Customer Lifetime Value

This dashboard helps you track how much returning revenue and profit you earn from each group of new customers over time.

What is Customer Lifetime Value?

Customer Lifetime Value (CLV) tells you how valuable each customer is to your business over time — not just on their first purchase, but across months of repeat purchases.

CLV is essential for tracking customer behaviour patterns, making informed decisions, and identifying valuable customers to retain.

It reveals how much revenue new customers generate over time and shows how long it takes to recover your recover your cost per acquisition (NCPA). The standard grouping in CLV is by the timeframe a customer made his first purchase. Therefore, once a customer belongs to a group he/she is there for life.

The CLV analysis also provides tools to refine the customer base which will help with making important decisions such as:

When do customers usually churn?
How much of your revenue comes from new vs long-time customers?
When is the best time to re-engage your customers?
How valuable each group of customers is over time.

Changing Metrics

Each new customer cohort can be modelled based on different metrics. The more popular use cases are Net Sales, Gross Profit and Contribution Profit by CPA.

Analysing a Group

In this example we have chosen to analyze the cumulative LTV of our customers in the last 6 months, with a month time frame:

Each row shows a group of customers who made their first purchase in a specific month.
Each column shows how much those customers spent in the following months after their first purchase.

Term	What it Means
COUNT	How many new customers joined in that month.
RETURN RATE	% of customers who came back to buy again.
NCPA	Net Cost Per Acquisition – how much you spent to get one new customer.
AOV	Average Order Value – how much a customer spends per order.
FIRST MONTH	Total revenue earned from those customers in their first month.
MONTH 1–6	Revenue from those same customers in the following months.

Colour coding:

To keep the data visualization simple and to help immediately spot troublesome areas of churn or positive areas of growth , the CLV table uses colour coding. We use various shades of blue to denote how the metric values fluctuate from the maximum to the minimum.

Once an interesting time period has been identified - you can further investigate the data by customizing the table.

Analysing Multiple Columns

Another interesting analysis is how cohorts are compared to one another - comparing 2 group can give insights on behaviours of your customers.

Its interesting to analyse and understand what is impacting this difference:

How are we targeting customers in each group?
Were customers in a cohort promoted with a specific campaign?
Are customers in a cohort using a specific discount code?

The Customer Lifetime Value dashboard also includes a Filters function—allowing you to drill down into CLTV by product tags, marketing channels, attribution types, and more. This gives you a clearer view of which factors influence the value and behaviour of your customers over time.

Filter Categories Explained

Section	Filter	Description
Products	Tags, Collections, Products, Categories, Brands, Groups	Focus your CLTV analysis on specific SKUs or groupings such as seasonal drops, core range, or brand lines.
Marketing Attribution	First Click, Last Click, Linear Paid, Linear Weighted	Filter CLTV by how conversion credit is attributed along the journey. Useful for understanding which tactics bring in long-term value.
Marketing Channels	Email, Google, Meta, Organic, etc.	See how different channels contribute to higher or lower lifetime value segments.

You can combine filters (e.g. “Jewellery” tag + Meta channel + First Click attribution) to isolate the highest value customer groups with precision.

Use Case Examples

Identify high-LTV products acquired through organic vs. paid
Compare LTV across brands or collections over the past 12 months
Understand if remarketing drives short-term revenue or long-term loyalty
Segment customers acquired via different channels and see who buys more often