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:
- Correct problems in the front end of the business that cause customers to defect in the first place
- 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!