What the heck is ad data unification?
Who here has tried comparing cross-platform advertising data, only to struggle trying to mash together misaligned data from different platforms? If you’re raising your hand right now, you should know that you’re not alone … and that you’re 10% crazy for raising your hand while alone at your desk.
The point being, the majority of digital marketers often face this data pain when analyzing cross-platform advertising campaigns. And this is exactly the challenge that ad data unification aims to solve.
In a nutshell, ad data unification is the process of cleaning, distilling, and aligning cross-platform advertising data into a single data set. Through unification, mismatched metrics become aligned, and all ad platforms speak the same data language. The result? Marketers who leverage data unification get better, accurate metrics that can be easily analyzed and visualized without manual formulas or data wrangling.
How does data unification differ from ad data standardization?
Maybe you’ve heard the term data “normalization” or “standardization” before. While similar in some ways, those largely miss the mark with delivering better data for marketers. Data unification is better suited to taking 3rd-party data sets, and cleaning them through a data pipeline for analysis. And with data unification, you get a secondary benefit: ready deployment to an analytical tool. Since unification aligns mismatched metrics, visualization tools like Google Data Studio can ingest the data easily and churn out graphs and reports to fit your reads.
Data unification–as done through Joinr’s pipeline–offsets both of these concerns by leveraging automation and efficiency of 3rd party tools. Forget any coding or manual data wrangling. All data passed into Joinr is cleaned and unified before being sent directly to a visualization tool like Google Data Studio. No coding required.
Read more about the difference between data unification and data standardization here.
Okay—But why does data unification matter?
After surveying 400+ digital marketers, we quickly learned that 89% of marketers waste hours trying to merge and manage ad data themselves. That’s a ton of time wasted for a manual process that opens the door to manual errors.
But there’s a reason marketers have been vying to unify their data for so long. Because simply put, unified data is better data. Here’s why:
1) Unified data is more accurate:
If you were to compare video views between Facebook Ads and TikTok, you’d have to sift through several metrics on each platform and decipher which metrics are actually equivalent. Then you’d have to run formulas that connect these metrics with sumifs and v-lookups or other merging techniques. With a data unification tool, that work is done for you, delivering one metric that touches both platforms equally: video views (not views by region or 3-second views, as an example).
2) Unified data easier to analyze and visualize:
When trying to compare two different sets, creating visuals is an exhausting task. Even creating something as simple as a pie chart highlighting comparative spend between platforms requires extra steps to harmonize metric labels, align date ranges and formats, and create blended sums of the data before finally wrapping it all up in a chart. Unified data delivers a single data set with singular metrics, allowing you to compare and analyze cross-platform with a single click.
3) Unified data cuts through the noise and delivers essential metrics:
To create unified data, tools like Joinr pass raw data sets through an Advertising Data Framework that strips away unnecessary metrics and highlights metrics and KPIs that marketers need to know. Through this process, marketers can save hours of time from sifting through data noise, and more time analyzing data to find important insights and optimizations.
How do I unify my advertising data?
There are both free and paid tools available for data unification, and each comes with their own benefits and drawbacks.
Excel or Google Sheets: perhaps the most ubiquitous of the bunch, many marketers attempt to unify their data manually through spreadsheets. The process includes downloading raw data each month from all different ad platforms, manually creating blended views of data through spreadsheet formulas, then creating charts and graphs based off those merged views. Here’s an example of a formula one might see during this process:
Essentially, this formula summarizes a certain metric over a specific date range from one data set (if the dates match), then sums that with another “sum product.” It’s a headache to look at, and even greater pain to execute.
- Pros: Free
- Cons: Everything else (a.k.a time consuming and difficult to maintain)
Paid Software Tools: For marketers who recognize there aren’t enough hours in the data for manual data unification, there are paid alternatives to get the job done. Tools like Adverity enable marketers to connect different ad platforms, and use their in-platform software to align metrics and visualize it.
- Pros: Automated(ish), trustworthy
- Cons: Expensive, and creates reliance on the platform.
How do I visualize my advertising data?
Okay, so we’ve spent a few paragraphs covering the different ways you can unify your ad data. Now comes the fun part: visualizing it for your reporting needs. While there are a wide range of paid tools that can help you connect your data and visualize it, we prefer to keep the wallet out of the equation. In this section, we’ll cover the top free tools you can use to visualize your ad data. Spoiler alert: our favorite is hands-down Google Data Studio.
Full Circle: When should you use unified ad data?
Now that you’re a bonafide expert in advertising data unification, it’s time to put the data to work and get some serious insights out of it. First step: pick your data unification methodology and create a single source of truth for your cross-platform advertising data. Next, choose a tool that will help you visualize the ad data seamlessly. After that comes the fun part: analysis.
Here, you should start by asking yourself the question, “Does it make sense to compare cross-platform data right now?”. If it does, then we suggest starting with a cross-platform advertising report template that can help you quickly and easily see which channels have the best performance, outlining how much you’ve spent against the conversions / revenue each platform has generated. Also, you can use the unified ad data to compare individual campaigns, ad sets or individual creative to see which copy or calls-to-action are driving the best engagement.
Unification > Standardization
The world is on pace to generate over 180 zettabytes of data by 2025. If you’re thinking, “only 180?”, then consider this: 1 zettabyte is equal to 1,000,000,000 terabytes (TB), and the personal hard drive most of us use is around 1 TB. If you were to stack 180 zettabytes of hard drives on top of each other, it would create a tower 138,565 miles high, about halfway to the moon. In other words: there’s a lot of data in the world.
To make sense of the ever-growing influx of information, marketers are in desperate need of systems and processes that convert data oceans into digestible data lakes. And that’s exactly what data unification is designed to do.
Is data standardization the same as data unification?
Let’s first clear the terminology waters. Marketing jargon is a real thing, and marketers tend to have a multitude of terms to describe similar concepts. Maybe you’ve heard the terms “standardization” and “normalization” used interchangeably. Perhaps “harmonization” even made the cut a few times. Although each of these terms carries similarities, there are subtle differences that help Data Unification stand out from the bunch.
Data standardization refers to the process of shaping data into an aligned internal format that enables large-scale analytics. Without internal consistency, data loses its value while being bogged down by duplicates, slight mismatches and incorrect definitions.
Marketers who leverage customer relationship software (CRM) tools like Salesforce would employ data standardization to create harmony between customer data from a variety of sources. Tools like Google Analytics, pixel tracking tools, and in-app tracking tools all deliver data in slightly different ways. If left unchecked, you might find that you’re double counting “engagement” without a clear universal definition of the term.
Data harmonization attempts to bring disparate data sets into a single, cohesive data set for easier analysis. This process helps improve data quality and trust-worthiness, while helping save time and reduce the cost of data analysis.
Unfortunately, data harmonization is generally an internal-facing initiative, requiring teams to create their own custom nomenclature that can then be used to merge data sets. Once a harmonized data set is created, it cannot be shared or reproduced by other teams unless they too agree with the predefined nomenclature
Data normalization mainly refers to the sorting of data within a particular database to align records and fields. In the context of digital advertising data, data normalization is off-track for what most marketers would want or need.
So how is data unification different from data standardization?
While the above data manipulation techniques work great for internal data, or custom data sources, advertising data unification thrives when combining pre-established external data sets. Data from tools like Facebook Ads and Google Ads come complete with their own nomenclature and metric definitions, which then necessitates a single process to smooth out inconsistencies and deliver a single source of truth.
Data unification, as done through Joinr’s methodology, funnels all external data through an Advertising Data Framework, one that’s been honed and chiseled through over a decade of ad data experience. This framework identifies a single definition for the most important metrics–so, when external data sources are passed through it, the output is a singular, consistent data set that tells marketers exactly what they need to know with no uncertainties or doubts.
What does ad data look like without data unification?
Here’s a simple example, but one that can be quite telling. Let’s say you were looking to compare Link Clicks across both Facebook Ads and Google Ads.
At first glance, most marketers can see a lack of parity in metric definitions. Facebook has a metric aptly called “Link Clicks”, which is defined as the number of clicks on links with the ad that led to experiences on, or off, Facebook”. However, they also have a metric called “Clicks (All)”, which refers to clicks that keep people on Facebook. Which one do you choose?
Alternatively, Google Ads has a simple metric called “Clicks”, which is counted if someone clicks your ad (even if they don’t reach your website).
If you’re a marketer aiming to get a holistic view of link clicks across a cross-platform campaign, you can already tell there may be some issues along the way.
Ad data transformed with data unification
Leveraging a data unification tool like Joinr, all possible metrics surrounding “Link Clicks” are evaluated, then either merged or ignored. The ultimate result is a singular metric that simply outlines a true link click across multiple platforms, with no data noise adding confusion to the analysis.
There are 3 reasons why this is immensely valuable:
- Data accuracy: when there’s one universal metric for an important KPI that all marketers assess, there’s a big increase in analytical trust. This takes some of the doubt and guesswork out of cross-platform reporting, which is notorious for sometimes including metric conflation.
- Time savings: Rather than sifting through hundreds of metrics to find the ones that matter, Data Unification leverages a framework to do the work for you. This saves time at the onset (reducing manual effort) and the outset (not having to review work).
- Long-term viability: Using a data unification approach creates a solid foundation for any updates or changes to the raw data you receive from platforms. If using a tool like Joinr, the data unification provider will always keep universal metric definitions up-to-date, so there will never be breaking changes to the reports you generate on a weekly, monthly or yearly basis.
How to effectively unify your ad data
Like most things in life, there’s an easy way and a hard way to achieve data unification. The hard way is to do the unification logic yourself, either through a database like Looker, Tableau or any other SQL based platform, or simply through a spreadsheet tool like Excel or Airtable.
Only one issue: most marketers are not experts in the realm of data logic. This opens the door to the highly-sought-after alternative of using a tool that connects to your various ad channels and unifies the data for you. These tools provide a metric layer on top of your raw data, giving you consistent metrics for steady reporting.
Joinr is an example of one such tool, but there are many others. While Joinr unifies your ad data automatically, some tools offer the ability to create your own metric layer manually on top of the raw data by defining relationships and various data definitions. Funnel is the paragon of that functionality, which they’ve dubbed “Data Transformation”. The only downside to the Funnel approach is that most marketers don’t have the time to manually set up their own metadata on top of the raw data.
Let’s start unifying data
The benefits of unified ad data are growing longer each day. Greater accuracy, increased metric focus, improved ability to compare multiple channels. To evolve the way you use and analyze cross-platform data, you need to keep ad data unification top of mind. Otherwise, you’ll be stuck analyzing data silos that create real obstacles in finding insights and optimizations. If you have a Joinr account, then keep on being awesome. If not, sign up for joinr now and experience what unified ad data can do for you.
How to unify advertising data manually with a spreadsheet
When marketers talk about “blending” their ad data, they’re referring to the process of creating alignment between data sets from different ad platforms. At Joinr, we prefer the term “advertising data unification”, as it directly connotes the ultimate goal of what we’re trying to achieve: perfect data unity for analysis.
To unify advertising data manually with a spreadsheet, there are four distinct steps.
Step #1: Download CSVs of data from each platform
The first step is to download the data from the respective platforms. For this example, we’ll explore how to unify Facebook Ads and Google Ads. To get data from Facebook Ads, here’s a help article that outlines how to download and share data from the Ads Manager.
For consistency in this analysis, we’ll use the last 3 complete months for the date range. Then we’ll break the data out by week so we can compare apples-to-apples across both platforms. Once you have those filters in place, you’ll navigate to the Reports button in the top-right of your table. Then, you’ll click Export Table Data, and select CSV as the file type.
For Google Ads, the process is similar. First, you’ll navigate to the appropriate section depending on what kind of data you’re looking to pull (campaigns vs. specific ads). From there, you’ll set the date range to the past 3 complete months, and segment the data by week. After that, you’ll click “download” and select CSV as the file type.
Step #2: Insert data into one common spreadsheet
For this step, you’ll want to Copy + Paste the data from each spreadsheet into a new spreadsheet, using a separate tab for Facebook and Google. We’ll use Google Sheets for this task, but you can use Excel or Apple’s Numbers as well.
Once the data has been inserted, you’ll want to sort the data by date so that we’re viewing each platform in ascending order. This will help with keeping things organized.
Step #3: Create a new tab for “blended” analysis
Next, you’re going to create a 3rd tab that we’ll use to combine the data from Facebook and Google. Once you’ve copied over the dates, we’ll want to create columns that help us identify the key metrics we’d like to pull. For this example, we’ll just use a simple metric: Ad Clicks.
Normally, this would be a simple IF statement with a VLOOKUP function wrapped inside to grab the value from each platform. However, because Facebook calls their metric “Link Clicks”, and Google Ads refers to it as “Clicks”, we’ll have to go full manual mode from here.
This formula tells the spreadsheet to grab the exact date from cell A2, then look for that date in Facebook Ads, then pull the value that is 16 rows over (where Link Clicks actually lives), and finally the FALSE prevents the data from being sorted incorrectly.
Step #4: Sum the values to create a blended view
The final step is perhaps the easiest. Here, you’ll use a SUM formula to sum the Click values from both FB and Google, giving you a holistic sense of how many clicks each platform generated over a specific week.
This same process can be applied to most metrics, including Impressions, Cost, Conversions and more. However, there’s a trap inherent in this manual approach: a lot of the metrics between Facebook Ads and Google Ads differ in both their nomenclature and their definition. This requires a savvy eye to ensure you’re pulling the correct metrics across your analysis.
Manually unify advertising data with a BI Tool
While there are lots of BI tools available for marketers (think Databox, Grow.com and more), Google Data Studio (GDS) is perhaps the most ubiquitous of the bunch. The value of using a tool like GDS is that a lot of the blending process can be done through various kinds of “joining”, which is a technical term for creating relationships between different metrics.
Here is how you can blend ad data in Google Data Studio with just a few clicks.
Step #1: Import the data sources as CSVs
Since we just downloaded two CSVs for Facebook Ads and Google Ads above, we’ll use those for this analysis.
First, you’ll navigate to Google Data Studio and create a blank report. From there, you’ll select File Upload to bring your CSVs into a report.
Step #2: Blend the data with join configurations
Once the data has been imported, you’ll navigate to Resource, Manage blends, and Add a Blend.
From there, you’ll select “Join another table”, and add in the Google Ads table. The next part gets a bit technical, so we’ll direct you to the official Google Data Studio documentation, but here’s the practice at a glance.
One by one, you’ll select common metrics and create Left Outer Joins of the date ranges (so they fully align). Then you’ll create Join Configurations between the two tables. A join configuration has both an operator and a condition – the operator is the value being linked, and the condition is the definition of how those values are linked.
Once you’ve completed these steps, you can repeat the process for all your ad platforms
Automatically unify advertising data with a free tool like Joinr
Some of you may have looked at the heaps of text above for the manual sections, said “No thank you,” and skipped down to this section all about automation. And we don’t blame you for that. Because automation is awesome, and it saves you loads of time while simultaneously improving the quality of your work.
With a setup like that, it’s no wonder why we’re so passionate about free and automatic ad data unification at Joinr. And be warned: incoming a serious product plug. Joinr does all the blending work for you with the click of a few buttons, all completely free of charge. And we mean it when we say all the work: it fetches the data from each ad platform, funnels it through an ad data framework to clean it up and merges both data sources into a fully unified data set for analysis.
That way, when you’re analyzing the data in a CSV or even in Google Data Studio, you get one fully complete, read-to-analyze data set that answers all the cross-platform advertising questions you have.
Don’t believe us? Check out our How It Works page, or watch a video to see the product in action. And yes, we do accept thank-you high-fives as a form of payment (the tool is free, after all).
Why marketers need unified ad data
Every digital marketer loves data. But too much data can be more of a curse than a blessing. Unfortunately, advertising platforms are key culprits for delivering heaps of ad data that create confusion over clarity. Instead of rejoicing in data-driven insights and analysis, digital marketers are forced to sift through a seemingly never-ending array of dimensions and metrics, all in search of the golden data points that will help them answer real marketing questions.
Lucky for marketers, unified ad data has entered the chat. When ad data is unified, it is stripped of its clutter, merged with other data sources, and neatly packaged into a single data set that’s primed for analysis. And when your advertising data is delivered like this, it comes with some serious benefits that give digital marketers a serious advantage.
Unified advertising data is consistent
Comparing data sets that differ in their nomenclature or definitions is an uphill battle. And with inconsistent metrics, trying to report holistically on cross-platform campaigns becomes next to impossible. That’s why data unification is crucial to ad data analysis: it provides a consistent foundation for the data that gives marketers the confidence they need to report on performance definitively.
Here’s a very simple example. Let’s say you were trying to find one metric that highlighted your overall campaign spend across two channels, like Facebook Ads and Google Ads. After downloading 12 months of data, you try to do a SUMIF function in a spreadsheet, looking for any column that includes the term “Spend” to summate (note: summate is a funny word). Only one issue: “spend” isn’t a term that exists in either platform. In Facebook Ads, the metric is Amount Spent. In Google Ads, the metric is “cost”.
Of course, this is a rudimentary example that could be solved with a quick column change. But what if you aren’t analyzing the datasets in a spreadsheet? What if you’re sending the data directly to a BI tool, where editing becomes a SQL formula instead? And what happens when you multiply this issue by a countless number of different metrics?
Suddenly, the problem goes from a smoking oven to a full out blaze.
Unified ad data extinguishes any analytical concerns by uniting misaligned values into consistent metrics with consistent definitions. Whether you’re analyzing conversions, impressions, spend, CTRs or ROAS, using unified data you can be confident in the accuracy it provides.
Unified advertising data is Accurate
After we surveyed 400+ digital marketers about their ad data, one of the biggest complaints we heard was that marketers weren’t sure if they were reporting on their data correctly. A lot of these marketers were using spreadsheets to keep track of their ongoing month-over-month data, which have proven to be fragile in the wake of any changes to reporting structure.
Using a tool like Joinr to unify your advertising data, all disparate data sets are passed through an Ad Data Framework that connects directly to ad platform APIs to deliver 100% accurate data that adapts to reporting updates. This immediately solves the challenge of CSVs, which only provide snapshots of data that can become obsolete, fast. Unified ad data enhances accuracy by updating the data every few hours, so everything you’re reporting on is always completely up-to-date.
Unified advertising data has improved reportability
Ask any marketer how they visualize cross-platform data in charts and graphs, and you’ll see sweat form on their forehead. Why? As mentioned above, data from different sources do not share consistent metrics, which creates a headache when trying to create something as simple as a pie-chart for reporting purposes.
With consistency acting as the undercurrent for unified ad data, reporting and visualization becomes a breeze. With aligned date ranges and metrics that all share a solid definition and nomenclature, there’s no second-guessing if you’re capturing the right values while creating a chart or graph.
Armed with this power, digital marketers can report as broad or as granular as they need knowing that the metrics are consistent across any scope.
Unified advertising data for the win
If you couldn’t already tell by now, we’re big fans of unified ad data here at Joinr. It’s why we’ve dedicated our existence to the very topic, creating tools and best practices to bring unified data to marketers with just a few clicks. Why are we so obsessed? Because before we were Joinr, we operated as an agency, pulling several thousands of cross-platform reports for our clients. And when the data was misaligned, we simply couldn’t do our jobs to the best of our abilities.
Today, it’s our mission to help digital marketers do their job even better, fuelled by unified ad data. Create a free account today, and experience the massive benefits of unified ad data for yourself.
Automatic vs. manual ad data unification
All marketers know the value of ad data unification: improved accuracy, enhanced reportability, and greater consistency, to name a few. But not all marketers know how to actually unify ad data. While the process can be done manually using spreadsheet software or a BI tool, doing so is both time-intensive and quite error-prone. Fortunately, there are a variety of data unification tools to choose from to get unified data in a more automated fashion. And in the midst of both free and paid solutions, we have a slightly biased recommendation of which is best in the world of ad data unification.
Top free tools to unify ad data
Before diving into free tools, a quick disclaimer: we are only recommending tools that are 100% free. No “14-day trials before payment” nonsense here.
Price: Free Tier
Windsor.ai has a free plan that allows you to connect 2 data sources for one user, and send the data into 2 data destinations. Available data destinations include Google Data Studio, PowerBI, Tableau and other BI tools, all of which include templates that can help automate the analytical process.
The only downside to Winsdor.ai is that it only achieves the first half of the ad data unification process: extracting data and funneling it into another software. Once inside that new data destination, you’ll still have to follow a relatively manual process for blending the data together.
Price: Free Tier
Dataslayer offers a free tier that allows for an unlimited number of reports with up to 15 API calls per day. While that API call limit can be a bit of a hindrance if you’re doing live analysis and shifting data around, the unlimited number of reports creates a lot of upside in analytical potential.
Dataslayer offers drag and drop functionality with their templates across a variety of sources, which helps marketers merge data together to create holistic analysis across their campaigns. While the data merge is still manual in nature, it beats using spreadsheet formulas any day.
Price: Free Tier
Incoming slightly biased product plug. Truth is, we only feel comfortable sharing facts about Joinr with you because we know the value it can unlock for digital marketers everywhere. Joinr is the only free tool that does ad data unification for you fully automatically, no manual effort required.
In future, we’ll be offering paid ad bundles that can help marketers tap into different data sources and send data to different destinations, but we recognize that “marketing budget” is occasionally oxymoronic. That’s why our free tier is free free. Forever. Allowing marketers to get started with analysis in a few clicks without any hassle or credit card.
Top paid data unification tools
While the prices may vary from one product to the next, each of these platforms offers marketers a ton of features that help with marketing data analysis.
Price: $399 monthly (with annual billing)
Funnel is an established name in the ad data space that automates data collection from 500+ marketing platforms, then allows marketers to create no-code rules to define relationships between data and unify it all together.
Offering a no-code layer on top of the raw data is both virtue and vice. On one hand, it gives marketers power to create their own relationships between data sources. However, on the flip side, it requires marketers to have intimate knowledge of their raw data so they know that the metrics they are connecting are indeed similar and defined the same way. With an Essentials plan that starts at $399 monthly, it’s a good solution for established marketing teams that have the budget.
Price: Starting at $2,000 for custom solutions
Rather than offering a stock product to every customer, Adverity helps customize a solution that fits each marketing teams’ data needs. Offering 600+ connectors, Adverity breaks down data silos and helps manage disparate data sets with point-and-click data governance tools. Similar to Funnel, this allows marketers to semi-manually create data relationships between metrics, to effectively unify everything under consistent labels.
Adverity also shines with its data transformation ability, allowing marketers to transform data into a desired format (think currency types, languages, etc.). Then, you can send the data into a 3rd party tool. With slight parity against other paid competitors in the space, Adverity does come with a steep price tag and potential learning curve that could be off-putting to some.
Price: $119 per month (billed annually)
Supermetrics’ single connector tier doesn’t help us to unify ad data, so we’ll bump up the starting plan to the Essentials package, which starts at $119 / month. This plan allows marketers to connect 9 data sources, and send data into a variety of tools, including Google Data Studio, Google Sheets, BigQuery, and more. If sending data to Google Sheets, marketers won’t get much value in the way of ad data unification. However, using the Supermetrics API plan, marketers can send their data into a 3rd party BI tool like BigQuery, where they can manipulate the data using SQL or other database querying languages.
While possibly the cheapest solution of the bunch, Supermetrics is more of an ad data aggregator than it is a unification tool. Leveraged in the right hands, it can give marketers the data they need, in the way they need it. But that’s about where the potential ends.
Price: $89 – $399 per month depending on plan
TapClicks offers a suite of features that give marketers varying arrays of capabilities with their data analysis. Two in particular stand out in the ad data space: tapAnalytics, and tapReports. TapAnalytics gives marketers the freedom to add metadata (custom definitions) to their data, which can help with normalizing naming conventions, visualization, comparative analysis and more. Also, it lets users integrate custom metrics, which can highlight different KPIs that might be important for certain stakeholders.
On the visualization side, tapReports helps marketers visualize their data to tell those data stories and answer marketing questions. Although their pricing model is opaque compared to their competitors, their reviews seem to indicate a satisfied audience who gets value from the product. Perhaps this tool is best for teams who have both the time and budget to go through an official sales cycle, and don’t need their data with the usual amount of immediacy.
Our not-so-unbiased recommendation
The way we see it, there are two kinds of marketers in the world. In the left corner, there’s action-oriented and data-first marketers who need the best data, with minimal expense, to make rapid decisions. Or in the right corner, there’s the patience-is-a-virtue type of marketer who prefers a slower pace, measure-twice-cut-once approach to their data. That slower pace might fit some of the paid tools which require research and learning curves to get the ball rolling.
But for us here at Joinr, we recognize that data should be perfectly accurate, always available, and immediately actionable. That’s why we love standing in a class of our own in the Free tier. It allows us to offer data-driven marketers the data they need, with zero hassle of managing data pipelines or other technologies.
Our not-so-unbiased recommendation: try Joinr for free, and experience what unified can do for you.
Visualizing Ad Data: Debunked
It’s easy to think that to visualize advertising data, you must simply create charts and graphs from your data. While technically accurate, the real focus of data visualization is found in its purpose: to portray data in a way that captures insights and clearly communicates value to multiple stakeholders.
The “multiple stakeholders” bit is crucial here. Once upon a time, digital marketers would create a line graph visualizing impressions-over-time in excel, copy and paste that into a PowerPoint deck, then attach it via email for any unfortunate team members forced to consume it. But there are two problems with this:
The data in that slide deck will be a snapshot of a specific date range, one that doesn’t update dynamically. This requires a new deck and chart to be sent out as often as a stakeholder is looking for current performance updates.
The data presented in a slide deck is coloured through the lens of the marketer that created it. They selected the date range, and the data granularity. If other teammates wanted to dig deeper, they’d have to pull the data themselves.
Fortunately, in the wake of the data revolution, data visualization dashboards have taken the marketing world by storm. These dashboards create fluid connections between data sources, allowing the data to be updated dynamically over time. Also, the dashboards offer additional interactivity, allowing marketers to drill into certain areas of focus.
Fuelling dashboards with clean data
To create data dashboards that simply work whenever they’re needed, it’s crucial that marketers import clean data into them. This type of data is tightly formatted, contains consistent metrics and dimensions, and maintains the same structure whenever it’s updated. There are two ways to obtain clean data: you can either download the data and scrape it for errors yourself, or you can tap into an ad data unification tool to obtain unified data that is naturally cleaned at the touch of a button.
If you don’t prioritize clean data, you’ll quickly find that your dashboards create ongoing maintenance headaches and create more hassle than they do value. Luckily, there are several free tools that help you visualize unified data:
Power BI Desktop
Power BI Desktop is a powerhouse in the data visualization world. Although it comes with a relatively steep learning curve, the benefits are pretty substantial. Featuring easy data uploading and a large variety of different slicers and graphical visuals, it’s a go-to for larger organizations who might already be part of the Microsoft ecosystem. You can also create data relationships with Power BI, which helps teams manually merge two data sources if the need arises. The free version of this tool also includes real-time updates whenever the source data is refreshed.
Although it’s only free for personal users who have their data hosted on-premise, Zoho offers the ability to connect to 250+ data sources, data blending, unlimited reports and dashboards, and data snapshots for comparative analysis. While it is one of the more intuitive platforms out there, the free tier only covers 1 user. Adding additional users can rack you up as much as $10/month per user.
Tableau Public is an open-source, collaborative data visualization tool that allows users to create interactive graphs and live dashboards in just a few clicks. You can integrate data from a variety of sources, including Excel, CSV or Google Sheets. While API connections to direct data sources are not available in this tier, downloading from platforms and uploading to Tableau could be a workable solution for some teams. The only downside to Tableau Public is that your ad data will ostensibly be available for anyone on the internet to see. Use with caution!
Google Data Studio
Google Data Studio is one of the most popular ways to visualize unified data. Jam-packed with visualization tools and an easy to navigate UI, Google Data Studio is (in our eyes) the best tool for creating and sharing marketing dashboards. And the best part? Rather than connecting to APIs directly, you can find Google Data Studio connectors that help you bring in the data dynamically from different sources. As an example, we have a free data studio connector that allows you to connect to Google Ads and Facebook Ads, 100% free. The Joinr connector does all the hard work, extracting and unifying advertising data before flowing into Google Data Studio for your use.
And there’s one more bonus: Google Data Studio has a massive library of free-to-use templates that can get you up and running (and reporting) in no time.
Joinr’s recommendation to visualize advertising data
We recommend that marketing teams get started with Google Data Studio for a few reasons. First, it’s 100% free and will be forever. Second, it offers a great deal of flexibility and functionality that is comparable (and sometimes better than) the paid options. Finally, its intuitive design and connectivity to a huge array of different data sources make it an absolute beast for creating dynamic dashboards that integrate data from multiple sources.
All these reasons are why Joinr has chosen to use Google Data Studio as its default data destination. We’ve created five dashboard templates that can level up your reporting in a few fast and easy clicks.
Try it yourself by signing up for Joinr today and get your first hand look at what Google Data Studio + Joinr can do.
The saga of Google vs. Facebook (Meta)
When combined, Google and Facebook Ads comprise over 52.4% of the total digital ads revenue in the United States. These two behemoths have been at the forefront of digital advertising since the early 2000’s, and have been the flag bearers of the industry ever since.
Google AdWords begins
Google AdWords officially launched in October of 2000, and instantly revolutionized paid search. Initially, the service offered only pay-per-impression, and rewarded the highest bidder with higher-position search results. In 2005, Google officially launched the display ad network, which allowed websites across the globe to capitalize on higher traffic and display relevant ads to their audience.
Facebook Ads launches in 2007
Realizing the immense opportunity of display ads within a confined network, Facebook officially launched Facebook Ads in 2007 as an attempt to combat Google and offer marketers a more direct line of communication with their specific target audience. Since Facebook inherently included demographic information like age, sex and region, and often highlighted psychographic information as well, the service was a massive hit for digital marketers.
At their core, the two platforms are different in many ways, and therefore hard to compare. Google Ads dominates in paid search, while Facebook Ads is a top player in the world of paid social. However, when you dig into the various types of advertising each platform offers, the similarities between the two begin to shine. For example, the Facebook Ads network is a direct competitor to the Google Display Network, and not the AdWords function of search results.
Because of these ranging similarities and differences, it doesn’t always make sense to compare Google Ads and Facebook Ads directly. Below, we’ll outline when (and when not) to consider analyzing the two platforms side-by-side.
When does it make sense to compare Facebook and Google Ads?
Marketers should be comparing Facebook and Google Ads when running campaigns that span across the entire customer journey. Typically, a customer will begin their “awareness” phase by researching the area of interest, often using Google as that jumping off point. Here, marketers can highlight impressions and click-through rates as a signal of intent.
Once intent is captured, the Facebook Ads and Google Display Network can be used to retarget users who have taken actions on your site (and are thus cookied for retargeting). Although iOS 14.5 has reduced ability to track users, improved usage of UTMs and first-party data can help target the right users. This is the first instance where you can compare digital ad performance across the two platforms. We recommend beginning by clearly identifying your business goals and objectives. This allows you to highlight campaign specific KPIs.
In the potential purchase phase of the buyer’s journey, marketers can use Google’s Display Network in conjunction with Facebook Ads to massively increase their awareness, and include high-value offers in an effort to drive conversions. In this phase, you can compare the two platforms while focusing on goal-based conversions as the north star metric.
When does it NOT make sense to compare Facebook and Google Ads?
Not all digital advertisers are running campaigns that span the entire customer journey. It sometimes may not make sense to compare the two platforms when your goals are isolated to specific parts of the funnel. For example, if your marketing goal is to increase awareness of your brand, then comparing Google Ads and Facebook Ads will be like comparing apples and oranges. From an impressions standpoint, Google Ads will massively outperform Facebook Ads, leaving you questioning why you’re advertising on the platform at all.
Alternatively, if your goal is to increase life-time value of your customers by targeting specific customers for conversion, then Facebook Ads will likely take the cake, offering a much more specific targeting tool than Google does at the onset.
Search vs. Display
Finally, marketers should not be comparing Facebook Ads with Google AdWords search. Although you might use the two to challenge-test copy (the words being used), it won’t be a fair comparison as Facebook Ads offers imagery and other CTAs that could entice a click better than Google’s limited number of isolated characters.
Instead, marketers should only be comparing Facebook Ads and Google Ads when using the respective display networks for digital advertising.
Wrapping up: is there a clean way to compare the two platforms?
Most marketers struggle to compare Facebook and Google Ads because they find it difficult to sift through the noise and isolate just ads that are part of the display networks. We recommend finding an ad data unification tool to handle this work for you by distilling the data into clean and accurate buckets that can then be used for direct comparison. If that tool can connect to visualization software, it can make your comparative reporting that much easier.