Analytics CEO makes a passionate case against marketing attribution

Marketing Attribution Myth

The following is a guest post by Sergio Maldonado, the founder and CEO of Sweetspot Intelligence. The above cartoon by Tom Fishburne was not part of the original article.

I have seen the Emperor walking naked for too long, and I wish I could be that naive kid in the crowd. I do not believe in marketing “attribution”. Beyond the combined power of algorithms, data, software and professional know-how, the concept is — at its foundation — flawed.

Here’s an attempt at explaining my standpoint, although it’s worth noting that more scientific avenues have already been explored with similar conclusions.


It all started with a beautiful idea. Cross-channel attribution (or “multi-touch attribution”) became a popular concept at the time when web analytics had just completed its journey from IT to the marketing department (circa 2008).

What was it about? Essentially, our ability to assign a specific value to each touchpoint or event — often a paid ad view or click — contributing to a final business outcome or conversion. This would allow us to stop giving undeserved credit to the last or first campaign or touchpoint logged in the chosen system of record.

Increasingly more sophisticated techniques for the integration of owned, paid, and earned media touchpoints within first-party analytics environments have subsequently increased our capabilities, eventually spinning off a software category of its own.

What is not to like about the whole concept? It embodies everything marketers ever wanted to get out of data!

The cross-departmental synergies it creates cannot be ignored either, starting with a translation of marketing outcomes/touchpoints/reach into dollars — a long dreamed bridge between the CMO and CFO camps — and continuing with an engineer-friendly understanding of the marketing process, which is music to the ears of CIOs and marketing technologists. Plus CMOs can now be held accountable, making CEOs happy. In other words, killer fuel for company politics.

But there is of course an ultimate promise: a mastery of the formula results in consistently getting more money out of it than the amount originally invested. Which is truly unbeatable.

Why question it, then?

Well, attribution — even when solely focused on digital channels — places a very tall order on prerequisites:

  1. It requires linearity. There is no single, common timeline sheltering all possible initiatives in the vast realm of marketing. Even less so one in which such investments and our desired business outcomes coexist. A fictional “snapshot” is required when cause and effect reside in parallel realities — one that is defined by our even more arbitrary decision-making milestones.
  2. It requires causality. Causality does not happen in aggregate, but instead at very granular level. More specifically, at customer, user, fan, visitor, lead level. This means cross-device and cross-media integration at a user level are imperative if attribution is to work. In other words, attribution requires the famous 360-degree view of a statistically significant amount of potential customers. More on that separate fiction in a minute.
  3. When pertaining to humans (always the case thus far), the understanding of attribution is limited by the understanding of the human mind. Our culture, life experiences, perceptions… affect a system (human brain) which we do not entirely comprehend. Now, can the human mind ever understand its own intricacies?

Lizard Brain

Okay, this was somewhat vague and abstract — highly relevant picture of the lizard trying to understand the lizard brain aside — but the ground is now paved for a more straight-forward explanation.

So here’s a second try. Attribution will not happen because:

  1. There is no common timeline. Many campaigns, channels, or media have a longer term impact on true business outcomes that we can even measure. Are you prepared to maintain never-ending conversion funnels so as to properly take into account the long-term impact of social reach and cultural associations? Will you really be able to compute all possible conversions?
  2. There is no single customer view. People are less “digitally unique” and traceable every day. First because they choose to be. Second, because they scatter their attention and touchpoints across multiple, isolated environments. Third, because privacy standards or data protection laws prevent further integration.

Sure, I will have to explain myself much better on this latter point:

  1. Useful as it is for its primary purposes, cross-device identification is not enough to get us to a single view of even a fraction of our potential customers. And it will only get worse unless we bring supercookies back into the picture, which is highly unlikely given the next point below.
  2. We easily brush privacy compliance aside, but the EU’s upcoming General Data Protection Regulation (“GDPR”) [PDF] will draw a red line in the sand (or the Atlantic Ocean), with the many US companies participating in the Privacy Shield program most likely dragged into a much harsher reality. Furthermore, the FTC has identified the compliance of cross-device identification activities as one of its top priorities, while the Federal Communications Commission (“FCC”) has just introduced an opt-in approach in its own Broadband Consumer Privacy Rules.
  3. Social trends run counter to a single identity. Surely much more important than regulatory limitations, as these only follow social unrest. But society keeps finding much more effective and enforceable means of defending itself against lack of transparency.

“But what about new models combining deterministic information (truly integrated at granular level) with probabilistic data? Have we not overcome technical and legal constraints with smart algorithms?”

For starters, even though I proposed this myself as a solution at the time, probabilistic models are now facing the same legal challenge: the EU’s GDPR will label this non-PII data as “pseudonymous” (rather than anonymous) if it can be used for profiling purposes, and the collection or processing of such data will be subject to the very same limitations/burdens as of May 2018. And this month’s ruling on IP addresses by the European Court of Justice will ensure that the very concept of PII as a threshold for compliance becomes a thing of the past well before that.

Secondly, does it really matter that you put together the best sounding algorithms and weight distribution alternatives when all you ponder are touchpoints within your sphere of control? The core limitations have not changed one bit, and yet we place our faith on the more sophisticated blend. Do we simply want to believe in magic?

Now, as convinced as I am that attribution does not work in itself, I can surely appreciate that attribution efforts (i.e., investments in the pursuit of such nirvana) do in fact produce positive and tangible results.


Taking it from that point, if attribution is impossible, useless, even illegal (!)… then why do we spend fortunes on this mission? Look at the amount of effort gone into the said blending of space and time:

eMarketer estimates that over 50% of US marketers are using digital attribution models in 2016, with over 60% expecting to expand attribution to offline channels in 2017.

A recent report by eConsultancy concluded that 43% of organizations reported having a single customer view — yet only 12% claimed to have the required technology in place. As pointed out by by David Raab (see his comment on Scott Brinker’s post), the companies who say they have built a complete view without the technology “are either magicians or fooling themselves.”

Perhaps more relevant are the findings of the multiple workshops and roundtables on attribution taking place across uncountable marketing events worldwide. Having attended a few of them myself over the years, I can confirm that the following summary from a recent (eConsultancy) Digital Cream gathering could have perfectly been transposed from any other:

“Marketers found that offline data was very difficult to match up with online data as there was a lack of customer identification at offline touchpoints. This meant that that measuring ROI for online campaigns in the offline space was nearly impossible.”

Has anybody actually got this thing to work? (Successful readers, please do comment!)

From a very cynical point of view, were attribution achievable or had it ever been attained by anyone alive, the never-ending impact of eternal ROI would have drained every other source of income on the face of the planet. Those in possession of such formula would drive unlimited resources towards one offering after another making the legend of Midas pale in comparison.

All of which leads me to conclude that there is no such Holy Grail.

A few recent studies would confirm that I am not alone: Gartner’s recent Hype Cycle for Digital Marketing and Advertising (2016) saw attribution sliding down the Peak of Inflated Expectations deeper into the Trough of Disillusionment. According to Gartner, reasons for the descent range from unrealistic expectations to vendor hype. In contrast, old-fashioned, aggregate data-based marketing mix modeling was considered to remain in the Plateau of Productivity.

Where, then, is the ROI of attribution? Despite all of the above, I could never understate the beneficial side effects of this effort. To name a few:

  1. A welcome understanding of campaign naming conventions across the entire organization (and its multiple agencies).
  2. An imperative to audit the organization’s martech and adtech stack to ensure maximum interoperability, discarding legacy solutions unable to provide reliable reporting APIs or bulk access to client data.
  3. A push for brand-driven data governance and first-party measurement as opposed to agency-driven, platform-driven, or media-driven measurement.
  4. Ensuring full control over the “data layer” for normalized data collection purposes.
  5. Insights on the sequence/story that customers have chosen to put together with the myriad of digital experiences that we enable, providing basic visibility on previously hidden touchpoints.

Do any or all of these justify your investment? Perhaps, if you ponder the amount of money you could have wasted in useless campaigns that you cannot truly measure anyhow.

But hardly so if you compute the huge cost of opportunity incurred. An attribution project happens at the expense of many others. And the side effects listed above can become far more agile stand-alone endeavors when purposely tackled.

(To be completely fair, some of these side-effects are already identified as the final goal in certain, definitely more sensible, approaches to attribution).


As a summary of all of the above, cross-channel attribution serves a much higher purpose than the attainment of ROI in itself. The alignment of resources and minds towards such common purpose results in a powerful driving force. Quite the paradox: emotions driving the quest to encapsulate emotions.

Should we then find a new dream? Is there an alternative source of inspiration that is actually attainable? Something that has indeed been achieved by colleagues or competitors and in fact resulted in a tangible competitive advantage?

Equally important: does turning our backs on attribution imply a denial of the possibilities of data? Should we stop believing in its ability to determine our priorities, discover anomalies, validate hypotheses, or unveil truly useful insights?

Not at all. There simply is something very different to “attribution” as the pinnacle of data-driven marketing.

How about starting with the acceptance of this new demand-led reality in which you cannot expect to shape or understand each customer journey, but instead you are finally able to obtain a single view of your own business. A single view of your brand. A single view of the experiences you provide. A “brand journey” for your customers.

Opening ourselves to this simple premise paves the ground for three more thoughts:

  1. Customer-level intelligence belongs in the space of data-powered automation/optimization/activation (data as an engine), not data-driven decision-making (data as a witness). In other words: Customer Data Platforms (CDPs) and Data Management Platforms (DMPs), not decision-support systems. As for the mentioned limitations of cross-media and cross-device identification, they will matter little when the focus is placed on the collection and storage of permission-based first-party data. That is, until it is customers who voluntarily store and share their own data at every demand-led interaction.
  2. We love to understand the inner components of every process because that is the world that many of us grew up in. But discerning the pieces is no longer needed to work with the whole. Aggregate data is not inherently inferior to granular data. And there is great power in correlations.
  3. The very nature of digital data (mostly unstructured or semi-structured) has provoked a database and data management revolution. The new models -or rather the fact that models are not a prerequisite for data collection- result in a peaceful coexistence between an in-depth understanding of the few (customers) and a shallow understanding of the many. This is aligned with the said new mindset to let go of the search for neverending causality/structure and, I believe, completely disrupts the traditional approach to “business intelligence.”

There are, in sum, enough open fronts to entertain the most hyperactive and ambitious of CMOs looking to reallocate a sizeable portion of their budgets — only this time backed by first-hand experience.

So, here are a few potential alternatives if you find yourself in the said Trough of Disillusionment, with the essential question being: what were you truly seeking in attribution?

  1. Was it all along about maximizing return on investment, understanding for “return” a very clear set of short-term measurable outcomes? Then the answer could be in marketing mix modeling (using aggregate data).
  2. Was it about making the most of the time and resources at hand? How about adopting agile marketing methodologies? (Get yourself a copy of Scott Brinker’s own Hacking Marketing book.)
  3. Were you hoping to find golden insights in the advanced analysis of multiple sources of data? Great, but why make that “data lake” project the new center of the universe when both decision-making (data-as-a-witness) and data activation (data-as-an-engine) can still fly much faster and further on their own?
  4. Were you instead looking for medium-specific optimization through behavioral, cookie-based analytics and testing? Digital analytics may then be what you need.
  5. Was it about nurturing one-to-one relationships with your customers? Do you really need ongoing, human decision-making for that? Customer data platforms (CDPs), primarily first-party data, and data management platforms (DMPs), primarily third-party data, will let you activate customer data in any content personalization or media buying platforms you may want to plug into them.

If however, what you really are looking for is the said holistic view of your owned, paid, and earned media, then we are back into the realm of human-driven decisions, information delivery, and performance management. And this requires special treatment.


We have discussed the growing importance of a 360-degree view of your brand and the experiences it creates in the face of the marketing revolution that a demand-led world has brought about. This purpose is fully aligned with the promise of omni-channel intelligence and not contaminated with customer-level data integration imperatives.

It is at such decision-making level, where ideas meet data, that the most crucial things happen. For the executive decision is the one step that defines the business and cannot be automated.

This concept, somehow sitting “between traditional BI reporting and advanced analytics,” with the specific challenges of digital data and marketing agility in mind, has finally been recognized as a space in its own right by Gartner’s recent Guide for Marketing Dashboards.

And even though “dashboard” is a very generic word that pretty much applies to everything these days, I believe the Gartner team has done a great job at both differentiating “pure players” from generic self-service BI or digital analytics solutions and setting all of them apart from data visualization (or visual discovery) offerings — something long overdue.

Most importantly, the authors mention a few of the things that are unique at this intersection of data/science/performance and ideas/decisions/collective intelligence:

  • Data connectivity for multiple owned, earned, paid media sources
  • Built-in marketing templates and metrics
  • Workflow features allowing the dissemination of insights

And I certainly believe that some of the promises of this new, growing space are just as exciting as those of attribution. To name a few:

  1. An aggregate summary of investments and partial outcomes. Knowing how you are doing half way into the race should be a good indicator of eventual success. Not to mention the cost savings associated to putting an end to countless hours of manual reporting work.
  2. Anomaly detection models that take advantage of a very large amount of behavioral and media performance data. Cost savings come in this case from even more expensive analysts’ hours.
  3. “Contribution models” or “journey distribution flows”, computing aggregate reach, response, or behavioral milestones along the path of online and offline experiences offered by the brand.
  4. A combination of social media metrics with intentional and brand equity benchmarks originated in trusted third parties better positioned to gather consumer feedback than most individual organizations.

All of them cost significantly less than attribution projects and happen within weeks. But given that I am biased — Sweetspot is both focused on the above and one of the four solutions listed as “pure players” in the Gartner guide — you should not take my word for granted.

There you have it, if you must: your ROI.

I very much expect a heated debate. Please bring it on.

Thank you, Sergio. Readers: do you agree or disagree? Share your viewpoint in the comments below, or if you have a longer rebuttal for marketing attribution, let me know — I’d be happy to publish a well-argued counterpoint. A more extensive report from Sergio’s perspective with examples is available on Sweetspot’s website.

Get directly in your inbox!

Subscribe to my newsletter to get the latest insights on martech as soon as they hit the wire. I usually publish an article every week or two — aiming for quality over quantity.

This field is for validation purposes and should be left unchanged.

26 thoughts on “Analytics CEO makes a passionate case against marketing attribution”

  1. Does this all assume that we know how to find the right prospect at the right time? I like the second #2 under “What is the alternative?” — agility. Most B2B marketers I talk to have real problems addressing new behavioral data sources quickly enough. If it can’t be automated through their website, they can’t respond quickly enough. This perpetuates the whole myth that customers are waiting much longer before they contact a vendor when their digital signals are an invitation for the vendor to wisely reach out. Thoughts? Are you seeing B2B players exhibiting the agility to act on Purchase Intent Insight?

    1. John

      At this point in the evolution of agile marketing an always on, it is very hard to rely on the client side org to take the lead and get out in front of this.

      As you know, our own business model shifted two years ago when we realized there is no true handoff of intelligence and data into an organization with the expectation of actual operational executional at a high level.

      If, as has been suggested both by Scott Brinker and others, that only a fraction of capabilities inside of linear, martech black boxes is being used – how can we expect some form of evolutionary leap from tadpole to New Soviet Man:-).

      It takes a village – and this is where it all falls down – martech companies led by tech, more SaaS not less, more breakthroughs in journey analysis and anomaly detection, further knitting together of diverse data streams and a cessation by the client side to be in such a hurry when B2B buying journeys are long to begin with – then you can walk in as a data provider and have the success you hope for.

      The amazing part of all of this – is where are the agencies? This is their natural traditional place in the eco-system. Seems most are back at the ranch – either organized around a fraction of the tactical channels that they have always inhabited, or just totally intimidated and under manned when it comes to this very complex, FAST, world we live in

    2. Thanks, John. I would say B2B marketing/sales are resembling B2C more and more as individuals take control from organizations in purchase decisions (and the “consumerization of IT” clearly affects MarTech). Demand-led also results in a great environment for agile to have an impact.

  2. Terrific post.
    The cry for marketing ROI, heeded by marketing people simply because it makes sense, and to get a seat at the most senior of tables, which you normally need to be able to justify with numbers, is flawed.
    However, there are two realities that remain:
    1. People, real people, those out there as ‘targets’ (have to think of a better word) for our marketing activities do not care about our algorithms, they behave in hugely non-rational and inconsistent ways, stuff algorithms have yet to accommodate very well.
    2. People, the same ones as above, whether that are consumers of your product or not, react to visuals and stories way, way more than they do the products of algorithms, it is the way we evolved.
    Martec has brought huge operational and productivity improvement to us, and that will continue and improve further, but it remains well short of the holy grail it is sometimes touted to be, particularly by those with a bottle of commercial hope to sell.

    1. I fully agree. It is all about real people but we keep treating each other as if we were robots ? . As for ROI, it does get quite silly – few things can be ideal for both short-term and long-term performance.

  3. I like the thoughts on the different alternatives and drilling down into each sub-problem as a help to organizations. At the end of the day, multi-channel & cross-device attribution should function in a similar manner to last touch (which also has its own attribution challenges incidentally). If one of the goals, which I think it should be for high spend organizations, is to try to understand media influence on final outcomes – you have MMM from the top down and multi-touch from the bottom up. Multi-touch is doable if data loss can be managed.
    I believe vendor promises are a big issue here…opportunity cost in terms of time is founded upon poorly communicated or unreasonable expectations of the time it takes to truly integrate a project like this – particularly if internal knowledge or skill sets are not adequately prepared for such an undertaking.
    I believe a light pure-play data side multi-touch/cross device solution is required in the market. One that does not allow itself to be corrupted on the media sell-side.

    1. Andrew, good point on the challenges of last touch itself. On the management of expectations, I believe the general assumptions on the possibilities of data or technology do not help either. We collectively seem to have contributed to the hype and it is often clients who ask to be sold on a grander dream (deployed within days), to which sales teams happily oblige.

  4. This is one of the best martech posts I have read in a long time. I think it clearly outlines the value of focusing on data driven decision making (in order to increase future marketing ROI), instead of finding the exact attribution of previous activities (tracking past time marketing ROI).

    Knowing the exact ROI of your previous investments is potentially very interesting of course, but knowing how to increase your future ROI is more important and more business critical. Gaining sufficient intelligence to make well grounded decisions to optimize your marketing performance (on Plan or at least overall Campaign level) should therefore be prioritized over tracking nitty gritty performance on individual activity or even post/like/ad level.

    One perspective does not necessarily have to replace the other of course, but I think many marketing teams would do better if they raised their view points and started to focus more on overall marketing plan performance – and relaxed a little regarding the attribution tracking.
    The first step is often to start aligning marketing (and marketing data) with sales (and sales data), meaning that from a Martech perspective the CRM system is often one of the most important sources of performance data.

    One example from a B2B perspective: Finding correlations between marketing investments and the development of various CRM metrics, such as the basic Lead, Opportunity, and Pipeline/Forecast values, will often tell you more about actual marketing performance than what you can learn from tracking traffic, visits, click data. This will require you to adopt a slightly longer perspective, and the longer your sales cycles the longer your perspective must be. But in return your marketing performance dashboards (at least on an overall plan/campaign level) will be more relevant and easier to understand, not only for You as a marketer but also for everyone else in the company – which serves a great purpose for the future role of the marketing function!

  5. Great article, we need way more critical discussions such as these with all the hype surrounding marketing attribution.

    I think “Attribution doesn’t work” is a little exaggerated. Marketers attribute all the time, there’s no way to do performance marketing or calculate a ROAS without attribution. That said, many marketers might only do so implicitly and do not realize, that they attribute when evaluating their campaigns on a last-touch basis.

    As the term “attribution modeling” implies it’s more about modeling attribution than coming up with a 100% attribution truth. As George E.P. Box stated, “all models are wrong, some models are useful”. So the task is to come up with a model (e.g. an algorithmic one), that is more useful than the existing one (e.g. last-click). When comparing last-click (only the goal-scorer gets paid) to an algorithmic one (other players get their fair share) it intuitively makes sense to at least look at the outcome of such a more advanced model.

    Even the Gartner article cited in the post says that “useful results emerge all the time from thoughtful attribution projects.”
    From our experience we can second that. Helping marketers to for example
    – figuring out that they heavily overvalued their retargeting campaigns by only looking at last-click attribution
    – or to track and include display ad impressions in our algorithmic models allowed to “model” a more realistic ROI of display ads (not last-click, but also not solely on view-through conversion basis)
    is very useful and led to a significant increases in marketing efficiency.

    Useful heavily depends on what you spend for marketing attribution (which in turn correlates to your expectations). Overinflated expectations (especially if you pay what the existing solutions such as Adometry, Converto and Visualiq cost) are as you mentioned the main reason for why attribution slides deeper into the trough of disillusionment. But since it’s called a hype “cycle” most tech went through this, as did marketing mix modeling (MMM). I am confident that with a new breed of attribution solutions (lightweight, independent, SaaS) attribution will meet MMM at the plateau of productivity very soon. (Keep in mind that Marketing Mix Modeling has pretty distinct requirements regarding the input-data and hence only really works for larger advertisers and brands).

    At we provide small and medium ad spenders as well as startups with a SaaS, “light pure data side multi-touch/cross device solution”, as one of the previous commenters stated, is required. Our aim is to allow our clients to move beyond last-click and other flawed static models, based on data science powered algorithmic attribution modeling.

    1. Thanks, Janos. The effort certainly results in many small wins for many clients (what I referred to as “side-effects”) and case-studies abound, but it is the ultimate promise (ROI) that I wanted to put the focus on. Good observations on your own market and I will definitely keep an eye on its evolution.

  6. Charlie TARZIAN

    Excellent job, Sergio!

    As the marketplace struggles with the next shiny objects while at the same time trying to cast off the addictions of past shiny objects – between what you have lucidly, articulated, Scott Brinker’s call to arms for Agile Marketing and David Raab’s articulation of Journey Orchestration – we have a triple crown of new think that should set us all in the right direction.

    Given the stultifying concepts of first touch, last touch anything and the primitive and limiting inbound marketing processes – we all need to understand the fact that all this spend and all this thrashing about on click through rates, followers, predictive, account based whatever, cross device nirvana, DMP’s, etc… negates the need to do and understand a few things:

    1. is there real operational readiness inside an organization
    2. is there the realization that the human condition is non-linear (ergo – why is inbound marketing oh so linear???)
    3. that cross-device is B2B is much less important – even almost meaningless when compared to how groups of people act in concert – something the cookie based, B2C world of impulsivity has no idea how to track or model
    4. historical understanding of the relationship between an account/people inside of an account and a product, brand, category (as proxy for the previous two) is critical – yes, that’s correct – how can someone claim a ‘surge’ of activity when historical data is not included in that declaration
    5. that inside/out – followed by outside in data – will tell you a lot more than vice versa and set you up for a way to manage and understand non-linear behaviors – because it is how content is consumed organically in a non-chaperoned environment – that gives you true intent
    6. That the culmination of your thinking (and Scott B’s and David R’s) is the embracing of an Always on middleware – an executional decisioning engine that, as you ably maintain – moves us from ‘data as a witness’ to data as an enabler
    7 and that finally, as you state: data connectivity across outside and inside data, built-in marketing tempplates and the aligned and automated workflows (which is what the API, middleware layer I speak of) will give us the view we need to look at the ‘aggregate summary of invests’, allow for anomaly detection, will be the death knell to first, last and all types of flim flmmery as it relates to attribution – that will make this collective vision a reality
    8. it will bring on the sudden death of SaaS based anything (why would we need another interface?), the happy exit of inbound marketing as the go to (based on the fact that nothing has challenged that limp thinking and a terra firma for making sound, data driven decisions on spend, direction, product and market development and more.

    I say death to campaigns and the beginnings of Always On.

    Now let’s all get to work!

    Nice job, Sergio

    1. Charlie, This was a truly insightful summary of many things ? – I definitely have to reflect on some of these points, loving the idea of always-on middleware and the end of campaigns!


  7. I agree.
    1) Strong brands are more efficient than weak brands. The strength of a brand is defined by customer perceptions. Even the customer can’t attribute their perception of a brand to any particular tactic.
    2) The more you know about a person, the more unfathomable that person becomes. Attribution modeling assumes that the more you know about a person, the more predictable that person becomes. Nothing from real life supports that idea.
    3) Top line tactics are an investment in building demand. Bottom line tactics are an investment in gathering up sales at the moment of purchase. Gathering sales appears to be more efficient than building brands because data is taking precedent over wisdom.
    > Strong brands are the water table that lifts all boats. Attribution modeling measures the boats relative to each other but struggles to understand the tide.

  8. Completely agree!
    The oversold attribution concept does not look big picture of entire marketing landscape. One can not fix every problem by just looking from a single lense of digital media. The conventional media has its own challenges that can be solved through strategic planning and better adoption of technology. A data driven marketing approach with econometric technique to measure ROMI can deliver far better result than unpromising cross channel attribution approach.

  9. That cartoon graphic is the truth, there are so many places that you can get a customer come into your funnel from, it can get overwhelming with the amount of referral sources.

  10. Well written article Sergio.

    What is very interesting (to me any way) is how, in B2B instances, our channels for contacting businesses has changed over the years. I feel that the attribution fields in CRMs are saying more and more “website” – but may or may not have anything to do with successful SEO campaigns… it could be an in person referral that is now searching for your company or someone heard your radio spot and wants to learn more. First, last, middle – it doesn’t matter, because we will never know the mindset of the prospect.

    One could suggest you use CRM data of leads, opportunities, wins as a guide to where you should invest, but that quickly becomes flawed. In most B2B sales you quickly move from a single point of contact – someone who researches the company, downloads the whitepapers, speaks with the sales people – to a boardroom of 15+ decisions makers from different backgrounds/specialties who ultimately have say on the purchase and who may or may not have ever interacted with one of your marketing pieces in a trackable manner, because the “researcher” in their company brought them all of the compiled information.

    Just my thoughts.

    Again well written article – gives me lots to think about.

    1. Thanks, Mike. Very good point. We do suffer that scenario often in the B2B space. We happened to discuss it recently (during a Disruptive Digital Marketing NYC Meetup) and it was good to hear about a different approach to CRM leads which is more focused on “accounts” (with their many stakeholders downloading papers and researching info as a team) than “contacts”. We are in this case speaking of an “Account Journey” in which we must cluster all team interactions (cookies, requests, calls…) under a single path to (in this very case) a clear goal.

  11. Hi Sergio,

    Great article and I admire you taking this stand. However, I would respectfully argue that “good enough” sales attribution and ROI mapped to certain points in the customer journey can bring real value to marketers.

    I’m wondering if that is what you meant by “journey distribution” above?

    For example, if you can track the click before a visitor becomes a lead, by mapping the click to the CRM activity, and then pull in the cost of that click, that would seem to have value. You then have legitimate tracking of successful lead generation. Furthermore, now that you have that customer journey point (we call it first optin), future sales can be tracked and attributed to the lead generation, giving ROI, or lack thereof, to your lead gen marketing efforts.

    1. Thanks for your comments, Scott. I would actually agree that attribution tied to certain points in the journey can indeed bring clear benefits. We would in this case not speak of multi-touch attribution but rather “staged attribution”, as it obliges us to clearly define stage-specific goals, giving us far greater control over the timeline.

      And yes, this is what I meant by “journey distribution”. I believe we are aligned.

  12. Dear Sergio,

    Very eloquent as always. Yet I feel the need to take issue with some of your contentions.

    First and foremost, your case against advanced attribution is fundamentally flawed. By your logic, given that attribution (particularly digital attribution) doesn’t effectively measure everything that happens, we’re better off attributing using aggregate statistical tools such as Marketing Mix Modeling or viewing the world through the prism of a DMP.

    1) You’re absolutely right that Digital Attribution platforms do not measure everything. However, they never intended to, and in fact, are modeled not to. Understanding the inner workings of advanced attribution models is sorely needed in order to understand their scope and outputs. While true that they attempt to view the digital journey at the most detailed level, various statistical methodologies are employed (such as hierarchical shrinkage, bias detection) in order to make up for the lack of observations. This means that Attribution models are simply machines with highly complex statistical models behind them, meaning that they too should benefit from the advantages of “Aggregate [not being] inherently inferior to granular data. And there is great power in correlations.”. This is important due to my next point:

    2) We can agree that the role of attribution in the organization is NOT the end-game of determining ROI on marketing. However, the alternatives you posit (DMPs, Marketing Mix Modeling) does not take into consideration the operational realities of the enterprise. Attribution models fundamentally serve the needs of those that operate media campaigns and need to optimize marketing spend within their organization. For someone running a campaign, they need to know which creative has performed the best, how organic compares to paid, which keywords are performing and which aren’t. When $ millions are invested in online marketing, simply stating that MMM is a valid alternative to attribution modeling completely misses the actual needs of organizations to optimize their day-to-day marketing operations and get the most out of their spend. Believe it or not, attribution modeling is very well suited for this purpose. MMM is not. And here I circle back to my first point: Attribution is a statistical representation of marketing performance at the level that is operationally important to many marketing campaigns. And by your very own argument, we should agree that it doesn’t have to observe every interaction in order to be useful and relevant.

    There are many other points to take issue with here – but my last point is that you take an “opportunity cost” view : if you implement attribution, you forfeit other investments. This is naturally true to some degree, but again I believe it is an oversimplification. We need systems that serve the needs of many parts of the organization. If your online marketing is able to (albeit only via statistical indicators) improve its efficiency by 10%, that should be a huge priority for your organization. If an MMM model shows that you can increase sales by 3% by investing in other channels, this should also be a priority. If via a DMP investment can improve retention rates or optimize other parts of your business, this should also be a priority. The question then becomes: how to chose which investment to make? The answer should be fairly obvious: the area where your organization is most capacitated today in order to take action and pursue improvements. That will be a discussion for another day.

    Keep up the great work Sergio. Despite my criticism, I thoroughly enjoy your writing…

    A “critical” fan

    1. Dear “Critical Fan” (I am intrigued!)

      Much appreciated answer. Your thoughts open the debate I was actually hoping for. They also help me understand that I was not that clear after all… So, to your specific points:

      – I never intended to advocate for Marketing Mix Modeling, which I believe is built on very shaky grounds: a clearly identified, easily measurable outcome. The only reason for its inclusion was a reference to a potential use for aggregate data when success has been so stubbornly tied to a very concrete milestone.

      – Fully appreciating the power of advanced statistics when it comes to “making up” for the lack of granular data (and fully embracing them at different levels) throughout the said “journey”, my contention is actually with their irrelevance in the face of isolation from the said defining milestone. In other words: we can use statistics to complete the picture given a large collection of “journeys”, but they cannot help nailing down a destination, for this one has a life of its own which is not measured through events or actions but instead disappears in the minds and private choices of our final target, consumers – we are missing an entire data set and I do not believe it is within reach.

      – It is certainly a very good point that attribution is actually employed at operational level, as a means to rationalize specific investments. The problem being of course that not only does it end up influencing the strategic understanding of the marketing function, but it also assumes certainty in the attainment of the goal (often tied to the lifespan of a very fragile cookie). I do not believe it is a coincidence that most large corporations are these days facing a “civil war” between traditional “Mad Men” marketers (yes, resting in the laurels of a logic built for mass media reach, but truly familiar with the nature of brands) and performance marketing die-hards (aiming to turn marketing into a programming exercise, as that is their background).

      – On the opportunity cost, you open a great debate. I agree, actionable improvements seem like the biggest priority. My question would however be: How can we be certain of a 10% improvement as a result of attribution when it is precisely the measurement of such improvement that we are calling into question? (rather than the methodology used to arrive at it through an enhanced combination of investments?). Which is why I am defending investments of any sort which accept multiple potential outcomes, with the optimization of sequential milestones (I have used “staged attribution” in a prior comment) appearing to be the low hanging fruit.

      All in all, I believe that we have taken measurability to the extreme – as someone wrote recently, “Google Analytics has killed marketing”. What would be the point of the marketing function if we had a clear understanding of outcomes – could delegate it to a piece of software? Perhaps it is only paradoxical that I have to stand up for the unpredictability of human decisions/emotions after a considerably long career fighting old marketers in search for better answers 🙂

  13. I agree that attribution as it has been done by many companies and ad agencies has been lousy. You are right about certain points. Gartner, Mobile Marketing Association, and Forrester would agree with some of your points. Here is our write up on the key problems with attribution:

    However, Forrester, Mobile Marketing Association, Gartner all agree that there are solutions.

    The best overall view of the problems and solutions, in my opinion, is Forrester’s 2016 Measurement & Optimization Wave. It discusses the problems and analyzes 67 measurement companies, rating the top 10 companies and what makes them different: (reprint at

    Mobile Marketing Association has successfully done a series of next generation attribution with marketers including Allstate, Coca-Cola, MasterCard, Walmart and more, publishing them for the benefit of the industry (here is peer reviewed article in Journal of Brand Strategy about the methodology and finding:

    Gartner refers to it as Total Marketing Measurement. Their Marketing Leadership Council has a “best practice” based on Regions Bank sharing how to connect brand to behavior at the person level:

    If you want to hear from marketer’s directly, Citibank’s expert, Mike Eichorst recently retired and is sharing what he learned from his 35 year career in measurement. He has tried it all. Mix modeling, attribution, person-level, design of experiments, you name it, he probably has tried it. The webinar is next week. I have the opportunity to interview him, and in our preparation call this week, he is going to be very specific about what doesn’t work and what does work. There will be time for audience questions too.

    In conclusion, almost everyone agrees that there are problems with both marketing Mix Models and Attribution, and most agree that the new generation of measurement, Unified Measurement or Total Marketing Measurement depending on the analyst terminology, is solving these shortcomings.

    Your criticisms are an accurate reflection of the OLD generation of measurement. But, the good news is we are already in the next generation.

Leave a Comment

Your email address will not be published. Required fields are marked *