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Marketing MathTM

Tag Archives: Algorithms

Increase Your Digital Coverage by 40% In One-Easy-Step

01 Aug

simple is goodConfucius once said that “Life is really simple, but we insist on making it complicated.”

Perhaps the same can be said of digital media buying. Too often it seems as though the onset and rapid growth of programmatic buying has created more problems than solutions. An expanded media supply chain with multiple layers of costs, increased levels of fraud, brand safety concerns, visibility challenges, a lack of transparency and perhaps most troubling, eroding levels of trust between advertisers and their agencies.

Growing pains? Perhaps. But something needs to change and this author would like to suggest one potential solution… abandon programmatic digital media buying altogether. Seriously? Why not?

Consider the following and the concept won’t seem so far-fetched:

  • In 2015, advertisers spent $60 billion on digital media, with close to two-thirds of that going to Google and Facebook (source: Pivotal Research).
  • According to the advertising trade group, Digital Content, today this duopoly is garnering 90% of every new dollar spent on digital media.
  • What happened to the magical pursuit of the long-tail and the notion of smaller bets being safer? Economics. The fact is that the notion of the long-tail simply didn’t work as researchers and economists found that having less of more is a better, more statistically sound pursuit. To wit, Google’s and Facebook’s market share.
  • Today, programmatic digital display advertising accounts for 80% of display ad spending, which will top $33 billion in 2017 (source: eMarketer).
  • Between 2012 – 2016 programmatic advertising grew 71% per year, on average (source: Zenith).
  • In 2018, programmatic will grow an additional 30%+ to $64 billion, with the U.S. representing 62% of global programmatic expenditures (source: Zenith).

Come again. Two publishers are getting $.90 of every incremental digital dollar spent and programmatic digital media buying accounts for 80%+ of digital media spend. What are we missing? Is there an algorithm that specializes in sending RFPs and insertion orders to Google and Facebook in such a manner that the outcome yields a 40% or better efficiency gain?

As we all know, there have been numerous industry studies, including those sponsored by the World Federation of Advertisers (WFA) and the Association of National Advertisers (ANA), which have suggested that at least 40% of every digital media dollar spent goes to cover programmatic digital media buying’s transactional costs (third-party expenses and agency fees), with only $.48 – $.60 of that expenditure going to publishers.

So, for an advertiser spending $40 million on programmatic digital media, if the law of averages holds true, $16 million will go to cover transactional costs and agency fees. That means that of the advertiser’s original spend, they will actually get $24 million worth of media. While we know that programmatic media can yield efficiencies, can it overcome that type of transactional deficit?

If that same advertiser eschewed programmatic digital and decided to rely on a digital direct media investment strategy, what would it cost them?

Assume that they hired ten seasoned digital media planning and investment professionals for $150,000 each (salary, bonus, benefits), they would spend $1.5 million on direct labor costs. Further, in order to afford their team maximum flexibility, let’s say that the advertiser allocated an additional $1 million annually for access to ad tech tools and research subscriptions to facilitate their Team’s planning and placement efforts. This would bring their total outlay to $2.5 million per annum.

If they were spending $40 million in total, this means that the team would be able to purchase $37.5 million worth of digital media. Don’t forget that placing digital buys direct will greatly reduce fraud levels that can eat up another 8% – 12% of every digital ad dollar, while also greatly improving brand safety guideline adherence. Compare that to the $24 million in inventory purchased programmatically. 

So how efficient is programmatic?

Sadly, most advertisers can’t even address this question, because their buys are structured on a non-disclosed, rather than a cost-disclosed basis. Even if they had line of sight into what the third-party costs (i.e. media, data, tech) and agency fees being charged were, they wouldn’t have a clue as to the fees/ charges that sell-side suppliers were levying, further eroding working media levels.

A simplistic solution? Perhaps. But the fact that the industry continues to drink the programmatic “Kool-Aid” without any significant progress toward resolving the dilutive effect that programmatic transactional costs, agency fees and fraud have on an advertiser’s investment seems a tad irresponsible.  

Ask yourself. What would you do if it were your money? 

 

 

Can AI Bots Solve the Agency Remuneration Issue?

21 Mar

commodoreIt was a simpler time in 1864, or so it seems, when the “Commodore,” James Walter Thompson, founded his namesake agency.

As the ad industry grew over the next several decades, a commission based compensation system was the predominant means of remuneration. Simply put, full-service agencies kept 15% of the gross media rate charged by media owners from whom agencies purchased advertising for their clients. At some point in the 1960’s commission based remuneration began to give way to labor-based fees that were predicated on an agency’s direct labor and overhead costs and a reasonable level of profit.

It wasn’t long afterward that the agency “holding company” was born and full-service agencies gave way to agencies that specialized in a particular area such as creative development, media planning and placement and sales promotion. Both of these trends directly impacted “how” and “what” agencies charged clients for their services. As importantly, advertisers became more acutely interested in understanding more finitely the details behind the composition of their agency partners’ fees. This in turn created anxiety and concerns on the part of ad agencies and clients alike. Advertisers sought to reduce the level of fees that they were paying and the agency community sought to protect their profit margins and maintain some level of privacy surrounding their financial operations.

Fast forward to 2017 and the topic of “non-transparent” agency revenue sources such as rebates, kick-backs, float income and media arbitrage has been at the forefront of contract and compensation discussions since the Association of National Advertisers (ANA) completed their landmark “Media Transparency” study in 2016. Rightly or wrongly, many in the industry feel that client procurement tactics, focused on squeezing agency compensation led to the rise in non-transparent revenue. Agencies for their part, feel as though they are overworked and underpaid, while clients continue to sense that they are paying too much for the resources being proffered by their agency partners.

Challenging times to be sure. Add in the shift from traditional media to digital, the attendant impact on workflow and resources, the rise of new competitors to ad agencies that include consultancies, publishers and ad tech providers and the rapidly increasing impact of technology on operational efficiencies and the topic of agency compensation becomes even more vexing.

And while agencies wrestle with their organizational, talent and cultural issues, the industry is poised for a giant leap forward in operational efficiency. Algorithms that can place media and inform resource allocation planning and artificial intelligence bots that can actually create advertiser content and oversee the production of creative materials have the potential to displace agency personnel across multiple functions. The question is: “What is the impact of these technology trends on agency remuneration systems?”

For an industry that has relied on labor-based fees linked to marking-up employee salaries and selling their time to advertisers, the notion of automation and doing more with less can certainly be daunting. As IBM Watson Chief, David Kenny, once said:

“If you are using people to do the work of machines, you are already irrelevant.”

Thus it is time for the ad agency community to rethink both how they organize themselves to deliver client services and how to evolve from labor-based compensation models to outcome based remuneration systems.

Wonder if there is an AI bot that can assist with this transition?

If you’re an advertiser and interested in learning more about how to compensate your ad agency. Contact Cliff Campeau, Principal, AARM | Advertising Audit & Risk Management at ccampeau@aarmusa.com for a complimentary consultation on this important topic.

 

 

 

Does Anyone Really Want Advertisers to Solve the Attribution Dilemma?

14 Mar

conspiracyIt has been decades since the concept of Marketing Mix Modeling (MMM), the forerunner to Attribution Modeling, was introduced. The concept was relatively straightforward, marketers would apply statistical analysis to sales and marketing data to quantify the impact that each element of the marketing mix had in driving brand sales and profit. Once the causal relationship had been modeled, marketers would then be able to accurately forecast outcomes and inform resource allocation decisions.

While the concept may have been straightforward, the solution, for most marketers, has been elusive. Why? First and foremost, MMM has some inherent challenges, particularly when it comes to quantifying the impact of longer term brand equity development tactics versus those focused on short-term sales. Secondly, these models have not fared well in accurately assessing the impact of various media types on outcomes to assist in refining allocation decisions.

Fast forward to the late ‘90’s when we experienced an explosion in online media, the birth of e-commerce and the introduction of “Big Data.” The emergence of digital media and the attendant level of data that marketers where now able to gather led to the launch of “Attribution Modeling.” The goal, to assess and quantify what marketing and media touchpoints influenced an advertiser’s target audience, and to what extent, across the purchase funnel in an effort to optimize media spending across the ever expanding gamut of media alternatives.

While there are multiple variations of attribution models to consider, most marketers have relied on single-source attribution models, often using a “last click” approach which assigns responsibility for an outcome to one event. While simple, this flawed approach to attribution modeling gives too much credit to digital media, at the expense of traditional media and other marketing touchpoints.

Sadly, for advertisers that are doing both MMM and Attribution Modeling, it is rare that the feedback from these related, but different approaches synch. Further, there remain audience delivery measurement (i.e. cross-channel measurement), multi-touch attribution challenges that introduce a layer of complexity that drives up the cost of attribution modeling.

That said, since the onset of these two modeling tools being introduced, the industry has dramatically evolved its data gathering capabilities, enhanced CRM and DMP capabilities, conceived of and launched programmatic media buying, where algorithms have replaced media buyers and now we’re seeing the use of artificial intelligence bots, such as Adgorithms’ “Albert” that can plan and place media and create content. Heady stuff to be sure.

This got the cynic in me thinking; “Well if we can master all of this from a technology perspective, surely we should be able to cost efficiently and effectively master attribution modeling.” That led to idle speculation about whether or not the ad industry really wants advertisers to solve the attribution modeling dilemma?

After all, what if John Wanamaker was wrong? What if more than half of his ad spend was wasted? Remember, the marketing and media choices available to him in the 19th century were considerably more limited than those available to advertisers today. Would accurate attribution models eliminate some of the following marketing and media options from consideration?

  • Television
  • Radio
  • Magazine
  • Newspaper
  • OOH
  • Cinema advertising
  • Product placement
  • Direct mail
  • Email
  • Sponsorships
  • Online display
  • Online video
  • Podcasts
  • Paid search
  • Organic search
  • Mobile
  • Social media
  • Native advertising
  • In-store advertising
  • In-store displays
  • On-package advertising
  • Trade promotions
  • Price promotions
  • Couponing
  • Affinity marketing
  • Affiliate marketing
  • Applications
  • Earned media

Crazy. Right? Reminds me of a quote by the American journalist, Gary Weiss:

“One problem with the focus on speculation is that it tends to promote the growth of the great intellectual cancer of our times: conspiracy theories.”

What do you think…

 

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