The age-old goal of marketing attribution sounds simple: to find out which touchpoints, channels, and/or campaigns are most effective at driving customers to conversion. Building A Better Performance Measurement .
In the digital world, our ability to attribute impact is bas on deterministic identifiers that let us stitch marketing interactions together. Then a model, either rules-bas or data-driven, is appli to these interactions to adjust the weight of crit assign to each interaction across the customer journey.
So attribution theoretically gives marketers the power to understand and evaluate the value of different kinds of brand interactions on a consumer’s decision to convert. The platonic ideal of an attribution model would produce a holistic view of every touchpoint in the user journey and accurately assess its importance in driving people to the end goal.
Some models have gotten closer, like multi-touch attribution, while others have well-known blind spots, like last touch.
But it is simply not possible to accurately track every touchpoint that may have influenc a conversion event. It probably never will be. And even if it was, attribution might not actually give us the full picture we ne.
Attribution alone is an inherently flaw goal
Perfect attribution has been a marketing pipe dream for a long time; since the days of John Wanamaker, marketers have been obsess with the idea of a universal measurement framework that will prove they are driving value.
Country-based e-mail lists are essential in driving business growth. You will ensure that targeting by country is done at the right level country email list with a view to creating more personalized and culturally relevant content that resonates with local audiences. Such a level of targeting enhances engagement, increases open rates, and lifts conversions. Furthermore, it nails down adherence to local regulations aimed at building trust and loyalty. On this basis, you will be able to increase the effectiveness of your email marketing campaigns and successfully enable long-term business growth.
But even if you could see all touchpoints and achieve perfect holistic attribution, would that give you all the information you ne to build the perfect marketing strategy? After all, attribution by its very nature is always looking in the rearview mirror. It doesn’t look to the future or provide a path forward. It also doesn’t account for critical mia investment signals like diminishing returns.
This is not the measurement solution you’re looking for
Measuring performance should focus on using data to understand where your next best dollar should go, not just how far the last dollar went. You ne to be able to look at your data and answer forward-looking questions like.
Where can we increase budgets to scale our conversions while maintaining our current ROAS?
What levers can I pull to optimize campaign performance?
You can’t answer those kinds of questions if you’re only looking at a model that assigns retroactive crit on top of an incomplete data set. The cold, hard truth is that deterministic multi-touch attribution isn’t a cure-all; if that’s the only model you’re depending on to make decisions, it can’t deliver those answers.
Data deprecation is making deterministic
Whether or not you agree that the very idea of attribution only gets us partway to our ultimate measurement destination, we can all agree that many marketers are still beholden to attribution models. As with all models, there is no such thing as perfect. While there have been advances over the years, with Google’s value-deriv data-driven attribution of particular note, there are still plenty of unknowns at play when it comes to the future of attribution.
That’s because the current state of marketing data is only making things harder. As platforms like Meta, Google, and Snap struggle to influencer marketing, how to create a perfect strategy cope with Apple’s App Tracking Transparency (ATT), the CFOs of those companies would be the first to admit that data deprecation is their greatest challenge.
At least some of the practical problems with attribution are human ones: people are obsess with connecting the dots and finding patterns, whether or not they actually exist. We often get questions from marketers about how attribution works within Google Analytics because of inconsistencies with other data sets.
You’re probably familiar with this challenge: the Facebook Business Manager UI claims the platform drove 10x more conversions than what you’re seeing report in Google Analytics. So which one is right?
The answer is they are both wrong, just in different ways. No amount of fun math (i.e. proxy calculations that look at the delta between the two data points over time) will help you solve the equation and perfectly calculate exactly much crit Facebook Ads should get.
At the end of the day, this is a data observability problem; the data is incomplete, but we look for an answer anyway.
Graph of benefits vs challenges of attribution
If that’s confusing, think about it this way: let’s say you’re on a phone call with patchy reception. For every 10 words, you miss singapore number one word. Chances are you can still understand the gist of the conversation because you have so much other context.
But when you start to lose entire sentences or every other word, you’re going to find yourself in trouble. That’s because the inputs are too limit and fragment to draw accurate conclusions. That is exactly what is currently happening with deterministic attribution across all advertising platforms, and it’s something that no amount of modeling can totally solve.
Remember that Super Bowl T-Mobile ad where Rob Gronkowski invites Tom Brady to retire in Florida, but Brady can only hear every other word and thinks Gronk is telling him to go play in Tampa Bay? That’s attribution today. That’s the reality that brands face with data loss.
Platforms are turning to models to try and bridge the gaps. Essentially, they’re taking the limit data they have, like the few words Gronk can hear Tom say in the commercial, and using technology to model the rest of the conversation.
This is comparable to how ChatGPT pricts the most likely next word as it compiles responses. Often it makes sense, but sometimes it hallucinates and tells you that Elon Musk is going to be the next president of the Unit States.
Of course, there’s always a range of error in modeling, but if the data loss is bad enough, you can’t build an accurate model. Then the real question starts to take shape: how much can you trust advertising platforms to get right?
That’s why you ne to expand
Mia mix modeling and incrementality testing can get you closer to understanding impact
Attribution is still a powerful construct, but the underlying methodology nes to change so it can evolve into the modern era. You ne to ask some hard questions to figure out what kind of measurement toolkit and framework will work for your organization, like:
What measurement do you ne in place to make smart decisions about investment planning across channels and platforms?
Where are the best opportunities to scale your existing mia mix as efficiently as possible?
As an industry, we’ve gotten us to thinking of an imaginary version of perfect attribution as the end-all, be-all, but it was only ever meant to be us as a guidepost.
That doesn’t mean it’s not useful. But you ne to shift your focus to the future and let attribution be a component of your decision-making, not the only arbiter.
And while there is no perfect solution, there is an imperfect one that gets us a lot closer to the goal: unifi attribution combin with mia mix modeling (MMM), where you use some deterministic data and model for the rest. The goal is to leverage past data to prict future investments. It’s root in growth, not held hostage to past performance.
Example of mia mix modeling framework
To get it right, you ne to invest in robust incrementality testing, which will help you both validate model performance data and get a clearer picture of how your campaigns are affecting the full customer journey.
Geo-bas incrementality testing is vital to mia mix modeling calibration. It is also the single most powerful measurement solution to determine where you’re over- or underinvest at a given moment in time.
Most brands are not very comfortable with incrementality testing. Some have done it before, but historically the majority aren’t great at it. If that’s where your brand is, you ne reliable partners with a prictable methodology that is customiz to the nes and quirks of your unique business challenges.
It’s time for a future-facing solution that integrates multiple tools: the performance measurement framework
One of the big challenges
With traditional mia mix models is spe to action. At Wpromote, we built a high-velocity mia mix model and investment scenario planning tool call Growth Planner as part of our Polaris marketing platform to address both data deprecation challenges and actionability.
Growth Planner forms the core of our performance measurement framework. Essentially, it forecasts across a client’s entire year to find the optimal investment of available dollars to hit revenue targets. It also can be us for optimizations on a weekly basis so your brand can stay agile and adapt to new developments.