Welcome to the third episode of The Digital Dish, where we dish on all things ad tech! This episode focuses on attribution, including an overview of what it is, what the industry challenges are, the pros and cons of four different types of methodologies, and best practices for maximizing your media strategy.
Prefer to read? Here’s the transcript:
This series topic is attribution. The digital age has brought a wealth of opportunity for businesses of all sizes. With that opportunity comes a new set of challenges, among them attribution.
Attribution, or the process of determining which channels are driving customer behavior, has always been important for businesses, and in this age, it’s more important than ever. With so many channels and so much noise, it’s more difficult to determine which channels are actually driving sales and conversions.
In this episode of the Digital Dish, we’ll look at:
What we know is that all media strategies are tied to outcomes. Whether your brand is looking to measure engagement or actual direct response, you need to know whether you are getting a return on your investment. It’s likely that as a marketer, you are running a combination of media and need to track the sources of your traffic and leads to determine what is and isn’t working.
This is where attribution comes in. While attribution isn’t always easy to understand, it is crucial. It’s hard to track a customer’s journey:
But ultimately, you do need to know what the customer journey was (from start to finish) and understand which channels played a part in the consumer’s journey to buy or engage with your product or service.
There are a LOT of attribution models out there, so today, we are going to uncover some truths and simplify them for you.
So why measure? Why is attribution so important? Well, to start, you can’t improve what you don’t measure. By tracking the success (or lack thereof) of your marketing efforts, you can determine what’s working and what’s not. And that knowledge will help you allocate your resources more effectively in the future, working towards the goal of increasing overall ROI.
The most common methodology you’ll hear of is the last-touch model of attribution. The flaw here is that last touch refers to just that – the last touch point a customer had interacted with before making a purchase. This isn’t always accurate, however, since we are all subject to so many screens, shared screens, and innumerable distractions. The single-user journey just isn’t the case anymore.
For example, if a customer sees a social media ad, hears an audio ad, and then sees a display ad that they click on and finally make a purchase, the social and audio ad would not receive credit in the last touch attribution model.
That’s why it’s important to use more than one attribution model. By using multiple models, you can get a more accurate picture of how your marketing is performing and where you need to make adjustments.
Now we’re going to review four attribution methodologies, as well as their pros and cons, so you can be informed and ultimately choose the right one for your business. So, what are they?
Let’s first start with last touch methodology, which is the most commonly used. Last touch refers to the last touch point a customer had interacted with before making a purchase and therefore giving this last touch point the entire conversion credit.
The single-user journey just isn’t the case anymore. If you are working with a long buying cycle or want to understand how the consumer goes from the initial brand awareness stage through the funnel to becoming a customer or user, this methodology isn’t for you.
The next is first touch methodology, which is the opposite of last touch. With first touch, the first time that a customer interacts with your company that is determined the most important reason they ended up purchasing with you.
Next up, we have linear attribution, which is a multi-touch model–meaning it takes into account and attributes credit to multiple touchpoints along the customer journey. In fact, this model actually takes into account every single interaction that a prospect has had with your brand prior to purchasing. This method is incredibly easy to understand as every single touchpoint is given the same amount of credit, so the equation is as follows:
100% of the overall credit / the number of touchpoints in the conversion path = the amount of credit per touchpoint
For example, if your customer has four interactions with your brand before deciding to convert, each would, therefore, have 25% of the credit.
Finally, there’s time decay attribution. This is similar to linear in that it’s a multi-touch model that gives some credit to all channels that led to the conversion. However, this is different with that amount of credit being less (or “decaying”) the further back in time the channel was interacted with. This assumes that the first channel your customer interacted with merely planted the seed, and the customer’s interest in committing to a purchase grew over time with repeated exposure to various marketing channels. Think of this as a rising level of interest and commitment from the customer over time.
Now that we can understand what attribution is and the different methodologies you can consider, the next step is to review your current strategies to see which ones are working and which ones need improvement. Here are a few tips to help you master attribution in the digital age:
Use a consistent methodology across all channels and platforms. This involves creating benchmark goals and making sure that all channels are performing according to those standards.
Set up advanced tracking capabilities, such as key conversion pixels on high-traffic landing pages or mobile app tracking for detailed insights. Having multiple data points will help you pinpoint any weaknesses in your attribution model and make timely adjustments.
Be proactive in finding ways to increase return on investment from existing channels. You don’t want to track just performance; rather, you should continually seek out ways to improve results through optimization techniques such as A/B testing or identifying new audiences with demographic data. If you are a brand or agency with a goal to understand which touchpoint or variable along the consumer path is driving conversions and in the past have been directed to a single touch model, for example (like last or first touch), start thinking outside of that assumed-consumer path and consider a methodology that would best suit your specific set of challenges.
Leveraging these best practices and taking advantage of the latest technologies, as well as our custom attribution guidance, will help you master each methodology and create a more sophisticated strategy to drive best results.
As technology advances, so too does our ability to measure and optimize attribution. Thanks to powerful data visualization tools, machine learning algorithms, and the ever-growing volume of available customer data, marketers now have access to a wealth of insights when it comes to understanding where each consumer interaction is coming from.
Detail is key here, with the right technology, we can analyze granular data about each marketing initiative – including specific channels, campaigns, and even individual creatives – to understand how each variable impacts your customer journey and overall bottom line. Our full-funnel attribution methodologies help all advertisers across all verticals make better decisions about which tactics are most effective for driving results, wherever they may lie in the marketing funnel.
Take an example that we recently worked on with a DTC e-commerce brand. They had a digital strategy spanning multiple channels, from Performance CTV to Podcasts, Social, and Display Retargeting, and it was becoming increasingly difficult to equate purchases to any one variable and, thus, hard to dedicate specific spend to any one channel.
Both the first and last touch methodologies were not a suitable solution for this client, as they were new to market, had a higher price point of goods, and a longer sales cycle. It meant that typically, a customer interacted with media multiple times across multiple channels before making a purchase. Our team at Digital Remedy assessed every impression of this campaign and provided traceable data correlating each impression to a conversion, whether that was an online purchase, homepage visit, or email sign up. This full-funnel data allowed us to flag variables that were significant in the customer journey and therefore recommend the allocation of higher spend to these channels, and ultimately drive down the CPA for this client.
Attribution is a core digital marketing function, and it’s more complex than most people realize. It’s not just about correctly identifying the channels that drove a conversion but also understanding how each channel works together to influence conversions and from there, determining the most effective allocation of dollars and resources.
Getting attribution right is essential for optimizing your return on investment. With these data sets and insights, you can make more informed decisions about where to allocate your budget, and which channels are most likely to drive down cost per acquisition.
The digital landscape is constantly evolving, and it’s crucial that, as marketers, we stay ahead of the curve and master the art of attribution. With the right tools and knowledge, you can make sure your marketing efforts are always working as hard as they can to drive conversions and growth. Think of attribution as step one. Once we get that right, we can build upon its foundation with specialized optimization, incrementality, and halo effect methodologies.
Interested in learning more? Check out our Trends & Insights report—or speak to a team member! Be sure to follow Digital Remedy on LinkedIn, Twitter, and Instagram for the latest updates.
Welcome to the second episode of The Digital Dish, where we dish on all things ad tech. This episode focuses on Incrementality, including an overview of what it is, what the industry challenges are, and what solutions advertisers and agencies can tap into when utilizing incrementality methodology.
Prefer to read? Here’s the transcript:
Incrementality addresses two major concerns, especially when you’re trying to assess a single, possible experimental media type. Native demand for products/site visits/leads etc., and all the other media, especially walled-garden media. With proper incrementality testing, we can factor both out entirely, getting right to the heart of the media type we’re analyzing to assess a true, bottom-line impact.
In its simplest form, incrementality is the percentage of total conversions that otherwise would not have happened. When we measure incremental lift: that involves what percent more likely someone who is exposed to a campaign is to convert than someone who is not exposed to a campaign.
We now live in a world where measurement is growing more and more important, and we start to bootstrap spending and really assess where and how marketing dollars are impacting our bottom line. And Incrementality answers questions like:
In this video, we’ll look at:
What we’ve seen in the industry is that traditional marketing performance metrics have been around for many years and marketers still rely on them. Traditional marketing metrics such as CPA, CTR, and VCR, while important, can leave room for additional questions requiring custom analytics to answer. With limited media budgets and advertisers now experimenting with different channels and formats, understanding the true impact of your marketing is crucial in making budgeting decisions and being able to justify scale. Most marketers would (understandably) only want to spend money on channels that drive those bottom-line results and ensure a positive brand experience.
Marketers are facing a looming cookieless future, evolving privacy regulations, fragmented customer data, and the accelerating complexities of identity management. They’re seeking an alternative approach to evaluating the effectiveness of their campaigns. In response to new privacy rules requiring mobile apps to get things like explicit user consent and access to an IDFA device. eMarketer states that 64% of marketing executives plan to invest more in other measurement solutions like incrementality and media mix modeling. When implemented correctly, incrementality can boost a brand’s revenue and help marketers prove their value.
An increasing share of the industry is looking to move away from last-touch attribution. The majority of marketers really do still depend on it because of its ease of use. While last-touch is a simple way to assign credit, it’s not the most accurate. Incrementality is really there to identify the causal event of a conversion, and it’s allowing businesses to properly allocate their budget and reduce wasted ad spend.
Based on the conversations I’ve had, it’s pretty critical for advertisers to understand how their digital marketing efforts are impacting their sales. Incrementality offers deeper insights and more accuracy when measuring metrics like attribution, lift, and return on investment. Incrementality done correctly, and in comparison to the other methodologies, it’s reinforcing the need for marketers to have the right tools in place to drive a brand’s growth and their marketing efforts forward.
The industry challenge for many different advertisers is to make the most out of their dollars. And that means maximizing their media budgets. Advertisers are always experimenting with different channels and formats, and they want to understand the true impact of their efforts. This is really crucial in making budgeting decisions, as well as justifying scale.
In today’s ad landscape, brands run across many different media channels—this can be from paid search to CTV, display, and direct mail, and customers are often exposed to several different media types—as well as several different ads within each media type—before making a purchase or whatever their conversion point might be. And so, therein lies the challenge: How do marketers know which specific ads, or which specific campaigns actually drove the conversion, when so many other campaigns and ads are running concurrently? And how do they separate an individual channel from the rest, or from their own organic or native demand? This is where incrementality comes into play.
One of the examples that I’m about to share will allow you to understand that incremental lift can be measured across numerous variables:
Our team was working with a very well-known QSR advertiser. One of the ways that we wanted to involve incrementality was through the measurement of their creative and publisher lists. We wanted to look at these two pieces and provide them with insights they would not have been able to derive on their own. When we launched incrementality with this QSR advertiser, we could get really granular and look at these different variables. We were able to see which publishers were showing the highest incremental lift as well as which creatives were driving the highest incremental lift, and then also which ones weren’t.
From there, looking at this data, we were able to make optimizations toward these findings. And not only that, but we were able to provide the client with more insights to enable them to make informed decisions around their creative strategy, and this client actually ran a handful of different creative messaging throughout the campaign. Incrementality allowed us to show them which creative messaging drove the highest incremental lift in 2022. And from those results, they’re actually currently shooting to provide us with additional videos that align with this same messaging so that we can have more variety within this creative batch, and then additionally, on the publisher side, we really saw that sports inventory was a top performer for them, so we’re keeping that in mind as we go into 2023 and really maximizing the inventory that is sports related for their 2023 campaign. That’s just one real-life example of how incrementality is allowing us to see the lift across these different variables and find those points of success and then make pushes in those specific areas for continued improvement.
When discussing incrementality, we’re talking about measuring conversions that otherwise would not have happened. The reason why this is important is because we wanna make sure that any conversions that we’re driving with our OTT or our CTV spend are actually driving conversions that are not duplicative with the marketer’s other channels.
Marketers are running search and social and maybe affiliate marketing, and those are doing a great job driving conversions. And there’s a middle of that diagram with the conversions that we will also drive. The way that we do this is we set up a test and what we want to do is we want to analyze the difference in behaviors between two groups.
The first group is an exposed group, that’s people who have seen the campaign that we’re running for a client. The second group is a control group, and these are people who are actively prohibited from seeing an advertisement from this campaign.
And what we’re doing is we’re measuring the lift of that exposed group over that control group, or we’re seeing how those ads impact the behaviors of the people who actually saw them. We do this by looking at conversion rates between the two groups. And the reason we look at conversion rates and not just the number of conversions is that the group size is different.
And the way that we actually create that holdout group is through a process called ghost bidding. What Ghost bidding is, is if you imagine we’re set to serve five ads to individuals, all the targeting that’s applied, goes, behaviors, devices, all of that targeting is applied.
And then, instead of serving one of those five ads, we actually end up holding that person who is eligible for that ad out of the campaign. In other words, four ads get served, one ad gets held out. And then we’re creating a holdout group that mimics the exposed group exactly from a behavioral-, from a targeting-, from a demographic perspective.
All of the targeting that is applied to the exposed group is also applied to the control group because those individuals are actually eligible for bits. There’s no skew this way. So we avoid any skew that would’ve happened with a random sample. And instead, we’re making sure the two groups look exactly alike.
And then the second benefit is, because we’re not actually serving the ad, we don’t have to pay for the impression. So we can run this analysis free of charge. After we, have created that holdout group, what we’re able to do is measure the conversion rate lift of the exposed group over that holdout group.
And not only can we do this at the overall level, we can do this below the aggregate, which means that we can measure which publishers, which creatives, which times of day, which audiences are over-indexing or or have a higher incremental lift, than other times of day or other audiences or, other creatives or publishers, whatever that variable set might be.
Because we’re able to get below the aggregate, we can actually apply this analysis into the way that we optimize. So as opposed to just optimizing toward a standard ROAS goal or a standard CPA goal, we’re able to actually optimize toward an incremental CPA or an incremental ROAS. Adding another touchpoint and making sure that the creatives and the publishers and the day parts and the audiences that we’re targeting and ultimately moving money toward are actually driving bottom-line results, not just an in-platform low CPA reporting result.
This is all part of an effort to unsilo OTT. We wanna make sure that whatever we’re doing for our clients is actually in the best benefit from a bottom-line perspective, not just that we’re looking good from a reporting perspective in-platform.
Interested in learning more? Check out our Intro to Incrementality series or our Trends & Insights report—or speak to a team member! Be sure to follow Digital Remedy on LinkedIn, Twitter, and Instagram for the latest updates.