Marketing Data Pipelines: Turning Data Into Insights

As a performance marketer, it can be hard to evaluate campaign results, glean insights, and choose the best next step on your own. To get actionable insights, you have to gather data from various sources, marry cost with conversion and revenue data, then analyze it and present the information in a digestible manner to the rest of your organization.

In this guide, we seek to give you the answers you need on how to collect and make sense of your marketing data – and how to select the right technology for your team to help you optimize and achieve better ROI. 

We’ll cover:

• Defining your marketing data pipeline (MDP)
• Understanding the different stages of MDP maturity
• Identifying common challenges teams face when building an MDP 
• Evaluating if you need a tool to elevate your MDP to the next stage

First, let’s get started with building a little deeper understanding of MDPs.

What is a marketing data pipeline?

The term marketing data pipeline refers to the integration of marketing data sources with data storage, analytics, and reporting tools. The MDP organizes marketing data into a single source of truth that the whole organization can use. With this, the team can easily retrieve, examine, tell the story of, and act on insights from your data.

It's important to streamline and automate translating your data into actionable insights because of the vast amount of data marketers handle from many sources. This can include web analytics, paid advertising, email, social media, and CRM. Without the right systems in place, marketers spend too much time on data collection, manual computations, and analysis. That's not even accounting for creating useful visualizations and reports. In other words, analysis paralysis.

However, many companies only scratch the surface by aggregating and transferring data from marketing channels to a data warehouse. This eliminates error-prone and time-consuming manual processes, but it doesn't provide any analysis or actionable insights.

Analysis and reporting can be recurring (like monthly marketing summaries or updating metrics for ongoing campaigns) or ad hoc. This is why it can be a game changer to have a flexible MDP that aggregates data and automates the analysis and reporting.

If you want to learn more about what goes into building an MDP, check out our breakdown on the various components of the marketing data value chain below.

What are the stages of marketing data pipeline maturity?

Companies take different approaches to developing their marketing data pipelines. The more mature you are, the better you’ll be able to use your data to make better decisions and drive growth. We've created an easy-to-use guide to evaluate where you are and where you want to be.

Now you have a better idea of what the different solutions are that exist at each level of maturity. If you want to do a deep dive into the various components of the marketing data value chain, check out our breakdown below.

What are some of the key challenges with building a marketing data pipeline?

Building out your MDP can be complex – and we know first-hand because we’ve helped solve marketing data challenges for our  complex customers. Here are some common hurdles that we’ve seen to help you see around corners and avoid them.

• Uniting Conversion Data: You have to integrate your business results, including downstream and offline conversions with your campaign performance. This includes connecting your CRM or CDP with marketing data to close your measurement loop. Making sure you have unique tracking parameters on your URLs, dealing with privacy restrictions in iOS and different browsers, and connecting downstream events to your ads is not for the faint of heart.
• Aggregating Fragmented Data: Speaking of integrations, you’ve probably got a dozen, if not way more, different data sources. Extracting data from one source is a chore – now multiply that by all your sources.
• Keeping Up With Changing APIs: Platforms frequently update their Application Programming Interfaces (APIs) for many different reasons, which requires API users to update their calls. This can be time consuming, or even a full-time job.
• Translating and Normalizing Data: There's no cross-channel standard measurement or naming convention. That means not all platforms in the ecosystem are measuring the same things – or sometimes they just don’t call them the same thing. Depending on the complexity of your ecosystem, translating all of this data into a common model can be time consuming and requires frequent updates.
• Addressing Marketing-specific Needs: General purpose and open-source platforms are not built for marketers. They might have the tools to do the data normalization, but leave the execution up to the marketer. This requires complex expertise that marketers may lack the skill or time to do. Marketing-focused solutions take care of that automagically. For this reason, we’ve seen that it’s critical to have a platform built by marketers for marketers.
• Getting Insights: Are your campaigns on track? Do you need to make adjustments to meet your goals? What’s the anticipated impact if you make those adjustments? All of these questions still need to be answered and it’s on the shoulders of marketers to make sense of it all.

What’s next?

Now that you’ve read up on all things MDP and you’re ready to take the next step to converting your data to intelligence, here’s how you can get started:

• Identify key data sources - take an inventory of all the places you’re collecting marketing data to have a good idea of what your data ecosystem looks like
• Collect existing reports - gather the latest reports from these sources to know what data points you’re collecting from them and what insights they’re likely to provide
• Identify missing insight - now you know what insights you do have, so what’s missing to give you that complete view of the impact of your marketing efforts on the business?
• Map manual / error prone processes - take stock of how much manual intervention your reports are receiving they're being converted from raw data sources to dashboards
• Check out our guide to marketing data pipeline solutions for suggestions on which platform might be right for you

Put your marketing data to work with Marin

Leveraging a marketing data pipeline solution is essential to get the most from your marketing data. The right platform ensures you have accurate and timely insights to optimize your marketing campaigns, track your progress, and improve your overall marketing strategies. 

Marin Connect is a comprehensive MDP platform that can handle all your data needs. This includes collection, preparation, storage, analysis, reporting, and forecasting – all in one platform. And with Marin, you can expand into additional capabilities, including optimization, automated budget management, dynamic spend allocation, and campaign automation. 

Bonus Content: Understanding the Reporting Value Chain 

What are the components of the marketing data pipeline?

Each of these pieces is independently valuable and many companies have built solutions to address fragmented parts of the marketing data value chain. Let’s dig into each of them to understand what they are and why they matter.

Data Collection

The goal of the marketing data pipeline is to understand how your marketing campaigns are performing and impacting your company’s performance. To do that, you need to bring your front end campaign data together with the back end analytics that show the impact of the campaigns. 

As you’re designing your data pipeline, you need to think about the granularity of the data required. Do you need hourly, daily, or weekly data? Do you need campaign-level data or keyword, creative, and audience? Typically the more detail you can capture the better, because you can always aggregate data.

Let’s review some of the most important data sources in your marketing pipeline.

Campaign Data

Campaign data comes from the publishers where you’re running the campaigns – including Google, Meta, Amazon, and dozens of smaller publishers. This typically includes cost data, impressions, and clicks. If you’re using publisher tracking pixels, it’ll also include conversion information. More on that next.

Conversion Data

Your conversion data tells you what impact the campaign had on customer behavior. The easiest way to track when your placement results in an action is using the publisher pixel, but these have their limitations – such as time constraints – so most sophisticated advertisers are using a cross-channel approach, like Google Analytics or Marin Tracker. Read on for additional sources of conversion data.

Offline Data

Offline conversion tracking is crucial in today's omni-channel landscape, where customer interactions span both online and offline channels. Traditionally, businesses could easily track a customer's journey in physical stores or via call tracking. Offline conversion tracking and call tracking software bridge this gap by identifying actions consumers take outside of a website, such as in-store purchases or phone purchases after clicking an ad. This data needs to be tied back to the campaign with a unique ad identifier or linked to the customer through first-party data. One important source for first-party data is call tracking, which can determine what ads, web pages, or keywords prompted the call. Conversational analytics further analyze the call's content to determine outcomes, such as purchases or appointments. This comprehensive data allows marketers to attribute marketing campaigns, optimize various advertising mediums, and understand the ROI of efforts driving sales calls.

Customer Data

Sales and marketing alignment has long been a challenge, with each department often having differing views on lead quality and the impact of marketing efforts. The solution to bridging this gap lies in integrating CRM and marketing data, which provides useful insights for both teams. You may want to have conversion events for your campaigns that tie to opportunities reaching a certain stage, or connect the actual revenue from a deal rather than an average lead value. Customer Lifetime Value (CLV) is another data point that can ensure you are optimizing your campaigns to the highest value customers. 

Business Data

For marketers to be viewed as a strategic, critical partner in the business’s revenue engine, they should always be making decisions with the goal of driving business value. One of the most important parts of this is leveraging all the signals coming from within the organization and adding clarity and insights rather than  complexity. The more you can leverage your proprietary data, the better results you can drive. 

There are several types of data that are not traditionally associated with marketing campaigns, but influence marketing performance. These include promotion data, inventory, internal and competitor pricing, and others – and it’s critical that these data points are a part of your marketing data pipeline. Such data can explain the performance of past campaigns and be leveraged to refine your forecasting models. And there may be a lot of different data sources that your business leverages. In fact, according to a 2023 survey by Matillion, a large majority of respondents say their businesses use greater than 50 data sources – with many using more than 100. This can translate to noise and confusion for marketing teams, but an intelligent data pipeline can connect the dots and find the signal. 

Tracking & Preparation

When you bring your data into a single location, there are two key integration activities needed in order to make sure it’s useful: 

1. Tracking of conversion data and aligning it with campaign data 
2. Preparation and normalization of the data across the different sources

If you’re using publisher tracking, the integration of conversion data happens automatically, but even that gets challenging when you’re trying to incorporate downstream and offline data.

As an example of how complex this can be, conversion data is typically integrated using tracking parameters connected to the ads. Usually this is incorporated into the URL and captured by the tracking system. When making sense of conversion and revenue data, you need to parse the tracking ID from the URL and join the conversion data on this ID. Stitching together this data can be a manual and time-consuming process without the assistance of an intelligent platform built to make sense of these metrics.

Data normalization is a really important step because it helps account for differences in data models between the different data sources. For example, Meta and YouTube have similar metrics for video “views” or “likes'' but they don’t call them the same thing. Your pipeline needs to account for these differences and align the data across channels – which can be done manually with data mapping models. But those can easily break if one of your data sources decides to rename a metric or change how they’re tracking it.

It’s critical to have a unified data model that aligns metrics across publishers to simplify analysis and reporting. Data sets differ from publisher to publisher in terms of objects and levels of hierarchy. Automating object mapping between publishers helps reduce the burden on your IT and analytics teams and helps you deliver valuable insights faster. As a bonus, you don’t have to spend your time staying on top of coming changes to make sure you’re prepared and have timely, accurate, functional insights at your fingertips.

Another important part of normalization is making sure that your data is comparing apples to apples. One of the steps that seems intuitive is to make sure that all your data is equal when it comes to currency and time. For instance, is your data looking at global sales at 8 am PT? If so, that would favor places like the U.S. or Europe, but greatly disfavor places like Asia. Instead, you can set your data to measure for all timezones at 8 am on the same day. And with flexible currency conversion, your accounts can be mixed and matched as needed so local teams can work in their own currency and also roll up to the company default. 

Data Storage

While today’s reporting needs may only cover a limited date range, you want your marketing data pipeline to provide a way to look back at historical data, for quarter-over-quarter or year-over-year analysis. Having data storage built in is important for both easy, on-demand YOY and QOQ analysis and backup for data resiliency. 

Reporting & Dashboards

Once the data has been collected, stored, prepared, and analyzed, the next step is to present the insights and findings in a meaningful way to stakeholders. This is where reporting dashboards come in.

Reporting with dashboards allows users to view and understand data in a visual and interactive format – making it easier to identify patterns, trends, and outliers. They can be used to communicate key performance indicators, track progress towards goals, and identify areas for improvement.

They also each provide more in-depth analysis of specific metrics and can be used to communicate findings to stakeholders who need more detailed information. 

Having a system that can streamline reporting with shareable dashboards and customizable widgets or automate Excel and Google Sheets reporting with web queries is helpful when sharing insights across your organization. It’s also helpful when your technologies help you stay informed of changes in your accounts with automated alerts and give you a real-time snapshot of your performance to goals with tools like visual pacing dashboards. 

Analysis & Asking Questions

Analysis is important in the context of an MDP because answering questions through data allows businesses to identify areas for optimization, measure the impact of their marketing efforts, and adjust their strategies accordingly.

Take a look at your own paid media campaigns. Ask questions such as "what audience segments are we not reaching effectively?" or "are our ads more effective on certain devices or times of day?" to gain further insights into their campaign performance.

The best systems allow you to cut out the middleman and ask questions of your data right in the system. Having access to things like an analytics grid, agile pivot tables, or custom rules helps you define your metrics to align with your business’s strategic initiatives.

Forecasting & Optimization

Forecasting involves predicting future trends and patterns in data based on past performance, accounting for current factors. By analyzing historical data, businesses can use statistical models and AI-driven algorithms to generate forecasts for sales, revenue, customer behavior, and other key metrics. 

Optimization, on the other hand, involves identifying the best course of action to maximize a particular outcome. This could include maximizing revenue, minimizing costs, or optimizing customer satisfaction. Businesses can use optimization techniques like linear programming, decision trees, and genetic algorithms to find the best solution based on various constraints and objectives – or leverage a best-in-class platform to do it for them.

In practice, forecasting and optimization often go hand-in-hand. For example, a business might use forecasting to predict customer demand and then use optimization techniques to determine the best pricing strategy to maximize revenue. Or a business might use forecasting to predict inventory levels and then use optimization techniques to determine the most efficient ordering strategy to minimize costs while ensuring that products are always in stock.

Automatically identify and implement performance opportunities with insights tools in your MDP and, if available, leverage AI-powered bidding to beat out the competition. And capitalize on your intelligence by implementing changes at scale across your campaigns to maximize the return of every marketing dollar.