Growth Accounting

2025-06-17

The core idea of growth accounting is to relate how the purchasing behaviors of different customer groups contribute to revenue and enterprise value.

It seeks to provide customer-centric firms with a standardized reporting framework for the customer metrics that drive financial performance.

Growth accounting is strongly contextual based on the revenue model of the company. We will provide examples to cover transaction and subscription revenue businesses.

Customer Types + Groups

Let's begin by defining an active customer.

While specific to each company, an active customer is a user that exhibits a defined behavior within a given time period.

For simplicity—and to align with our financial analysis—in our examples we'll define an active customer as anyone who completes at least one transaction during a fiscal period.

A company’s definition of an active customer should align with its products and revenue model. Below are frameworks for defining active customers across common revenue models.

For subscription revenue models — an active customer is one whose subscription status is “active” (i.e. paid and not canceled or lapsed) during the measurement period.
For transaction revenue models — an active customer is one who has completed at least one transaction during the defined activity window.
For consumption revenue models — an active customer is one who has recorded any usage (or exceeded the agreed minimum usage threshold) during the specified period.

Active Customers in Current Period, $t_n$

N_{active} (t_n)=N_{new}(t_n)+N_{resurrected}(t_n)+N_{retained}(t_n)

Within any current period $t_n$ , the population of active customers is the sum of three mutually exclusive groups:

$new_c(t_n)$ — customers who where acquired (have made their first-ever purchase) in $t_n$ .
$resurrected_c(t_n)$ — customers that were inactive in the immediately preceding period $t_{n-1}$ but have purchased before and again in $t_n$ .
$retained_c(t_n)$ — customers who have made purchases in both $t_{n-1}$ and $t_n$ .

Active Customers in Previous Period, $t_{n-1}$

Using this same arithmetic we can express the prior period $t_{n-1}$ ‘s active customer base as

N_{active} (t_{n-1})=N_{retained}(t_n)+N_{churned}(t_n)

Where $churned(t_n)$ refers to customers who were active in period $t_{n-1}$ but not in $t_n$ .

Active Customers Growth Between $t_n$ and $t_{n-1}$

We can re-arrange the identities above to provide a concise customer growth equation

N_{active} (t_n)-N_{active} (t_{n-1})=N_{new}(t_n)+N_{resurrected}(t_n)-N_{churned}(t_n)

Analyzing Active Customers Over Time

Sample Active Customer Growth Accounting

This stacked bar chart shows the components driving active customer growth. It visualizes the components of an active customer base, and overlays two important ratios — retention and quick ratio.

Quick ratio calculated as $\frac{(new + resurrected)}{churned}$ , shows whether customer growth is outpacing churn. Values above 1.0 mean you’re adding customers faster than losing them, while values below 1.0 signal decline.

Cohorts

Active customers can be acquisition cohorts based on when they were acquired. Active customers can be segmented into acquisition cohorts based on when they were acquired. This approach focuses on tracking the retained customer group within each cohort, providing clear insights into how different acquisition periods retain and perform over time.

Active customer counts by cohorts can be visualized by a “Number of Active Customers” matrix

“Number of Active Customers” Matrix

Revenue Accounting

Traditional accounting treats revenue as a discrete figure tied to a specific fiscal period. In contrast, a customer-centric accounting approach views revenue as the sum of all individual customer transactions within that same period. Essentially, revenue is seen as a snapshot within a continuous time series of customer activities—the sum of many individual customer revenue streams evolving across their respective lifecycles. This approach allows us to analyze revenue by examining the specific customers and behaviors that drive it.

Deriving Revenue in Current Period, $R(t_n)$ by Behavioral Groups

R(t_n)=R_{newC}(t_n)+R_{resurrectedC}(t_n)+R_{retainedC}(t_n)

Building on our earlier customer definitions, during the current period $t_1$ , revenue is generated by three customer types: new, resurrected, and retained.

The revenue contribution of each category can be expressed as the product of its number of active customers, $N_{category}(t_n)$ , and their average revenue per user, $ARPU_{category}(t_n)$ .

R_{category}(t_n)=N_{category}(t_n) \ \cdot \ ARPU_{category}(t_n)

New and resurrected customer revenue calculations are straightforward as these segments had no prior-period interactions. While their numbers and ARPU can be benchmarked against historical data, they represent distinct customer sets with no continuity. Revenue from retained customers, however, requires a more sophisticated analytical approach.

Within subscription revenue models, revenue from retained customers can be explored through an ARR/MRR waterfall framework. The starting values do not begin at zero but rather carry forward from previous periods. Consequently, all revenue from the previous period must either be retained, churned, or contracted in the present time period. This perspective is exceptionally useful when analyzing growth rates across time.

Retained Revenue Breakdown [Subscription]

R_{retainedC}(t_n)= R_{retained}(t_n)+R_{expansion}(t_n)

$R_{retained}(t_n)$ represents revenue sustained from customers who continued their transactions from the previous period into the current one.

$R_{expansion}(t_n)$ represents dditional revenue from existing customers who increased their spending relative to the previous period.

\therefore R(t_n)=R_{newC}(t_n)+R_{resurrectedC}(t_n)+R_{retained}(t_n)+R_{expansion}(t_n)

For transaction revenue models, this approach doesn't apply. Instead, retained revenue is best understood by analyzing changes in the number of retained customers and variability in Average Revenue Per User (ARPU) among those retained customers.

Retained Revenue Breakdown [Transaction]

To analyze revenue from retained customers in period $t_n$ , we split in into two components: baseline retention revenue and incremental ARPU shift.

R_{retainedC}(t_n)=\underbrace{N_{retained}(t_n)\cdot ARPU(t_{n-1})}_{R_{retained}(t_n)}+\underbrace{N_{retained}(t_n)\cdot (ARPU_{retained}(t_n)-ARPU(t_{n-1}))}_{R_{arpuShift}(t_n)}

$R_{retained}(t_n)$ represents the baseline retained revenue had retained customers maintained their previous period’s spending behavior, $ARPU(t_{n-1})$ .

$r_{arpuShift}(t_n)$ captures the incremental revenue generated through changes in buying behavior..

This decomposition provides analytical clarity by separating the revenue effects of customer retention from the effects of evolving usage patterns among the retained customer base.

$\therefore R(t_n)=R_{newC}(t_n)+R_{resurrectedC}(t_n)+R_{retained}(t_n)+R_{arpuShift}(t_n)$

Deriving Revenue in Current Period, $R(t_n)$ by Cohorts

An alternative customer-centric decomposition of revenue is by the cohorts while have contributed. This can be visualized through a Customer Cohort Chart (C3).

Customer Cohort Chart (C3)

Deriving Revenue Growth Rate

Period-to-period revenue changes consists of components: revenue from newly acquired customers and changes in revenue contributed by existing customers. The existing customer component includes revenue from resurrected customers, spending changes from retained customers, and revenue losses from churned customers.

Revenue from new and resurrected customers is discrete and tracking changes in the counts and ARPU of these groups from period provides valuable insights; we will focus more so on understanding revenue changes from retained customers.

The analytical approach to understanding revenue changes from retained customers differs between retained transactional and subscription revenue models. Subscription models allow for clear categorization of revenue changes into expansion, contraction, or churn based on subscription tiers, seat counts, and discount modifications. In contrast, transactional models require a different metric: ARPU shift, which measures how the average revenue contribution from retained customers changes compared to their performance in previous periods.

Subscription Revenue Model

Revenue Growth

R(t_n)-R(t_{n-1})=R_{new}(t_n)+\underbrace{R_{resurrected}(t_n) +R_{expansion}(t_n) - R_{contraction}(t_n)-R_{churn}(t_{n})}_{revenue\ change\ from\ C_{existing}}

Revenue Growth Rate

Growth\ Rate \approx r_{new}(t_n) +r_{resurrection}(t_n)+r_{expansion}(t_n)-r_{contraction}(t_n)-r_{churn}(t_n)

r_{netChurn}(t_n)= r_{resurrection}(t_n)+r_{expansion}(t_n)-r_{contraction}(t_n)-r_{churn}(t_n)

\therefore

Growth\ Rate=r_{new}(t_n)+r_{netChurn}(t_n)

Transaction Revenue Model

Revenue Growth

R(t_{n-1})=N_{retained}(t_n)\cdot ARPU(t_{n-1})+ N_{churn}(t_n) \cdot ARPU(t_{n-1})

R_{churn}(t_n)= N_{churn}(t_n) \cdot ARPU(t_{n-1})

R(t_n)-R(t_{n-1})=R_{new}(t_n)+\underbrace{R_{resurrected}(t_n) + R_{arpuShift}(t_n)-R_{churn}(t_{n})}_{revenue\ change\ from\ C_{existing}}

Revenue Growth Rate

Growth \ rate \approx r_{new}(t_n)+r_{resurrected}(t_n) + r_{arpuShift}(t_n)-r_{churn}(t_{n})

r_{netChurn}(t_n)=r_{resurrected}(t_n)+r_{arpuShift}(t_n)-r_{churn}(t_n)

\therefore

Growth\ Rate=r_{new}(t_n)+r_{netChurn}(t_n)

Enterprise-Value

Thus far, we have deconstructed revenue as a temporal snapshot of a collective of customers each making transactions along their customer lifecycle instead of as a discrete financial metric. We can apply a similar framework to derive enterprise value (EV), the total value of a business’s operations.

Deriving EV via DCF

As a proxy for enterprise value, we will leverage the principles behind a discounted cash flow (DCF) model. Formally:

EV = \sum_{t=1}^{\infty} \frac{FCF_t}{(1+r)^t}

Where ${FCF_t}$ is the free cashflow generated in period $t$ and $r$ is the discount rate (sensibly the weighted average cost of capital).

The idea is simple — the value of a firm is the present value of all the future free cashflows it will generate. Since customers generate these cash flows through purchases, subscriptions, and renewals, we can reframe the DCF model in customer terms: firm value equals the sum of present values from all customer relationships—both existing and future.

EV = Customer Equity + PVGO

Enterprise\ Value = Customer\ Equity + PVGO

This framework divides enterprise value into two distinct components: customer equity representing the value of the current customer base, and PVGO (present value of growth opportunities) representing the value of customers to be acquired in the future.

Customer Equity

In recognition that each customer represents a free-cashflow stream to the firm, customer equity aggregates the net present value of all current customers.

\text{Customer Equity} = \sum_{i=1}^{N} NPV_{\text{customer}_i}

$\mathrm{NPV}_{customer_i}$ is the net present value of $customer_i$
$N$ is the total number of existing customers.

Each customer’s NPV can be approximated by applying the firm’s gross margin to their lifetime revenue.

NPV_{customer} \approx LTV_{rev} \times gm

$LTV_{rev}$ (Lifetime Revenue) is the net present value of revenue a customer will contribute over their lifespan.
$gm$ (Gross Margin) the share of revenue retained after variable, per-unit costs.

PVGO

PVGO sums the value of customers the firm is to acquire in the future. We approximate PVGO by forecasting the number of customers the firm will acquired, the unit customer acquisition costs, and the inflows the customers will bring with them.

PVGO = \sum_{j=1}^{N} \left( LTV_{\text{rev}_{j}} \times gm - CAC_{j} \right)

$N$ is the total number of future customers expected to be acquired
$LTV_{\text{rev}{j}}$ **is the lifetime revenue of future customer $j$
$*gm$ * is the average gross margin
$*CAC{j}*$ is the acquisition cost for customer $*j*$

Estimating $N_{future\ customers}$ requires analyzing both external market factors and internal growth capabilities. Externally, consider the total addressable market (and its growth rate) for current and near-customer profiles. Internally, evaluate the efficiency of existing and future acquisition channels, along with CAC and the company's available cash for customer acquisition.

Understanding the connection between customers and enterprise value is crucial for growth teams, who should focus on building long-term customer value rather than chasing short-term revenue targets. When growth strategies focus on building the customer base's underlying value, they naturally drive sustainable increases in shareholder value - the ultimate goal of any growth initiative.