected to generate in the next 12 months. Similarly to CLV segments, predicted profit segments are determined by first predicting future contribution profit for each customer then leveraging k-means clustering to group customers by Low, Medium, or High predicted profit.

Accuracy metrics gives you the percentage of accurate predictions for each segment---that is, the number of customers who were predicted to fall in a segment that actually fell in that segment using predictions from one year ago.

Note that accuracy metrics change daily when the graph is updated. To learn how we calculate predicted profit, see the SoundCommerce CLV Data Model. 

 

While dashboards can provide many insights, they aren't always actionable. Pre-built CLV segments and predicted profit segments are useful for pulling lists of customers for marketing campaigns. For example, you can target promotional offers to appease high CLV customers whose last order was late and caused a negative shopping experience. Or, if you're planning to promote a new loyalty program, draw on your high-value CLV segment to promote it. For a description of pre-built CLV queries, see Query Library.

This dashboard can help decision makers answer the following questions:

• What will customers spend in the coming year?

• On average, how will each new customer affect contribution profit over the lifetime of that customer?

• Do online customers affect contribution profit more over their lifetime than in-store customers? 

• Will a subscriber contribute more to contribution profit over their lifetime than a non-subscriber? 

• Which marketing sources and mediums generate high-value customers?

• Which product categories and SKUs from first-time purchases result in high-value customers?  

Reports and metrics 

This section describes each view and how to use it effectively. 

Lifetime value of a customer

At a glance, find out what each customer has spent (on average) and how much they're predicted to spend. The main view shows you the lifetime profit value for all customers who ever placed an order. In addition, it gives you the total predicted contribution profit of your existing customer base for the next 12 months, and how much each new customer affects contribution profit (on average) over the lifetime of that customer.

Note: In this dashboard, you may notice a discrepancy between the total customer count ("Customers" tile) and other customer counts. To improve the accuracy and performance of the SoundCommerce CLV model, we've excluded the following customer records from the sample:

• Anonymous customers (read more about Anonymous customers here).
• Wholesale customers
• Customers with negative contribution profit
• Customers with historic contribution profit above or below five standard deviations from the mean

Use filters to explore areas of interest

Filters let you change the default time range and isolate order channels, SKUs, or member status in the dashboards to see how they tie into CLV and profitability.

For example, to determine whether online customers affect contribution profit more over their lifetime than in-store customers, use the First Order Channel filter to select fields that reflect your in-store orders. 

Tables

Directly below the visualizations are a series of tables that list important indicators of lifetime value.

CLV by first order source

This table lists each of your attribution sources and the predicted profit for each one. By knowing how much profit an ad campaign is predicted to generate, you can more accurately budget your costs for acquiring new customers (CAC).

Note: Marketing data must be present for this table to populate.

CLV by first order medium

Learn how customers are getting to your site and which navigation channels are resulting in high CLV. Depending on the tools you're employing for your marketing strategy, mediums may include direct marketing emails, click ads, social media, and so on. A medium of “none” means that no referral information was available for the attribution.

Note: Marketing data must be present for this table to populate.

CLV by first order SKU

From this table, determine which SKUs from first-time purchases generate high-value customers. If the first order included multiple items, we list the item with the highest price.

CLV by first order category

Use this table to find out which product categories from first-time purchases generate high-value customers. If the first order included multiple items, we list the category for the highest-priced item.

Note: Merchandising data must be present to populate this view.

 

Source Datasets

This dashboard is built using the following DirectAccess views. You should be able to construct queries to calculate similar metrics using the below:

• sc_orders
• sc_customers