# Calculation of LTV ## 1. What is LTV? LTV, or *Lifetime Value* (Customer Lifetime Value), refers to the estimated amount a customer will spend on your product over the expected total duration of their subscription, including renewals. It allows you to estimate the total value of each subscriber based on the revenue they will generate over their “lifetime.” It is a **summary metric**, often used in the following contexts: * Assessing the profitability of a channel or customer segment * Tracking the overall performance of a business model (especially in SaaS or e-commerce) * Calculating the LTV/CAC ratio, a key indicator for determining whether acquisition cost is sustainable Since the total expected duration of a customer's subscription is generally not directly observable, LTV is **estimated** using your customers' ARPA and churn rate. *** ## 2. How does Fincome calculate LTV? At each data point date, LTV is calculated using the following formula: $$ LTV = \frac{ARPA\ moyenné\ sur\ la\ rolling\ window}{Taux\ de\ churn\ moyenné\ sur\ la\ rolling\ window} $$ In practical terms: * **Averaged ARPA** : Fincome takes the average ARPA (Average Revenue Per Account) observed over the X months **preceding the data point date**, where X corresponds to the length of the chosen rolling window. * **Averaged churn rate** : likewise, Fincome takes the average churn rate (by value) observed over those same X months preceding the data point date. #### What is the rolling window? The **rolling window** (or sliding window) defines the number of months over which Fincome calculates the averages of ARPA and churn for each LTV data point. > 💡 **Concrete example:** with a 6-month rolling window, the LTV data point on **June 30** will be calculated as follows: > > * **Averaged ARPA** = average ARPA for January, February, March, April, May and June > * **Averaged churn rate** = average churn rate for January, February, March, April, May and June > * **LTV on June 30** = Averaged ARPA ÷ Averaged churn rate > > The data point on **July 31** will follow the same principle, but over the February → July window, and so on. This sliding-window mechanism makes it possible to **smooth out one-off variations** (seasonality, churn spikes, promotions…) and obtain a more stable and representative estimate at each date. *** ## 3. Practical example: calculating LTV in SaaS Let's take an example to illustrate the LTV calculation with a **6-month rolling window**, at the data point on **June 30**. Here are the data observed over the previous 6 months: | Month | ARPA | Churn rate | | -------- | ------ | ---------- | | January | €13.00 | 2,8 % | | February | €13.10 | 3,1 % | | March | €13.20 | 2,9 % | | April | €13.30 | 3,2 % | | May | €13.10 | 3,0 % | | June | €12.90 | 3,0 % | #### Step 1 – Calculate the averaged ARPA over the rolling window $$ ARPA\ moyenné = \frac{13{,}00 + 13{,}10 + 13{,}20 + 13{,}30 + 13{,}10 + 12{,}90}{6} = 13{,}10\ € $$ #### Step 2 – Calculate the averaged churn rate over the rolling window $$ Churn\ moyenné = \frac{2{,}8 + 3{,}1 + 2{,}9 + 3{,}2 + 3{,}0 + 3{,}0}{6} = 3{,}0\ % $$ #### Step 3 – Calculate the LTV on June 30 $$ LTV = \frac{13{,}10}{0{,}03} = 436{,}67\ € $$ The estimated customer lifetime value on June 30 for this SaaS company is therefore **€436.67**. > 💡 On July 31, Fincome will recalculate LTV by shifting the window by one month (February → July), with the corresponding new ARPA and churn averages. *** ## 4. The importance of the LTV/CAC ratio The LTV/CAC ratio (Lifetime Value / Customer Acquisition Cost) is a key strategic indicator in SaaS for assessing the profitability of your acquisition model. It relates: * **LTV** : the average value of a customer over their entire lifetime (average revenue generated before cancellation). * **CAC** : the average cost to acquire a new customer, including associated marketing, sales, tools, and human resource expenses. | **LTV/CAC ratio** | **Interpretation** | | ----------------- | --------------------------------------------------------------------- | | < 1 | The customer приносит less than it costs → non-viable model. | | ≈ 1 | Acquisition at break-even, but with no margin → not very sustainable. | | 2 to 3 | Profitable model, but margin is still limited. | | > 3 | Excellent: each customer brings in 3× or more their acquisition cost. | *** ## 5. Best analysis practices (in Fincome) * **Segment LTV** (by plan, country, industry, acquisition channel) to identify where to invest and where to optimize (pricing, packaging, onboarding). * **Adjust the rolling window** according to your context: a longer window stabilizes the indicator against seasonality; a shorter window detects a trend change more quickly (e.g., businesses with usage spikes, seasonal reactivations). * **Interpret LTV together with GRR/NRR and churn** for a complete view of retention and expansion. *** ## 6. FAQ **→ LTV, CLV, CLTV: what's the difference?** None in common usage: these three terms are synonyms for Customer Lifetime Value. Fincome uses **LTV** for consistency. *** **→ Why can LTV vary from one month to the next?** The variations come from: * changes in**ARPA** in the rolling window (upsell, discounts, changes in the customer mix), * the evolution of the **churn rate** over the same window, * or from **seasonality** effects entering or leaving the rolling window. > 💡 Extending the rolling window can reduce these fluctuations in your reporting. *** **→ What is the rolling window and how do you choose it?** The rolling window defines the number of months preceding each data point over which Fincome averages ARPA and churn rate. A shorter window (e.g., 3 months) reacts quickly to recent changes; a longer window (e.g., 12 months) smooths out seasonality effects. Choose based on the volatility of your business. *** **→ Does Fincome LTV include gross margin?** No, by default. The standard Fincome formula is: **LTV = Averaged ARPA (rolling window) / Averaged churn rate (rolling window).** However, you can manually calculate an after-margin LTV: **After-margin LTV = (ARPA × gross margin) / churn**, for a “post-direct-costs” view. *** **→ Which churn is used in LTV?** The **value churn** (MRR lost following the cancellation of a customer's last active subscription), **averaged over the selected rolling window** — that is, the average of the monthly churn rates over the X months preceding each data point. *** **→ Why does my Fincome LTV differ from an internal Excel calculation?** Several possible reasons: * your Excel uses **logo churn** (number of customers lost) and not **value churn** (MRR lost), * the length of the **rolling window** differs (e.g. 3 months vs 6 months vs 12 months), * the**ARPA** is not calculated on the same scope (discounts, excluded customers, etc.), * your Excel uses one month's churn instead of the average over the rolling window. *** **→ What should I do if my LTV/CAC is low?** Act on the three main levers: 1. **Increase ARPA** : upsell, cross-sell, pricing optimization; 2. **Reduce churn** : onboarding, activation, proactive customer success; 3. **Improve acquisition targeting** : prioritize segments with better payback. These analyses are available in Fincome via **analytical segmentation**. *** **→ Edge case: churn averaged over the rolling window ≈ 0%** Mathematically, LTV tends toward infinity and becomes difficult to interpret. In this case, extend the rolling window and/or supplement the analysis with **GRR / NRR**. *** ## Need to go further? Combine **LTV by segment** and **CAC by segment** to optimize your acquisition budgets. Track the **LTV/CAC ratio over time** to measure the concrete impact of your actions (pricing, packaging, Customer Success). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://fincome.gitbook.io/help-center/en/understanding-the-numbers/calculation-methodology/calculation-of-ltv.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.