One plus one equals one and a half?

One of the most frustrating aspects of marketing right now is over-attribution when comparing Facebook reports to Google reports.

This occurs when you log into Facebook and it tells you it earned you $100,000 in a period, then Google says it earned you $100,000 in that same period, but you only received $125,000 worth of orders during that same time period.

This, unfortunately, is the new norm in the attribution war. Both Facebook and Google want your advertising money to go to them, so when it comes to tracking and reporting, there are a few things you have to understand:

• Even though the two platforms integrate with each other, each is entirely separate. They have different goals, definitions, standards, and abilities for tracking.
• Each platform only owns their own data. That means, when you go into the reporting aspects of Google Ads or Facebook, you will have mathematically biased information. Each platform only sees one variable (their ads) as an impact on your sales. However, there are always multiple variables involved—multi-channel marketing, public relations, organic posts… even the weather and political climate can impact your sales.

So, when you log in and see varying information, they’re not trying to lie, they’re just presenting their side of the story.

Everyone knows that there are three sides to any story. Each person has their version, and then there’s the truth, which is somewhere in the middle. So, when it comes to Facebook and Google reporting, neither is lying, but also neither is showing you the entire picture because they both are inherently biased. Facebook, for example, counts any conversion that has seen an ad on their platform and then converts as a “view-through” conversion; and Google uses last-click attribution by default in their reporting because that favors them.

Then how do I get data that I can trust?

There are two steps to get accurate reporting on your marketing efforts in your systems.

#1: Tracking

Get as much information as possible. Information is simply multiple points of data brought together to allow you to see patterns and gain answers to questions, like:

• How much overlap do we have in reporting?
• Are there clients that have been exposed to multiple marketing efforts?
• If so, are we tying together their customer journey with accurate tracking efforts?
• What are all the possible impacts on our sales?
• How have they impacted sales before?
• Are there correlations?

How are you going to answer these questions to get the insights you desire? You must have the data in order to be able to analyze the data to get insight.

That means, tracking is the first and primary component of accuracy in your reporting:

Are you tracking your client’s journey?

As we discussed earlier, Google uses last-touch attribution to assign credit to conversions. This slants credit towards Google, as by the end of a customer’s journey they tend to be aware of your brand, and therefore more likely to search for your name and click on a search ad or organic search result.

Google Analytics has many attribution models that you can try out to see which one works best for you. From position based (Which assigns 40% of the conversion value to the first and last touch, and then distributes the remaining 20% across all other touch points) to time decay (which assigns credit based off how close to the conversion date it was), it’s important to make a conscious choice of which attribution model you want to use. Each attribution model has its pro’s and con’s, but by staying aware of how the model affects your reporting, you can reduce bias in your reports.

Are you using pixels?

Tracking pixels have exploded in popularity. Many popular advertising platforms now use tracking pixels in order to track conversions and user interactions with the ads. Pixels provide amazing reporting because you can install them almost anywhere, from emails to landing pages, and, as of now, they can’t be disabled by a browser.

Pixels can help you gain greater understanding over how users interact with your advertisements and your website. Providing granular data about user’s behavior based off the platforms that they visit your site from.

Do you have unique identifiers for your clients that allow you to see their customer journey?

Specifically, you need a way to assign a user-id to your clients so that you can track their behaviors across devices. If you don’t have this set up, then when a user changes devices, you will lose all of the data from their initial visit. This can lead to incomplete customer journey’s and skew your attribution data.

Do you have organized UTMs setup?

The very best solution for the attribution problem is to utilize UTMs in all of your marketing efforts. UTMs allow you to tell Google Analytics exactly how you would like to categorize your traffic. Every external link that directs to your site should have UTM parameters appended to them in order to help assign credit to the proper source.  You can even add in campaign data in order to track which of your campaigns drives the best traffic to your site.

UTMs can be one of the most powerful tools available to marketers, or they can be their downfall. UTMs need to be standardized and utilized consistently, or they will make the data even more convoluted and confusing. You need to implement standardized rules for your UTM usage across the organization in order to make sure that your data remains as accurate and clean as possible.

If you don’t already have these things in place, that is your top priority.

By organizing your tracking efforts, you can start gathering the data you will need in the future. If you need help with your tracking, visit the Praxis Metrics – Google Analytics Audit service page to read about how we can help you get your tracking in order.

#2: Reporting

Once you have tracking in place, you can typically manually create Excel reports that give you a much more accurate depiction of your marketing efforts (including lift effects and other variables). However, over time, that becomes tedious and time consuming and allows for too much human error.

The next logical step is to automate via ETL (extracting, transforming, and loading the information from these systems into a singular place) and then to visualize the combined, clean data with a dashboard.

This enables you to eliminate wasted time, effort, and give you insights in a quick and digestible manner. This process can be very intense and require the help of a data scientist.

Fortunately, we specialize in exactly this type of process and can help you revolutionize your data reporting. If you’d like to learn more about how we can help you with ETL and visualization, visit us here.

Bonus #3: Democratize your data

This one may seem out of the blue, but it can change the way that your entire organization interacts with data.

Democratizing data means providing access to data to everyone in your company. Not just information that pertains to their specific corner of the business, but the business as a whole. We have clients who have walls of TVs dedicated to displaying their data for the entire company. Everyone from entry-level employees to C-suite officers has access to the same data.

You may be asking yourself, “How on earth would that help my business?” Everyone has different backgrounds and experience, so when one person looks at a metric they will see one thing and come up with an action item based off their experience; but if you bring in another set of eyes, that person may see something totally different and come to a different conclusion. Democratizing data and making it accessible to more people will lead to greater insights and more options for ways to proceed.

Accountants can be creative, and marketing people can help solve operational issues. Democratizing your data can help you gain a myriad of insights and give you an edge over your competition.

You have tons of data; but data alone will not grow your business. It’s the insights from the data that will inform your team on how to grow. Companies that focus on causation will scale. Those that don’t, will fail.

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