B2B Attribution is attribution focused on and tailored exclusively to the B2B customer journey.

This is because the B2B customer journey has two core defining elements:

1. The buying decision is typically made by multiple stakeholders.

That is, the B2B customer is not a single individual but a company where a host of employees are typically involved in the buying journey. Indeed where the individual starting the journey is very unlikely to be the one with the credit card.

2. The buying cycle is typically months-long; on average lasting between 6 and 12 months.

B2B sales cycles can easily last for many months after the first touchpoint (demo, trial, negotiations, etc.) 

This is a huge element of B2B attribution that not enough people take into account: time to revenue. Or, in other words, how long it takes from the first time somebody from a potential account visits your website until the account is closed as won.

B2B Attribution is attribution modelling that caters for these two defining elements. B2B attribution is account-based and, as we’ll inspect in much greater detail below, tracks across the months-long B2B buyer journey.

By definition therefore, solutions such as your CRM’s Original Source or B2C-focused tool like Google Analytics, are inadequate for the B2B business.

Let’s see why.

Your CRM ‘original source’ is insufficient

Unfortunately, there are many B2B companies relying on their CRM system’s “original source” field to attribute towards the lead’s first-touch.

While this might offer revenue leaders a glimpse into what touchpoints might be converting leads, it paints only a fragment of the overall picture. This is for three main reasons:

• Single-touch: it takes into account the impact of just one touch.

• First-touch: it places all value for the conversion to the earliest visit.

• Single source: it factors in just one source.

Combined, these three elements carve out only a narrow picture of everything that’s taking place in the customer journey prior to entering the CRM as an SQL. It means potentially not capturing the entirety of the customer (account) journey. 

And without taking them all into account as a holistic customer journey, it’s impossible to know what your ROAS, ROI or LTV is on all your ads, campaigns and activities. Which in turn means it is impossible to start making data-driven decisions on what’s working and what isn’t. 

More on the CRM Original Source here.

B2C tools don’t work either

In a nutshell, the difference between B2C and B2B attribution is the added complexity and length of the B2B customer journey. There are way more touchpoints and people involved in the B2B buying process, which also means it takes a lot longer to close a deal. A deal, as Steffen highlights in the episode, takes three, six, 12 months to close!

Let’s unpack this a little through a comparison.

A customer journey for, say, buying a new jacket, will look something along the lines of:

• Clicks ad/ goes to site directly/ organically and goes through with purchase immediately, or

• Clicks ad/ goes to site directly/ organically but does not go through purchase, so is retargeted and then moves to purchase.

Granted, the user might be using two (or more) devices which complicates matters a little. 

Getting to the purchase might even take a few weeks, but unlikely much more than that.

Google Analytics is geared mostly to this B2C setup. Here’s a closer look.

1) Google Analytics tracks individual devices not accounts;

As we’ve already mentioned It’s very likely that two or more stakeholders are part of buying your B2B service or product. 

But Google Analytics is unable to track this multi-stakeholder buyer journey. It doesn’t know that multiple IDs represent one and the same account, so you end up with gaps all over the place. 

This is inadequate for optimising the efforts and user journeys that perform well. It also makes it unfairly difficult to identify where things are (significantly) underperforming. 

2) Google Analytics can’t handle long user journeys

Google Analytics only tracks GCLID data for a maximum of 90 days.

Not exactly ideal for the B2B user, right?

From our own customer data, we’ve found that the average B2B buyer sits in the pipeline between 6 to 12 months - from the first touch to the deal being closed as won.

So tracking this vital data for no more than 3 months means most B2Bs are unable to ever see the true impact of their ad campaigns. 

3) Only Google Analytics tracked data is taken into account 

Google Analytics only takes into account information tracked through GCLID and Google Ads. Which leaves the rest of your paid (and non-paid) activities to be tracked and measured independently, if at all.

The fact is, Google does tracking well, and it does it automatically. Because of this, and because it’s typically the paid channel that takes the lion’s share of the attention (and budget) other activities are tracked much more loosely, if at all.

Ideally, you want to be in a position where you’re tracking (and storing) all your activities with the same vigour and accuracy.

More on Google Analytics’ B2B marketing attribution limitations here.

So how does a B2B Attribution software solution overcome the challenges posed by the long and complex B2B customer journey?

B2B Tracking Script

The objective of the Tracking Script is twofold: (1) gather as much user and wider account behaviour as possible. (2) link this behaviour to an identified user.

To achieve this, the data tracking starts from the moment a user comes onto your site (and accepts ‘statistics cookies’) - whether through paid ads, direct, or organically. With UTMs on all links and ads to your site to ensure complete tracking.

Your tracking then records users’ interactions with your site, e.g. reading a blog post or subscribing to a newsletter, as well as any properties that describe that action. From which you are able to assign ‘traits’, such as first name and email, to these users’ actions.

The stored data is eventually converted from an anonymous user to an identified user - complete with all relevant details (email, company, etc.). 

Integrations with all tools on the go-to-market ecosystem

The rest of your data comes from the tools you’re already working with, e.g. CRM, Ads, Automations, and Customer Success tools, as these already hold critical details about customers across your pipeline.

The integrations in themselves are not technically challenging, and so easily set up.

What does present a challenge is how these tools are defining their data. That is, how they’re labelling their fields, etc. But this relates to the cleaning process outlined in the next point on Data transformation.

Data transformation

Cleaning and Merging

All the data you’ve collected needs to be structured in a uniform way. In particular, you need the data coming from all your tools and site to have the same fields matched and merged under one field with a single label.

For example, your CRM might use “customers”, your ads platform “users”, and your success tool “clients”. These need to be matched and brought under one label, e.g. “users”.

As so for every single item of data.

A B2B Attribution tool cleans the data through:

• UTM mapping

After UTM parameters values are extracted (e.g. utm_source, utm_campaign, utm_content) from the URL, the data processing will map certain combinations of UTM parameter values to a customized source and channel name. For example: map (utm_source=hs_automation) to (Source: Hubspot, Channel: Email).

• Deduplication

The data coming in from your tools and the site will often overlap with each other. This is sorted and those duplicate fields are removed.

• Removing unwanted data

The algorithm then takes into account any null values or not set values within fields, and excludes irrelevant tracking like the activities from your own employees.

• Merging to account

As already mentioned defining component in the data set for B2B attribution is the company (account). It is this that places the various users from each company into one single customer account.

The most important element here is to find the company from amongst the data. The B2B attribution platform will set the following hierarchy:

• Set a default source for this, e.g. Salesforce (CRM);

• Add an alternative tool if it is missing, e.g. when it’s a lead;

• If alternative tools have not got that information, your merging algorithm could pick up the email account and assess whether that is a company email address. Or if not, reverse look-up IP address.

Attribution modelling

The B2B attribution software will then move to model the data. This includes: 

• Setting conversion goals, i.e. what is a conversion, and crucially, be able to identify these from your data.
  

• Setting up the parameters for your sessions and events on all your different data sources. Most companies rely on Google Analytics to define their sessions for web traffic (which carries some risks in its own right, more here) but when you have your own tracking and combine more sources, these are defined by the B2B attribution tool.
  

• And whether some sessions or events should be excluded, such as we might track sent emails, but we do not want to attribute value to a sent email, but rather to emails that was clicked.
  

• Setting up attribution goals/benchmarks by defining what is being attributing towards. For example, your MQLs, SQLs, or New Business.
  
  This includes switching between these models in order to compare what is working against each goal. For instance, MQLs and not for New Bizz, etc. This helps see what occurred at different points in these deals with different marketing/sales models.
  

• If you’re working on campaigns or channels with different currencies the B2B attribution modelling will also solve for the currency you want to be reporting in.
  
  

Activation

Finally, there’s the activation. That is, where you’ll be working with the data and how.

B2B attribution software providers will offer their own application for this. Dreamdata is no exception. However, Dreamdata also opens up the data sitting in BigQuery where you can:

• Run your own queries on our data models

• Copy, manipulate, join and use the data within BigQuery

• Connect your favourite BI, ML and Operational Analytics tools to the Bigquery data. These include: Hightouch, Google Data Studio, Tableau, PowerBI, Sisense, Looker, and Metabase.

Check out Dreamdata’s latest destinations and dashboards here.

We’ll look at these in more detail below.

The plain answer is no. At least not unless you’re an organisation of a certain size with the necessary resources to go down this route painlessly.

And definitely not if you have not tested an off-the-shelf solution first. The last thing you want to do is spend a ton of time, money and effort (which come at the expense of other projects) on a solution you don’t yet know if you need.

Most decisions to go down the build route are based on the following three assumptions.

First Assumption. You don’t need some of the features an off-the-shelf platform offers

Yes, there are some features from an off-the-shelf platform which you might not make use of - at least as far as you’re presently aware of.

Building your own means you would only have those features which you know that you need (at this stage).

However, as we learned in the post, these typically sit at the very top of the platform. That is, they are part of the final set of algorithms and reporting models.

Focusing only on these when building your solution in-house risks not handling the data properly at an earlier stage, e.g. when cleaning and merging your data. 

This means that should you find out later that you do in fact want to run an attribution model you excluded initially, you will have to re-do almost the entire data processing and all subsequent steps again.

With an off-the-shelf solution like Dreamdata, you can simply choose not to use a feature without affecting the data collection and cleaning process. With Dreamdata all your data will be cleaned and merged irrespective of whether you end up using that data or not. Which means, should you decide to start applying a different attribution model in the future, you will immediately be able to run these without re-doing algorithms.

Second Assumption. You have the resources to build a solution in-house

If you’re making this assumption it is likely that you have given some thought to what it will take to build the platform in-house.

However, from the post we can see that there are many details which can easily be overlooked. In fact, at Dreamdata we encounter an oversimplified understanding of what happens in an attribution tool quite often. 

Many B2B CMOs, ops professionals, and growth leaders (those typically wanting the solution) do not realise just how complex revenue attribution is until they start the process of setting up a solution themselves - this includes setting up both bought off-the-shelf and built in-house.

As laid out in some detail in the post, it really isn’t just pulling data out of the tools and putting them into a dataset. The complexity lies in the sum of all the intricate individual steps in the process.

The devil really is in the detail. If you haven’t set about collecting the right data, or things aren’t being mapped in a correct fashion, or you are not carrying out the necessary maintenance of the platform, you lose the all-important overview and the ability to derive maximum value from doing revenue attribution.

Third Assumption. An off-the-shelf platform doesn’t give you the bespoke tailored solution you want

It is true, almost by definition, that an off-the-shelf platform cannot offer the same level of bespokeness that an in-house solution can.

But, when considering this factor you really need to understand precisely what bespoke solution you’re considering.

Some B2B marketing attribution platforms like Dreamdata, offer a very high degree of customisation and support. The best of off-the-shelf options offer a partnership solution with continual support to ensure the platform is adapted to your needs to the greatest degree possible.

You can limit the scope (and thereby the cost and risk) by utilising an attribution platform like Dreamdata to do the time-consuming data crunching and use the resulting cleaned data to power your own BI or dashboard solution. In this way, you can build bespoke solutions tailored to your business in-house, without having to build up all the data infrastructure.

It is also worth noting the point made in the ‘maintenance’ section above. You need to weigh in the fact that an off-the-shelf platform like Dreamdata will be constantly using all its resources to innovate and find better and faster ways of doing things.

Creating this enduring competitive advantage (which is very unlikely to be done in-house) carries considerable value in its own right, especially if it factors in customisation.

Check our product videos ➝

What an off-the-shelf B2B marketing attribution offers

• A safer investment of time and treasure

With an off-the-shelf B2B attribution platform like Dreamdata you are spared from the higher risk investment of building a solution yourself.

An off-the-shelf tool B2B attribution tool will already have most, if not all, the features you’re looking for.

Whether it’s to get a taster of what scope, functionality and features you want, or to trial the value of B2B attribution itself, or even to wait till you are further in your growth journey with more resource, an off-the-shelf tool like Dreamdata is the better choice.

• Attribution models for every flavour

Out-of-the-box, a B2B marketing attribution software platform like Dreamdata allows you to analyse your revenue-generating journeys through many different attribution models, including:

• First Touch

• Linear

• Last Touch

• U-Shaped

• W-Shaped

• Linear Non-Direct

• First Touch Non-Direct

• Last Touch Non-Direct

• U-Shaped Non-Direct

• W-Shaped Non-Direct

• Customisability

One of the motivations behind wanting to go it alone is a business requirement for high customisability. Nothing quite compares to doing this yourself, right?

Many off-the-shelf B2B marketing attribution software offer high levels of customisation within their applications. Dreamdata for instance offers a very wide selection of filters which enables a high level of dashboard customisation.

At a more technical level, Dreamdata offers custom attribution models which can be designed entirely by you.

But even if this doesn’t go far enough, Dreamdata offers free access to the data platform. Meaning that you get full access to all the modelled data. Which you can then connect to your favourite BI/visualiusation tool, ML model, and/or send back to your go-to-market tool using reverse ETL tools.

Maintenance

• Ensuring the dashboards are presenting the correct data.

  You’re going to need systematic monitoring of the data that’s being pulled, i.e. that your algorithms from top to bottom are all doing exactly what they should be. Otherwise, any analyses and attribution your running will be as good as useless.

• Changes in requirements

  What if your CMO or growth leader changes and sets out new performance targets and metrics? Here is where the extent of automation built into your modelling and reporting becomes very useful. Unless automatic, not a question of pressing a button. More redoing.

• Changes in commercial tech stack

  Say you buy another tool for your commercial tech stack, or even replace your CRM, how is your system going to manage the changes necessary?

• Troubleshooting

  There’s the obvious problem of troubleshooting any errors that prop up across the system. A particularly important point to bear in mind is if there’s a break taking three weeks to solve, what happens with the tracking data then?

• Innovation or improvements

  Are you planning to run with the pack (or peloton if you’re into cycling) or are you striving for a leadership position where you’re creating an enduring competitive advantage?
  
  Sure, keeping up with trends and changes in the tech stack might suffice, but to really draw continued maximum benefit, you will need to have some innovation.

In summary, off-the-shelf B2B marketing attribution software like Dreamdata will:

• Ensure the dashboards are presenting the correct data.

• Accommodate changes in requirements

• Accommodate changes in commercial techs tack

• Help with troubleshooting

• Ensure constant innovation or improvements

Dive deeper into the Build vs Buy question in this post.

Ok, so now we’ve covered what’s under the hood. But how does all this help B2B go-to-market teams build, repeat and scale success?

Well, here are some examples for Marketing, Sales and Data teams:

For Marketers

• Unprecedented clarity of the B2B customer journey

By connecting all the dots on the B2B customer journey, B2B marketing attribution paints as complete a picture as possible of what is happening in that journey.

This means you will know when and how your leads are interacting with your efforts. And ultimately find what’s working and what isn’t in driving them down the funnel.

You also learn the crucial Time to Revenue metric - from first touch as anonymous user to deal closed as one (and beyond). Meaning that you can better plan and time your efforts.

Check out this post for much more on the benefits of Time to Revenue.

Dreamdata’s B2B marketing attribution software has a Customer Journey map that plots every touch every user and account makes.

• Confidence to Scale

This clarity into all your channels, campaigns and other revenue-generating efforts means you finally gain the confidence to scale. 

By connecting your efforts to pipeline and revenue you learn which are your high-converting channels and touchpoints, what campaigns are performing and which need scrapping. You can reduce wastage and double down on what works, so that when the order comes down to scale your efforts by X, you know exactly where to go.

With this clarity, forecasting becomes more accurate, as does improving loyalty, retention and brand reputation.