Regardless of product type, marketing strategy, and objectives, every marketer has something in common: we all want to know whether our efforts are moving the needle on sales. That’s why revenue attribution is so important; in order to gauge your return on investment (ROI), optimize your campaign, and make smarter budget allocation decisions moving forward, you first have to know how your current ad investment is measuring up.

Building an attribution model is all about gauging marketing performance by matching your marketing spend to your sales revenue. Where things get tricky is that making those connections isn’t always cut and dry. For example, it’s a common mistake when finding the meaning of attribution to attribute conversions and results solely to your own marketing efforts, when in reality there could be a lot of other influencing factors at work.

These factors can be either inside or outside the marketing funnel—think external events, competitor actions, or customer behavior. Since these can play an influential role on conversions, marketers have to dig deep into the data by performing an attribution analysis while also considering the broader context of market dynamics and customer trends.

The secret to measuring marketing attribution more accurately, and mitigating attribution errors that could lead to invalid data, is to approach attribution like it’s another aspect of the purchase funnel. This can provide a better understanding of your market and customer research, and also help with conversion rate optimization.

Using multiple sources and methods for data collection and validation is vital, as is regularly adjusting attribution models and parameters to reflect changes and uncertainties in the market and customer landscape. All of this can help you avoid attribution mistakes.

Bottom line? No attribution model is perfect, but you should use the one that best matches your customer’s journey to avoid drawing the wrong conclusions—and making the wrong decisions, too.

The 4 Main Common Attribution Errors to Avoid

For marketers, knowing which channels drive the best results is your bread and butter and the most reliable way to achieve ongoing success. Even if your ads are generating revenue, identifying the top touchpoints for those outcomes can empower you to make further improvements and replicate your current advertising success.

Studies show that in 2020, 84% of North American brand marketers felt they were facing “new pressure to prove the effectiveness of their ad spend since the onset of the pandemic.” That makes attribution models an attractive option for marketers eager to demonstrate the value of their campaigns.

In theory, the attribution principle is simple: use reliable tools to identify high-performing channels so you can hold your marketing accountable. That said, there are several mistakes marketers tend to make along the way. Avoiding these is vital if you want to make sure your data is accurate and keep campaign performance going strong.

Let’s break down the attribution mistakes you may encounter so you can navigate this must-have marketing toolset, which is a critical part of every advertising campaign.

Attribution Error #1: Not considering multiple attribution models

When choosing an attribution model, it’s important to consider the whole picture of the funnel and external factors such as product features, number of channels, and the length of your sales cycle. Relying too much on one—or very few—models, or neglecting to test them, can reveal too narrow of a picture and lead to misinformation.

Assessing attribution models to determine which will lead to the best overall ROI is time-consuming, but as tempting as it is to go with a single model, that simply doesn’t provide enough detail about the customer journey. For this reason, your attribution analysis should involve testing different models to see which one provides the most reliable results over a given period of time.

Consider a scenario like shopping for a new mattress. There are so many variables to consider when you’re measuring customer interactions with a mattress brand, from sales and promotions to seasonal needs like back-to-school time for college students or an impending move. Even other forms of advertising, like a TV spot, could be impacting your online sales. Without deeper insight into your customers’ lives, media preferences, and day-to-day behavior, how can you be sure why they’ve decided to buy?


A good attribution strategy allows you to derive actionable insights from multiple attribution models, all of which focus on a different part of the customer journey. When combined, these will give you the big revenue picture you’re looking for.

Attribution Error #2: Relying only on single-touch revenue attribution models

Just as you’re selling yourself short with one attribution model instead of employing numerous options, relying solely on single interactions can lead to costly mistakes. Sure, this strategy can work with digital advertising like pay-per-click (PPC) banners that advertisers only pay for when the ad incites a click. But such attribution models are linear in nature. They place equal value on every touchpoint on the path to purchase, when customer behavior is, in fact, a lot more complex.

While single-touch models (like first-touch) can provide ROI measurements fast, you might get something far less desirable too: attribution bias, and an inaccurate view of how your customer got to the point where they’re ready to make a purchase.


The same can be said of last-touch models, which places the value on the customer’s last touchpoint prior to conversion. Analyzing the customer journey requires a more sophisticated model that can delve into multiple layers of information about customer behavior, experiences, and interactions.


You can avoid this common error by moving beyond single-touch models to consider multi-touch models that provide more nuanced information about customer interactions. Choosing attribution models that address more touchpoints and potential paths to purchase will mitigate the risk of error in your data sets.

Attribution Error #3: Not aligning your attribution model to your business objectives

There’s another factor to consider when you’re developing attribution modeling strategies, and that’s which model is most appropriate for your business goals (no two brands are created equal, after all, so why should everyone use the same attribution model?). The first step here is to connect with your various marketing teams, from sales to brand management, and establish the objectives you collectively hope to meet.

Why is this so important? Imagine that your brand’s current goal is to drive sales of a new product model before the end of the quarter. Tracing sales back to the channels that initiated them is all well and good, but a single model like last-touch could present a different picture than a model like first-touch, leading you to provide your stakeholders with an incomplete impression of your marketing success. To be more specific, using the last-touch might suggest your best-selling channel is paid search, whereas applying the first-click model on the same result might show that native ads deserve credit for most of your recent sales.

The way around this misstep is by conducting (and repeating) tests that focus on individual goals. If, for example, your quarterly goal is to boost revenue, final touchpoint attribution can be very useful. If your aim is to prioritize brand objectives, though, first touchpoint attribution might be a better fit.

As your business grows, your priorities might also change—which could mean your attribution models aren’t as useful as they used to be. As you work toward establishing your goals, and finding the attribution models that can help you meet them, a mix of consistency and variety is a recipe for success.

Switch up (and test!) your attribution models from time to take to deepen your analysis of your KPIs and business performance. Remember to consider attribution biases, too (more on that below).

Attribution Error #4: Avoid considering the bias within your analysis

While every attribution model has its perks, none are perfect—but keep in mind some of the errors you’ll encounter will have more to do with human bias than anything else. The most common are correlation and confirmation bias, which are two well-studied psychological phenomena. These types of bias can affect decision-making about the validation of attribution models as a whole.

Let’s start with Correlation Bias. This is best described as the tendency to inaccurately link an action to an effect, and it happens when you’re overestimating or underestimating the influence of a channel, giving credit to different paid channels in the customer journey when what you’re seeing is a natural customer conversion. Some conversions do happen organically, so crediting them to an ad can skew your data and lead to inaccurate results. It’s therefore crucial to distinguish between correlation and causation when making these kinds of attributions.

Confirmation bias, on the other hand, is about interpreting information in a way that reflects your personal beliefs and desired outcomes (i.e. if you have preconceived ideas about the effectiveness of a particular channel, you many subconsciously transfer those beliefs to your results, giving more weight to a channel than it actually deserves). In any attribution model, this kind of bias can lead you to over- or undervalue touchpoints across channels, again exposing inaccurate data.

To combat these biases, and get to the true heart of your campaign results, marketers should do all they can to maintain an objective mindset about ad channels. That way you won’t be inadvertently diluting your results, but instead sticking to the hard facts.

Final Thoughts on Most Common Attribution Errors

If this all feels a bit overwhelming, take a breath and rest assured that finding the right attribution modeling strategies for your brand is a goal that’s well within reach. These mistakes are common, particularly if you’re new to attribution analysis. But if you find that you’re limiting yourself to one attribution model, or giving in to a bias that could lead to inaccurate data, just revisit this guide to help you get back on track.

Choosing the right attribution model may be a big deal, but that shouldn’t stop you from trying out different options—cross-platform attribution, first touch, multi-touch, and others—to find the one that suits your business best.

Taboola’s services and attribution models enable marketers to choose the model that’s right for your brand, while also modifying and adapting to meet your evolving needs. When you’re able to identify the attribution model that best matches your customer journey and KPIs, you’ll be ready to drive, track, and continuously improve your digital marketing results.

Interested in learning more? Contact Taboola to hear about our attribution models today.

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