Discover how to set up ETL pipelines, save time on reporting, and maximize ROI with advanced analytics.
How can you create effective marketing strategies if you don’t have timely and accurate data to inform your decisions?
Enter ETL - the solution for getting the right data at the right time.
In this guide, we’ll cover:
ETL is an abbreviation for “extract, transform, load” - the three stages data engineers use to get data ready for analysis.
So ETL is ultimately a data processing workflow. A workflow that pushes raw data through a set of stages and gets it ready for data analytics (in marketing).
You’ll often see ETL also called a data pipeline - because we pipe data through the three stages.
Let’s explore the stages of the ETL workflow in more detail.
“Wait, wait, wait … *4* stages? Doesn’t ETL stand for Extract, Transform, and Load - making it three stages?”
Yes, that’s true. But we are extending the concept to include a fourth critical stage: using accurate data to drive action.
In the data extraction phase, you collect data from different marketing data sources.
Every marketing team uses data from different sources:
However, raw data is often not ready to be used. You need to transform it first, to get it ready for data analysis.
Data transformation involves the following steps:
Now that you’ve prepared the data, you need to save it somewhere.
Data loading, in simple terms, means saving data. Your options include:
📚Recommended reading: Power BI Data Preparation in 5 Steps
The last step helps you make use of the data you just prepared via ETL. Now it’s time to analyze and put the newly minted data into action. How? Keep on reading.
You can use ETL to achieve many marketing goals:
ETL enables marketing teams to collect and integrate customer data from various sources like CRM systems, social media platforms, and website interactions.
After cleansing and transforming this data, marketers can create unified customer profiles as email lists in CRMs or audiences on Facebook and Google ads. This enables highly targeted and personalized marketing campaigns, resulting in increased engagement and conversion rates.
See Rohlik, an e-commerce unicorn, for example. They use Keboola and real-time machine learning algorithms to identify the food items nearing expiry, apply discounts, and automatically advertise them to price-conscious consumers discovered through customer segmentation. This end-to-end automated marketing initiative helps Rohlik reduce food waste while addressing the needs of a targeted customer profile.
In digital marketing, data is generated from multiple sources including Google Analytics, advertising platforms (e.g., Google Ads, Facebook Ads), and email marketing tools. ETL helps aggregate and consolidate this data into a central data warehouse.
By combining marketing performance metrics, marketers can gain a holistic view of their campaigns, assess the success of various marketing channels, and optimize their strategies to improve overall performance and return on investment (ROI).
For example, Enjoy Digital, the fastest-growing UK digital agency, creates consolidated reports across all marketing channels to consistently identify opportunities for growing ROI. Their secret? They automate marketing reporting with Keboola.
A clear understanding of the customer journey is vital for effective marketing.
By extracting data from different stages of the customer lifecycle and transforming it into a standardized format, marketers can map the entire customer journey, identifying pain points and opportunities for improvement at every touchpoint.
Example: Marketing Intelligence, a business intelligence firm specializing in marketing, uses Keboola to reconstruct customer journeys along multiple touchpoints and across different attribution models.
With careful analysis of how different channels and pathways interact with each other and what customer segment tends to convert, Marketing Intelligence used Keboola to help save their client 30% on marketing costs while increasing acquisition by informing them what conversion paths work best.
👉Read the full customer success story here.
ETL allows marketing teams to set up data pipelines that continuously extract and transform real-time data from various sources.
By loading this data into an analytics platform, marketers can monitor campaign performance, track key metrics, and make data-driven decisions on the fly.
This ensures marketing strategies remain agile, keeping pace with rapidly changing market conditions. Real-time insights enable agile marketing strategies, ensuring that marketing efforts are responsive to rapidly changing market conditions.
Introducing ETL in your data operations will unlock many benefits
When you consolidate data into a single source of truth, you provide everyone with access to consistent, high-quality data. This removes any discrepancies and varying interpretations as all individuals are viewing the same datasets and metrics.
An added perk? The next time your stakeholders require data, you can save time by utilizing the same dataset or rerunning the same ETL pipeline.
Remove manual workload by picking an ETL tool that can be automated. This way, you just set your ETL pipeline on a schedule and get fresh data each time. For example, the team at North Labs helped their client save 10+ hours per week by automating their reporting end-to-end,
ETL workflows help you to save only the customer data you need by filtering out errors and aggregating small data into a big picture. This results in a significant reduction in storage costs for your organization or agency.
When you use ETL as part of your marketing analytics, you can automate the data collection process end-to-end. This gives you more time for analyzing insights instead of collecting data.
The ETL process ensures that the final data set is accurate and reliable with data quality tests. This is crucial in marketing, where decisions often rely on data accuracy.
Data quality leads to better analysis, accurate reporting, and ultimately, better decision-making.
Often, creative marketing momentum is hindered or halted by data issues - gaps in data can leave unanswered questions in decision-making, while flawed data can mislead you.
With an ETL tool, you don't need to rely on others (IT, data engineers, etc.). You can independently run your ETL workflow, store data as needed, and garner the insights necessary to direct your creative efforts. This also allows business users to access the data they require without dependency on IT or engineering departments.
All of these benefits are amplified by the right ETL tool that helps you create and automate ETL data pipelines. Let’s see how to select one.
Picking the right ETL tool can seem challenging. Don’t panic. We’ve made a list of the best ETL tools. All you have to do is match them against the most important criteria:
Many ETL tools may not integrate with your marketing data sources (Hubspot, Salesforce, Google Analytics, …). If an ETL tool lacks specific data source extractors, it could hinder you from obtaining all the data required for informed decision-making.
Make a list of all the data sources you use in marketing and compare them against your shortlisted ETL tools. For example, Keboola covers 250+ data sources and destinations to make sure you can quickly get data from all the data sources in a couple of clicks (and without needing to code!).
While some tools market themselves as ETL, they may focus more on Extraction (E) and Loading (L). Without Transformation, you may struggle to clean, aggregate, or compute the metrics vital for your marketing decisions.
Without transformed data, raw data is mostly deceiving - leaving you to look at trees instead of the marketing forest.
Make sure you can perform all three transformation types in your ETL tool.
How? If you and your team are familiar with coding, look for ETL tools that support a variety of programming languages. Keboola, for example, supports Python, SQL (dbt), R, and Julia. This flexibility allows you to manipulate your data in your preferred programming language, optimizing your data transformation process.
Unfortunately for many marketers, ETL tools are usually built for data engineers who know how to code data pipelines in Python or SQL.
This means you need to have a dedicated data engineer on your team or even learn to code yourself. Talk about time-consuming activities!
Luckily, exceptions exist. Some ETL tools, like Keboola, offer no-code and low-code transformations, along with other visual features like the Visual Flow Builder These user-friendly tools allow you to either drag-and-drop or code your data pipeline, offering the flexibility to work independently or alongside data engineers as needed.
[insert visual - an example of data template]
👉Pro tip: Use pre-built ETL pipelines. The best tools - like Keboola - offer already configured ETL pipelines you can launch with a click. Use data templates to launch fully developed marketing reports in minutes. From Shopify e-commerce analytics to Google Analytics deep dives.
An important benefit of ETL tools is their ability to automate processes. This reduces manual errors, eliminates the need for weekly pipeline reruns, and keeps your data fresh.
For example, in Keboola you can automate your ETL pipelines end-to-end by running them on a schedule or designing them to stream data in real-time whenever a new piece of information is generated at the data source.
Marketing teams can sometimes find it challenging to manage the technical aspects of ETL pipelines, such as setting up and managing a data warehouse for data storage.
Some tools, like Keboola, offer an additional service alongside ETL - a Snowflake data warehouse Keboola manages for you out-of-the-box. This eliminates the need to set up your data warehouse and reduces related storage costs on other tools.
A good ETL tool can make a big difference in using data to optimize your marketing strategies and work processes. It can help you unify all marketing data sources, enables fast transformations, automates that pesky manual reporting, and even lend a helping hand with data storage.
Keboola helps you do all of the above and more. It’s an all-in-one data operations platform that helps its users collect, transform, orchestrate, and analyze data:
ELT is a similar workflow to ETL, but the stages come in a different order. Instead of transforming data before loading it, we load data first and transform it later.
ELT is useful for unstructured data (such as images, videos, text files, …) that are later used by data scientists to train machine learning algorithms.
Curious about all the differences between ETL and ELT? Check out the 11 critical differences between ETL and ELT.