Dashboard: Customer Lifetime Sales (CLS)

Measure and predict the financial impact a customer will have on sales over their lifetime. Leverage products, marketing channels, and strategies that drive high customer lifetime sales. 
The Customer lifetime sales (CLS) dashboard is included with SoundCommerce Campaign and 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 Sales".

Dashboard description

At a glance, you can find metrics for what each customer has spent (on average) and how much they're predicted to spend.

This dashboard features Customer Lifetime Sales (CLS) metrics for individual customers and segments. Customer Lifetime Sales (CLS) represents the revenue that a customer generates over the lifetime of their engagement with a company (between first purchase and churn).

 

CLS segments

These visualizations categorize customers by Low, Medium, or High CLS.

Segments make predictive CLS models easier to interpret. They visually demonstrate trends within your customer base and are highly actionable for marketers and operations managers who want to improve strategies and processes by learning from, or appealing to, specific segments. 

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

Predicted sales segments

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

Accuracy metrics gives you the percentage of accurate predictions for each segment---that is, of the customers who were predicted to land in a segment, how many actually landed in that segment.

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

While dashboards can provide many insights, they aren't always actionable. Pre-built CLS segments and predicted sales segments are useful for pulling lists of customers for marketing campaigns. For example, you can target promotional offers to appease high CLS 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 CLS segment to promote it. For a description of pre-built CLS queries, see Query Library.

This dashboard can help decision makers answer the following questions:

• What are the predicted sales for my existing customer base for the next 12 months?

• Do online shoppers have a higher CLS than in-store shoppers?

• Which marketing sources result in high CLS customers?

• Which marketing mediums result in high CLS customers?

• Which first-purchase product SKUs result in high CLS customers?

Reports and metrics

This section describes the dashboard views and how to use them effectively. 

Lifetime sales of a customer

The main view shows you lifetime sales for all customers who ever placed an order. It also tells you the total predicted sales of your existing customer base for the next 12 months, and what percentage of sales need to come from new customers in that time period to meet your annual sales target.

Note: In this dashboard, you may notice a discrepancy between the total customer count here and customer counts elsewhere. To improve the accuracy and performance of the SoundCommerce CLS 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 specific order channels, product SKUs, or member status) to see how they drive CLS.

For example, to compare in-store customer CLS to online customer CLS, select First Order Channel, and select fields that reflect your in-store orders. 

Table views

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

CLS 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 will generate, you can more accurately budget your costs for acquiring new customers (CAC).

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

CLS by first order medium 

Learn how customers are getting to your site and which navigation channels are resulting in a high CLS. 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 first-order medium of “none” means that no referral information was available for the attribution.

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

CLS by first order SKU 

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

CLS by first order category 

Use this table to find out which product categories from first-time purchases generate high-sales 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