A lot is made of attribution and rightfully so, but one must remember what attribution really is: a story of the participation of the different touch points along the customer journey. Now, as a marketer, it is our job to understand when and what story tells us the truth about what really happened.
This seems like a super daunting task, but today, we're going to simplify things. First, we're going to briefly go over the most common attribution models and what they mean. Then, we'll briefly touch on why you may want to leverage different models for different clients.
Ok, let’s jump in.
Attribution modeling is the process of determining how much credit to assign to different marketing channels or touchpoints in the customer journey for a particular sale or conversion. The goal of attribution modeling is to understand the effectiveness of different marketing efforts and allocate marketing resources appropriately.
Marketers use attribution modeling for many reasons. These include:
From acquisition to retention, marketers need to understand the impact of each digital marketing channel. Attribution modeling helps them do this by evaluating which channels are most effective in driving desired customer outcomes.
What's more, attribution modeling helps marketers to identify which channels are working and which are not. This allows them to allocate resources more effectively.
Attribution modeling can also be used to optimize digital ad spend. By analyzing the customer journey with different attribution models, marketers can determine which channels should receive the most budget in order to maximize ROI.
Also, attribution modeling helps to identify which channels are more likely to drive conversions, so marketers can adjust their ad spend accordingly.
Now that you know the purpose of attribution modeling, here are a few examples of how you can apply it to your eCommerce business.
If you have a fashion eCommerce store, for example, you can use attribution modeling to identify which channels are most effective in driving sales. By understanding the customer journey and attributing conversions to the right channels, you can ensure that your ad spend is allocated in the best way.
Additionally, if you have an eCommerce store with multiple products, you can use attribution modeling to identify which channels are most effective in driving conversions for each product. This allows you to customize your marketing efforts for each product, resulting in a more effective overall strategy.
Let's chat about the first interaction for a bit, shall we? Picture this: It's like the first page of a mystery novel. Just as the first page sets the tone for the entire story, the initial customer interaction with a brand often shapes the rest of their journey.
In attribution models, this first interaction holds a whole lot of weight. Why, you ask? Because it's the first impression, the introduction, the spark that gets the customer journey started. It’s the moment when potential customers first connect with your brand, and it's absolutely crucial to trace back to this touchpoint when we’re talking about attribution modeling.
Think of it as a starting point in a race. You can’t really track the performance of a runner without considering where they kicked off, right? The same applies to customer journeys. Understanding which marketing channel or tactic was responsible for that initial 'hello' between the customer and your brand can give you some pretty invaluable insights.
And here's the kicker - knowing this can help us shape our entire marketing attribution process. We can figure out which strategies are working great as conversation starters, and those... Well, not so much. By focusing on the importance of the first interaction, we can better allocate resources, strategize, and ultimately, enhance the effectiveness of our marketing efforts. How cool is that? So remember friends, never underestimate the power of a first impression!
Lastly, there are several different methods for attribution modeling, each of which assigns credit to different touchpoints in the customer journey in a different way. Some common attribution models include:
First Click attribution is going to allocate all the credit of the purchase to the first touch point. So if you consumer had the following journey, Facebook would receive all the credit for the purchase. See the graphic below:
Now, when is First Click attribution useful. Well, a lot of times. Again, this will be contextual to the business and media buying strategy. For example, do you think the first touch point is more important than the last? If that is the case, then this would be the attribution model for you.
A use case for first click attribution is understand how efficacious your TOF efforts are. If they invited the person to the party, they should get the credit :)
Last Click is going to be the inverse of First click; all the credit will be given to the last touch point. So in the same example above of the user journey of Facebook to Google to Purchase Google would receive the credit.
Again, I find it useful to think of it in terms of “causal” intensity. What do you value more? The person who brought the person to the store? Or the sales person that closed said person?
Last Click is a great way to measure your channel health. However, be careful with it because it can be misleading sometimes.
For example, if customer journey below, the FB ads seem to be doing the majority of the lifting and the Google ad closed.
This isn’t good/bad per se, I just want you to be cognizant of what if that was a branded search ad? Would you really want your branded search taking all the credit for the previous 3 touch points?
Probably, not.
Ok, maybe you aren’t an absolutist and you don’t think one touch point deserves 100% of the credit. I can dig it and this is where attribution models become muy interesante. Fractional attribution, the next model we will explore.
Fractional attribution is will credit every touch point in the user journey and spread credit across all the touch points from first to conversion. However, there a few types of multi-touch attribution models. The one we offer in Triple Whale is Linear, but we can briefly touch on all of them.
This model gives all the touch points on the customer journey the exact same credit for the purchase. All the touch points are assigned the same weights.
What have you done for me lately ;) This model gives more credit (weight) to touch points that happened closer to the conversion.
This is kind of like how you remember things. You remember the beginning and the end, but very little in the middle. This model gives the majority of the credit to the first and last touch points, while recognizing the touch points in between.
Same same, but different as the U-Shaped model. The W-Shaped will give you three milestone touch points, and then credit the in between touch points evenly with the remaining credit.
Alright, folks, it's time to talk about probably the most famous kid in the attribution neighborhood - the Last-Touch Attribution Model. This one's got a reputation! Most often used in the marketing world, this model points the spotlight on the very last touchpoint before a prospect becomes a paying customer. It's like saying, "You, my friend, you're the star who closed the show. Take all the applause!"
So, let's envision Last-Touch Attribution modeling as a game of soccer. It's like only crediting the player who scores the goal, and forgetting about the rest of the team who passed the ball, blocked the opponents, and set up the play. In technical terms, this model assigns 100% credit to the final marketing touchpoint before the purchase decision. So if that last touchpoint was, say, a Google ad, then Google ad takes all the glory!
Now, this model has its perks. It's straightforward, easy to implement, and suitable for simple, short sales cycles where customers don't interact with your marketing efforts very often before buying. It's like when soccer fans only watch the highlights of the game - they still get to see the goal, right?
But, my friends, marketing is often more like a full-on, 90-minute game with a lot of back and forth. Today's customers might see your ads on Facebook, read your blog post, receive an email newsletter, then finally click on a Google ad before making a purchase. In such complex customer journeys, using Last-Touch Attribution can be the equivalent of missing the entire game and only catching the final goal. It overlooks earlier touchpoints that might have played a significant role in sparking interest and nurturing the lead towards the purchase.
So, while Last-Touch Attribution modeling can be a handy tool in your marketing toolkit, remember it's not a one-size-fits-all solution. Consider it as part of a broader marketing attribution strategy that acknowledges the complexity of modern customer journeys. In other words, don't forget the midfielders and defenders - they need love too!
Triple Attribution gives credit to the last ad the customer clicked through on the particular platform (Facebook, Google, etc) currently being viewed. (This model is sometimes referred to as “Last Platform Click”.) In this model, each marketing channel’s final click-through for the particular customer receives credit for the purchase.
Example: If a customer clicked on Facebook ad #1, then Facebook ad #2, then a TikTok ad, and finally made their purchase, then Facebook ad #2 would receive credit when viewing the Facebook data, AND the TikTok ad would receive credit when viewing the TikTok data. In this model, the final click a user made within each ad platform is deemed the most significant, and credit is assigned thereby.
Triple Attribution + Views is a unique attribution model for use with Facebook ad data. Facebook tracks both click-through as well as view-through data.
We cannot independently verify Facebook’s claimed view-through data, since we are only tracking click-through events from your ads. However, what we can do is layer Facebook’s view-through attribution data on top of our Triple Pixel click-through attribution.
This way, you can see the impact of Facebook’s claimed view-through attribution when added to Triple Pixel’s own click-through attribution.
Example: If the Triple Pixel ROAS was 2.35, and Facebook’s view-through attribution claimed an additional 0.5 ROAS, then toggling to the Triple Attribution + Views model will add that additional 0.5 ROAS to our own click-through ROAS.
Alright, so now let's chat about the importance of the "Last Non-Direct Click" in attribution models.
Imagine this - you're at a party, and you meet someone who tells you all about this incredible new cafe in town. A few days later, you walk past the cafe and decide to pop in for a coffee. Now, who do you think influenced your decision to visit the cafe? The friend at the party, or the sign outside the cafe? In attribution language, the friend is the 'Last Non-Direct Click,' while the sign is a 'direct' interaction. Last Non-Direct Click is defined as the last non-direct interaction that led to the purchase.
This model is significant because it takes into account all the touchpoints that a consumer interacts with before making a purchase, not just the final click or interaction before buying. It recognizes that consumers are often influenced by multiple marketing efforts along their journey toward making a purchase decision.
Okay, so why is this important? Well, direct interactions – like typing the website URL directly into the browser or clicking on the site from a bookmark – are often the last touchpoint, but it's the non-direct interactions – like clicking through a newsletter link, an ad, or a social media post – that create awareness and interest first.
So, in the cafe scenario, if you only gave credit to the sign (the direct interaction), you'd miss out on understanding the whole story of how you ended up there. It's the same with marketing attribution. This is why considering the 'Last Non-Direct Click' is crucial for a comprehensive understanding of conversion paths. It helps to reveal the actual journey that leads to conversions, especially in scenarios where direct interactions are not the final touchpoint.
In other words, we need to give credit where it's due. If we want to understand the full picture of what's driving our customers to convert, we can't just look at the final interaction. We need to acknowledge those key players who did the groundwork.
So, let's dive into how the 'Last Non-Direct Click' can shake things up in the marketing world, shall we? First off, knowing the true influence of each marketing channel is like having a superpower. It allows you to optimize your channel strategy based on actual performance rather than guesswork. "Last Non-Direct Click" gives you that power.
Picture this: you've been pumping money into your Instagram ads because they are the last click before a customer makes a purchase. But it turns out that email newsletters are the unsung heroes, doing the heavy lifting in the background, sparking the initial interest that drives the customer to your Instagram. By not giving the newsletter its due, we're missing out on opportunities to better engage our audience at the pivotal moment of their journey.
So what does this mean for you, rockstar marketer? It means you've got to work smarter, not harder. Use the 'Last Non-Direct Click' insight to reassess your marketing channels and what role they play in your customer’s journey. Perhaps you need to up your email game or maybe your blog content needs a little more love. The key is to understand how these channels work together, not in isolation, to drive conversions.
And hey, let's not forget that 'Last Non-Direct Click' insight can also help you identify potential budget leaks. If a channel is not playing a significant role in the customer's journey, it might be time to divert those resources to more impactful channels.
So, in a nutshell, when we consider the 'Last Non-Direct Click', we’re giving credit where it's due and using that insight to make savvy, data-driven decisions that optimize our marketing strategies. It's all about getting the full picture and understanding the true heroes of our customer journey.
So, how do attribution modeling and marketing strategies work together? It's kind of like a dance, really. Attribution modeling steps in and lets us know who's leading, who's following, and who's stepping on whose toes. By understanding these dynamics, we can adjust our dance – I mean, marketing strategy – to create a harmonious performance.
Think about it this way: Every time we gain insights from attribution modeling, it's like receiving a private dance lesson. We learn what moves are winning the applause (conversions, anyone?), and which ones are making our audience want to leave the dance floor (yikes, let's avoid those!). Armed with this info, we can then tailor our marketing strategies - our dance routine - to include more of the crowd-pleasing moves and less of the, well, not-so-pleasing ones.
The beauty of it all? It's a continual process. We're always learning, always improving. As we apply our new moves to our strategy, attribution modeling is there to give us feedback on how well those moves are working out. And then, we adjust again. It's a fabulous cycle of learning, implementing, analyzing, and improving.
Let's get real, folks. Marketing attribution isn't just some fad – it's the backbone of any solid marketing strategy. Imagine playing a game of darts blindfolded. That's kind of what running a campaign without attribution insights is like. You might hit the bullseye once or twice by pure luck, but mostly, you're just throwing in the dark.
With marketing attribution, we remove the blindfold. We get to see what's working and what's not, where our audience is engaging, and where we're just shooting arrows into the void. It allows us to channel our resources exactly where they’re needed, target our campaigns with laser-like precision, and squeeze out every bit of ROI from our marketing dollars. In a nutshell, marketing attribution is the secret sauce that takes our marketing from guesswork to a well-coordinated, data-driven strategy. Now, who wouldn't want that?
So, let's bring in the power of visuals, shall we? Infographics, my friends, are like fun, colorful cheat sheets for complicated stuff like marketing attribution models. They take all those tricky concepts and turn them into simple, easy-to-grasp visuals.
Imagine, instead of wading through dense blocks of text, you're now looking at a neat little flowchart or a diagram that pretty much tells the same story, but in a way that's snappier (and way more fun!).
Infographics are fantastic for making complex ideas universally understandable. So, whether you're a marketing pro or a newbie just dipping your toes, you're going to appreciate a good infographic.
For example, you can use an infographic to showcase your marketing funnel or customer journey. You can also use it to explain how each model works, which channels it's best suited for, and give real-life examples of its applications.
But hey, don't take my word for it – check out some cool infographics on marketing attribution models from around the web! Trust me; your brain will thank you.
So, folks, let's switch gears a bit and talk about the different marketing channels and their roles in our wonderful world of attribution modeling. Think of these channels like players on a sports team – each has a unique skill set, but they work together to score that goal (or in our case, a conversion).
So, there you have it! Remember, different channels excel in different scenarios. The key is figuring out which channels work best in your game and tailoring your attribution model accordingly and using the best marketing attribution tools to capture this information. But that's not all...
Alright folks, it's time to get our hands dirty with 'Cross-Channel Attribution Modeling.' Now, what on earth is that, you ask? Imagine you're hosting a party, and each guest (or marketing channel, in our case) brings their unique flavor to the mix. Now, instead of just focusing on individual guests, Cross-Channel Attribution models look at the entire soirée. They take into account how each guest interacts with the others and how they all contribute to the success of your party (or conversion, if we're getting technical).
Why is this so crucial, you wonder? Well, in the grand scheme of things, your customers are partying across multiple channels - they're liking your posts on social media, they're clicking on your emails, they're finding you via search engines, and they're clicking on your ads. Each of these interactions plays a key part in their journey towards a conversion.
By considering all these interactions across multiple channels, Cross-Channel Attribution provides a more realistic and accurate picture of your customer's journey. It helps you understand how your marketing channels work together, so you can better allocate your resources and optimize your strategy. In essence, it’s like putting on a pair of 3D glasses and seeing the full picture in all its glory!
Let's hit the gas and dive into the world of content operations. When it comes to attribution modeling, content operations are like the rhythm section of a band - it sets the tone and keeps everything in sync.
It's all about creating high-quality, relevant, and engaging content that resonates with your audience. The better your content, the more likely your audience will interact with it, click on those links, and, cross fingers, convert. And that, my friends, is what feeds into the accuracy of your attribution models. It's like a feedback loop - better content equals better engagement, which equals better data for your models.
But let's explore this further:
Content operations play a massive role in your attribution modeling. Here's what you should consider:
By focusing on these content-related variables, you'll be well on your way to a more accurate and insightful attribution analysis. And I'm sure we can all agree, that's what we like to call a win-win!
Alright, let's take a breather and talk about the MVP of Attribution Modeling: 'Key Takeaways.' I hear ya thinking, 'Why the hullabaloo about key takeaways?' Well, let me break it down for you.
Attribution modeling can be, let's face it, a bit complex. You're juggling a ton of data from a whole bunch of channels and trying to make sense of it all. Key takeaways are like your trusty compass in this data storm. They help distill this mountain of information into easy-to-grasp insights.
These distilled insights, my friends, are your gold nuggets. They help you understand which channels are rocking your boat and which ones need a little more love. They give you the lowdown on your audience's behavior and interactions, helping you steer your strategy in the right direction.
In essence, key takeaways make the complex simple. They translate a plethora of data into actionable insights, enabling you to apply your learning directly to your marketing strategy.
Alright, buckle up, folks! We're about to venture into the common pitfalls of Attribution Modeling. We all face them, but it's how we sidestep these missteps that counts!
Avoiding these common pitfalls will set you up for attribution modeling success. Remember, it's all about understanding your customer's journey in its entirety, not just focusing on the final destination. The more accurately you can track that journey, the more effective your attribution model will be. And that, my friends, is how you win the attribution game!
Alright, folks, time to dive into the deep end of the pool: advanced concepts in attribution modeling. Let's get our geek on!
First up, let's talk about Machine Learning Based Attribution. Yeah, it sounds like something straight out of a sci-fi movie, but trust me, it's as real as it gets. This approach uses machine learning algorithms to dissect the customer journey, determining the importance of each touchpoint. It takes into account a plethora of variables and interactions that traditional models may overlook. It's like having a super-smart, data-crunching sidekick helping you figure out what's what. Ready to get your hands dirty with some data mining?
Next on our advanced concepts tour is Algorithmic Attribution. This method is all about utilizing statistical algorithms to assign value to different touchpoints. It's like having a math whizz at your disposal, breaking down complex journeys into understandable chunks. And the best part? The more data you feed it, the smarter it gets. Talk about a win-win!
Alright, folks, time to introduce you to the belle of the ball, the creme de la creme, the Triple Whale's Total Impact Attribution Model.
This baby is like the supercharged, fully-loaded, all-inclusive holiday package of marketing attribution. It's not just about counting clicks, no siree, it dives deeper than that.
It rolls up its sleeves and sifts through a whole heap of data points to tell you how channels like TikTok and Facebook are influencing your biz. It's all about giving you a crystal-clear picture of your ad investments, letting you know what's hitting the bulls-eye and what's missing the target.
Plus, it decks you out with key insights to help you nail those future marketing tests. And how does it do all this? It's a trifecta of first-party data, zero-party data, and their nifty proprietary algorithm. That's right folks, with the Triple Pixel, Total Impact is the new gold standard in attribution. It's like your marketing GPS, guiding you toward success. So, are you ready to take it for a spin?
See how Triple Whale makes attribution simple and effective for ecommerce businesses.
Finally, we can't talk advanced without discussing the rise of Predictive Analytics. This approach uses historical data to predict future outcomes. It's like having your very own fortune teller, giving you insights into what your customers might do next. Now, who wouldn't want that?
As you can see, attribution modeling is not just a static concept; it's a dynamic field with ongoing advancements. Embracing these sophisticated approaches can give you a serious edge in your marketing game. So, don't be afraid to venture beyond the traditional and explore these advanced concepts. After all, fortune favors the brave!
Alright, time to put on your decoding glasses because we're about to get up close and personal with attribution model reports. These babies can look a little intimidating at first, but once you get to know them, they're more like a treasure map leading you straight to marketing gold!
Typically, these reports are loaded with jargon and metrics, but don't worry, we'll break them down together. Here's a little cheat sheet for you:
Once you get to know these elements, understanding reports becomes a whole lot easier. Just remember that these reports are telling the story of your customer's journey. Each touchpoint, conversion, and pathway is a chapter in that story. Your job is to understand this story, learn from it, and use those lessons to write even more successful marketing stories in the future.
Google Analytics 4 Properties offers three distinct models for attribution in the Attribution Reports: cross-channel rules-based, Ads-preferred rules-based, and data-driven. Each model provides powerful insights into how conversions are generated across channels, allowing you to measure your marketing performance accurately.
This model gives 100% attribution of the conversion to the last Google Ads click, with secondary attribution to other channels in the user journey.
This model gives full credit for conversions according to the rules you set. From last-click attribution to first-click attribution, you can assign weights to different channels as desired.
This model uses machine learning algorithms supported by historical data to accurately attribute conversions to each channel. It requires large sample sizes and is the most accurate of all three models.
To find these reports, log into Google Analytics and select the “Attribution” tab. From there, you can view all of your attribution reports in one place.
With these reports, you can gain insights into how each marketing channel contributes to your overall conversions and make more informed decisions about future campaigns.
There is no right or wrong attribution model. The best attribution model is the one that helps you make the best decisions. I like to think about attribution as a story telling mechanism. What is the story that is closes to the truth?
That is the attribution model you want to use.
With that being said, you might want to compare the attribution models to ensure you are “listening” to most truthful story.
To see the comparison, just drill down into Pixel screen and click on the rubik’s cube. You can now see all the attribution models compared. What a time to be alive :)
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