Data is crucial for marketing agencies today, but it’s only useful if it’s interpreted properly.
Making data-based decisions off of bad data or incorrect assumptions could lead to revenue loss and degraded campaign performance.
In marketing analytics, one of the most common mistakes is failing to understand the attribution models being used to point conversions back to their originating campaigns.
Do you know if your data use last-click, linear, position-based, time-decay, or first-click attribution?
If not, it’s time to find out what each is and how it impacts your reports.
Here’s everything you need to know about attribution in digital marketing.
What Is Attribution in Digital Marketing?
Attribution in digital marketing governs the way that credit for conversions is applied back to marketing touchpoints. It allows you to attribute revenue directly back to marketing campaigns.
Let’s say, for example, that your agency is in charge of marketing for an eCommerce business.
You post updates frequently on Facebook and Twitter, run PPC ads, and send out weekly marketing emails to promote new products, sales, and deals. After a month of executing all of these campaigns, sales increase.
With attribution, you can see why sales increased.
Attribution reports show which specific campaigns and channels generated the most sales during that time period, allowing you to ramp up successful campaigns and slow down or stop those that are less effective.
Common Attribution Models
In its most basic form, attribution is a simple concept.
Someone clicks an ad and makes a purchase. The revenue from the sale is attributed back to the ad.
But the buying journey is rarely so straightforward.
The average consumer uses six touchpoints before making a purchase. Users click ads, visit landing pages, read reviews, skim marketing emails, see social posts, and request demos; they collect information from a variety of sources before deciding on and making a purchase.
This makes attribution more complicated.
Which of those six channels is responsible for the conversion?
Or are they all responsible, in part, for the conversion?
To provide clarification when running the numbers, multiple attribution models are used:
- First-Click Attribution – 100% of the credit for a conversion is attributed to the very first campaign/channel that the buyer interacted with.
- Last-Click Attribution – 100% of the credit for a conversion is attributed to the final campaign/channel that the buyer interacted with.
- Last Non-Direct Click Attribution – 100% of the credit for a conversion is attributed to the last campaign/channel a buyer interacted with—but ignoring direct website visits.
- Linear Attribution – The credit for a conversion is distributed evenly across each channel a buyer interacted with through the entire buying journey.
- Time-Decay Attribution – The credit for conversions is spread across all touchpoints, but touchpoints nearer the conversion receive more credit than early touchpoints.
- Position-Based Attribution – The first and last touchpoints both receive 40% of the credit for a conversion and the remaining 20% is spread evenly across any touchpoints in the middle of the journey.
The table below illustrates how each different attribution model applies credit for the conversion to each touchpoint that preceded the conversion:
Why Multiple Attribution Models are Needed
Here’s the truth about all attribution models: none are perfect.
First-click, last-click, and last non-direct click only provide credit for a single step of the buying journey. Sure, first and last clicks are important. They highlight which campaigns/channels are effective in introducing leads to your brand and bridge the gap between consideration and conversion. But they eliminate the impact all other touchpoints had on the sale.
Linear, time-decay, and position-based attribution are better in that they take all touchpoints into consideration. But they’re also limited because they applied for credit generically—there’s no way to determine which specific channels and campaigns were the most convincing or compelling in driving the decision to buy.
And while none of the attribution models are perfect, they all provide some value. For example:
- The value of first-click attribution is that it provides a picture of which campaigns/channels are most effective in increasing awareness of your brand, products, or services. Looking at all of your conversions based on what the initial touchpoint was shows which campaigns/channels are most effective for top-of-the-funnel prospects.
- The value of last-click attribution is that it provides a picture of which campaigns/channels are most effective in helping prospects make a purchasing decision. Looking at all of your conversions based on what the final touchpoint was shows which campaigns/channels are most effective for bottom-of-the-funnel prospects.
- The value of linear attribution is that it provides a picture of how many stages are in your buying journey, the process that prospects go through before they make the decision to buy, which campaigns/channels are most-used and most effective in moving prospects along the journey, and the types of information needed at different stages.
The bottom line is that relying on a single attribution model is an ineffective way to measure marketing performance.
To gather truly actionable data, you need a way to filter data using multiple attribution models.
Marketing agencies are no longer able to retain clients with surface-level metrics like traffic and brand awareness. To prove the value of clients’ investments in your agency, you have to be able to show exactly how much revenue your efforts are generating.
Let’s say you’re in charge of social media and content marketing for a client whose owner runs his own PPC campaigns separately.
You track conversions using first or last-click attribution, and more often than not, it attributes conversions to PPC ads.
It’s difficult to prove the value of what you’re doing because the data shows that PPC is most effective.
However, if you switched the report to show data using a linear attribution model, you could find, for example, that most buyers are reading a lot of content and interacting with a lot of social posts prior to the conversion.
It could even be that the ads people are clicking on before making the purchase are brand name bids that buyers mistake for the organic search result.
By filtering the data, you can prove that your efforts are contributing to incoming revenue.
Additionally, you can use the information you gathered, that the owner is spending too much on clicks for brand name keywords, to suggest cost-savings opportunities that boost revenue even more.
Ruler Analytics Provides a Solution to the Attribution Problem
Ruler Analytics was built by marketers for marketers with the goal of solving for the data limitations that plague the industry.
Our system allows you to apply multiple attribution models, first-click, last-click, and linear, to get a complete picture of the effectiveness of different campaigns.
Furthermore, Ruler has a customer journey mapping feature that provides a complete list of every single interaction that took place between the buyer and the brand before the conversion.
View interactions across a timeline to see exactly which campaigns and channels comprised the journey for every lead—even anonymous ones.
Finally, Ruler also allows you to take into account non-digital touchpoints and conversions.
It integrates directly with your CRM to make sure sales conversations and conversions are accounted for in marketing’s data, allowing you to track the impact marketing has on generating revenue for both online and offline sales.
Using Data to Make Better Decisions
Marketing data can tell you exactly what campaigns, content, and channels are most valuable for your brand and your clients, but only if you fully understand how values are calculated and applied.
This doesn’t mean you need to be a data scientist to report on the outcomes of your efforts, but it does require the right knowledge and tools.
When you understand the benefits and disadvantages of different attribution models and have a tool that helps you collect and filter that data, you can use it to improve your campaigns, reduce waste, and prove to your clients how your efforts are building revenue for their businesses.