Dates, Text & Analytics: 20 Functions & Datediff PostgreSQL

Early-stage startups require fast insights and independent work. PostgreSQL is built to enable both, but only if you speak its language. SQL is an important skill, and those who master it are more competent, independent, and well-rounded in their skillset. Those who don’t rely on someone more technical, which slows down the workflow and their ability to execute.

Thankfully, you don’t need to be an engineer to write SQL. As there’s only a handful of queries you’ll likely use, mastering these few can have a big impact. This guide delves into 15 basic PostgreSQL functions that enable marketers and product managers to draw useful insights while working faster. If you’ve previously found yourself Googling for datediff PostgreSQL or asking LLMs for help, this is one to bookmark.

Let’s delve into the “Pareto principle” of SQL skills.

Understanding datediff PostgreSQL for Date Calculations

A common way to get marketing and product insights starts with date calculations. You can track existing campaigns, daily active users, subscription timelines, or the effectiveness of tested assumptions. But, on PostgreSQL, date and time calculations work a little different than other databases.

Unlike MySQL or SQL Server, Postgres does not include DATEDIFF. Instead, it works with arithmetic between DATE types, interval calculations, and functions that offer a more detailed control. So, instead of datediff in PostgreSQL, the database works by subtracting dates directly:

1SELECT end_date - start_date AS days_diff
2FROM events;

The query above gives you the interval between two specified dates, which supports most analytics tasks and forms the basis of reporting.

The AGE function

The AGE() function also comes in handy for days, months, or years. The PostgreSQL age function is, in fact, one of the most handy ways to check subscription timelines, customer loyalty, and product lifecycle metrics, all of which affect marketing decisions.

One situation where AGE() is useful is measuring subscription tenure. Instead of giving you the number of days, AGE() divides them into monthly, daily, or yearly timeframes. This also helps to make cohort reviews and lifecycle analysis more intuitive. For example, if you want to figure out how long a paying user has used your product before making an upgrade, you can use the following command:

1SELECT 
2    customer_id,
3    AGE(upgraded_on, subscribed_on) AS time_to_upgrade
4FROM customers;

You can use the same function to track how long a free user will use your product before upgrading to a paid version. Doing this regularly improves your onboarding funnels and activation campaigns:

1SELECT 
2    user_id,
3    AGE(activated_on, signed_up_on) AS activation_age
4FROM users;

Is there a DATEADD function in PostgreSQL?

Postgres also relies on intervals when it comes to date addition. If you’re familiar with SQL Server, you’ve probably searched for dateadd PostgreSQL, hoping to find the equivalent function. Yet, adding dates depends on a more expressive interval syntax:

1SELECT start_date + INTERVAL '7 days' AS follow_up
2FROM leads;

As you probably noticed, PostgreSQL offers more flexibility, making it easier to use once you get a hold of it. So if you’ve searched for dateadd in PostgreSQL, you’ll have to adopt this interval-style pattern. The same syntax applies to reports, simplifying week offsets, deadlines, and cohort windows.

If your queries require today’s date, you will need the current_date in PostgreSQL. This function tracks the server’s current date and is a simple but powerful function to keep in mind for dynamic queries and dashboards that provide daily analytics. On the other hand, if you wish to track experiments and lifecycle metrics, you’ll have to rely on the PostgreSQL date difference query. The alternative to the above would be to use the NOW() function.

The queries described above provide clarity and consistency to date logic, and also make you more capable of debugging them if needed. Once you know your way around them, the rest of PostgreSQL’s date functions become easier to navigate.

More Date Utilities that Simplify Reporting

Having gone over the adaptation of datediff in PostgreSQL and the above date functions, let’s briefly mention a couple of linear utilities to keep in mind as well.

When building cohort tables or setting up alerts, you might think of SQL Server’s syntax and search for PostgreSQL dateadd or similar. There is no such command on Postgres; again, the platform works with interval logic, which is more flexible and precise in most cases.

1SELECT signup_date + INTERVAL '7 days' AS next_week
2FROM users;
3

You can notice here how PostgreSQL uses time intervals instead of DATEADD function commonly associated with other databases.

If you want to build custom dashboards, you may require summary statistics or exact period splits. The model of PostgreSQL date difference is the foundation for most of this work. Therefore, it might be good to keep it in mind.

Let’s illustrate with an example. Let’s assume that you want to calculate the number of days it took users to complete their onboarding so you can visualize the average onboarding time in your next report:

1SELECT 
2    user_id,
3    completed_on - signed_up_on AS onboarding_days
4FROM users;

Since you can subtract dates directly, the result (completed_on - signed_up_on) becomes the foundation of summary calculations like averages, medians, or charts on specific time intervals, which you can then visualize in custom dashboards and use for reporting.

When it comes to dates functions on PostgreSQL, there are usually two or more ways to tackle the same issue. Hence, getting comfortable with the basics helps you resolve more complex issues down the road. With the above commands, you are well underway to doing so, claiming back your independence at work, and elevating your skillset.

Text and String Functions That Simplify Reporting

Manipulating text is very common in marketing and product analytics. You adjust campaign names, replace UTM parameters, convert timestamps, or merge multiple fields to get results that you can then interpret.

A common text manipulation is the need to combine strings. PostgreSQL string concatenation is achieved using either || or the CONCAT() command:

1SELECT first_name || ' ' || last_name AS full_name
2FROM users;

The CONCAT() function lets you easily merge parts of text and code to generate clean URLs, and ensures that these won’t break if it's incomplete (null-safe combination).

Date formatting is also essential for marketers and product managers. TO_DATE() converts values into dates and is often described as PostgreSQL string to date function:

1SELECT TO_DATE('2024-01-15','YYYY-MM-DD');

You may find this command useful when importing spreadsheets, normalizing inconsistent data, or when working on CRM exports.

Split_part in PostgreSQL is also a useful command if you wish to split fields like UTM parameters or other web links. Just indicate at which symbol the parse should occur and what part you want:

1SELECT split_part(url, '?', 2) AS query_params;

This automates a lot of manual work when marketing reports are due.

Handling empty or missing data (nulls) is another common issue. The database can help you with the NULLIF PostgreSQL function. This command returns NULL when two expressions match:

1SELECT NULLIF(a, b);

The PostgreSQL NULLIF function is also often used to prevent division-by-zero errors, because when the denominator is 0, then numerator / NULL → NULL:

1SELECT numerator / NULLIF(denominator, 0)

For users who come from SQL Server, checking for missing values is also a little different, as there is no PostgreSQL ISNULL function. Instead, the database slightly adjusts the command to: IS NULL. While the two inherently serve the same function, you may still find people searching for ISNULL PostgreSQL, or asking about it in relevant forums, so it’s important to be aware of the difference.

Another function people often search for in online discussions is PostgreSQL group_concat or group_concat in PostgreSQL. Again, such a command does not exist in the Postgres database. If you need to group strings, the corresponding function of GROUP_CONCAT in MySQL is STRING_AGG()or array aggregation. Here’s what it looks like:

1SELECT STRING_AGG(name, ', ')
2FROM categories;

String aggregation is a great way to group tags, campaign lists, or product specs in one cell.

Window Functions for Ranking and Ordering

Window functions may seem challenging to those with little SQL knowledge, but they offer insights faster. These commands are important for more complex customer insights, like customer retention and churn, cohort ranking based on several metrics, lifecycle transitions, funnel stages, etc.

The first function to keep in mind is PostgreSQL row_number, which gives a number to each row in an ordered result set:

1ROW_NUMBER() OVER (ORDER BY created_at)

You’ll often find this command paired with partitioning, like PostgreSQL row_number over a customer segment or relevant category:

1ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY created_at)

A function like this helps you identify user actions like a first purchase or first email open.

When tracking performance or comparing products, another type of command is needed. These are known as ranking functions. One function commonly used among product managers is dense_rank in PostgreSQL, which provides rankings without gaps:

1DENSE_RANK() OVER (ORDER BY revenue DESC)

PostgreSQL dense_rank use cases mainly involve leaderboards, tracking best-selling products, or campaign performance. It can help you figure out what works and what doesn’t, so you can double down on the former.

Overall, window functions support complex analyses that would otherwise require exported CSVs or Business Intelligence tools. For most beginners, using them can be quite complex. For this reason, and if you want to build up some confidence before you start, you may want to check out PostgreSQL's official documentation on Window Functions. After understanding the basics, you can also check the Advanced Queries section.

Grouping, Rollups, and Aggregate Enhancements

Aggregations are at the core of data reporting. Unlike other databases, PostgreSQL offers a great toolbox to help you track multiple performance aspects in a precise manner. Not all of them can be discussed here, of course, but let’s go over some basic ones you should know.

One great feature is PostgreSQL rollup, which helps you calculate subtotals and totals in multiple grouping levels:

1GROUP BY ROLLUP(channel, campaign)

This is perfect for campaign reports where you need totals per channel and a grand total.

For marketers specifically, this function is useful when reporting on campaigns that require channel totals and a grand total.

Next, date functions like PostgreSQL date trunc can group data by specific timeframes spanning days, weeks, or months:

1SELECT DATE_TRUNC('month', created_at) AS month, COUNT(*)
2FROM events
3GROUP BY 1;

This command provides detailed time-based reporting without messy date formats.

And remember - in this situation, you may also add or subtract days using the PostgreSQL dateadd alternative function using intervals, as explained earlier in the Date Functions. This way, you can customize reports to display only the data that is required.

All in all, these grouping functions should simplify your daily reporting and improve your tracking.

But is it really necessary?

Searching for SQL functions like datediff PostgreSQL reveals not just a personal, but also an organizational bottleneck. Lack of SQL expertise can slow down your work, as you will probably rely on devs or LLMs to find the commands that deliver the data you seek to use. And in agile environments like those of early-stage startups, every repetitive task that can be automated should be automated.

As serial founders, we recognized this issue early on. For this reason we built TalkBI, a simplified business intelligence tool that enables you to “chat with your database” instead of building complex SQL queries.

After browsing through the demos on our platform you may wish to use the tool for your own project. This is easy to do. Simply sign up, connect your Postgres database to the tool, and start chatting. We have a freemium model so that you can see if the tool works for you. The best part? After retrieving the data you are looking for using natural language, you can showcase it visually through beautiful dashboards that are created instantly.

Conclusion: You Can Learn SQL… or Make SQL Optional

As a marketer or product manager, learning some basic SQL is definitely a good idea. As a founder, on the other hand, hiring for these roles with SQL experience is really tough and expensive. You can simplify this process by giving your team the tools to work independently. TalkBI is a great tool in this toolbox.

Summing up, the commands described in this article should simplify basic data analysis that is needed for proper reporting with your team. Learning the more advanced functions is simply a process that builds upon the basics we described above.

So, whether you plan to talk to your database using SQL or plain language, you now know how to do your job in a better and faster way. If you still find yourself tired of database-related frictions, consider booking a demo with our team. We’ll walk you through the steps so that you won’t ever need to search for basic SQL support.

Mastering Dates, Text, and Analytics in PostgreSQL: 15 Essential Functions for Everyday Queries