How platform bias and faulty integrations undermine your marketing tech stack
From social media to paid search, there’s no shortage of marketing technology on the market today. But do more platforms mean better measurement? Not necessarily. Our new Attribution Report found that 75 percent of marketers using four or more platforms say they spend too much time on reporting. For marketers using five or more platforms, a whopping 89 percent say they waste part of their budget on unnecessary tools.
These stats don’t mean you should outright avoid adding new technology to your stack –– measurement is and always will be critical, especially as companies add new channels. However, these stats do indicate two main problems with marketing technology: Insufficient integrations and platform bias.
Integrations don’t always work perfectly
If you’ve ever had to plug multiple data sources into a spreadsheet, you know that getting an accurate, holistic view of your marketing data can be difficult and messy. Fortunately, many major marketing platforms integrate with each other, or at least offer APIs for your developers to create their own integrations.
That said, even when platforms do integrate with each other, they don’t always integrate correctly, leaving you susceptible to inaccurate reporting. They may also use different attribution models that create inaccurate reports. For example, your paid search tool may give credit to the first touch, while your social media tool gives credit to the last. This creates discrepancies that make your reports messy and unreliable.
Platform bias skews your marketing data
At the end of the day, social media and advertising platforms need to prove that your investment in them actually drives the conversions you want to see. Unfortunately, this means that platforms often default to show the most optimum view of the results they’re driving and don’t offer a complete picture of what marketing tactics really drove results –– unable to show how one platform worked in tandem with another to generate, say, a lead. That’s why your analytics data can vary depending on the platform you plug it into or the attribution model you use. The more of these platforms you add to your tech stack, the more contradictory your analytics findings can become.
For example, let’s say you’re running a Facebook Ads campaign for a new product. A customer clicks the ad, abandons the page, searches for your product via Google a couple weeks later and then makes a purchase. If you ask Facebook Ads, their platform should get credit for the sale, considering it was the first touch and Facebook cannot track data from Google. If you ask Google Analytics, organic search should get credit because it was the last touch before conversion. You will get different answers depending on the platform you’re trusting for the insight.
In reality, neither Google nor Facebook are “correct” in this case. First and last-touch models don’t uncover the totality of the steps that were taken to generate a certain conversion. That’s where marketing attribution platforms like CallRail come in.
You can connect data from your disparate marketing tools into a single lead tracking platform, eliminating the need to bounce from report to report or spreadsheet to spreadsheet. Even better, CallRail is a platform-neutral arbiter that measures your campaigns accurately. You can choose which attribution models you want to use to get a holistic and accurate report on your marketing efforts, instead of your marketing tools deciding for you.
Integrations and platform neutrality are the tip of the iceberg when it comes to achieving marketing attribution. To learn more about the state of marketing attribution, download our full Attribution Report for free.