#Digital #Attribution is a misnomer

March 9, 2012

The concept of “last click” is as flawed as attributing the purchase of an adult beverage to the neon sign hanging outside the liquor store. The concept of “Digital attribution” simply tries to count the number of beer signs the person saw. What about TV, bill boards, demographics and socioeconomic factors?

While reading a Digiday article this morning, “The Last-Click Attribution Dilemma“, two arguments presented by the author struck me as worthy of comment…

  1. Authors point – Brand marketers are staying out of display ads because of the inherent inability to properly attribute spend to results. Really?!? Is TV a good example of being able to attribute spend to results? Of course not. Yet, this has been the haven for brand dollars for generations. I suggest that while attribution is AN argument to this issue, the main argument is that display ad technologies target consumers very poorly and that those targeting capabilities have little to do with the knowledge and needs of brand marketers. Comscore identified that 80% of targeted ads fail to reach their intended audience. Pause for a second… yes, 80% failure. Why? They all rely upon poor proxies of the real, underlying predictive insight required… bad and incomplete data. 3rd party cookies, context and behavior are not sufficient. Individually or collectively.
  2. Authors point – focus on expanding perspective of digital touch points to do attribution properly. Three research points come to me: 1) about 40% of ad impressions occur in digital channels; 2) Forrester estimates that 70% of consumers exhibit multichannel behavior – researching in one channel and purchasing in another; 3) multichannel customers contribute 4-5 times the revenue per customer than single channel customers. Doing a perfect job at assembling all digital touch points will never be enough. I suggest it’s a false objective. It misses the perspective of consumer behavior, information necessary to support executive media mix decisions and simply creates focus on the minority of ad spend.

All of this makes me thirsty… Sierra Nevada’s my favorite beer!


Narrow solutions lead to false impressions

December 30, 2011

Subtitle: If all you have is a hammer, the whole world looks like a nail

I recently read an article in digiDay, written by the CEO of a modestly large online personalization firm. The title drew me in… “Outsourcing Data Management is a Mistake“.  As I read, I was consistently impressed with the idea that narrow solutions, while interesting, lead to profound mistakes.

An open reply to the article –

If the proposal is that better management of online data needs to drive towards online personalization, I believe the premise and conclusions of the article are too narrow. While interesting, they are incomplete. Consider this, even if the premise is executed perfectly, advertisers will still have not solved 60% to 80% of the problem. A growing portion of media is being consumed online and a growing number of transactions are occuring online, but it’s still a minority.

The root of my point is that the writers premise solves only a small portion of all consumers interactions with a brand… not all portions of some consumers. In a world where more than 60% of consumers act in a multichannel manner and bring 4 to 5 times more value (Forrester research), solving the larger problem of multichannel insight has become the new table stakes. Using the writers premise, relying solely upon online actions to drive personalization, success would rely upon shere luck that a media impression would actually be triggered by the appropriate marketing reason.

 Until we have a data management solution that leverages the knowledge, segmentation and targeting of a brand as the primary data select and targeting methodology, we’re going to be chasing after the big money with small solutions. Consumer behavior is more complex than the distillation of online data. Consumer expectations are greater. The problems marketers are trying to solve are larger.

 What you propose isn’t wrong, I feel it’s just incomplete.

Love to hear your thoughts!


Comscore identifies 14MM US mobile users have scanned QR codes

August 12, 2011

Boy, that’s a LOT more than I would have anticipated. Maybe I live under a rock but I rarely see QR codes. I wonder what percent of US mobile users have seen a QR code, bet it’s not a lot… that would make that 14MM users a huge percentage, relative to those who have seen a code:-)

On a similar note, have you seen the Autonomy Aurasma application yet? Extremely cool! Here’s a YouTube video demo. Basically, it turns the entire offline world into a potential QR code. Take a look, it’s worth it!


The fit of independent variables to the personalization scenario

October 1, 2009

Regression analysis can determine the “fit” of independent variables to a dependent variable.

Not all independent variables are a good fit.

Relying upon a small set of independent variables may produce an incorrect fit -> destroy your chances of doing anything that’s successful in your personalization program.

Thought for the day:-)

Mark


Website personalization vs customization

September 26, 2009

Customization is derived through explicit, stated preferences, while personalization is driven by both the explicit and implied – behavioral, demographic and brand specific information. How did the user get to the site (referral information like URL or search keywords), prior purchases, and onsite activity are key to driving relevance on a website.

Consider this, your brands’ website probably constitutes less than 0.5% of a visitors life experience, if you’re wildly successful… there’s a world of insight necessary and available to the purview of  your personalization scenario. A world that requires integration with a more comprehensive data set: your marketing database, third party data and analytic models to decipher it.


Forrest Gump was a mass marketer

June 11, 2009

forrest-gump-chocolates“My momma always said, ‘Life was like a box of chocolates. You never know what you’re gonnaget'”. Yeah, right… in a mass marketing spray and pray world, sure!  But  Lieutenant Dan…

It’s a marketers job to figure out which chocolates taste best and then figure out how to find more of just those. Most people would think this is a great place to stop. I think we should also go and figure out how to find bigger chocolates! Forget the box Forrest, pull up a truck!

mandm-persoWouldn’t be a great world if we could each go to a Godiva store and order a box with your name on it, with just your selection of chocolates… “I’d like a box of Mark Ogne, please”. You may not know this, but did you know you can order personalized M &M’s? What a cool idea… check it out!


Behavior, algorithms, consumer relevance and the advertiser

February 19, 2009

Can a single algorithm deliver relevance… across seasons, different web properties, between global cultures and among differing offering categories? Personally, I have troubles trying to predict behavior in the people I’ve known for many years. The human heart and mind act in sometimes strangely unpredictable ways. Isn’t that the part of humanity that’s really great? I think so!

Algorithms to deliver relevance need to accurately reflect consumer information – behavior, demo / psycho-graphics, and other.  The difficulty with this model is that only the smallest of slivers of a consumers life revolves around any single brand – though we would all like to believe differently:-)  Also, until you get reams of data surrounding an individual, how do you actually recommend. In the on line world this is particularly debilitating because over half of website traffic bounces from good sites right away and only a small sliver (used the word twice) of traffic actually makes it to a conversion… and an even smaller sliver (gotta stop using that word) comes back and makes it to a second second conversion. So, algorithmic personalization or recommendations really are only capable of helping a small portion of your consumers, after you get to know them.

An actuary can build statistical models that deal with vast population samples, telling the breakdown of what will proportionally happen in certain events. That’s interesting but it also deals with averages across large groups of people. Not necessarily valid to the point of algorithmically driven recommendations or optimization of an individual and their purchase potential or drivers.

I don’t intend to close the door on the subject, I do believe these approaches are helpful when other data is not available or when you have A LOT of information and you want to solve a retention / lifetime value issue… which are both great issues to solve. From my experience though, many brands believe they can use these technologies, in particular recommendation engines, to help solve an acquisition problem. Hmmm.

Love to hear your thoughts!

Mark


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