Let's Bury John Wanamaker

There were so many interesting aspects of this year’s Masters of Marketing conference. One in particular caught my attention right up-front on Friday morning…

In this, the 100th year of the ANA, there are still lots of questions surrounding which 50% of advertising is “wasted”. I find that astounding. That 100 years later we’re still having this debate.

Maybe it’s because the very nature of advertising defies certainty.

Or maybe the definition of “wasted” is too broad.

Or maybe the reality is that the actual waste factor has been reduced to significantly less than 50%, but no one famous ever said that “15% of my advertising is wasted, I just don’t know which 15%.” And it wouldn’t make for a provocative PowerPoint slide even if they did.

It’s difficult to ignore the many signs of great progress we’ve made as an industry towards better understanding the financial payback of marketing and advertising. For example:

• Research techniques have improved and the frequency of application has increased to provide better perspective on how actions affect brands and demand.
  
  
• We’ve not only embraced analytical models in many categories, but have moved to 2nd and even 3rd generation tools that provide great insight.
  
  
• We’ve adopted multivariate testing and experimental design to test and iterate towards effective communication solutions.
  
  
• We’re learning to link brand investments to cash flow and asset value creation, so CFOs and CEOs can adopt more realistic expectations for payback timeframes.

All of this is very encouraging. Most of the presenters at this year’s conference included in their remarks evidence that they have been systematically improving the return they generate on their marketing spend by use of these and other techniques. So where is the remaining gap (if indeed one exists)?

First off, it seems that we’re often still applying the techniques in more of an ad-hoc than integrated manner. In other words, we appear to be analyzing this and researching that, but not actually connecting this to that in any structured way.

Second, while some of the leading companies with resources to invest in measurement are leading the charge, the majority of firms are under-resourced (not just by lack of funds, but people and management time too) to realistically push themselves further down the insight curve. In other words, the tools and techniques have been proven, but still require a substantial effort to implement and adopt.

Third, not everyone agrees with Eric Schmidt’s proclamation that “everything is measurable”. Some reject the basic premise, while others dismiss its applicability to their own very non-Google-like environments.

So what will it take to put John Wanamaker out of our misery before the 200th anniversary of the ANA?

1. Training – exposing more marketing managers to more measurement techniques so they can apply their creative skills to the measurement challenge with greater confidence.
2. A community-wide effort to push down the cost of more advanced measurement techniques, thereby putting them within reach of more marketing departments.
3. An emphasis on “integrated measurement”. We’ve finally embraced the concept of “integrated marketing”. Now we have to apply the same philosophy to measurement. We need to do a better job of defining the questions we’re trying to answer up-front, and then architecting our measurement tools to answer the questions, instead of buying the tools and accepting whatever answers they offer while pleading poverty with respect to the remaining unanswered ones.
4. We should eat a bit of our own dog food and develop external benchmarks of progress (much like we do with consumer research today). Let’s stop asking CMOs how they think their marketing teams are doing at measuring and improving payback, and work with members of the finance and academic communities to define a more objective yardstick with which we can measure real progress.

As we embark on the next 100 years, we have the wisdom, technology, and many of the tools to finally put John Wanamaker to rest. With a little concerted effort, we can close the remaining gaps to within a practical tolerance and dramatically boost marketing’s credibility in the process.


From Time’s a Wasting – for more information visit anamagazine.net.
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Pat LaPointe is managing partner at MarketingNPV – objective advisors on measuring payback on marketing investments, and publishers of MarketingNPV Journal available online free at www.MarketingNPV.com.

"That is the Big Challenge" says Eric Schmidt

I had a chance to talk briefly with Eric Schmidt, CEO of Google, at last week’s ANA conference. He’d just finished sharing his take on marketing and advertising with 1200 of us representing marketers, agencies, and supporting service providers. He said:

• Google backed away from managing radio and print advertising networks due to lack of “closed loop feedback”. In other words, they couldn’t tell an advertiser IF the consumer actually saw the ad or if they acted afterward. Efforts to embed unique commercial identifiers into radio ads exist, but are still immature. And in print, it’s still not possible to tell who (specifically) is seeing which ads – at least not until someone places sensors between every two pages of my morning newspaper.

• Despite this limitation, Schmidt feels that Google will soon crack the code of massive multi-variate modeling of both online and offline marketing mix influences by incorporating “management judgment” into the models where data is lacking, thereby enabling advertisers to parse out the relative contribution of every element of the marketing mix to optimize both the spend level and allocation – even taking into account countless competitive and macro-environmental variables.

• That “everything is measurable” and Google has the mathematicians who can solve even the most thorny marketing measurement challenges.

• That the winning marketers will be those who can rapidly iterate and learn quickly to reallocate resources and attention to what is working at a hyper-local level, taking both personalization and geographic location into account.

On all these fronts, I agree with him. I’ve actually said these very things in this blog over the past few years.

So when I caught up with him in the hallway afterward, I asked two questions:

1. How credible are these uber-models likely to be if they fail to account for “non-marketing” variables like operational changes effecting customer experience and/or the impact of ex-category activities on customers within a category (e.g., how purchase activity in one category may affect purchase interest in another)?
   
   
2. At what point do these models become so complex that they exceed the ability of most humans to understand them, leading to skepticism and doubt fueled by a deep psychological need for self-preservation?

His answers:

1. “If you can track it, we can incorporate it into the model and determine its relative importance under a variety of circumstances. If you can’t, we can proxy for it with managerial judgment.”
   
   
2. “That is the big challenge, isn’t it.”

So my takeaway from this interaction is:

• Google will likely develop a “universal platform” for market mix modeling, which in many respects will be more robust than most of the other tools on the market – particularly in terms of seamless integration of online and offline elements, and web-enabled simulation tools. While it may lack some of the subtle flexibility of a custom-designed model, it will likely be “close enough” in overall accuracy given that it could be a fraction of the cost of custom, if not free. And it will likely evolve faster to incorporate emerging dynamics and variables as their scale will enable them to spot and include such things faster than any other analytics shop.
  
  
• If they have a vulnerability, it may be under-estimating the human variables of the underlying questions (e.g., how much should we spend and where/how should we spend it?) and of the potential solution.

Reflecting over a glass of Cabernet several hours later, I realized that this is generally a good thing for the marketing discipline as Google will once again push us all to accelerate our adoption of mathematical pattern recognition as inputs into managerial decisions. Besides, the new human dynamics this acceleration creates will also spur new business opportunities. So everyone wins.