B2B Revenue Attribution: Build vs Buy

Aug 23, 2021
Author: collin stewart

Revenue attribution is something that many companies aren’t doing properly. That’s understandable – revenue attribution is complicated. But if you don’t have a strategy for how to calculate attribution at your company you’ll be stuck relying on guesswork as to what does and doesn’t bring in revenue.  


Ole Dallerup, a recent guest on the Predictable Revenue Podcast, comes from a software background. In development and technical roles, it was natural to measure everything from who logged onto your platform, how people interacted with it, to which features they were using. And in order to measure this effectively, they would simply look at the data. 

When Ole moved into the revenue world, he realized that the teams making up the revenue-generating organization view success metrics differently. Specifically, marketing teams often measure their performance against vanity metrics, “metrics that make you look good to others but do not help you understand your own performance in a way that informs future strategies” (Tableau). For instance, marketing often measures clicks or impressions to gauge the success of an ad campaign. But clicks and impressions don’t take into account whether the ad is touching the right people, or if they’re ever buying from your company because of this ad. Essentially, these metrics don’t give marketing teams any sense of whether the money they spent on a strategy was fruitful or a total waste. In practice, marketing teams should be trying to find out what impacts the number the whole revenue organization cares about: revenue. 


Marketing teams don’t skip tracking more truthful forms of data just because they feel like it. Unlike software developers, these teams lack the tools and the skills to track and utilize the data from disparate sources in varying formats. This is a complicated task. 

Even in the simplest of b2c scenarios, like an ecommerce company selling shoes online, there is a lot of data to track. If the company is displaying ads on just one channel, like Google, then they can look at google analytics. But as soon as they mix in another channel for ads like Facebook, they have to look at another analytics tool. This second analytics tool won’t integrate with the first and doesn’t necessarily offer up accurate attribution. Each analytics tool might tell the ecommerce company that it’s responsible for 80% of revenue generated.

Two people working and pointing at a laptop screen


Similar discrepancies occur in b2b revenue attribution when no standardized system is set up. Each tool says something different about revenue generated. Just like each department might have a different view on how revenue is generated. 

Ole once worked with a business trying to figure out its revenue attribution. He spoke to the CRO, the CMO, and the CTO. The CTO thought he was responsible for 0% of revenue generated. The CMO and the CRO both thought they were responsible for 80% of the revenue generated while the other was responsible for the remaining 20%. The CMO and CRO both thought they needed the lion’s share of their yearly budget to grow their teams, while the CTO was content to accept whatever money was leftover. After revenue attribution was designed correctly at this company, it was evident that the CTO generated 80% of revenue, the CRO generated 20%, and the CMO generated 0%. 

Without accurate revenue attribution, it’s easy to invest money in the wrong things. Each leader is working off of a hunch or their own set of vanity metrics. When attribution is accurate, companies can see which departments and which channels generate the most revenue and figure out exactly where their spend should go. Unfortunately, a lot of the data that is available and visible to leaders through the tech they use is misleading. That’s why companies need to establish a process that is purpose-built for revenue attribution.


Revenue is the most important metric to track. It is the single most important factor for every department whether it be product, marketing, or sales. Sometimes, however, it’s not ideal to track just revenue because it takes so long to see impacts on revenue. For that reason, Ole suggests looking at metrics further up the funnel such as SQLs or opportunities in pipeline. These are leading indicators of revenue generation.


To build a revenue attribution system in-house, Ole maintains that you’re going to need to build some relatively heavy infrastructure. You’ll need to hire real engineers who know how to build things that can scale and build models that are dynamic. As your organization grows, so do your datasets. As different tools are added or subtracted, your dataset changes. As new information becomes available on a certain contact, your model needs to be able to adjust historic attribution information.

First, you need to build something that will help you track and extract all the data. This would mean placing cookies on your website, extracting data from CRMs, marketing automation tools, support systems, chat systems, and whatever other tools you use that would contain and produce meaningful information. 

Next, you need to import this data into a database or data warehouse (you can cheat at this step and purchase one like Snowflake instead of building something from scratch). 

Now you have a large quantity of raw data sitting in your data warehouse. You have duplicates, data coming in different formats in a random order, structured and unstructured. So, you need to clean it. You need to standardize the format, and remove duplicates.

Once your data is standardized, you can link all of the data with a unique identification variable for each customer. Use something like an email address that is tracked in every tool. From here, you can link contacts with companies and activities with contacts.

In the end, you have one uniform table complete with contacts, companies, activities, timelines, etc. Now you can start talking about attribution. So, you’ll come up with an algorithm that is appropriate for your company. 

Finally, you need to visualize the data in a meaningful way so that non-technical people can make sense of it (this is another step where you can cheat a little and purchase an established tool like Tableau). 


Fortunately, if you don’t want to hire a team of 10 engineers and spend 2 years building out your in-house revenue attribution system, there are tools on the market that can do it all for you, like Dreamdata. Ole and his cofounder build Dreamdata when working at a past company, Trustpilot. They were starting to focus on revenue attribution but realized it was an almost insurmountable task and there were no tools out there that could help with all the different steps. Ole realized that because of this, even though it means they can’t make good business decisions, a lot of companies just don’t attribute revenue accurately. Dreamdata connects all your tools to automatically pull, clean, and visualize data so you can measure what you want to measure and spend more time and money on what actually brings in revenue.


The marketers reading this right now are thinking, “what, so brand exposure doesn’t matter?” It does. But for activities that do not directly generate revenue, marketing should be finding ways to do them cheaply so that budget is saved to invest in what does directly generate revenue. 

That said, revenue attribution is never as simple as a salesperson prospects a customer and they book a meeting. There, revenue attributed. Customers often take long, circuitous journeys before eventually making a purchase. They might go onto your website and sign up for a webinar. Then, their colleague might sign up for your newsletter and see an ad, and so on. This is where using technology to help you track attribution becomes vital. You need to be able to map out that customer’s journey, have an understanding of how they touched you, and which people from their company were involved. Figuring this out is both difficult and time-consuming as the data comes from different systems and sources such as your CRM, marketing automation tool, support ticketing system, and website analytics. 

Most attribution models are too simple – they attribute to either the first or the last touch. This doesn’t help companies figure out how to replicate a successful customer journey. On the other hand, you don’t need to come up with some complicated algorithm in an attempt to perfectly segment the revenue generated between each activity. In all likelihood, you won’t have visibility into every single activity a customer took, so your attribution will be imperfect.

For these reasons, the algorithm you create that shares the eventual outcome or revenue between activities doesn’t matter as much as looking at the journey as a whole. Attribution is not an exact science. You just want it to indicate, directionally, which activities generate revenue so you figure out how much time you spend on said activity, approximately how much revenue it generates, and then you can decide whether to optimize, double down, or axe it.


Even if you don’t want to get revenue attribution fully up and running right away, Ole implores you to set up tracking on your website today. Find a system that will help you link an email address to a website visitor. Each day that you don’t track the people coming to your website you are deleting that data and removing the possibility of being able to go back in time and see what works and what doesn’t for bringing in customers on this particular platform. You’ll have no idea what messaging, banners, pop-ups, buttons were a good idea and which were a total miss.   


When companies start doing this, Ole sees the relationship between marketing and sales transform. The teams no longer duke it out over who generates the most revenue. They work together to analyze which types of customers are best, which channels they come from, and how you can get more. This allows companies to control their spend and their lead flow. It also gives marketing the ability to experiment and see data on which campaign and channels work best rather than relying on someone’s gut. 


Once upon a time, b2b companies were encouraged to find one channel that brought in revenue and lean into it. Today, one channel is not enough. Customer behaviour changes. New channels emerge and old channels become saturated or obsolete. Complexity and cost grow every day, and it is more vital than ever to be able to see what works and move your money there. If you won’t have a way of effectively attributing revenue, you’ll be shooting in the dark forever.


More on being data-driven and figuring out what really generates revenue:

Rev Ops: The Missing Link That Will Increase Your Revenue by 26%

Building a RevOps structure to increase revenue and customer LTV



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