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The Attribution Problem: How Poor Data Leads to Poor Decisions

In the past few years, attribution has been dragged through the mud, and for good reason. 

Initially, it felt like it would be *the thing* that would finally allow marketers to be data-driven. But attribution was oversimplified over time and now, is a biased judge that we use to determine things like:

  • what the exact ROI of something is,
  • which team gets credit for a deal, and
  • which channel drove a conversion. 

This (flawed) view of attribution has a lot of negative consequences for businesses and their go-to-market teams. For marketers, it ultimately means to continue optimizing for short-term gains at the expense of long-term strategies (which is, ironically, something we need to focus more on). 

If attribution claims that Google drove the most conversions last quarter, how do you convince leadership to keep investing in, let’s say, the podcast that you know impacts your pipeline and sales cycles, but never shows up in your attribution software?

This article will break down everything wrong with how we perceive attribution today. Then, we’ll dive into what the role of attribution should be – and how to practically apply it in that manner.

We’re using attribution the wrong way

There are two core problems with attribution today:

  • Most attribution software vendors have limited technological capabilities
  • We’re looking to extract the wrong insights from attribution software

Here’s one common scenario caused by these two problems:

  1. Your attribution software uses a first or last-touch attribution model and doesn’t show you all the touchpoints of your customer’s journeys
  2. Leadership wants binary answers to revenue attribution (e.g. what is the ONE channel that drove the conversion? Was it Marketing-influenced or Sales-led?)
  3. The difference between where demand was created (i.e. the channels, platforms, and mediums where people learn about your brand or product) and where it was captured (i.e. the method through which a prospect declared interest and you received their contact information) is not taken into account
  4. The context and complexity of a B2B customer’s journey are lost on you, and the conversion is credited only to what your attribution software can track (e.g. a Google ad)
  5. You’re led to believe that you should invest more in Google Ads, when in fact, it only captured the demand that was created somewhere else (and you’ll most likely neglect or pull resources from that place moving forward since it’s not being credited by your attribution software)
  6. You continue investing primarily into demand capture channels that, on their own, mostly generate low-quality leads (i.e. prospects that are only looking for information, were only interested in some specific content you created, or are otherwise not looking to buy right now)
  7. Attribution software doesn’t have any post-conversion data (i.e. it stops tracking the journey once a prospect turns into a customer), and you continue optimizing for the customer without understanding their activation, churn, or expansion stats

If you only look at a single data point in your customer journey (e.g. last-touch), and you pull insights from it to inform your strategy and execution, you can create a vicious circle of misinformed decisions.

The simplified version of the customer journey as our industry sees it today (first-touch, last-touch) is quite different from the reality of complex B2B customer journeys.

The best way to think of B2B customer journeys is as a convoluted mess of touchpoints or interactions. And it’s a different combination of interactions for each buyer. On top of that, these combined interactions also become more and more varied when you consider customer journeys across different industries, verticals, and companies. 

To try and gain a better sense of this complexity, check out Gartner’s visualization of the B2B buying journey: 


For the longest time, attribution software vendors have told us that this journey is linear and simple because the technological limitations of these vendors don’t allow them to show us the real picture.

On the other hand, traditional business operators don’t want to acknowledge the complexity of this process either and are fine with simplifying it, believing that you can funnel your prospects and convert them as easily as Google ad → book a demo → closed/won.

And as a result, we’ve spent the better part of the past two decades focused on only a few touchpoints (one could argue, the least essential touchpoints), while neglecting 90% of the customer journey.

The problem is that the touchpoints we’ve been overlooking are usually key.

And yet, most attribution software solutions won’t show you common touchpoints like:

  • LinkedIn ads engagement and impressions
  • Brand awareness touchpoints
  • Podcast episode listens
  • Gifts you’ve sent them
  • Sales touchpoint

Without this data, you optimize your entire marketing engine around direct-response campaigns on demand-capture channels (i.e., you’re only going after the people who are already in-market looking for a solution). This (inevitably) leads to a drop in win rates and the effectiveness of your marketing decreasing over time.

Your marketing team stays focused on lead generation and attracts low-quality leads that convert at zero-point-something rates, while neglecting one of the major purposes of marketing (i.e. planting seeds in the minds of more and more out-of-market buyers and making your product the first one they think of when they are ready to buy).

Attribution is more than proving ROI

Most attribution software vendors sell you a simple story (one that suits them): The purpose of attribution is to give credit to a single channel and/or a single team, allowing you to prove ROI.

If your only goal with attribution is to get these insights – that is what you’ll get. 

This line of thinking is based on three false premises:

  • The customer journey is linear and fits into pre-made models
  • All marketing activities are measurable and are supposed to drive immediate revenue
  • Different teams and initiatives can operate in silos

So, let’s do some myth-busting on each one of these now.

You cannot truly understand ROI until you have the ability to analyze your customer’s journeys

Let’s return to that example of attribution software crediting Google Ads (or another demand-capture channel) for a conversion.

Here’s what happens when you don’t question this data and let it dictate your decisions:

  • You optimize activities/channels that had little to no impact on your buyer’s decision
  • You invest more time and money into these touchpoints
  • You neglect the touchpoints you can’t identify

The touchpoints you neglect often turn out to be key in building brand affinity and association in your buyer’s mind and collectively impact conversions down the line. 

Again, Google ads (or any other demand capture channel/activity) simply capture the demand and are often irrelevant, as in, a determined buyer could’ve found your site/pricing page even without your ads or high organic search rankings.

The (ugly) truth is – you should invest more in the touchpoints you usually can’t see in your attribution dashboards.

But how do you do that when there’s no objective evidence of their impact and, therefore, no leadership buy-in since none of these touchpoints are visible?

Well, “if it’s not measured, it cannot be improved,” right? 

So, let’s start measuring it.

With HockeyStack, you can plug all your data sources into one place and turn all that data into visual customer journeys. This will show you all the touchpoints you’ve been missing out on. 

Once you’re able to gain a better view of all the different channels, content, and efforts that were involved in your customer’s journeys (from the time they heard about you to the point they became a customer and beyond), you can begin analyzing the data, and reiterating your strategy according to what’s working. 

Sustaining this cycle of customer journey data → insights → refinement helps you gradually remove low-payoff efforts from your strategy and do more of the things that actually impact and drive revenue or business growth.

Only once this is in place can you truly begin to understand and prove your ROI.

Core marketing activities are often long-term plays that aren’t supposed to  drive immediate revenue

We’re all customers of something, so we know that things like brand awareness, association, and affinity play a role in buying decisions.

And yet, we let poor data lead us to assume that our customers operate differently. 

When was the last time you made a high-ticket, complex purchase right after seeing a Google ad for the first time? Or a Facebook ad? Or just because an SDR sent you a nice, personalized DM on LinkedIn?

So, why do we expect our customers to behave any differently?

The truth is, we all know this to be true anecdotally, from first-hand experience, but technological limitations don’t allow us to understand the complexity of our customer journeys and truly ‘measure’ the impact of our efforts, which leaves us running direct-response, lead generation campaigns with follow-up sales cadences that convert at 0.01%.

Another critical factor here is the fact that only 5% of your B2B customers are in buying mode at any given time. The other 95% simply cannot be swayed into making purchasing decisions when you want them to.

If all (or most) of your marketing efforts are aimed at converting the 95% of your market that is not actively looking to buy, your marketing efforts (messaging, content, etc.) won’t fulfill their purpose of educating and evangelizing them. 

Additionally, you’ll most likely fail to convert them because data shows that when they finally are ready to buy, they’ll go to the brand they’re most familiar with. And if all you’ve been trying to do is convert them, that won’t be you.

Funny how that works, huh?

The touchpoints that are meant to educate, associate your product with an outcome, and build admiration towards your brand exist throughout your customer journey. It’s just that, at the moment, you don’t have the data to register them and prove their ROI (i.e. show their impact on business growth) to leadership.

The main reason we built HockeyStack is for marketing teams to finally have a way to shed light on these hidden interactions that turn your ICP into customers.

When it comes down to it, the purpose of marketing (at its core) is not to convert, but to make sure that your brand is the first to come to mind when someone is ready to convert. And we do that by creating memorable experiences for those interacting with our brand through different types of content (educational, tactical, and entertaining).

Such content won’t show up in your traditional attribution touchpoints but is a key factor, and when overlooked, leaves you chasing leads/MQLs, one month at a time.

No channel or team can operate effectively in silos

We’re all familiar with ongoing conflicts between functions that should, in theory, be best buddies working towards the same goals.

  • Sales and Marketing
  • Growth and Sales
  • Product and Marketing
  • Finance and everyone else

Here are two examples of how flawed data creates silos and dysfunctional relationships among different teams in companies:

  1. A product-led business that attracts free users from their ICP with high activation rates. However, Sales never follows up with these users because they’re only incentivized to talk to customers from cold outbound since those are the ones they can be credited for closing.
  2. A sales-led business that relies on lead generation as the only model they can effectively track causing Marketing to chase low-quality leads to send to Sales, who spend most of their time following up on leads that won’t convert.

Incentives and goals directly affect behavior. And if your incentives are built around goals that are built around flawed and siloed metrics, you’re going to end up with misaligned departments that are working towards different goals.

So, what’s the main cause of this problem?

A single source of truth doesn’t exist, success metrics are different for each team, and long sales cycles make it difficult to make data-driven decisions since we can’t analyze these journeys. Once again, technological limitations influence our mental models, and we’re stuck tracking the wrong metrics. 

Marketing only tracks and cares about getting more leads because that’s what they’re measured against. 

Who cares if they convert? Well, Sales do, but…

Sales is only interested in prospecting and closing deals because that’s what they’re tasked with. Who cares about activation and retention? 

Well, Product and/or Customer Success do, but…

You get the point. 

Poor data and tracking limitations lead us to draw the wrong conclusions, which over time inform bad decisions, and then our strategies follow suit.

Now, imagine if each team could understand how their actions directly impact the business in the long run:

  • Marketing would know which campaigns, channels, etc., generate high-quality leads that convert at higher rates, activate quicker, and stick around longer
  • Sales would understand the customer journey of leads they follow up with and would know how well they activate and retain post-conversion
  • The product would be able to see where (which verticals, companies, channels, etc.) the best customers come from, how they discovered the product, which features they spend the most time with, etc.

It is also crucial that all this data sits in one place, where you can get unbiased answers to all your questions (*cough* HockeyStack *cough*).

Start tearing down the silos your teams operate within by first implementing a way to understand the entirety of your customer’s journeys and then making that data available to everyone on the team.

Attribution is about analyzing more data the right way

The solution to the attribution problem is simple, but it requires us to abandon how we currently think:

  • The B2B customer journey is linear and simple
  • A single touchpoint or team can be responsible for the conversion and be given credit to
  • Simple attribution models provide enough context to feed strategy

If we acknowledge that this line of thinking is outdated (and simply wrong), we can start solving the attribution problem with a new way of thinking:

  • The B2B customer journey is very complex; we need more data to truly understand our buyer
  • A single touchpoint or team cannot be fully credited with a conversion; we need to understand the entire journey and figure out what touchpoints collectively impact conversions before having conversations about ROI
  • No attribution model is perfect; a combination of attribution models is the way to go

These new premises provide us with a clear solution to the attribution problem:

Focus on collective impact > continuously get more data > better understand customer journeys > refine your strategy 

Now, let’s go over how you can apply this today.

Self-reported attribution is not enough

If we agree on how flawed the notion that a single Google Ad can be responsible for a conversion is, and that we shouldn’t let a single data point inform our strategy, why should we treat self-reported attribution (i.e. asking “how did you hear about us?” on your contact sales form) any differently?

Asking someone how they first heard about your brand still gives you a single data point, but this time – it’s a data point that originates from imperfect beings and relies on human memory, marketing/tech literacy, and the willingness to waste a minute on giving you more information about themselves.

I have personally implemented self-reported attribution, and while some entries were literal gold nuggets of information on customer journeys, roughly 85% of entries were single-word channel mentions (Facebook, Google, etc.).

Let’s be clear – self-reported attribution is a great start, it means more data & context for you, and you should definitely implement it (especially if you’re still only using software-based attribution).

It helps marketers show leadership with at least some data that reveals the impact of the hidden touchpoints we discussed earlier.

It helps us convince them to invest a little more into that podcast, employee advocacy program, or other channels that traditional attribution software doesn’t consider.

However, self-reported attribution is still a single (flawed) data point that can’t give you the full context of the complex buyer journey.

It’s great for agencies that use 2-3 marketing channels, whose audiences consist of marketers who are also fans that are willing to spend a few minutes reconstructing their journey to help out the brands they love.

On the other hand, if your company…

  • runs omnichannel marketing with 5+ channels involved,
  • has different motions at play (product-led, sales-led, marketing-led),
  • sells to different verticals/ICPs,
  • has a more complex customer journey with different stakeholders,
  • consists of different teams that all have different goals,
  • and/or has more than 50 customers,

… then you will need more data.

You also need better ways to analyze that data if you want to use it to refine your marketing strategy.

How HockeyStack helps you understand your customer journey

At HockeyStack, we’re working on building a single source of truth that marketing teams and leadership can count on to make their marketing more efficient.

At the moment, HockeyStack allows you to:

  • Connect all your ad platforms, website, and CRM data in one place
  • Turn all of that data into visualizing customer journeys that you can analyze
  • Uncover touchpoints that have consistently impacted sales cycles but went untracked by attribution software
  • Understand how your team’s efforts and initiatives contribute to business goals and growth
  • Build custom reports around any metric/funnel/activity, 100% no-code
  • Track touchpoints ranging from LinkedIn ad impressions and podcast listens to gifts opened and pricing page visits

Along with creating content that dives deep into understanding and approaching Attribution 2.0, we’re also building the tools that provide us the technical capabilities and features we need to adopt it.

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