# Calculations of Analytics

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### Revenue

#### Overall Revenue per period

Overall Revenue per period = Sum of All payments filtered by dates - refunds (money) filtered by these dates

#### Overall Revenue without commission

Overall Revenue without commission = Sum of All payments filtered by dates - refunds (money) filtered by these dates) × 70%

If the user will switch % for any app as 15, then: Sum of All payments filtered by dates - refunds (money) filtered by these dates) × 85%

#### LTV

LTV = Total revenue generated since the install date or during\
a defined period / Total # of users who installed on that\
date or during the mentioned period

{% hint style="success" %} <mark style="color:green;">**Example**</mark>

Suppose you have a mobile app that generates revenue through in-app purchases. You want to calculate the LTV for users who installed the app during a specific month. You have the following data for that month:

500 users installed the app\
The total revenue generated from those users since their install date is $10,000\
To calculate the LTV for this app, you would use the following formula:

LTV = Total revenue generated since install date or during a defined period / Total # of users who installed on that date or during mentioned period

LTV = $10,000 / 500\
LTV = $20

Therefore, the LTV for users who installed the app during that specific month is $20. This means that, on average, each user who installed the app during that month is expected to generate $20 in revenue for the app over their lifetime.
{% endhint %}

#### Revenue by Platform

All payments filtered by dates filtered by platform

#### Revenue by Country

All payments filtered by dates filtered by country of an end user

#### Revenue by SKU

All payments filtered by dates divided by SKUs

#### MRR

MRR shows revenue from all active subscriptions normalized to one month. So the formula is all paying users average payment per month.\
For example, for a yearly subscription, instead of counting full revenue from the start, revenue is split into 12 equal parts which are evenly spread across a 12 month period.

If different SKU, then: all annual users × annual payment ÷ 12\
if weekly subscriptions, then: weekly users × weekly payment × 4

{% hint style="success" %} <mark style="color:green;">**Example**</mark>

Suppose you have a mobile app that offers a premium subscription plan. The monthly price of the subscription plan is $10, and you have the following data on paying users:

100 users are on a monthly subscription plan\
50 users are on an annual subscription plan, paying $100 per year\
25 users are on a weekly subscription plan, paying $3 per week\
To calculate the MRR for this app, you would use the following formula:

MRR = (Monthly subscribers x Monthly payment) + (Annual subscribers x Annual payment / 12) + (Weekly subscribers x Weekly payment x 4)

MRR = (100 x 10) + (50 x 100 / 12) + (25 x 3 x 4)\
MRR = 1000 + 208.33 + 300\
MRR = $1508.33

Therefore, the MRR for this mobile app is $1508.33.
{% endhint %}

### User Types

User Types = Sum of all users that have the same attribute below:

#### Standard User types

* by OS: iOS, Android
* by device
* by country
* by timezone
* by language
* by acquisition

#### Custom User types

* subscribed/unsubscribed
* trial/install/subscription
* renewed/first subscription
* all/churned

### Churn Rate

Churn Rate = Churned for the start of the period ÷ users for the start of the period × 100%

#### How do we calculate the churn amount?

1 - Sum of the number of users canceled from trial, turned renewal off, refunded during the period, non-active users (no triggers from them during 30 days)\
2 - Sum of non-active users (no triggers from them during 30 days) per period

{% hint style="info" %}

* Left user: user who canceled trial, canceled auto-renew or refunded, plus here we added a non-active user
* All users: all of the app users, including trial, subscribed or just opened
* Non-active user: from whom we didn't receive triggers (actions during the last 30 days)
  {% endhint %}

### Cancellation

Cancellation reasons amount - the amount of reason amount per period per platform per reason

#### iOS Cancellation Reasons

* issue with app
* Other

#### Android Cancellation Reasons

* Remorse
* Not\_received
* Defective
* Accidental\_purchase
* Fraud
* Friendly\_fraud
* Chargeback
* Other

#### Cancellation for both Platform

* Issue with app
  * Android: Not Received and Defective reasons
  * iOS: Issue with app
* Other
  * Android: Everything else except of Not Received and Defective reasons
  * iOS: Other