Category Archives: Web Analytics

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.

Continue reading Marketing Funnel Not Dead, Using Funnel Model for Attribution Is →

“Missing” Social Media Value

Analytical Culture, DataBase Marketing, Web Analytics
I have no doubt there is some value in social beyond what can be measured, as this has been the case for all marketing since it began ;)  The problem is this value is often situational, not too mention not properly measured using an incremental basis (as you point out).
For example,  to small local businesses who do no other form of advertising, there is a huge amount of relative value to using social media, versus no advertising at all.  Some advertising is much better than none, and since it’s free, the incremental value created by (properly) using social is huge.
On the other hand, I wonder why social analysis seems to forget that people have to be aware of you to “Like” you in the first place.  Further, it seems unlikely a person would “Like” a brand or product if they have not already experienced it, and are already a fan.  If this is not true, if people “Like” a company even thought they do not (paid to Like?), then the problems with social go way beyond analysis…
But if true, , the number of “Likes” doesn’t have as much to do with awareness as it does with size of customer base, and is much more aligned with tracking customer issues (retention, loyalty) than anything to do with awareness / acquisition.
Add the fact many companies are running lots of advertising designed to create awareness, and the incremental value of social as a “media” may be close to zero, or at least less than the cost to analyze the true value of it.
And this last, really, is the core of the issue.  It’s simply not possible to measure “all” the value created by any kind of marketing, and there are hugely diminishing returns as you try to capture the last bits.  I think it’s quite possible the optimism for “value beyond what can be measured” is less than the cost of measuring it *if* people keep looking in the awareness / acquisition field.
Folks who want to find this “missing” social value should start doing customer analysis, and look in the “retention / loyalty” area, where the whole idea of social is a natural, rather than a forced, fit.

Has to be There

I find it really interesting that whenever there is a discussion of measuring the value of social media, there’s such a bias towards believing there is value in social beyond what can be properly measured.  See the comments following this post by Avinash for a good example.  Speculation is fine, but the confidence being expressed that a new tool or method will uncover a treasure trove of social media value seems un-scientific (as in scientific method) at best.

I don’t doubt there is some value in social media beyond what can be measured, as this has been the case for all marketing since marketing measurement began.  These measurement problems are not new to social either:  Marketing value created is often situational, it depends on the business model and environment.  What works in one situation may not work in another.

For example:

To small local businesses who do no other form of advertising, there is a huge amount of relative value to using social media versus no advertising at all.  Social advertising is much better than none, and since it’s free, the incremental value created by (properly) using social is huge.  It’s also really easy to measure the impact and true value, since the baseline control is “no advertising”.  Lift, or actual net marketing performance, can be pretty obvious in his case.

On the other hand, many companies are running lots of advertising designed to create awareness, and the incremental value of social as a “media” may be close to zero for these companies, or at least less than the cost to analyze the true value of it.  Possible explanation:  Social events such as “Likes” or comments are simply representations or affirmations of awareness already created by other media, so by themselves, create little value.  In other words, events such as Likes might track the value of other media spending, but may not create much additional marketing value.

Continue reading “Missing” Social Media Value →

Increase Profit Using Customer State

DataBase Marketing, Driller Q & A, Measuring Engagement, Newsletters, Web Analytics

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Q: We’ve been playing around with Recency / Frequency scoring in our customer email campaigns as described in your book.  To start, we’re targeting best customers who have stopped interacting with us.  I have just completed a piece of analysis that shows after one of these targeted emails:

1. Purchasers increased 22.9%
2. Transactions increased 69%
3. Revenue increased 71%

A: There you go!

Q: My concern is that what I am seeing is merely a seasonal effect – our revenue peaks in July and August.  So what I should have done is use a control group as you described in the book – which is what I am doing for the October Email.

A: Yep, that’s exactly what control groups are for – to strain out the noise of seasonality, other promotions, etc.  But don’t beat yourself up over it, nothing wrong with poking around and trying to figure out where the levers are first.

Q: Two questions:

1.  What statistical test do I use to demonstrate that the observed changes are not down to chance

2.  How big should my control group be – typically our cohort is 500-800 individuals

A: Good questions…

Continue reading Increase Profit Using Customer State →