You have a **micro SaaS**. You have added many analysis tools. Some tools you even built yourself. But if you miss the **most important statistics**, can be disastrous.

So what are these statistics? Which tools can we use to find them? Or how do we make this tool ourselves, what formulas, what algorithms do we use to make the tool? Let’s get started.

## Most Important Statistic List for Micro SaaS

### 1. Most Important Statistics: Growth Rate

It’s been 6 months since you launched your **micro SaaS**. So what is the growth rate? What does growth rate mean?

A growth rate is the percentage increase of data added to an existing value at regular intervals. My base here is the number of members. How much does the number of members increase? Do you get 10 members a day? And how many members come in a month? And how do we calculate the rate?

#### Growth Rate Formula

Growth Rate = (Final Value / Start Value) * 100

##### Let’s take this as an example:

You have 4200 members. Every day 10 people become members. To calculate your monthly growth rate, we first need to find out how many people should become members per month.

###### Step 1:

Number of daily memberships * Number of days of the month i.e. 30 =

10*30=300 new members per month. The number of new members per month is our final value.

###### Step 2:

Divide the final value by the number of your current members.

300/4200=0.0714285714285714

###### Step 3:

We multiply the result by 100 to form a percentage.

0.0714285714285714*100=7.142857142857143

If we round up the result, we get a growth rate of 7.14%.

I have converted this into SQL code for you. When you run this code you can see your monthly growth rate.

```
SELECT
((SELECT COUNT(*) / 30 AS daily_average FROM users WHERE created_at >= DATE_SUB(CURDATE(), INTERVAL 30 DAY)) * 30) / (COUNT(*)) * 100 AS sonuc
FROM
users;
```

### 2. Most Important Statistics: Number of Active Users

The easiest way to learn this is to add a column named “*login_number*” to your members table in your database. Each time a member logs in, increase the number here by **+1**. In this way, you can see its activity.

For more advanced statistics, you can also add a timestamp to the “loginlogs” table every time a member logs in. In this way, you can find out which days are logged in or which hours are active.

### 3. Most Important Statistics: Conversion Rate

The conversion rate is the calculation of the rate at which new members subscribe. We will do the same process here as we did above. But this time we have a condition. It must also include paid members!

Suppose there is a Confirmation column. If the contents of this column are “0”, let’s assume that they are regular members. Then we can extract the following code if we want to include those who are not “0”.

```
SELECT
((SELECT COUNT(*) / 30 AS daily_average FROM users WHERE confirmation != 0 and created_at >= DATE_SUB(CURDATE(), INTERVAL 30 DAY)) * 30) / (COUNT(*)) * 100 AS sonuc
FROM
users;
```

### 4. Most Important Statistics: Retention Rate

Retention is the rate at which your members or subscribers, or perhaps your employees, choose you or your product. The longer you keep a customer, the more effective your work will be.

When calculating the retention rate, we first need to find the number of customers/members/employees remaining at the end of the period. To do this, you first need to subtract the total number of subscribers at the beginning of the period from the subscribers who canceled their subscriptions during the period. We have reached the number of your actual subscribers remaining at the end of the period.

(Total number of subscribers at the beginning of the period) – (number of subscribers who left during the period) = (number of subscribers remaining at the end of the period)

You should now divide the number of subscribers remaining at the end of the period by the total number of subscribers. We now have the retention percentage. This result is a decimal number. If you multiply the result by 100, you will calculate it as a percentage.

(Number of remaining subscribers) รท (total number of subscribers) = (retention percentage)

(retention percentage) * 100 = (retention rate)

**Let me write the retention rate as SQL code:**

```
SET @periodSubscribers = (SELECT COUNT(*)
FROM users
WHERE conf != 0
AND created_at >= DATE_SUB(CURDATE(), INTERVAL 3 MONTH));
SET @lostSubscribers = 27;
SET @totalSubscribers = (SELECT COUNT(*)
FROM users
WHERE conf != 0);
SET @remainingSubscribers = @periodSubscribers - @lostSubscribers;
SELECT (@remainingSubscribers - @totalSubscribers) * 100 AS resultSubscribers;
```

Here you need to look at the payment channel you use for **lostSubscribers**. Here’s how to look for it in LemonSqueezy as an example.

From LemonSqueezy, under the chart on the Subscriptions page from the Store menu, there is *“Status: All Subscriptions”.* Click here and select *“Past due”, “Expired”, “Unpaid”, “Cancelled”, and “Paused”* in order. Each time you select, you should click on the date option above and list it as the last 3 months. When you look at these 5 options, add the total number with “xxx results” at the bottom of the page for each page.

So **total Past due + total Expired + total Unpaid + total Cancelled + total Paused** = number of lost subscribers.

### 5. Most Important Statistics: Churn Rate

Churn means a lost user. They come and sign up, then cancel their membership and leave. One of the worst things about churn is members who disappear without giving any reason and without deleting their accounts. Since there is no member activity, it makes it difficult to calculate churn.

Users who do not delete their accounts and disappear are called *“ghost members”* or *“ghost users”*. There is a possibility that these ghosts can be reactivated by sending an extra email.

The easiest way to understand these one-offs is the number “2” in “login_number” in the “*users*” table. “1” works when you log in for the first time. But “2” or above indicates that you have logged in more than once.

This alone is not informative enough. If you open a table called “*userlogs*” and record the user’s movements or open a table called “*userloginlogs*” and record at least when the user logged in, then you can get stronger results!