Say you launched a car race with a fleet of cars varying in brands, models, size, production year, and types of engine — that need to get from point A to point B.
To be able to determine which cars got to the finish line the fastest and in the most efficient way possible (i.e. cost-effective ratio between petrol consumption and mileage), you need to keep your eyes peeled on the race track, right?
B2B Marketing Attribution models work exactly the same.
They give you mission-critical insights into the best & fastest marketing activities that generate the highest ROI and produce the most highly-converting leads.
It’s how you measure the impact of your efforts throughout marketing investments and accounts’ journeys, by mapping out which channels or campaigns are most effective in driving conversions and which are just draining your budget.
Because if you don’t understand where your revenue is coming from, you’re likely to continue to invest in ineffective channels and lose out on potential revenue. And no one wants that.
The challenge – B2B Marketing Attribution is not all peaches & cream
As a marketing leader, you need to fully understand the impact of your marketing channels on the bottom line results of your business. Problem is, this is becoming difficult to do in today’s intensely fragmented B2B marketing landscape.
With so many channels available and new ones being introduced into the market all the time, account journeys are becoming more and more complex. Hundreds of touchpoints, both online and offline, and multiple personas and contacts within a single account — all contribute to fragmented, messy data points that could be extremely challenging to analyze in a holistic way.
So despite its clear value and undeniable need, B2B SaaS marketing attribution could be a tricky topic to tackle.
To help you make sense of it all, there are quite a few attribution models available out there, each with its own set of advantages and disadvantages.
Which is the best model to use in B2B SaaS and what is the best way to measure the effectiveness of your marketing efforts? Read on to find out.
Let’s break it down – Comparing B2B Marketing Attribution models against one another
To make this comparison as clear as possible, let’s start with a simple use case and then apply each attribution model to showcase how it works.
The use case – Good ol’ Megan
Megan is the VP Marketing at a B2B SaaS company and is looking for a solution to help her build, track, and optimize her marketing budget. Your company offers a B2B Marketing Attribution platform. A match made in heaven. ????
Megan’s journey was as follows:
- She was first introduced to your brand when she met your team at a conference that you sponsored (EVENTS)
- Later, when searching for “B2B SaaS marketing planning” on Google and reading one of your blog posts, she signed up for your newsletter (SEO)
- A couple of weeks later, Megan clicked on a CTA in your newsletter and signed up for a free trial (EMAIL)
- After viewing a relevant post on LinkedIn, she clicked on the link to read the full article on your website (LINKEDIN – ORGANIC)
- Finally, after clicking on a retargeting ad on LinkedIn to attend a webinar, your Sales team took ownership of Megan’s account and converted her into a paying customer (LINKEDIN – PAID)
Megan ended up signing an annual contract of $50K (ARR). Whoo-hoo!
Now that we have our use case for a simple customer journey, it’s time to apply our attribution models and see how each would attribute your $50K ARR.
1. Single-Touch Marketing Attribution
The earliest model of marketing attribution, single-touch credits the revenue value to a single touchpoint in the customer journey.
For obvious reasons, this model offers a problematic approach for B2B marketers who need to navigate a wide variety of touchpoints, as it ignores the impact of all other marketing channels, campaigns, events, and initiatives in the buyer journey — in favor of a single point of contact.
While certain touchpoints may be more significant than others, it doesn’t make much sense to disregard the rest, especially in the realms of B2B where longer sales cycles and an average of 100 touchpoints per account are entailed.
Under the single-touch attribution category, nestle two main model types — Introducer (First-Touch Attribution) & Converter (Last-Touch Attribution). Let’s dive into both:
Introducer or First-Touch Attribution Model
This model is based on the view that the channel that initially introduced a customer to your product or service is what ultimately led to the conversion, and therefore assigns full credit to the first touchpoint in the customer journey.
How does it work?
Going back to our use case scenario, EVENTS would get the full credit of $50K ARR.
Attributed revenue:
- Events: $50K
- SEO: $0
- Email: $0
- LinkedIn – Organic: $0
- LinkedIn – Paid: $0
Converter or Last-Touch Attribution Model
This model also takes on a narrow approach to attributing revenue, only it assumes the opposite of the Introducer model. According to the Converter model, the last touchpoint in the customer journey, e.g. the one that took place right before converting, is what ultimately sealed the deal.
How does it work?
Going back to Megan, applying the last-touch model would credit the entire $50K ARR to LinkedIn – Paid.
Attributed revenue:
- Events: $0
- SEO: $0
- Email: $0
- LinkedIn – Organic: $0
- LinkedIn – Paid: $50K
2. Multi-Touch Attribution (MTA)
The latest category of marketing attribution and much more of a realistic approach for modern B2B marketers, the basic notion behind MTA is that all touchpoints in an account’s journey have a certain impact on conversion. The only question is — what is the most accurate or logical credit distribution.
In today’s ultra complex marketing reality, most B2B marketers prefer to rely on MTA. Logically speaking, it also makes the most sense. The downside? It ain’t cheap.
According to a recent study by Forrester, small businesses typically spend between $10,000 to $50,000 annually on MTA tools. Medium-sized enterprises allocate between $50,000 and $200,000/year, while large corporations often invest north than $500,000 annually. Clearly, good attribution solutions are a justified yet significant investment.
What about the types of MTA models available for you?
There are a number of frameworks that nestle under the MTA category, each offering its own answers to the fundamental question of “who gets credit”. Generally speaking, MTA models can be divided into two main buckets:
- “Rule of thumb” models follow general credit attribution rules, or a specific percentage of attributed credit for certain touchpoints. Among these we’ll find the Linear, Time Decay, U-Shaped, W-Shaped, and Full Path (Z-Shaped) models.
- Data-driven models apply machine learning to collect data from customer journeys, and are more dynamic in their credit attribution.
Let’s break down each model to get a clearer picture:
“Rule of thumb” Attribution Models
Linear Attribution Model
This model is all about equality, based on the premise that each touchpoint impacted a conversion, the Linear attribution model assigns equal credit to every touchpoint along the customer journey.
How does it work?
Applying the Linear attribution model to our use case, all channels would be credited evenly with $10K each.
Attributed revenue:
- Events: $10K
- SEO: $10K
- Email: $10K
- LinkedIn – Organic: $10K
- LinkedIn – Paid: $10K
Time Decay Attribution Model
This model is more advanced, based on the assumption that the more recent touchpoints in a customer’s journey had a greater impact on a conversion compared to earlier, older touchpoints.
How does it work?
Going back to Megan’s journey, the time decay model would credit each touchpoint on a reduction basis. Meaning, the farthest the touchpoint from the point of conversion — the less credit it receives. In our example, LinkedIn – Paid would get the bulk of the credit.
Attributed revenue:
- Events: $1K
- SEO: $3K
- Email: $26K
- LinkedIn – Organic: $2.5K
- LinkedIn – Paid: $3.5K
U-Shaped Attribution Model
This model is based on the view that the first and last touchpoints (or in some cases, the first and lead creation touchpoints) are the most impactful — and are therefore assigned greater credit.
How does it work?
The U-Shaped model has a pretty specific credit assignment formula:
40% is credited to the first touchpoint and last touchpoint on the customer journey, with the remaining 20% evenly allocated to the other touchpoints.
In Megan’s case, Events and LinkedIn – Paid would receive 80% of the revenue credit, with the remaining 20% evenly distributed among the rest.
Attributed revenue:
- Events: $20K
- SEO: $3.33K
- Email: $3.33K
- LinkedIn – Organic: $3.33K
- LinkedIn – Paid: $20K
W-Shaped Attribution Model
This model is based on the view that the touchpoint leading to the actual creation of the lead is as significant as the first and the last touchpoints prior to conversion.
How does it work?
Similar to the U-Shaped model, the W-Shaped model dictates a set credit assignment formula:
The first touchpoint, the touchpoint that led to the lead creation, and the last touchpoint before conversion — each receive 30% of the revenue credit, whereas the other touchpoints settle for the remainder.
Attributed revenue:
- Events: $15K
- SEO: $2.5K
- Email: $15K
- LinkedIn – Organic: $2.5K
- LinkedIn – Paid: $15K
Full Path (Z-Shaped) Attribution Model
The most holistic of the attribution models, Z-Shaped assigns different weights to different touchpoints across the customer journey, depending on the effectiveness of each individual interaction. While this method provides a more comprehensive view, it’s also the most difficult to execute.
How does it work?
The Full Path (Z-Shaped) model also has a set credit assignment formula. It prioritizes four primary touchpoints: First Touch, Lead Creation, Opportunity Creation, and Last Touch. These events are assigned 22.5% of the credit (90% in total), with the remaining 10% allocated between the rest of the channels.
Attributed revenue:
- Events: $11.25K
- SEO: $11.25K
- Email: $11.25K
- LinkedIn – Organic: $5K
- LinkedIn – Paid: $11.25K
Data-Driven Marketing Attribution
The data-driven or custom marketing attribution models apply machine learning algorithms to the data collected from the customer’s journey, in order to identify and properly credit the marketing channels that played a significant role in a conversion.
Unlike the “rule of thumb” models, this framework doesn’t include a set formula. Instead, attributed credit is dynamic and changes according to the data, meaning results are bespoke to a given business and you get an accurate picture of what truly drives results.
How does it work?
A key part of data-driven attribution is using probabilistic models like the Markov chain. This model helps predict the likelihood of a conversion by analyzing the order of all touchpoints, identifying which are most likely to convert, and assigning credit accordingly.
Machine learning further enhances this process by continuously learning from the changing data, and improving the accuracy of credit assignment as lead behavior and marketing strategies evolve.
The benefits of data-driven attribution
- It helps you visualize your marketing efforts across multiple channels
Data-driven attribution offers an interconnected view of how various marketing channels work together along the customer journey, making it easier for you to pinpoint which channels pull the most weight and how they complement each other.
- It helps you optimize your strategy based on historical data
Applying this framework, you can use historical data to identify the touchpoints and channels that have delivered the best results over time. For example, you might find that combining paid search with organic content often leads to high-value conversions.
Armed with this knowledge, you can tweak future campaigns to mirror successful patterns, ensuring a more efficient budget allocation.
- It helps you boost ROI
Data-driven attribution gives you a more accurate way to measure the impact of each touchpoint, allowing you to more easily demonstrate how your marketing activities drive revenue.
Suppose you’ve been investing heavily in display ads but struggled to see a clear connection to conversions. Data-driven attribution can reveal that these ads play a critical role in the early stages of the customer journey, justifying further investment. This level of insight can help you defend and optimize your marketing spend.
- It provides valuable insights into buyer behavior
This model does more than just show you which channels are performing — it cuts to the heart of how and why customers are engaging with your brand. For example, if you discover that leads who watch your video content early on are more likely to convert later, you can adjust your messaging and tactics to better connect with their needs.
- It directs your ad spend more effectively
Data-driven attribution helps you allocate your ad spend and budgets more wisely. Let’s say your model identifies that retargeting ads contribute massively to closing deals. Knowing this, you can confidently increase spend on retargeting ads and spend less on low-impact channels, ensuring every dollar you spend is working hard to drive the best possible outcomes.
Which marketing attribution model is right for your org?
Unfortunately, there is no one-size-fits-all solution to attribution modeling for B2B SaaS companies. When considering the right approach for your needs, be sure to factor in your sales cycle, customer journey, resources, and goals — before adopting a model.
In some cases, you might need to employ multiple attribution models. For example, marketing activities for your self-serve SaaS product will greatly differ from the marketing activities for your enterprise offering, calling for different attribution models within your segments.
Here are three questions you need to ask yourself when considering attribution models:
- Does this model make sense for my business (sales cycle length, business model, decision-making hierarchy, etc.)?
- Is this model flexible? Can it adapt over time to meet the growing needs and changing nature of my business?
- Do I trust it? Is there a solid reason to believe in the accuracy of the credited revenue associated with my marketing channels? Unsurprisingly, not all marketing attribution tools can claim to offer trustworthy insights.
TLDR
- Attribution models offer a way of measuring the impact of marketing activities across the customer journey. These models analyze customer interactions with various touchpoints, and work out how much each one has contributed to a conversion.
- Effective attribution modeling shows marketers what’s working and what’s not, offering insights that support better decision-making and resource allocation.
- Multi-touch attribution or MTA is the most holistic and accurate category for B2B SaaS companies, because of the highly dynamic and complex nature of B2B marketing. That said, it could also be the most complex to implement.
- MTA credits all touchpoints in the journey, because every touchpoint DOES contribute toward a conversion. This model provides the greatest clarity into the impact of the marketing channels you invest in and their ability to generate revenue for your business.
- Consider your overall marketing mix, strategy, needs, and resources when choosing an attribution tool. While attribution is certainly not everything you need in order to effectively optimize your marketing budget, it’s certainly a critical component in the process.
- And most importantly, once you’ve opted for your attribution model of choice, make sure to whip your data into shape before applying any model on top, or any results you’ll get will be completely unreliable. Remember — your insights are only as good as your data, so start with cleaning it up.
