When to use SQL for data analysis (and when not to)

When to use SQL for data analysis (and when not to)

Most businesses start with out-of-the-box analytics tools like Google Analytics, Mixpanel, or Amplitude. They're fast, easy to use, and answer 90% of your questions with a few clicks.

But eventually, you hit a wall.

The tool can't answer a specific question about customer behavior, combine data from multiple sources, or create the custom metric your business cares about.

That's when SQL becomes necessary.

SQL (Structured Query Language) is a programming language for querying and managing data in relational databases. It gives you direct access to your raw customer data, letting you answer questions that pre-built analytics tools aren't equipped to handle.

But SQL isn't always the answer. It requires technical skills, takes longer to get results, and adds complexity to your analytics workflow. Knowing when to use SQL versus when to stick with analytics tools determines whether you're spending time efficiently or overcomplicating simple questions.

This guide covers what SQL is used for in data analysis, when SQL makes sense versus out-of-the-box analytics tools, and specific use cases where SQL is necessary.

What is SQL and why is it used for data analysis?

SQL (Structured Query Language) is a programming language designed to interact with relational databases. It allows you to retrieve, manipulate, and analyze data stored in tables.

Analytics tools provide pre-built reports and visualizations, but SQL gives you direct access to query your raw data however you need.

Why SQL is used for data analysis

SQL is used for data analysis because it provides flexibility and power that out-of-the-box analytics tools can't match. With SQL, you can:

• Define custom metrics that matter to your business
• Combine data from multiple sources (your product database, CRM, marketing tools, support system)
• Answer highly specific questions about customer behavior
• Create behavioral cohorts based on complex criteria,
• Build custom dashboards with the metrics you need

Analytics tools like Google Analytics, Mixpanel, and Amplitude are great for specific use cases but struggle with questions outside their scope. SQL databases give you the raw data, and SQL queries give you unlimited ways to analyze it.

How SQL works for analysis

When you query data with SQL, you're writing commands that tell the database what information to retrieve and how to organize it. For example, a simple SQL query might retrieve all customers who made a purchase in the last 30 days. A more complex query might identify customers who viewed your pricing page, didn't purchase, but later returned after receiving an email.

SQL databases store data in tables (rows and columns), and SQL queries let you filter, join, aggregate, and transform that data to answer specific questions. Common SQL analysis tasks include filtering data with WHERE clauses, joining tables from different sources, aggregating data with SUM, COUNT, AVG functions, grouping results by categories or time periods, and calculating custom metrics specific to your business.

Out-of-the-box tools can get most of the job done

Hopefully, you're owning (or at least have access to) your raw customer data, even if you don't need to directly query it just yet. If your product or business is young, you probably should leave it be and answer all of your questions from a set of event analytics and reporting tools. This is preferable, because you want to move fast and let those tools do all of the heavy lifting!

Services like Google Analytics, Mixpanel, and Amplitude are great for basic product and marketing questions. They'll help you analyze trends, usage patterns, cohort and funnel analysis, etc. With these types of tools you can answer the following questions:

• Which sites send us the highest converting traffic?

• Where are the key points people drop off in my funnel?

• Which features are customers using? Which ones are they not?

• Did people who signed up around our funding announcement act the same as other cohorts?

It's not trivial to figure these questions out in SQL, and the out-of-the-box analytics tools will get you your answers much faster — it just takes a few clicks.

But at what point does it make sense to use SQL? The section below enumerates reasons why people migrate off these hosted tools to SQL databases/BI tools like Looker, Mode, and Periscope. If learning SQL seems like too much of a lift, you can also lean on BI tools like Tableau and Chartio that specialize in providing drag-and-drop reporting on top of SQL databases.

When to use SQL for analysis

SQL is best used when you need to answer specific, granular questions about your business that out-of-the-box tools aren’t equipped to answer. For example, SQL is useful if you want to organize customer behavior into different cohorts and analyze it, combine data from multiple sources or platforms for integrated data analysis, or build your own customized dashboards with unique metrics.

There comes a point in every business where the trend lines, funnel analysis, and cohort reports just don't provide the granularity you are seeking: that's when SQL comes in.

Analyzing behavioral cohorts and customized sessions

For example, say you want to do a cohort analysis by a particular behavior. How do people who take this action compare to others? Is there one behavior that indicates increased activation and retention? You really need SQL to figure that out. Many folks in the industry call this analysis finding your aha moment.

How about if your business has a unique definition of a "user session?" For example, an e-commerce site and a run tracking app probably have different preferences for setting sessions. Most out-of-the-box tools choose a way to measure sessions and give you very limited options to customize it. In SQL, you can define sessions the way that makes the most sense for your business.

Drilling down into customer behavior

SQL also provides you full access to get as granular as possible with your analysis. Here is a line of questioning that can be answered easily with SQL, but that would be difficult for managed tools to address:

• How many users created an account on my website, added a product to their cart, and then completed that purchase on their phone?

• What links and referrers did most people in this group come from?

• How many users added additional items to a cart on their phone?

• Who also saw a push notification about completing their order?

Joining in other data sources with your customer data

For many companies, customer event data becomes more valuable for analysis when used with operational or other kinds of business data. For example, SQL lets you connect marketing channel information with deals closed in a CRM to measure channel effectiveness or product purchases with a product inventory to help manage retail logistics.

• Salesforce + Marketo = What campaigns drive the most revenue?

• Zendesk + Product = What's our average number of tickets per users per plan?

• Transactions + Web = Which browsing behaviors lead to the most purchases?

Building custom dashboards

In addition to answering ad-hoc questions about your business and product, you can also use SQL to power real-time dashboards with completely customizable metrics that are a lot more meaningful to your business than standard metrics.

Though you can get far with hosted analytics tools--they're always getting better--not every company is the same. Each company measures its own success by its own self-defined metrics (hopefully not vanity ones!).

Here are some examples of metrics that we power with SQL:

• number of qualified opportunities by channel: We combine Salesforce opportunity data with website and Marketo data to identify which channels and campaigns drove the most leads.

• number of integration health score: To understand the state of integrations at Twilio Segment, we assign each integration a health score that combines the last time the integration was updated (Github), how many support tickets it has (Zendesk), total revenue associated (Stripe and Salesforce), and total users (Twilio Segment). This gives us a nice and easy view to prioritize integration updates.

Select from sql.knowledge

SQL gives you the ability to easily manipulate data and tables to answer any question you could have about your customers and business. If you're just getting started with advanced analytics, here are some helpful resources and courses to get up to speed on SQL quickly.

• Mode SQL Tutorial

• Udacity SQL for Data Analysis Course

• Codeacademy Learn SQL

• Kahn Academy Intro to SQL: Querying and managing data

While out-of-the-box tools are easy to use and they help you get most of your analytics work done, they won't always provide the granularity you need to understand your business. With SQL, if you can dream it, you can query it!

Use SQL for analysis with clean data from Twilio Segment

SQL is only as powerful as the data you query. When your customer data is fragmented across different tools, SQL becomes frustrating instead of empowering. You spend more time cleaning and reconciling data than analyzing it.

Twilio Segment solves this by collecting customer data once and storing it consistently in your data warehouse. Instead of wrestling with messy data from a dozen different sources, you get clean, standardized customer data ready for SQL analysis.

How Segment enables SQL analysis:

• Unified data collection: Track customer behavior across your website, mobile app, email, and other channels, then automatically send it to your data warehouse.
• Consistent schema: All events follow the same structure and naming conventions, making SQL queries straightforward.
• Identity resolution: Stitch together customer actions across devices and platforms so your SQL queries reflect complete customer journeys.
• Automatic warehouse sync: Send clean data to BigQuery, Snowflake, Redshift, or other SQL data warehouses automatically.
• Pre-built schemas: Segment's Protocols feature enforces data quality rules so you never have to clean data before querying it.

Once your data is in your warehouse, you can use SQL to answer any question: create behavioral cohorts, join product data with CRM data, build custom dashboards, or analyze complex customer journeys that span multiple channels.

Start powering your SQL analysis with clean, consistent customer data. Sign up for free to get started.

Frequently asked questions

Q. When should I use SQL for data analysis?

Use SQL when analytics tools can't answer your question. This includes behavioral cohorts, custom sessions, granular customer behavior, joining multiple data sources, or custom dashboards. If Google Analytics or Mixpanel can answer it, use those instead.

Q. What is SQL used for in data analysis?

SQL retrieves, filters, aggregates, and manipulates data in databases. Common tasks: filtering with WHERE clauses, joining tables, aggregating with SUM/COUNT/AVG, grouping by categories, and calculating custom metrics.

Q. Why use SQL instead of analytics tools?

Analytics tools are faster for standard questions. SQL is necessary for custom metrics, combining data from multiple sources, complex behavioral analysis, or company-specific dashboards. Tradeoff: SQL requires technical skills but offers unlimited flexibility.

Q. Is SQL used for data analysis?

Yes. Data analysts, scientists, BI teams, and product managers use SQL to analyze behavior, measure performance, and make data-driven decisions. SQL consistently ranks as one of the most-used languages among data professionals.

Q. What do you use SQL for?

Data retrieval, analysis, reporting, dashboards, manipulation, and integration. SQL answers questions like "which customers will churn," "what channels drive revenue," or "how do behaviors correlate with retention." Used across e-commerce, finance, healthcare, and SaaS.

Q. Do I need to know SQL to analyze data?

No. Analytics tools answer common questions without coding. But SQL expands your capabilities to let you query raw data and answer specific business questions. Start with analytics tools, learn SQL when you hit their limitations.

Q. What are some alternatives to SQL for data analysis?

A. Analytics tools (Google Analytics, Mixpanel), BI tools (Tableau, Looker), spreadsheets (Excel), programming languages (Python, R), and NoSQL databases. Each has tradeoffs in speed, flexibility, and technical requirements. Most teams use a combination.