Analyze CLV as it changes

Identify the key drivers of CLV to close the gap between revenue and retention.

Analytics Challenge

Business Challenge

KPI Impact

Analytics Challenge

Analyzing Customer Lifetime Value (CLV) is never fast because not every customer is created equal. CLV predicts the amount a customer will spend on your product or service over an undetermined amount of time, making it one of the more difficult ones to assess. And before you can calculate CLV, you’ll also need to calculate the Average Order Value (AOV), purchase frequency, customer lifespan, and customer value.

Since CLV is also a forecasting metric, it’s not set in stone and needs to be continually monitored as customer behavior evolves to be actionable. And with varying contract lengths, differing acquisition channels, unique customer segments, discounts, refunds, and churn, diagnosing CLV has a high margin for error.

CLV

Customer Lifetime Value (CLV) measures the amount of value a customer contributes to the business over their lifetime.

CLV = (ARR per customer x Length of Relationship) — CAC

Analytics Challenge

Analyzing Customer Lifetime Value (CLV) is never fast because not every customer is created equal. CLV predicts the amount a customer will spend on your product or service over an undetermined amount of time, making it one of the more difficult ones to assess. And before you can calculate CLV, you’ll also need to calculate the Average Order Value (AOV), purchase frequency, customer lifespan, and customer value.

Since CLV is also a forecasting metric, it’s not set in stone and needs to be continually monitored as customer behavior evolves to be actionable. And with varying contract lengths, differing acquisition channels, unique customer segments, discounts, refunds, and churn, diagnosing CLV has a high margin for error.

Business Challenge

Of all the metrics you could monitor, CLV is one metric that informs strategies across the business — from acquisition strategies to product decisions to revenue projections and more.

Diagnosing CLV and the key factors impacting the metric faster is critical to shifting from transaction-based thinking to creating the long-term value of repeat business. Further, having a CLV analysis you trust is vital, as this number dictates what you can spend to acquire new customers. When you understand the factors behind increasing CLV, you’ve unlocked the future of growth for your business.

It’s simple math; the faster you increase CLV and lower customer acquisition costs (CAC), the quicker you grow your business.

KPI Impact

It’s easier to retain customers and grow them than it is to acquire new ones. By unlocking CLV and the factors behind it, you give the business insight into who the most profitable customers are, and what approaches are most effective in acquiring and growing them.

There are two ways to look at CLV, and each is valuable at different stages of your business:

  • Overall CLV: Total revenue for a customer, averaged over the typical customer tenure (e.g., “Our 9-month customer CLV is $650.”)
  • Net CLV: Total revenue for a customer, less costs to acquire and serve. More complex, this is your “unit profitability” for a customer.

Looking at Overall CLV allows you to forecast the net present value (NPV) of a new campaign or product line as you grow. Net CLV is a running measure of your overall business efficiency — grouped by customer acquisition date so that you can monitor efficiency over time.

Best practices for analyzing changes in CLV

Calculating Customer Lifetime Value varies slightly between subscription, SaaS, and retail companies but no matter what industry you’re in, you’ll need a wide dataset to identify the key drivers behind CLV.

As you build your dataset, each row should represent a unique customer, with one record per subscription period. Start with the recommended fields below and add as many descriptive variables as you can to augment your analysis.

Schema Blueprint:

  • One row per customer
  • One record per period
  • Metric column =
    Subscription Value

Recommended

Account ID

Txn_ Amount

Product SKU(s)

Customer First Order Date

Customer Last Order Date

Calendar Month

Customer Acquisition Date

Customer Age

Optional

Customer Tenure (months)

Customer Tiering

Customer City, State, Zip

Transaction Type

Product Category

Product Type

Promotion

Loyalty Program

Marketing Channel

Account Source

Schema Blueprint:
  • One row per customer
  • One record per period
  • Metric column =
    Subscription Value
Recommended

Account ID

Txn_ Amount

Product SKU(s)

Customer First Order Date

Customer Last Order Date

Calendar Month

Customer Acquisition Date

Customer Age

Optional

Customer Tenure (months)

Customer Tiering

Customer City, State, Zip

Transaction Type

Product Category

Product Type

Promotion

Loyalty Program

Marketing Channel

Account Source

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