Social media advertising has become a mainstay for brands wanting to promote their products and services to vast and attentive audiences. While social media ads are targeted and personalized to specific users for relevance, they are also seen by audiences outside that scope. For example, when you browse Facebook Marketplace, you may see a sponsored ad for products and services related to the category, not to your specific search criteria. These platforms are now essential to driving awareness, generating leads, and even directly selling products through in-app experiences and influencer marketing. Social media ads are powerful and the data they can provide to companies is rich with insights that can help brands refine social media marketing to be more successful. But how do you extract that data and analyze it?
If you’re like most marketers, you and your teams are running multiple ad campaigns and extracting data from numerous social media ad platforms, like Facebook, Instagram, LinkedIn, Snapchat, and TikTok. And, since they all have different metrics, you’re stuck custom coding scripts or manually extracting the metrics you need and compiling them in a spreadsheet before entering into your data warehouse or business intelligence tool. It may seem like tediously wading through is the only way to get the holistic data you need, but there is a way to automate this process. Even better — you can do it without any more spreadsheets or waiting around for data engineers to find time to build out a script to replicate your data!
Before we dive into how to automate it, let’s take a look at social media advertising data analysis in general. Social media ads compile a ton of data that can be useful in refining more targeted ad campaigns to ensure your ad spend is most effective. This is key to performance marketing, where digital marketing is driven by measurable results. You want to reach the targeted audiences most likely to convert on the sites where they spend the most time. Understanding how your ads are performing in real time and historically will help you improve performance to drive more revenue.
We don’t need to convince you of the value of the data social media ads can provide. The problem is extracting that data to use it properly.
Collecting ad campaign data from social media platforms is a cumbersome process, not to mention time consuming. Every platform has different APIs, requires different coding, has data in different formats, etc. Marketing teams often extract data from each channel individually into spreadsheets and then the data needs to be compiled into a dashboard or data warehouse. Different teams end up with pieces of the whole data puzzle, but no one has the full picture. Only after its loaded into the data warehouse can the data be used for business intelligence purposes. By the time this process is completed, the data is often outdated. It’s a never-ending cycle where digital marketers never feel caught up or on top of the holistic data they need to drive effective social media marketing strategies.
Typically, the key data marketers need include the following: reach, impressions, frequency, engagements/views, clicks, visits, conversions, CPM (cost per mille), CPV (cost per view), CPC (cost per click), CPC (cost per conversion), CTR (clickthrough rate), and CVR (conversion rate).
This data provides insights into how each ad is performing and whether it is effective in converting into sales. Marketers need this data to prove attribution and support their marketing spend.
Social media platforms often use different names for their metrics and have their own glossaries that marketers need to be up to speed on to work with each platform. Synthesizing these metrics can be challenging.
Extracting social media ad data is — no doubt — a complex, tedious process for many. But it doesn’t have to be that way.
Social media platforms provide APIs for marketers to extract data. Through a process of ETL (extract, transform, load), they can move the data they need from the source into a data warehouse, where it can then be utilized for data analysis and business intelligence purposes.
This process can be done manually — or it can be automated.
Stitch automates the ETL process and replicates the social media ad data marketers need into a data warehouse so it can be easily analyzed. It pulls both real time and historical social media ad data into Stitch, and then you can replicate only the data you need into your data warehouse.
Stitch rapidly moves data from 140+ sources into a data warehouse so you can get to answers faster, no coding required. It is an extensible ETL platform built especially for teams. You can extract data from your social media ad sources and other platforms, load it into the warehouses of your choice, then analyze it with leading tools.
What does this look like in real life?
Heap is a company that provides a data infrastructure and behavioral analytics platform that automatically captures user behavior across websites and mobile apps. They have a lean operations and reporting team. Their business needs to shift quickly, so they needed to be able to customize the data they were ETLing to Amazon Redshift. Heap didn't have the time or the resources to build out data pipelines for all their data sources, such as Salesforce, Google AdWords, and Facebook Ads. They chose Stitch because they needed flexibility in which columns and tables they were syncing.
Once their data is in Redshift, Heap uses BI tools for dashboarding. They primarily focus on their go-to-market function and do a lot of reporting on productivity of their sales reps and conversion rates across stages, joining Salesforce data with Heap data so they can get a better sense of the ROI on their ad spend. They work on determining the cost of a marketing-qualified lead (MQL) and a qualified op.
"Thanks to Stitch, we get the granular insight we need into our data. Having our Salesforce and internal data in one place was a key factor in helping us scale out our new business function, focusing on our inbound and outbound channels. For example, we determined that our Facebook Ads spend was not converting into closed-won revenue, so we were able to decrease our spend there,” said their Director of Marketing Operations and Analytics. “Stitch empowered our Operations and Analytics team with the data we needed in Redshift to give us actionable insights into our sales pipeline, without us having to dedicate resources for maintenance or developing a data pipeline on our own."
Automating social media advertising data extraction with Stitch makes it easy for marketing teams to synthesize their social ad data to drive more effective campaigns.
Learn more about how Stitch can make your social media ad data extraction quick and easy.