Marketing Data Warehouse: The Ultimate Guide with Examples and Benefits

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Data warehousing is not a new concept by any definition. But for marketers, it’s rapidly becoming all the rage — for a good reason.

The latest Gartner survey revealed that more than half of marketing leaders are disappointed with the output of their data analytics, while Forrester claims that only 48% of decisions are made based on quantitative data and analysis.  

And marketers have plenty of data: real-time traffic analytics, advertisement reports, CRM logs, A/B test reports — you name it. 

But this data is so scattered across hundreds of tools and platforms that 28% of marketers consider disjointed marketing tools to be their main problem, whereas 95% of businesses fail to make any sense of the information they gather. 

In other words, marketers struggle at putting their own data to work. 

The ultimate answer to this problem is a marketing data warehouse.

But what exactly marketing data warehouse is, why do you need it yesterday, and how to build one yourself? 

Let’s clear this up step by step.


What is Marketing Data Warehouse?

There’s no lack of marketing data these days. We have data on customer website behavior, content engagement, advertising efficiency. But there’s one problem: all of this data is disjointed. Our best efforts to bring it together end with countless chaotic and cryptic-like Excel sheets. 

When marketers and stakeholders talk about a marketing data warehouse, they often imagine a single cockpit-like dashboard that contains graphs and numbers from several different sources: your ad expenses, traffic data, and so on. 

In reality, the marketing data warehouse is much more than that.
It doesn’t simply serve as a hub of information — with a thoughtfully executed warehouse marketers can find and establish new data connections, quickly build custom dashboards, and analyze data in multiple contexts,they can also have a solid bedrock to run more advanced projections and forecasting. 

Put simply, a warehouse allows you to organize data from different sources in one place and then transform this data however your company likes. 

How To Design Marketing Data Warehouse

Building a marketing data warehouse is an ongoing process as you’ll constantly be adding new sources and finding new ways of transforming your data into actionable forms. 

Here are the five main steps to this process:

Step 1. Identify your Sources

This one is easy. What marketing tools are you using daily? What kind of data are you looking at? What sources do you deem important? Compile a list of all the marketing data sources your team uses at the beginning of your data warehouse design. 

Examples of marketing data sources: Google Analytics, Facebook Ads, CRM data, A/B reports.

Step 2. Choose Storage

Pick where you will be storing your data. By 2022, public cloud services will be essential for 90% of data and analytics innovation, so it’s preferable you pick a cloud storage provider for your data warehousing needs instead of a local one. 

Examples of cloud storage services: Google BigQuery, Amazon Redshift, Snowflake, Azure SQL

Step 3. Extract, Transform, and Load

Although technically these are three different steps, more often than not all three steps are handled by a single ETL tool or data-management solution.

Extract step defines how your data will be extracted from marketing sources into your data storage.

Transform step is where your data will be cleaned, merged, and transformed. For example, you might extract Facebook advertising cost-per-click data, transform it into your local currency, and combine with your average revenue per customer data from your CRM.

Load step is where you feed the transformed and cleaned data into a specific tool for further analysis. For example, you might visualize certain data streams in a Tableau dashboard or send data from your support chats into an NLP-analysis tool.

Examples of ETL / data management tools: Xplenty, Panoply, MayaInsights

Example of Marketing Data Warehouse in Action

As mentioned earlier, the most basic implementation of a marketing data warehouse is creating a single dashboard with data from several sources.

You might combine data from Google Analytics, Optimizely, and SEO tools such as Ahrefs to gain a full picture of how users interact with your web pages.

Or you can create a dashboard specifically for analyzing your advertising expenses by bringing together data from Facebook Ads, LinkedIn, Google Ads, and Youtube. 

The team at Macy, a Fortune-500 department store chain, took it one step further when they gathered and distributed data from hundreds of A/B experiments that ran on their websites. 

The data warehousing approach allowed the Macy team to present A/B data in real-time and present highly-customizable dashboards without any manual input or extra preparation.

The process involved gathering clickstream data, organizing it using the Hadoop platform, and feeding into Tableau to build reports with varying levels of detail.

The whole approach allows Macy analysts and marketers to easily switch between high-level A/B testing data and results for specific experiments to quickly check their ideas and find actionable insights.

Benefits of Marketing Data Warehouse

  • High-quality data insights. Having several sources of marketing data organized together allows analysts to achieve higher levels of data granularity and analyze data across several layers, which contributes to more data insights. 
  • Reduced cost of data insights. Data warehouses allow marketers to create new data pipelines within seconds without needing too much help from the IT department or support staff. 
  • Closer to customers. Nothing brings you closer to customers than analyzing what they do with your products and how they interact with your marketing campaigns in real-time. With data warehouses, it’s much easier to process data in real-time rather than manually updating Excel sheets or logging into dispersed analytical platforms. 
  • Increased flexibility. The marketing environment is constantly changing. The upcoming transition to the cookieless web, emerging country-wide data policies such as GDPR, and ever-evolving customer trends force marketers to quickly adapt to new realities. Data warehouses enable marketers to quickly test new marketing pipelines without having to re-establish existing workflows from the scratch. 
  • Enhanced visibility of data. Data warehouses integrate with a myriad of transformative and visualization tools which allows for presenting actionable marketing data in several ways from real-time dashboards to AI-driven recommendations. 
  • Faster access to data on all levels. Data is easily accessible by employees at only 14% of companies. Companies that aim to become truly data-driven need to make data accessible on every floor from business users to front level workers; data warehousing allows us to speed up and simplify this effort. 
  • Improved return on investment for all tools. Warehousing facilitates synergy between various tools as combined data potentially has more value than an isolated one. At the same time with data warehouses, it becomes much easier to track which tools and data sources are not being used and thus optimize expenses on marketing software.
  • Bring stakeholders closer to marketers. There are many reasons why business stakeholders and data analysts often don’t speak the same language. A shared visual medium and access to accurately presented data brings various business departments closer to each other and allows them to build a source of a single truth.

How to Make the Most out of your Marketing Data Warehouse

  • Don’t focus on dashboards, focus on insights, and workflows. Building a data warehouse doesn’t mean building a dashboard. In fact, the decline of point-and-click dashboards in favor of dynamic data stories is one of the trends projected by Gartner for 2020.

    It’s important to remember that warehouses present marketers with unique means for organizing and transforming their data, testing new data workflows, and finding new ways to look at their data.
  • Watch your tool count. Marketers can use many tools and platforms. In fact, there are over 7,000 martech tools on the market right now. But your team doesn’t need to use all of them, and neither does your marketing data warehouse.

    When starting, focus on a handful of key marketing data sources and KPIs that your team already utilizes, and then gradually add new sources and tools to enhance existing workflows or test the new ones.
  • Facilitate agile data management culture. According to McKinsey, even the most digitally savvy marketing companies experience revenue uplift of 20% to 40% by shifting to agile marketing.

    Agile methodology allows marketers to quickly explore and engrain data insights into their workflows and works perfectly with data warehousing principles of continuous data pipeline improvement.

The Best Time to Implement Marketing Warehouse is Yesterday

We already entered an era when companies that don’t have enough data or can’t use it are quickly becoming first outdated and then irrelevant.

Data-driven companies are growing 30% faster and are projected to earn $1.8 trillion by 2021. 

Data warehousing has already helped multiple industries and departments to gain a competitive advantage, and now it’s time to turn the tide for marketers.

Frequently Asked Questions

A marketing data warehouse is a DW that mainly contains marketing data, acquired from different sources (like multiple advertising channels, your website, your CRM systems, etc.), and stores & transforms it in away that makes sense to your business. 

First, you need to identify all the data sources that you need, then choose the most suitable storage solution (e.g. is it going to be Google BigQuery or Azure SQL?) and, finally, select an efficient ETL tool like Maya to create a single hub for your marketing metrics.

Having a clear overview of your marketing data in one place can help your business perform better in the digital world, and eventually increase return on investment (ROI) for all your channels by becoming completely data-driven.

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