Marketing Funnel Not Dead,
Using Funnel Model for Attribution Is

It’s become fashionable to declare the “Marketing Funnel Model” dead.

For example, here is a post worth reading on this topic by Rok Hrastnik.  There are some very good points in this post on why using a funnel to attribute media value is really a troubled idea.  I was flagged on this post because it has a quote from me that seems to support Rok’s thesis about the death of the funnel model and the related idea, “Direct Response Measurement is a Wet Dream”.   The quote is from a comment I made on a post by Avinash where we were discussing the value of sequential attribution models:

There are simply limits on what can be “proven” given various constraints, and that’s where experience and a certain amount of gut feel based on knowledge of customer kick in.  If you can’t measure it properly, just say so. So much damage has been done in this area by creating false confidence, especially around the value of sequential attribution models where people sit around and assign gut values to the steps.  Acting on faulty models is worse than having no information at all.

But none of this means the Funnel Model is dead, or that Direct Response Measurement overall is a Wet Dream.  What’s (hopefully) dead is  people using the funnel model inappropriately for tasks it was never designed for, in this case multi-step attribution of media value to goal achievement.  On the other hand, if this specific funnel use case is what Rok was coming after, I agree, because it didn’t make any sense to use a funnel model for this idea in the first place.

Let’s unpack these ideas

Funnel thinking is based on a relatively reliable model of human behavior, AIDA.  This model from human psychology does not specify tools, channels, or media.  It simply says that there is a path to purchase most humans follow.  That is:

A – Attention: (Awareness): attract the attention of the customer
I – Interest:  (Intent) promote advantages and benefits
D – Desire: convince customers the product will satisfy their needs
A – Action: lead customers towards taking action / purchace

Example:  I’m Aware of tons of products I would never buy.  There are lots of products I think are Interesting but I have no Desire for.  There’s a short list of products I Desire but have not Acted on.  The list of products in my head worthy of purchase consideration gets smaller and smaller at each stage of the AIDA model.  This is the funnel.

The AIDA funnel has not changed and it’s not dead.

It’s a model of human behavior, not media consumption.

Said another way, it’s not the tools, channels, or media that are a funnel, it’s the way humans process buying decisions that is a funnel, left side of chart below.  Media touches and impact, relative to the AIDA path, typically do not happen in a linear or funnel fashion, see right side of chart below, click to enlarge:

In a chaotic environment such as the one above, it’s near impossible to determine the contribution to end goal conversion of any one media touch or step in a multiple-step the sequence; there’s just too many uncontrolled variables.  I guess Rok knows this, because in a follow-up post, he presents his alternative view he calls the purchase cycle:

Yea, I know, looks like a funnel to me too.  In fact, it’s the AIDA model, right?  So, I think what Rok was saying in his original post is the using these funnel ideas for media attribution is screwed up, not the funnel idea itself.  And I would totally agree with that notion.

What Went Wrong

Where I think using a funnel model for attribution purposes starts to go off the rails is confusing measurement of  funnel state / position during  the sales cycle and attributing state to particular media.

These are two completely different ideas.  The first idea is simply a position, state or status with no value implied; it does not really matter “how” someone got to a state, just that they are there.  State tells you about what message might be most effective; where someone is in the process.   The second idea is brave but lacking data; given nothing but  a media touch in a particular sequence, let’s determine what value a touch contributed to end goal.  This idea really has nothing to do with funnel states at all.  Stir in an incomplete and inaccurate picture of all the media touches happening during the sales process (see Rok’s first post), and you have a real mess.

If what an analyst wants to achieve is similar to this second idea, funnel thinking is probably not a great model to use.  The best way to attack this idea is through media mix modeling.  Analysts still won’t be able to see the contribution of specific touches to value creation, but will be able to accurately measure the overall contribution of the different media types used in the mix to outcome.  The actual sequence or level of media exposures at an individual level is still not knowable  but a mix model can determine, in the aggregate, which media types and mixes drive optimal sales or other actions.

The fact many orgs don’t have the tools or institutional stamina to do media mix measurement properly should not result in people declaring they have measurement of these media step effects.  In fact, all they really have is a list of media exposures that is clearly an incomplete and broken picture, and guesses about the value creation at any step.

Hopefully, this approach is dead (or more likely dying) as well.

In fact, the whole topic of  attribution needs  a more complete discussion so analysts and marketers fully understand the benefits and pitfalls of various approaches to attribution and set themselves up for success in the future.  Those interested in this topic from a media / goal conversion perspective should check out my recent post for Econsultancy: Online attribution models: getting close.

By the way, I think this sequential touch data is fabulous for gaining a greater understanding of media interactions, it’s a great step forward in the evolution of digital analytics.  The vendors can’t keep people from abusing this data, and it can be used for some very creative testing ideas, which I cover in the Econsultancy post.

If analysts can’t measure these media contribution to goal effects properly, they should just say so and ask for the resources to measure them correctly.  There’s no shame in saying you just do not have the right tools or enough resources.  If you have to provide metrics, just be honest about what they do and don’t mean.  But please, don’t sit around and assign arbitrary values to exposures based on budgets or whatever else.  A list of events is not a media value measurement, and you hurt the marketing measurement cause when you just make stuff up.  And people wonder why analysts don’t get respect…

What You Can Do

If you have the capability to get a bit more sophisticated but are not ready for mix modeling or controlled testing, here’s a framework for approaching this media value attribution issue.

For Awareness, secure Awareness-generating media and measure the effectiveness of those media at generating Awareness using Awareness metrics.  Forget the rest of the funnel for these Awareness media, the next steps in the AIDA funnel are not the primary success metric of Awareness media.  If you are successful here, some percentage of the audience will move to the Interest stage.

Secure Interest-generating media and measure the effectiveness of those media at generating Interest using Interest metrics.  Forget the rest of the funnel for these Interest media, the next steps in the AIDA funnel are not the primary success metric of Interest media.

And so forth.  Those interested in more on this idea check out the Marketing Bands series here.   A similar approach can be taken for the entire Media / Marketing / Service effort, one that after initial goal achievement adds customer centricity as part of the marketing mission.  The financial results of such an approach are here.

I’m sure some people will say, “But Jim, one piece of media can have effects in different AIDA layers.  For example, a campaign designed to generate Awareness might also create some Interest, Desire, or Action (see the chart above).  How do you account for or measure these spillover effects using your approach?”

The correct answer is a media mix model.  Do you have a media mix model?  No?  Then I’m sorry, you’re just going to have to deal with an imperfect marketing measurement system, and realize there will be beneficial effects you cannot measure.  If what you can measure properly is successful, you can take comfort in knowing the actual results are probably even better!

Yes, the Marketing Bands framework is an imperfect approach, but without a mix model, it’s at least a realistic way to approach this marketing measurement challenge.  And it’s a whole lot better then fabricating attribution out of thin air using a list of sequential exposures, a model that is broken in so many obvious ways if you want to measure campaign effectiveness.

Direct response measurement is not a Dream or Wet for that matter, but it’s important to use it when it makes sense. Direct response metrics don’t take into account  all the vagaries of  media touches, real or imagined.  That’s true, but who said they were supposed to?  The advantage of direct response measurement is consistency and predictability, not accuracy.  If you want to measure Awareness, don’t use a direct response metric, use an Awareness metric.  Problem is, measuring Awareness properly is not easy and it’s expensive.  So sure, conversion has always been a shallow metric, but if it’s all you can afford, it’s pretty important.  Cost per order or campaign, preferred.  Better still is to use profit not cost, and where you really want to go is value created over time because profitability can change dramatically, see this example:  Freemium Customer Conversion.

Media Not the Only “Attribution” Play

The discussion above is just the beginning of the attribution journey if your definition of “Marketing” is larger than “Advertising”.  All of the above is just about getting the customer to one action or purchase.  For some companies, that is all they care about.  But what happens once a person is a “customer”, however your company defines it?  How do you measure and optimize the downstream relationship?

There are Marketing Bands for these downstream stages of the relationship too, as well as when customers begin the dis-engagement process.  As with acquisition, different approaches are most effective for each stage of the dis-engagement process, and those should also each be measured on their ability to do a specific job.

As the LifeCycle of the customer plays out across time, as the funnel continues to narrow down to the final defection of the customer and resulting terminal LifeTime Value, there are optimal contact approaches for each stage or Marketing Band that you will need to discover for your business (click to enlarge chart).

It all starts at Satisfaction with the first goal achievement.   Those not satisfied defect (left side of chart).  Those who are Satisfied usually end up in a General Information communication cycle, e.g. weekly newsletter.  This is pretty much where current customer marketing practices end; people remain satisfied for some time then quietly move down through the At Risk and Dormant stages to Defection without Marketers noticing and are purged from the list (small black arrows).

However, those looking to maximize the profitability of the business  ask a further attribution question: what is the source / cause of At Risk and Dormant stage customers?  Is it the acquisition method?  Initial customer experience?  Product quality relative to expectation?  Service / experience after initial goal achievement?

What is learned from attribution analysis of At Risk and Dormant customers is then used to:

  1. Correct problems in the front end of the business that cause customers to defect in the first place
  2. Develop customized communications (Behaviorally Targeted / Last Chance) to drive customers back up into a state of  Satisfaction where they enter the Lifecycle anew, e.g. they start opening weekly emails again (blue arrows)

The right message, to the right person, at the right time.

For examples of  a measurement and action framework for driving increased business value from these “lower funnel” customers, see the Measuring Engagement Series.  Those not having the required customer analysis capabilities to attack this area could upgrade their digital analytics platform or use a tag-based tool optimized for this purpose like the LifeCycle Grid feature offered by Listrak.

Folks, the Marketing Funnel is not dead, for most it’s yet to be discovered.  What’s dead (or should be) is the way people currently think about the Marketing Funnel and measure the implications of it.

For more on the future of attribution and how you can leverage it, come to my eMetrics Boston presentation or grab me at the show, I’d be glad to wrestle the topic to the floor with you!

It’s become fashionable to declare the “Funnel Model” to be dead.
For example, here is a post worth reading on this topic by Rok Hrastnik.  There are some very good points in this post on why
trying to measure marketing value using a funnel model is really an insane idea.  Primarily, assuming you can track all possible
touch points and understand the value of each is not reality.
But what this really means is sequential attribution measurement approaches do not create insight into the value of each marketing
contact.   The funnel itself is not dead, what’s dead is the current model of measuring success.
Funnel thinking is based on a relatively reliable model of human behavior, AIDA.  This model from human psychology does not
specify tools, channels, or media.  It simply says that there is a path to purchase most humans follow.  That is:
A – Attention (Awareness): attract the attention of the customer
I – Interest: (Intent) raise customer interest, promote advantages and benefits
D – Desire: convince customers the product will satisfy their needs
A – Action: lead customers towards taking action / purchase
This AIDA funnel has not changed and it’s not dead.
People have to become Aware of a product or service to be interested in it.  They have to be Interested in order to Desire it, and
unless they Desire it, they won’t take Action to acquire it.
Example: I’m Aware of tons of products I would never buy.  There are lots of products I think are Interesting but I have no Desire
for.  There’s a short list of products I Desire but have not Acted on.  The list of products in my head worthy of purchase
consideration gets smaller and smaller at each stage of the AIDA model.  This is the funnel.
Said another way, it’s not the tools, channels, or media that are a funnel, it’s the way humans process buying decisions that is a
funnel.  And it’s pretty much always been impossible to measure marketing impact on this process in any kind of linear way at the
individual or even the segment level, because as Rok and others have pointed out, that impact is (usually) not linear.  Never has
been.
That is why media mix modeling was developed.  Since it’s really impossible to measure the incremental contribution of any one
marketing channel or message at the individual level without controlled testing, and controlled testing is often not possible in
media environments, you have to model the mix in the aggregate.
It’s never really been possible to create a step-wise marketing measurement funnel or the representative sequential attribution
model.  Lots of people wanted one, for sure, and of course vendors stepped in to provide one.  But these sequential attribution
models do not actually measure anything, they are simply lists of events.  Interesting stuff, and can be used to speculate or
imply, nice to know, great for the development of tests to prove incremental behavior and profit – but not measurement.  This is
what’s broken with a lot of people’s current thinking on the “funnel model”, and deserves to be dead.
.
In other words, what’s dead is the idea one can:
1. Accurately measure the path of people through Marketing exposures
2. Attribute value contributed by each exposure or funnel step simply by finding the step was involved in a path to purchase
3.  Use this information to control or optimize the sequence of exposures
Exposure does not automatically affect value; otherwise, surely we would see negative attribution, correct?  Have you ever been
negatively impacted by an ad or social exposure, like product reviews?  Of course you have.  At the same time, has anyone ever
seen one of these sequential attribution models give a negative value to certain campaign or media exposures?
Why is that?  How can that be?
The proper way to actually measure this kind of funnel idea is through media mix models, controlled testing, or both.  Here still,
you won’t be able to look at the effect of different exposures on *individuals* or specific sequences, but the effect of different
levels of media mix on groups or segments.  You cannot predict or control the sequence or level of media exposures, but what you
can do is try different levels and mixes of media, and correlate to sales or other actions.
The fact many orgs don’t have the tools or institutional stamina to do this kind of measurement properly should not result in
people declaring they have measurement of these effects when in fact all they have is a list of media exposures that is clearly an
incomplete and broken picture.
Hopefully, that approach is dead as well.
If folks can’t measure these funnel effects properly, they should just say so and ask for the resources to measure them correctly.
There’s no shame in saying you just do not have the right tools or enough resources.  If you have to provide metrics, just be
honest about what they don’t mean.  But please, don’t sit around and assign arbitrary values to exposures based on budgets or
whatever else.
A list of events is not a measurement, and you hurt the marketing measurement cause when you just make stuff up.  Trust me, senior
management knows you are full of crap.  And people wonder why analysts don’t get respect…
For those of you who want to proceed on attribution measurement but don’t have the resources to do it properly, there are
alternatives.  They’re not perfect, but at least it’s a real measurement that provides direction you can act on.
The easiest alternative is to focus on first click or action and measure value creation over time, since it’s the first action
that is generally most predictive of end value.  Why?  The facts surrounding the way a potential customer becomes aware of a
product or service and takes action to move down the funnel reliably predicts the experience they have on the rest of the journey
to final action.  You can’t control this journey, but you can measure the end result.
For example, compare the value of new customers 6 months after they became customers by source of first action.  The media, offer,
copy, process experience is reliably predictive of segment value at 6 months on a relative basis.  If certain campaigns generate
“high value customers” and others “low value customers” (however you define those ideas), they will continue to do so in the
future.
If you have the capability to get a bit more sophisticated than the above but are not ready for mix modeling or controlled
testing, here’s a framework for you.
Most experienced marketing people would agree different types of media are more efficient at addressing the various steps in the
AIDA path versus others.  I refer to this idea as “Marketing Bands” versus a “funnel”, it’s more like a stack of layers people
pass through rather than a specific path.  For each layer, there then to be unique media and messages that  are most effective at
moving people to the next layer and toward final action.
In other words, instead of looking at steps in a funnel and trying to attribute the contribution of each media step to the end
value of the journey, look at each AIDA step by itself and the success of a media *within* that step.  Why is this a better
approach that sequential attribution?  People can and will bounce around between the AIDA layers as they move through different
platforms, media, and messages, but that’s not a problem, it’s an opportunity in this framework.
For Awareness, secure Awareness-generating media and measure the effectiveness of those media at generating Awareness using
Awareness metrics.  Forget the rest of the funnel for these Awareness media, the next steps in the AIDA funnel are not the primary
success metric of Awareness media.
If Awareness is generated, Interest may follow.  Secure Interest-generating media and measure the effectiveness of those media at
generating Interest using Interest metrics.  Forget the rest of the funnel for these Interest media, the next steps in the AIDA
funnel are not the primary success metric of Interest media.
And so forth.  A visual of this idea is here:
http://www.jimnovo.com/images/i-marketing-funnel.jpg
(1993 HSN media choices in black type, 2008 media choices in red type)
Those interested in more on this idea check out the Marketing Bands series here:
http://blog.jimnovo.com/marketing-bands-series/
The financial results of such a Media / Marketing / Service effort, one that adds customer centricity as part of the marketing
mission, are here:
http://blog.jimnovo.com/2008/06/29/marketing-bands-numbers/
I’m sure some people will say, “But Jim, one piece of media can have effects in different AIDA layers.  For example, a campaign
designed to generate Awareness might also create some Interest, Desire, or Action.  How do you measure and account for these
spillover effects?”
The correct answer is a media mix model.  Do you have a media mix model?  Do you have the staff and corporate stamina to properly
execute the testing required to build one?  No?  Then I’m sorry, you’re just going to have to deal with an imperfect marketing
measurement system, and realize there will be beneficial effects you cannot measure.  If what you can measure properly is
successful, you can take comfort in knowing the actual results are probably even better.
Yes, the Marketing Bands framework is an imperfect approach, but without a mix model, it’s the best you’re going to get.  And it’s
a whole lot better then fabricating attribution out of thin air using a list of sequential exposures, a model that is broken in so
many obvious ways if you want to measure campaign effectiveness.
Summary
Sequential attribution provides us interesting information but it cannot properly measure the contribution of various campaigns to
an end marketing result.
The best way to so marketing attribution work is to run a media mix modeling project or for specific situations, controlled
testing.  However, there are significant challenges to doing this work and most companies will not be able to pull it off for
logistical or resource reasons.
The easiest alternative to media mix models and / or controlled testing is to figure out which campaigns / media / experiences
start the most valuable trips through the Marketing Bands – trips taken in whatever sequence the customer chooses.  Once you have
some learning from doing this, try optimizing these ideas by working each of the Marketing Bands as a distinct media impact model.
Finally, once you have been through these two approaches you will have plenty of data and success stories to ask for the resources
required to step up to media mix models and / or controlled testing – and a solid reputation with management that supports asking
for the resources to go for this end game.
However…that’s just the beginning of the journey if your definition of “marketing” is larger than “advertising”.
All of the above is just about getting the customer to first action or purchase.  For some companies, that is all they care about.
But what happens once a person is a “customer”, however your company defines it?  How do you keep a customer, measure and
optimize the downstream relationship?
If you looked at the graphics at the links above, you saw there are Marketing Bands for these downstream stages of the
relationship too, when the customer beings the dis-engagement process.  As with acquisition, there are different media /
approaches that are most effective for each stage of the dis-engagement process, and those should also be measured on their
ability to do a specific job.
As the LifeCycle of the customer plays out across time, as the funnel continues to narrow down to the final defection of the
customer and resulting terminal LifeTime Value, there are optimal contact approaches for each stage or Marketing Band that you
will need to discover for your business.
The right message, to the right person, at the right time.
Folks, the Marketing Funnel is not dead, for most it’s yet to be discovered.
What’s dead is the way you currently think about this Funnel and measure

5 Responses to “Marketing Funnel Not Dead,
Using Funnel Model for Attribution Is”

  1. Rok Hrastnik says:

    Jim, first of all, thank you for taking the time for such an in-depth response & post. I’ve already enjoyed our conversation on Avinash’s post, and this new discussion even more.

    And, in reality, I have nothing to add to your blog post. I agree with all the points you’ve made 100%.

    I never intented for my posts to state that “Direct Response Measurement overall is a Wet Dream”.

    In The Dangers of Direct Response Metrics for Online Retailers my intent was to demonstrate that it’s dangerous to rely only on the simple direct conversion funnel (click > convert), which is still mostly used by (direct) marketers to evaluate media spend / campaign efficiency.

    My key point was that the purchase decision process in most cases isn’t instant, and the tools we have available today do not make it easily possible to measure the impact of all the touch points leading to the purchase, and we therefore should not rely only on direct response when optimizing our marketing investments.

    I’m a direct marketer by origin. I love direct response metrics. However, as stated in my post, they are not enough. We can no longer rely ONLY on direct conversion rates, cost per order etc.

    Unfortunatelly, most still do that today. It’s not just the marketers. I’ve met many who try to do things differently, but are then blocked by “black & white data” oriented CEOs and CFOs.

    In Moving Beyond Direct Conversions (1): Adapt to the Purchase Cycle I tried to expand on the idea, and proposed a) adapting goal KPIs to campaign goals (directly related with the purchase cycle stage they are primarily targeting) and b) expanding our basic direct response metrics “pool” with not only assisted conversions, but also engagement and other softer metrics.

    But, the bottom line is, I completely agree with all of your points.

    I might have been somewhat too aggressive in trying to bring my point home. A large part of that is due to the mistakes I’ve made myself in the past.

    Almost a decade ago, when I was more or less starting in online retail and building my team, my focus was almost exclusively on direct response metrics. I was all about direct CRs, direct CPOs etc. All I cared about was direct campaign / media investment profitability (not counting lead generation, though). And I trained my team the same way.

    My first real eye-opener was an in-depth analysis we did on the impact of our email program, finding that it has about a 3x stronger indirect impact VS direct conversion impact.

    Later, with more experience, I started implementing more complex analytical frameworks, but a lot of the damage has already been done. I had already very successfully “converted” a lot of the people to the “wrong path” (yeah, I know, sounds a little too poetic:) — to such an extent, that it than proved difficult to correct my own mistakes.

    So, much of my vigor comes from my own frustration over the mistakes I’ve made in my early days as a marketer/online retailer.

    The second reason is that I’ve seen far too many marketers, CEOs and CFOs focus only on measuring and optimizing for the direct response, consequently blocking their customer growth.

    Jim, thank you for the discussion. Enjoyed it as always!

  2. Jim Novo says:

    Rok, thanks for clarifying, I think you got people’s attention based on the number of “Jim, have you seen this?” notes I got from people about your post. I myself have presented numerous examples of how focusing on conversion rate can distort the true value creation of a campaign.

    Glad to see we’re not throwing out direct response measurement completely; it may be an incomplete picture but at least it’s precise and reliable, leading to sound business decisions. As a decision maker, give me this alternative over guesswork every time.

    What’s kind of surprising to me is an increasing willingness among analysts to substitute guesswork for facts when faced with complex analytical challenges. As we move up the complexity chain, it’s up to the analyst to recognize what can and cannot be measured properly, and speak up rather than being bullied into turning speculation into fact.

    There’s nothing wrong with speculation and gut feel based on experience – unless this information is labeled as a fact. If an analyst can’t measure an idea properly, they should say so and offer an alternative approach or outline what resources are needed to do the job properly.

  3. Mark Stern says:

    Firstly I would like to say that I have used First Click (within 45 days from registration) with Long term value generation and its results were accurate in the sense that we saw strong relative differences between long term value generated by different acquisition media: In some cases we took it right down to the Keyword bid phrase.

    Some insights we found have stuck with me: those acquisition sources that generated the largest long term value per customer also generated the most value as an acquisition source compared to those media channels that generated customers with low long term value.

    The conversion ratio was insignificant in determining if an acquisition media channel was going to generate big long-term returns or small return. For example we had one media channel in the top 5 ranked (based on long term value) with the second best conversion rate and another with the 61st best conversion rate

    Thus I now know that long-term value of a customer is more important to judging an acquisition media channel than conversion rate.

    The media Mix model sounds interesting but tough to implement. I like the metaphor with the targets but got confused when you said you go for precision rather than accuracy and wanted to clarify this with you. As with regards to outcomes I would, as a rule of thumb, focus on accuracy first and then precision after (if there was value in making the outcomes more precise). Does not mean this is correct in all situations though.

    If I look at your target diagram it illustrates to me that while the outcome is always the same for the same inputs, it is off target. And it’s always off target by a similar amount. Whereas, The target that represents accuracy is more volatile in its outcomes (less precise) but overall the outcome will be, on target.

    Why would I want a model that is less volatile, but off target when I can have one that is on target but more volatile (i.e. occasionally the shot is way off target – hits the outer ring)?

    After reading further through your text you state the following

    “If you are looking to build credibility and consistency in your analytical practice, I’d lean towards precision over accuracy; most management people really do not care about the details of how you got there anyway.”

    “These models are quite precise in terms of predicting outcome. But they never attempt to be accurate if you are looking for “how” the outcome occurs”

    Maybe this is the difference between transparency and encapsulation (hidden information) on how the model works (how it obtained the result).

    Taking stock, I think you are saying, from your experience, that you will sacrifice the accuracy of the model results/outcomes to gain the advantage of reduced volatility (more precision): thus you are reducing Risk as the expense of reducing the quality of the model: its accuracy.

    I guess this is fine so long as the accuracy off the model is not scarified too much.
    Do you have any more info on the process of building he matrix model?

  4. Jim Novo says:

    Mark, in the end I think you and I agree on the ideas I was trying to get across, perhaps from different perspectives. I absolutely would love to have a model that is both precise and accurate, as far as the outcome goes. But indeed, I would be willing to “reduce risk at the expense of reducing the quality of the model” – especially in marketing, and especially if quality implies trying to nail down every last variable using a faulty dataset in the pursuit of being “accurate”.

    Said another way, these “sequential touch models” imply a level of accuracy that simply does not exist, given we know there is so much missing data form technical issues, cookie problems, cross-device / platform usage, etc. So I question why people would want to spend a lot of precious time trying to quantify this kind of detail in the “process”.

    I’m willing to say, Hey, I don’t know exactly how this works, but the outcome is consistent and I’d rather spend my time finding more of these consistent ways to make money than chasing down the details of exactly how it works. It’s much more efficient to work at the “mix” level testing different levels of inputs than to try to chase down the individual contact sequences, which in many cases are not accurate anyway.

    After all, what would you *do* if you knew a certain specific sequence was desirable, how would you force it to happen?

    If by “Matrix” you mean “media mix”, that’s beyond my skill set…

  5. Mark Stern says:

    Thanks Jim. Yes I agree, good response and you make some interesting points

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