Marketing attribution is not easy. For businesses selling B2B, it’s even harder. Multiple people are involved in decisions and are part of a buying journey. To make the right decisions all their touchpoints have to be captured and analysed.
In this article, we’ll cover:
- An overview of what B2B marketing attribution is
- Types of attribution models
- The biggest problems in B2B marketing attribution
- Recommendations on where and HOW to start with B2B marketing attribution
But let’s begin with some basics to stay on the same page later on.
Generally, B2B marketing attribution is a set of rules to assign credits to marketing touchpoints according to their influence on a key metric. In layman’s terms: A framework giving marketing decision-makers an understanding of which of their activities got the customer closer to a purchase
A complex B2B journey looks like this:
A B2B customer journey involves multiple parts of an organization before a decision is made. The bigger the conversion (contract) value, the more complex the journey. Attribution helps to quantify the impact of each touchpoint.
There are 11 attribution models that you can use to measure performance. None of them is an absolute truth. Each of them tells its own story. So it’s your job to ask for the right one.
Below, you’ll find a list of 11 attribution models with a short explanation. If you want to read more about them, you can do it here: https://windsor.ai/list-of-marketing-attribution-models/
- First Click Attribution Model I saw your LinkedIn post and landed on your website through the company profile. I visited your website for 3 more times and converted a few weeks later. All the credits go to my first touch – LinkedIn organic.
- Last Click Attribution Model I’ve been visiting your blog weekly for 6 months. Today I saw a Facebook ad with a discount on your product and bought it. All the credits go to that Facebook ad.
- Last Non-Direct Click Attribution Model I’ve been to your website many times via different sources. I saw a winter bundle sale through a Google retargeting ad and clicked it. Had some payment issue and couldn’t buy. I got back the next day by typing your website directly into a search bar and bought it. The credits still go to that Google retargeting ad.
- Linear Attribution Model, I’ve come to your website via blog for the first time. Then I came again from social. And then I came to your website from a Google ad and converted. Every touchpoint gets an equal credit percentage.
- Position-Based Attribution Model (U-shaped Attribution Model) The first and last touchpoints get 40% of the credit each, and the rest of the touchpoints divide the remaining 20%.
- W-Shaped Attribution Model 90% of the credit is equally divided among the three touchpoints: first touch, lead creation, and conversion. The remaining 10% is distributed among other channels within the journey.
- Time Decay Attribution Model I’ve come to your website via blog for the first time. Then I came again from social. And then I came to your website from a Google ad and converted. Google gets the most credit, then social, and then organic (the farthest from the conversion).
- Last Google Ads Click Attribution Model I’ve been to your website 2 times. Then I came again via a Google ad. After that, I’ve been to your website multiple times coming from many sources except Google Ads. I initially converted from direct. All the credits go to the Google ad in the middle.
- Lead Conversion Touch Attribution Model I found your blog via Google organic. I subscribed and became a lead. You nurtured me and I bought your product. All the credits go to Google organic since I became a lead from there.
- Full-Path (Z-Shaped) Attribution Model
90% of the credit is equally divided among the four primary channels: first touch (22.5%); lead generation (22.5%); opportunity creation (22.5%); customer close (22.5%). The remaining 10% is shared among the rest of the channels.
- Custom, Algorithmic, or Data-Driven Attribution Model The most precise and advanced one. It performs an in-depth analysis of the customer journey to identify all marketing channels playing a role in bringing visitors to your website and converting them into customers and assigning credits they deserve.
B2B marketing attribution involves multiple people. Multiple people have multiple devices. It is a complex task. Creating an attribution model for a B2C is a much easier task as in most cases it only involves one person.
The customer journey in the B2B space is long and non-linear. It’s hard to predict the interactions. It is even harder to measure the impact of those.
Leaving the technicalities aside, the biggest issue in most cases is the organizational alignment towards becoming data-driven. B2C organizations, especially eCommerce, have long been data-driven and they had the pressure to be data-driven because of the lower margin these companies operate in.
In B2B in comparison, sales teams were the rain-makers that bought in the revenue through their network. In recent history, this is dramatically changing as B2-B buyers are radically more empowered than ever. Harvard Business Review and CEB report:
65% of customers tell us that they spent as much time as they’d expected to need for the entire purchase just getting ready to speak with a sales rep.
Research is usually done online and it is your job as a marketer to understand what the prospective buyer is doing online.
Fragmented tracking and data silos
In B2B attribution the post online conversion, in other words, what is happening after a form fill is as important to understand as what happens online.
Often these journeys are not connected and aligned. If you are working in a larger organization chances are high that your data is siloed and has different owners. Here is an example of a typical setup we see a way too often:
This led to the following issues:
- Teams track their own KPIs, cannibalizing conversion numbers
- Conversions tracked are not realistic (do not include actual pipeline contribution and lead quality)
A big challenge is that teams track their own KPIs, this cannibalizes conversion numbers of other teams. What needs to be done is that the whole customer journey is connected and each team gets a part of the credit for the conversions. This needs top-level alignment in any organization trying to break down the data silos.
We often see that optimizations are done on website events that do reflect reality. Organizations should connect their CRM and post online conversion information to understand the impact of cancellations, returns, or unqualified leads on their campaigns.
Multiple people aka a buying center are involved at different stages of the customer journey
Try to remember some of your past purchases from a corporate account. How many of them did you buy with no other people involved?
I mean, sometimes even for small things you have to convince someone that you need it. The bigger your purchase is and the more it impacts others, the more people are involved in the decision-making process.
When I purchased a professional plan from Hubspot at my previous job, I had to talk to the marketing team, to the sales team, to the CEO. We needed to discuss the benefits and pitfalls, the technical support, and the resources needed to implement it.
So Max (the CEO) took the negotiations and eventually bought the product. He was the person holding sales calls and giving his credit card.
But that’s not he who initiated that process.
So multiple people came across the different stages of the journey. And if you do attribution for an individual, you’ll most likely optimize for Max, because he’s bringing revenue to your business.
Needless to say, the Hubspot purchase decision was not a real enterprise purchase. If my company would have been a larger organization, there would have been an even more complex purchasing process including complex due diligence.
Traditional sales methodologies here talk about the buying center and you’ll likely find the roles as shown in the graphic below.
Applied on a customer journey that might look similar to this:
So in short: It’s much better to track the customer journey for the account instead, not an individual.
That’s a good moment to realize why Google Analytics is not good for most of the B2B cases.
But no panic! There is a way to solve that problem. I will share how to later on.
Complex lead nurturing where not everything is measured by revenue
I wish B2B marketing was as simple as forming a lookalike audience from your existing clients, putting out a Facebook ad, and waiting for revenue to flow. But even for a $5/mo B2B product, it still seems utopian to have such a process generating enough revenue.
It is hard to educate and convince your customers. It always takes time. It may take a month to make a decision to buy a $100/mo product. Not to speak of $100,000 checks.
So you most likely have a long journey with multiple touchpoints between your brand and a customer.
How do you measure the performance of those touchpoints?
If you measure revenue generated by a channel, it is a place to start. I cannot criticize you for that. Remembering the 80/20 book, it’s close to doing what it recommends. But if you’re big enough and a 1% improvement of the ROI equals a lot of money, it is probably time to look at the “80” part.
So what approach can you take instead?
Let’s break it down on real customer data examples.
Let’s start with the most common attribution model – last touch. A default option for most of the analytics solutions and the one that usually defines what will get an investment and what will be shut down.
On this graph, you can see that Google paid campaigns are driving the most conversions and revenue for this business, with Google organic and direct traffic following them closely.
I believe this graph reflects how most the companies would allocate their budget.
But let’s look further.
Here are the channels driving awareness. While Google ads are still leading, organic and direct became closer contenders here.
What’s interesting, is that Direct traffic became more significant with the first touch attribution model. Usually, it is vice-versa. It can say about strong brand awareness or trade shows, partnerships, etc. And this may help you to get the budget for those hardly measurable things.
However, with a custom attribution model, the impact of Google paid traffic becomes undeniable.
Here you can see the comparison of three attribution models. Comparing those gives a much stronger perspective on the performance of the channels and how you should allocate the budget.
But that’s not where the real magic happens.
While this data might be enough for a CMO to make a budgeting decision, there’s nothing a performance marketer can do with this graph.
If Google ads are that massive already, you most likely squeezed something from it with proper targeting, negative keywords, bid optimizations, etc.
That’s where attribution comes in.
Here you can see the attribution model comparison on a campaign level.
It is quite clear with the campaigns #1 and #2. They are top performers for driving revenue.
Campaign number #4 is good at driving revenue as well, but it works exceptionally well for driving awareness. So putting more budget there might be a good idea if you want more people to come to your website.
But here’s the magic. That little highlighted campaign at the end of the graph. If you reallocate your ad budget between those campaigns according to the last-touch model, that one would probably be shut down. It has the second weakest performance according to the last-touch attribution model.
But in reality, that campaign is the fourth-best in driving revenue for your company. So maybe it deserves a chance?
Nobody would blame you for shutting down these two, right?
Let’s dig deeper.
Here we have ROAS as a new green bar. And those two underdogs don’t look that bad now. While the campaign #4 is barely hitting the positive ROAS according to the custom model.
Would you ever think about shutting it down and reallocating the budget for campaigns #5 and #8 without seeing this chart?
By having this granularity, we can clearly see what is worth our attention and how to re-allocate the budget to have solid revenue streams and impressive ROAS.
Multiple target audiences and buyer personas
Our website is the biggest lead generation and conversion point for most of the cases. So we experiment with it sometimes (or always). We are all glad when our website conversion rate increases after a change. And we all get upset when it drops. But what we rarely do is asking “what for?”
It’s not a discovery to say that every business has multiple target audiences. If you have one, you just don’t know yet about others or don’t do segmenting well. But let’s drop it now and say you have multiple user cohorts coming to your website or whatever.
And when you go more precisely about any significant changes happening, like conversion rate drop, you should look at how it changed for those cohorts.
Let’s imagine you locally sell coffee machines in London. You started blogging some time ago on how to choose and maintain a coffee machine. And you encountered a problem of people outside the UK coming through your blog and calling you to buy a coffee machine.
It just drains your resources. So you added a big banner “LONDON ONLY” to your converting website pages.
If non-UK countries have a big part of your traffic, your conversion rate can drop dramatically. But for the London-based users, it may increase because of even higher relevance.
It would be great to have that cohort analysis holistically, isn’t it?
Here’s how we do it at Windsor.ai :
- A user lands on your website and gets a unique ID that is stored in a database.
- The anonymous user awaits further actions and identification like product signup or subscription.
- Additional parameters are added to a user record like:
- Behavioral or geodata extracted from cookies and CRM data.
- Advanced data from data enrichment tools like Clearbit, LeadGenius, etc.
- Users with certain parameters are pulled from the database into a report with an SQL request.
With this, you can evaluate your marketing performance for the user segments with the highest LTV and avoid optimizing for low LTV/high CAC users.
So these were the most common problems a B2B marketer can face with attribution modeling. I hope those are not obstacles anymore for you to make more data-driven decisions. But problems are not the starting point, so let’s break it down.
Here are the three important steps to start with B2B attribution modeling.
Start tagging every link that matters
Well, you might not track every single link in general, but there are parts of your funnel that are more important and more controllable than others.
Let’s break the funnel into such parts (image?)
- Lead generation
- Lead nurturing
- Product usage
The funnel can be extended with Referrals or Product engagement tracking which is especially applicable for B2B SaaS to identify your power users and get viral exposure with a referral program.
Under “Acquisition” fall your activities towards getting an anonymous user to land on your website and/or provide information about them.
Organic search, paid search, paid social, and affiliate programs are some of the examples of acquisition channels.
Google made it harder for marketers to attribute conversions to certain keywords with organic traffic, so it’s a bit different story.
But with most other channels you have more control to set up parameters and track them later on.
UTM parameter consists of:
utm_source=Identify the source of traffic (Google, Facebook, newsletter)
utm_medium=Define the nature of traffic (CPC, sponsorship. etc.)
utm_campaign=Find a specific campaign (“us-retargeting”, “attribution-playbook”, etc.)
And for more in-depth tracking:
utm_term=To specify keywords for paid search campaigns (attribution+software)
utm_content=To differentiate A/B tests or specify content promotions.
More information and a template on how to structure UTM tags here https://windsor.ai/utm-tagging-fundamentals/You can find Google’s campaign URL builder here https://ga-dev-tools.appspot.com/campaign-url-builder/ to easily build UTM-tagged links.
The example of the UTM-tagged link will look like this:
So start putting UTM parameters into links you share online. Even if you don’t do attribution modeling now, it will pay off by having historical data to optimize your marketing when the time comes.
That moment when you receive permission to talk to a person directly. It can be a blog subscription form, guide download, demo request, or free tool signup. You name it.
It is an important step to track in a B2B space. So how do you make sure to match this submission to initial actions from the anonymous user? Let’s break it.
Important note: In most cases, people sign up for a form while browsing the website, but there are cases when you link to the form from an external source. In this case, you should UTM-tag the link as in the acquisition step.
To track how the user interacts with you pre and post a form fill here is a summary on the steps which need to be taken to match Google Analytics and CRM data:
- Create a unique identifier that is captured with every form submission.
- Store the identifier in Google Analytics
- Store the same identifier in the CRM system (through a hidden field in the form).
- On form submit create a trigger that sends the identifier to the CRM.
- Match both the IDs you have saved in Google Analytics and your CRM.
You have an email and identifier of your lead now. But that’s far from revenue yet. There is this chasm you need to cross called nurturing.
You can argue with that, but most of the nurturing process is taking place in a user’s email inbox.
You can show demos, schedule webinars, and share articles. But it all mostly happens via email. So you need to properly track what happens within your email, and if people take action from there.
The journey ID we have from the lead generation stage is now stored in your CRM system. Now every demo, in-person meeting and email you exchange with the lead or opportunity will be tracked inside your CRM. You can now understand the acquisition journey and combine it easily with the lead nurturing journey all the way towards deal closure.
This stage begins from the moment a user has static credentials to log in – the registration. It might seem counter-intuitive to track every user login with identification and event tracking like in the lead conversion stage. But in reality, it is your opportunity to get all the missing pieces together. Here’s why.
Every customer had its own journey to becoming your customer. Redundant, I know. So this path might be taken from multiple devices and locations. They might see your article on mobile, browse the website from a home computer, and make a transaction from a laptop at work.
But remember that all of those interactions are in a database waiting for their time. By being patient, you give your customers an opportunity to log in from those other devices. And when they do, the missing data pieces combine into one user journey.
The mobile device, the home computer, and the work laptop are now having a strong identifier of login credentials to bridge the gap.
Get a strong alignment between the departments
Needless to say that everyone in the organization should follow the rules of tagging links and also using the same format to avoid polluting your data with junk no one can read. It’s a whole topic in itself on how to get buy-in from the stakeholders.
I’m definitely not the best person to cover it. But what I can surely say is a good starting point is explaining the benefits to each department in their own language.
For a sales department, it won’t make sense to “tag every link to have clean data” but it would make sense for them to help you bring their best leads who’re closing easily and have high checks.
So find that lever for your stakeholders and make some time for it.
Get an attribution software like Windsor.ai that solves your problems
There are quite a few attribution modeling providers that can solve your problems. Most of them are targeting big enterprises, which makes them ridiculously expensive.
Do your own reasoning, and choose what fits your needs best. You can start from an attribution modeling software list on Capterra https://www.capterra.com/marketing-attribution-software/
So look around, and don’t forget that you’re always welcome at Windsor.ai.
You might also be interested in…
- Microsoft Power BI Multichannel Marketing Attribution Dashboard Template
Data-driven attribution per keyword or ad content