Marketing Data Warehouse: Getting Started Guide For Digital Marketing Teams

Are you looking for a marketing data warehouse but need help figuring out where to get started?

In this guide, we’ll go over the main benefits of using a data warehouse as well as the drawbacks so you can understand if this solution is for you or not.

Then, we’ll explore the most used data warehouse tools on the market and how to choose one when your marketing team needs it.

What is a marketing data warehouse?

A marketing data warehouse (DWH) is a cloud-based storage solution that empowers teams to aggregate data from multiple data sources in one place and use it for analysis and reporting purposes. 

Data warehouses vs. databases: What’s the difference?

While a database is a collection of records that share the same property types, a data warehouse is where you centralize data coming from different databases.

You can look at a data warehouse as a large repository of multiple datasets from different sources. Think of all your Hubspot or Salesforce CRM data merged with your Google Ads campaign data and your social media platforms. This allows you to track and analyze your customer acquisition omnichannel strategy.

On the other hand, a database is a set of organized data too, but at a smaller scale, used mostly for the purpose of storing and retrieving specific data. For example, you might use a database to store all your online shop sales data that includes details like price, transaction date, and customer ID for each sale.

Why does your business agency need marketing data warehousing?

Data warehouses are the backbone of business intelligence reports. But even though the practice of using a DWH is more popular for performing financial analysis at the business level, that doesn’t mean they can’t be useful to a data-driven marketing team.

Let’s look at some use cases and benefits marketing teams get by using a marketing data warehouse for data management.

Align your team around a single source of truth

Every marketing team focuses on different channels and campaigns. When you start scaling, you’ll have separate teams responsible for performing and reporting on results across different channels. Each with its own focus.

When all those reports are generated individually, there’s no way of understanding your marketing effort performance as a whole. Not to mention tracking ROI properly.

A data warehouse for marketing can help marketing teams remove data silos and align all stakeholders around a single, most comprehensive source of truth. This leads to better decisions when it comes to marketing campaigns and spending the budget.

Combine data analytics from multiple marketing campaigns

By combining data from across channels and different marketing campaigns, your team can identify cross-channel attribution and understand how different touchpoints across the customer journey impact conversion rates.

For example, if you are running paid ads on Facebook, Google Ads, and a retargeting email campaign, looking at individual analytics, you will get some insights into how each performs.

When you sync your data to a marketing data warehouse and combine the insights, it will be easier to see which campaign is most effective and which is generating the highest ROI and optimize your strategy based on actionable insights.

Reduce human error in manipulating and analyzing customer data

Sometimes custom reports in analytics tools can be limited, so exporting data into a spreadsheet and manipulating it for analysis is a common practice across marketing teams.

But when you do this manually and repeatedly, there’s a high chance your data won’t be accurate anymore due to human errors, like copy-pasting the wrong line or accidentally deleting some data.

You can use software to automate data exports from your desired tools into your data warehouse, eliminating manual data transfers and the errors associated with it.

For example, using a tool like Coupler.io, you can set up automated data pipelines from 70+ data sources to a data warehouse destination like BigQuery.

Automate real-time data insights and save time

Automating data flows between your marketing tools and a data warehouse not only reduces human error but can give you access to real-time data vs. dated insights.

While your analytics reports inside each tool you use might update constantly, your team can’t get those updates unless you also update your team’s reports.

When using a data warehouse to centralize your data, you can set up automatic data sync between your tools and the DW, giving your team access to real-time insights across the entire customer journey.

Get better visualization with self-updating marketing analytics dashboards

One of the key benefits of using marketing data warehousing is that you can get access to self-updating analytics dashboards. Once you’ve automated your data flows, you can go a step further and set automatic sync.

This way, all your marketing dashboards built using the data will also update.

We’ve seen the main benefits of using a data warehouse for your marketing team. But how do you choose the right one?

Next, we’ll look into some of the main DW functionalities you should consider.

How to choose the best data warehouse for your needs

When choosing marketing data warehousing SaaS tools, you must ensure they have what it takes to get the job done. Price can also be a deciding factor, but it shouldn’t just come to that.

Here are the main things to consider when choosing the best marketing data warehouse:

• Compatibility with your tech stack and your team’s skills: Ideally, your chosen data warehouse should integrate with multiple data sources, making it easier for you to collect and analyze data from all your tools.

• ETL tools compatibility: If you are planning to use an ETL tool to automate your data flows, you should look for a data warehouse that is supported by your chosen ETL tool. For example, Coupler.io supports automated data flows directly to the BigQuery data warehouse that you can set up without having to code or understand how an API works.

• Storage and computing costs: Depending on how much data you are planning to store in the DWH, but also process, this will influence your final cost. Most data warehouses will offer some free storage and processing power. For example, Google’s Big Query offers on-demand computing pricing. This means that you only pay for the computing power of your queries needed for analyzing data. The first 1TB per month is free, and then you are priced at $6.25 per additional TB. You also get 10 GB of free storage. Learn more about BigQuery pricing.

• Data visualization and BI tools integrations: Integrations with tools like Looker Studio, Power BI, or other data visualization tools of your choice should also be a deciding factor when choosing the best data warehouse for marketing teams. This will simplify the process of getting from raw data to insightful analytics from various sources in one comprehensive dashboard without needing a data engineering team.

Best cloud-based data warehouse solutions to choose from

If you’re not sure where to start when looking for the right cloud-based data warehouse solution for your marketing team, here’s a list of the top players on the market:

• Google BigQuery is a widely used data warehouse in marketing that is easy to get started with and offers pre-built connectors with other Google tools, like Google Analytics or Google Sheets. You get 10GB storage and the first 1 TB of computing power for free. This is a good place to start if you are a small team, plus the scaling costs are granular, too. BQ is also equipped with machine learning capabilities so it makes it easier to analyze large amounts of data and use data modeling to predict future customer behavior based on existing data.

• Amazon Redshift is part of Amazon Web Services (AWS) cloud solution and is a comparable marketing data warehousing solution to Big Query in terms of performance and scalability, but their pricing structure can get a bit complex for beginners. Their on-demand pricing is calculated by the hour based on the node types you choose to run your data warehouse on. The setup and maintenance for this requires help from an engineering team, making Amazon Redshift better suited for enterprise-level teams.

• Snowflake is a cloud-hosted relational database that enables storing and analyzing data using SQL queries. Snowflake sits on top of AWS, Microsoft Azure, and Google Cloud infrastructure, allowing you to scale your data warehouse across multiple locations. Being a more complex tool to use, this is also recommended for enterprise teams.

• Azure Synapse, the data warehouse by Microsoft, is a great option for a data warehouse that offers a good price/performance ratio, but it’s more expensive than BigQuery. If you are using Power BI or other Microsoft tools like Excel, it’s still an option to consider due to its native integrations that can streamline your data flows.

How Coupler.io can help you with marketing data warehouses

Coupler.io is a no-code automation tool that allows you to export your data from various data sources directly into BigQuery and other destinations, so you don’t have to do it manually.

One of the main advantages of using Coupler.io to export your data into a data warehouse is that it enables you to import multiple data sources and manipulate and transform the data before sending it to BigQuery.

The process is simple and straighforward, and consists of three steps: Export, Transform, and Manage.

Let’s look at each of them in more detail.

Start by creating an account at Coupler.io (you’ll automatically get a 14-day trial). Click on Add new importer to get started with exporting data. Next, you will need to select your desired data source and destination.

We’ll continue this example using BigQuery as our data warehouse destination. You can select one data source at this point but will be able to add multiple in the next step.

Step 1. Extract data

You will then have to follow a quick setup wizard and connect your data source to Coupler.io. Once this is done, you can continue adding more data sources or move on to transforming your merged data.

Step 2. Transform data

When you have finished adding all your sources, click Transform Data, and you can start managing columns, sorting existing data, and manipulating it using formals. Click Proceed to move on to the next step.

Step 3. Manage data

To manage your data, you will first have to connect your BigQuery account. Click on Connect to launch the connection and authorization wizard.

You can always go back and add more data sources or change and manipulate your data in the Transform Data section. Once all is set, the final step is to set up automatic data refresh on your preferred schedule, enabling data sync inside BiqQuery. Click Save and Run, and your data will start flowing.

When should your marketing team start using a data warehouse?

A data warehouse can be a perfect fit for some marketing teams and a burden to others.

If you’re looking to decide if this is for you, here are a few situations where you might want to start using a data warehouse:

• Drowning in spreadsheets and struggling with attribution: If your marketing team is tracking performance using several spreadsheets and it’s becoming hard to understand ROI, centralizing all your data inside a warehouse for analysis and reporting should be your next step.
• You need a more reliable data storage solution: While tools like Google Drive or Dropbox are easy to use for storing and sharing data, when it comes to analyzing data across multiple sources, you should opt for a solution that not only stores your data but allows you to process it and use it for clear reporting.
• Google Analytics reports are too limited and do not give you enough insights: GA works great for reporting on website traffic and acquisition mostly but when you are trying to dig into the data, the preset or custom reports can be limited.
• Managing multiple marketing platforms and needing to analyze metrics across the entire customer journey: Manipulating and analyzing large sets of data from different marketing channels can become time-consuming and prone to human error when not using a data warehouse to bring your data in one place.
• Reporting across different projects and campaigns for better decision-making: Large teams usually apply different marketing strategies at various stages of the customer journey. A marketing data warehouse will enable you to track omnichannel performance and create insightful reports.

The downside of data warehouse in marketing

We’ve explored the main use for data warehousing in marketing and its benefits and in order for you to make an informed decision, let’s go over some of the drawbacks of using one.

First, the cost of storing and processing data will depend on the platform you choose, but most tools offer usage-based pricing, making your monthly cost unpredictable. If this is a concern for you, close monitoring will be needed to make sure you don’t exceed your budget.

On top of pricing, a data warehouse requires maintenance. This includes the cloud architecture for tools like Amazon Redshift, or simply managing and maintaining the data inside your data warehouse tool.

Ideally, you should have at least a marketing data analyst on your team who can manage data manipulation and maintenance on top of data analysis.

This takes us to another drawback: the need for in-house skills or team expansion. Although not exactly a downside, this can still be a blocker for teams with budget constraints.

Despite the drawbacks, if your team is growing and you’re looking for scalability, the benefits of using a data warehouse, such as better data insights and omnichannel reporting, far outweigh the implementation and maintenance downsides.

FAQ

Data warehouse vs. data lake

A data lake is primarily used for storing raw data, including log files or multimedia files. In comparison, a data warehouse is used to store processed data that is ready for analysis and reporting.

Data warehouse vs. data mart

A data mart is similar to a data warehouse in that it’s used for storing specific data. The main difference between them is that a data warehouse is used as a centralized repository for the entire business, while a data mart is more focused on data needed for a specific analysis.

Should your marketing team start using a data warehouse?

In this article, we explored some benefits and drawbacks of using a data warehouse in marketing for your analytics needs.

The main drawbacks we covered are tied to setup, maintenance, transferring data, and cost. These can easily be outweighed by the benefits of not having siloed data reports.

Using a data warehouse in marketing to collect your analytics data from all the marketing reporting tools you use will allow your team to have insightful omnichannel reports. 

Better data analytics leads to better decisions. That means, overall, it could be more expensive not to use a data warehouse.

And with tools like Coupler.io that allow you to automate data flows from multiple sources to BigQuery, getting started should not be a “should?” question but a “when?” one.

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