Tag Archives: Analytical Culture

Digital Customer Analysis Going Mainstream?

Analytics Education

Is it possible the mainstream digital marketing space is about to finally move on from a focus on front-end measurement (campaigns, etc. ) to creating knowledge around how enterprise value as a whole is created?  And actually enabling action in this area?

Judging by the material coming out of the recent Martech conference in Boston, one would think so.  And it looks to me like I’m not the only one thinking “it’s about time”.

A couple of years ago I lamented:

It’s been very popular among marketing types to talk about “the customer” but seek metrics for affirmation other than those based on or derived from the customer. Digital analysts have followed their lead, and provided Marketers plenty of awareness, engagement, and campaign metrics. As I’ve said in the past, this is a huge disconnect. Does it make sense (analytically) to have discussions about customer centricity, customer experience, customer service, the social customer, etc. and measure these effects at impression or visit level?

If you’d like to review some commentary on the conference, see a list of 5 posts here.  I found the list of tweets here particularly indicative of Martech’s potential, for example:

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Marketing Funnel Not Dead, Using Funnel Model for Attribution Is

DataBase Marketing, Web Analytics

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.

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Tortured Data – and Analysts

Analytical Culture, Web Analytics

Fear and Loathing in WA

You may recall I wrote last year about the explicit or implicit pressure put on Analysts to “torture the data” into analysis with a favorable outcome.  In a piece called Analyze, Not Justify, I described how by my count, about 50% or so of the analysts in a large conference room admitted to receiving this kind of pressure at one time or another.

Since then, I have been on somewhat of a personal mission to try to unearth more about this situation.  And it seems like the problem is getting worse, not better.

I have a theory about why this situation might be worsening.

Companies that were early to adopt web analytics were likely to already have a proper analytical culture.  You can’t put pressure on an analyst to torture data  in a company with this kind of culture – the analyst simply will not sit still for it.  The incident will be reported to senior management, and the source of “pressure” fired.  That’s all there is to it.

However, what we could be seeing now is this: as #measure adoption expands, we find the tools in more companies lacking a proper analytical culture, so the incidents of pressure to torture begin to expand.  And not just pressure to torture, but pressure to conceal, as I heard from several web analysts recently.

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