In the world of media buying, the devil is in the details. This is evident across industries like retail, where nearly 60% of surveyed marketing decision-makers report poor return on investment (ROI) from retail media compared to other channels. These ROI struggles suggest that many companies still rely on higher-level data to inform decisions and neglect granular data.
Platforms like Meta, Google, and Amazon are treasure troves of this detailed consumer data waiting for people to harness them. In fact, 80% of digital ad spend in the US is going to these walled gardens — a total spend expected to reach over $ 100 billion by 2024.
So how can media buyers leverage granular data from walled gardens to improve return on advertising spend?
In this post, we’ll unravel the significance of granular data, its potential to transform your media buying strategy, and the tools needed to navigate this landscape with confidence.
Why Marketers and Media Buyers Need Granular Data
As data collaborators, media buyers and marketers rely on access to data from a variety of websites, social platforms, and networks to better understand and reach audiences. Keeping track and optimizing audience targeting, frequency, reach, and conversions all contribute to returns as you gain from your ad investments. But do you understand how different types of data from Facebook, Instagram, Google, and Amazon can inform and enhance your decision-making?
Oftentimes, advertisers and media buyers aren’t aware that they’re using aggregate data to inform and run campaigns and that there’s more granular data available to improve your return on ad spend (ROAS).
Read on to learn about the differences between aggregate and granular data, why granular data allows for a higher level of control in campaign planning, and ways to drive better business outcomes.
Spectrum of Granularity — Aggregate vs Granular Data
As your customers navigate the web, they leave a trail of digital breadcrumbs that come in a spectrum of detail: aggregate data providing a higher level view with less detail and context and granular data that provides individual elements rich in context.
Aggregate Data
This data deals with the overall performance of a campaign on a specific platform, which is why it’s also known as ‘platform reporting’. Aggregate data is typically shown in metrics, such as total impressions, clicks, or cost per acquisition (CPA). This data gives you an overall picture of how your campaign performs at a high level.
Granular Data
Granular data looks at individual elements of a marketing campaign. Granular data — such as log-level reporting — breaks down each piece of the puzzle and allows for more precise targeting decisions. For example, looking at the demographic breakdown of an ad or pinpointing which geographic areas have had the best impressions can help you fine-tune your audience targeting and reach your ROAS goals. The other valuable layers of information available from log-level reporting include impression-level details, such as geolocation data, URLs, time stamps, brand safety data, and auction mechanic information.
Why Is the Granularity of Data Important?
Essentially, the granularity of data available to you determines which types of analysis you can perform on a data set and determines how useful the outputs are for specific business functions.
Unlike aggregate platform-level data, granular data is much more precise. It unlocks a deeper layer of consumer insights that brands can use to increase the efficacy of media buying and other advertising endeavors with targeted, personalized content.
For example, say you’re a media buyer running campaigns across Google and Facebook Ads. If you just take an aggregate view, you’ll miss out on key log-level data from a single impression or transaction, like:
- Geographic location
- URLs
- Timestamps
- Auction ID
- Vendor fees
Armed with this information, you can leverage granular data from the platforms’s walled gardens to tighten up your digital ad strategy and increase ROAS.
The Challenge: Accessing Walled Gardens for Granular Data Analysis
Media buyers, advertisers, and other data collaborators don’t always leverage granular data from Facebook, Google, and Amazon through each platform’s free walled garden clean rooms. Facebook Advanced Analytics (FAA), Google Ads Data Hub (Google ADH), and Amazon Marketing Cloud (AMC) all provide in-depth data about audiences and campaign performance.
Here’s an overview of the data you can get through the walled gardens offered by each of the big three tech platforms:
Despite the richness of data in walled gardens, there are some major obstacles that advertisers face when working with these clean rooms.
Siloed Analytics and Lack of Interoperability
First, marketers must log into each walled garden clean room separately. That’s not so different than how advertisers run and review campaigns today. Instead of logging into Facebook, Google, or Amazon, you’d log into FAA, ADH, or AMC. Insights from these clean rooms pertain only to that specific domain, making cross-platform analysis complicated.
In addition to digital silos, there may also be organizational silos slowing down the process as marketing teams interact with different internal and external data owners. Privacy regulations and information security best practices place administrative and technical barriers between data stores and those who need to access them. If data partnerships are built around outdated clean room systems, the ability to share data access and collaborate efficiently reduces greatly.
Complex and Inhibitive User Experience
Although the walled garden clean rooms are rich with granular marketing data, they weren’t built for your average marketer. To extract meaningful insights, users need to have some level of proficiency in database query language or rely on the basic functionality of walled garden cleanrooms.
In some organizations, separate data science or data analysts perform this type of analysis in-house. Not only does this block advertisers from quickly and efficiently gaining learnings and making optimizations, but it also eats up budgets due to departmental overlap. In others, marketers receive meaningful insights — causing them to lose out on opportunities to increase ROAS, reduce media waste, and optimize ad spend.
The overlap between IT, data, and marketing departments — and the increase in cost it incurs — has the potential to increase as we move forward. Deloitte’s Marketing Trends 2023 report highlights how marketing leaders are leaning into this trend, with one CMO stating:
“If you want to do really good personalized and automated marketing, you need help from your IT colleagues, you need data colleagues, and the systems have to work together”.
The Deloitte report also states that 38% of CMOs hope to collaborate with data science teams to assist with personalized marketing initiatives this year.With this in mind, it’s critical that companies invest in solutions that enable data collaborators without driving costs through departmental overlap. One promising option is the use of data clean room platforms.
A ROAS-Friendly Solution to Walled Garden Clean Rooms
Advertisers need a user-friendly way to access granular data from the walled garden clean rooms. One that simplifies data analysis for non-technical collaborators to keep costs from eating into ROAS. This means obtaining insights on their own, without involving data scientists for their most important questions, and, ideally, being able to see the performance across Facebook, Instagram, Google, and Amazon.
Specifically, marketers and media buyers need a data clean room platform with the following capabilities:
- Toggled no-code interfaces for those without technical experience
- Powerful ML and AI models to extract advanced insights and make better projections
- Interoperability to connect and unify data clean rooms across different cloud environments
- Privacy-enhancing technologies to protect consumer data and maintain compliance with new laws
Habu was built with those objectives in mind. As an interoperable clean room, Habu helps data collaborators extract insights from the walled gardens (and even publishers and media partners) to reduce spending time across different ad platforms. The solution is also business-user friendly, meaning advertisers can select from pre-built queries represented as business questions to efficiently gain actionable campaign insights so they can quickly make decisions and optimizations.
Best of all, it helps lower costs by reducing the need for involvement from data science departments.
Maximize Media Buying Returns with Habu’s Data Clean Room
By leveraging Habu’s data clean room platform, beauty industry leader L’Oreal reduced overlap between marketing and strategy teams by 25% while over-delivering campaign performance to the tune of $300,000 in monthly savings. Here’s what L’Oreal’s SVP Head of Media, Shenan Reed, has to say about the use of an effective clean room platform:
“The ability to safely and securely access and analyze more data, without tapping into data science resources, has empowered us to better understand our customers and measure the true impact of our marketing activities”.
Retail brands in other niches, like ASICS and PepsiCo, are driving 3x the economic benefit of their data by safely sharing their data with strategic partners using Habu’s collaborative intelligence platform.
Ready to see what Habu can do for you? Book a demo.
