As Internet memes go, one stands out as pretty good advice: Skip to the good stuff, a.k.a. the Wadsworth Constant

I know you’re busy, so I’ll explain it quickly: The Wadsworth Constant states that you can easily jump past the first 30% of any YouTube video and be assured of missing the inevitable build-up, the talky, pointless chatter before the main event — the guy falling off the bridge, the kitten nodding off to sleep, the backboard-bending alley-oop, or whatever the really good part might be.

For marketers, the widespread belief that nothing good happens in the first third of a video begs the question: What length will they watch? And if you need to get their attention in the first few seconds of an ad, why not just make the whole ad a few seconds long?

When you think about the web and attention spans online in general, shorter video begins to make a lot of sense. After all, one of the greatest animal memes of all time, Dramatic Look, clocks in under seven.

So why do ad videos run long? It comes from TV conventions, the golden ratio of ad inventory to programming that dictates that a quarter of an hour of primetime television is ads, then proceeds to chop those 16 minutes into a graspable, clock-driven convention of 15-, 30- and 60-second chunks.

But the online experience is not the TV experience. We do not “settle” into a programmed evening of internet crawling. Instead we grab snatches of video viewing, rarely fixating on a single channel or source for long.

In web time, 30 seconds is how long we might look at a whole page. Will the web audience dedicate that entire time to an ad? Maybe. Probably not. So it makes sense to crunch down the ad math accordingly. What’s less than a third of 30 seconds? Try seven.

Seven seconds changes the game on ad content. Wadsworth’s Constant is now in full force. You don’t have seven seconds to try to build up to anything. In seven seconds, it’s over. Creative must get more creative, and more meme-like, in its thinking.

What’s the payoff? If the ad engages the viewer — if it distracts, even momentarily, from the content — then it’s done its job. Consumers realize that they will be served ads, and they don’t necessarily reject a well-targeted ad, which is what online is all about.

But video has to rise above the trap of Wadsworth’s Constant, the presumption by a viewer that there’s just nothing there to see. The seven-second ad can be the creative burst that delivers brand impact without punishing the viewer. It’s a happy solution for everyone involved.

Two months ago, I wrote an overview of our research paper on multi-touch attribution. The premise of the post was that although attribution measurement for digital advertising has come a long way in the last year, the attribution space is still fragmented and, to be frank, a bit confusing. We proposed three guiding principles that we think will enable multi-touch attribution to be a core part of every marketer’s ad measurement and optimization plan: standardization (and in some sense, simplification), ad effectiveness and goal alignment. We promised to elaborate on each of these three points. This post details how ad effectiveness fits into attribution.

Attribution measurement is really a study of cause and effect. We show ads in order to drive some type of conversion event (let’s put aside for now whether that be purchases, research, brand engagement, brand awareness or simple clicks). The core of attribution measurement is determining who (or maybe what) is driving the conversion event of interest. This determination of post-campaign cause and effect is the product of controlled experimentation and/or carefully managed analysis of observational data (i.e., the raw campaign data you have). We won’t dive too deeply into the pros and cons of various causal analysis methods here (but if you want to witness one of the nerdiest debates in existence, read the comments here) but some causal analysis must be adopted by any attribution analysis in order to arrive at the right conclusions. A few examples of methods that have been publicly introduced to the marketing community have been the experimental A/B test, as written about by Collective and quasi-experimental observational methods, as written about by our own research team at m6d.

Again, the motivation behind considering causality within attribution analysis is that a smart media mix optimizes towards those channels that can demonstrably drive conversions, and any channel that serves ads with no effect should be dropped. It is an intuitively obvious statement, but the practice of attribution measurement may not always prove to adhere to it. I don’t want to suggest that causal analysis is a trivial thing to accomplish, and the lack of causal analysis in attribution measurement may have more to do with practical considerations than fundamental disagreement with the principles. Even the conceptually simple A/B test can be fraught with problems (Ron Kohavi of Stanford/Microsoft has a lot to say about this.

Imagine designing a controlled experiment that incorporates 15 different channels across 15 different software and data platforms that each use different targeting triggers on proprietary data. In such a case, “difficult” might be better replaced with “impossible.” In such scenarios, often the only possible approach towards measuring causality is the “observational” approach. This leads me to my final point.

Observational approaches towards measuring causal effects can be biased when strict assumptions about your data have not been met. In most realistic cases (i.e., the 15 channel case mentioned above), data on every possible attribute that could have affected conversions will not be available. Such attributes might be offline ad exposure, conversations with friends or characteristics that bias an individual towards the action being analyzed. When faced with the nearly impossible challenge of perfect attribution (basically, causal attribution where every possible data point in the world has been observed and collected), we need to be realistic. We need to be careful with the language we use, and not try to convince others that perfect attribution has been achieved. Adometry in this post used the term “relative attribution,” which I like and support. Such honesty in terminology goes a long way. We also need to not be intimidated by the complexity of the problem. Armed with the right set of directional principles, a good model is better than no model. And the principle that I believe to be the most fundamental is that attribution is all about ad, and by extension, channel effectiveness.

Attribution is the certainly the topic du jour in online advertising (as it has been for several years). The good news for the industry is that firms are finally emerging to address the challenge. The call to move beyond the last-click is finally being answered! The problem, though, is that attribution measurement is fragmented and inconsistent (typical of any new industry). Many proposed solutions exist to the same problem, and the diversity tends to undermine the effort, and delay adoption of the best solution.

We have thought long and hard about this at media6degrees (m6d), and being scientists, we felt compelled to develop our own proposal for what attribution should look like. We postulated three core concepts for multi-touch attribution: 1. standardization, 2. ad effectiveness, and 3. goal alignment. This post is the start of several in a series that we will be putting out to answer the question: what should good attribution measurement look like? We will be discussing in more detail these three points in future posts. The whitepaper is a bit math-y, but the intuition is certainly there between the formulas. If that is not your style, this post and the posts to follow will cover the core concepts without getting to heavy on the technical jargon. Enjoy!

Attribution is measurement, and measurement should be standardized.

Imagine you are a busy ad industry executive (probably not too far off) and you have to juggle a schedule that involves sales calls, vendor meetings, internal strategic planning, investor sessions and the like. Now imagine that everyone you meet with defines an hour differently. Internally, an hour lasts 60 minutes, but your clients and investors (admittedly important people in your life) define an hour as 50 minutes and 75 minutes, respectively. Whether it’s you or your assistant who has to coordinate with everyone, wouldn’t it be easier if you could all agree that an hour lasts 60 minutes?

This is what the attribution landscape looks like right now. Some vendors offer heuristic attribution allocations based on position in the marketing funnel. Some offer differing statistical approaches. Many even have different definitions of what constitutes an ad exposure. Most parties tend to agree that last-touch/last-click attribution is flawed. But what is stopping us from moving beyond it full force? Lack of standardization seems to be the culprit. So let’s explore what standardization might look like.

Attribution is first and foremost an ad effectiveness problem.

Going back to our role-playing scenario, you, the busy ad exec, have to decide on compensation for your sales staff. You have one sales person who made lots of calls. You have another who made fewer calls, but brought in several seven figure accounts. You likely gave the bigger bonus to the sales person who brought in more revenue. This same compensation strategy should apply to attribution. A good attribution system, plain and simple, rewards advertising strategies that drive conversion. The tricky part is knowing who is driving conversions. This is why causal ad effectiveness measurement is a prerequisite for good attribution measurement. Whether it is through A/B testing (such as that proposed by Collective Media or statistical modeling (such as that done by m6d, attribution systems need to credit the strategies that create real value, and reward them accordingly.

Attribution is what ultimately aligns brand marketer and advertiser goals.

This brings us to our final point — incentives. Online advertising can fall victim to the principal-agent problem. Brand marketers contract advertising agencies and their vendors to serve ads on their behalf. The marketer wants ROI and the vendor wants credit. The oft-cited problem with last-touch attribution is that it motivates vendors to game the system, where they optimize towards their own credit allocation as opposed to creating value for the brand. This ultimately causes a misallocation of the the brand’s advertising budget. In this scenario, both the brand and the advertising ecosystem suffer as value-takers flourish at the expense of value-creators.

Every attribution system should be designed with incentive alignment in mind. By creating an attribution system that is based on commonly accepted principles, one of which being that attribution is at its core an ad effectiveness problem, a standard can finally emerge that serves to align the goals of brand marketers and the agencies and vendors serving them.

February is always the high point of the movie season. Oscar nominations are out and people are rushing to catch up on the movies they have yet to see. To date, I am 3 for 9. Not a very good average, but not bad if I were a left-handed power hitter.

Of the three I have seen, Moneyball really struck a chord. I liked it not only because I’m a life-long baseball guy and a fan of the book, but also because the message rang true for the current state of advertising.

One scene particularly resonated, in which Billy Beane (Brad Pitt), General Manager of the Oakland A’s, is speaking to Paul Podesta (Jonah Hill), his assistant GM, about the idea of using sabermetrics to draft players for their team.

BILLY Why–You’re not the only computer science major who likes baseball. If what you and Bill James are saying is right–
PAUL It’s right.

BILLY It sounds right.

PAUL It is right.

BILLY If math isn’t a theory–

PAUL It isn’t.

BILLY If this is right, why isn’t everybody doing it? In fact why isn’t anybody doing it?

PAUL Because it’s not what they were taught.

Marketers today are challenged more than ever to find new customers. The old ways of finding “prospects” – such as targeting with demographics – are growing as outmoded as thinking that a team shouldn’t draft a player because his girlfriend is ugly (honest to god, it’s in the movie). But it is the way we all have been taught. It’s also safe and comfortable.

Is it fair to assume that our traditional ways of finding new customer prospects are akin to scouts looking at batting average, home runs or slugging percentage? Are we using old and inferior techniques to solve for new problems? Can we apply advanced statistical analysis to find new customers for brands, much as the A’s found prospects in 1999? I, and many others, absolutely think so.

As with Billy Beane and his staff, marketers are now using data more than ever. In fact, we would all agree, there is more data available than we can usefully process. The mantra now has shifted from the amount of data collected to how it is utilized. How is that data put into action?

Much of that data is less valuable and actionable than we had expected. A browser that comes across a website with automotive reviews is not necessarily interested in buying a car, let alone a Ford. However, if that browser demonstrates certain web patterns, and it can be matched to other browsers who have proven to be strong Ford customers, then empirical evidence proves that it’s a great “prospect” for Ford.

So, what does this new world look like? The new coda is to target browsers that will work for your brand, not your competitor’s brand, not your product category, but your brand. The players that Billy Beane drafted for the A’s were drafted specifically to play a role for that team. They were valued for the contribution they could make to the A’s, and wouldn’t have worked for another team (think Scott Hatteberg post-A’s).

Our industry challenge is to find new customers, or prospects, that will engage with your company, brand, sub-brand, and even SKU. Why not pursue that challenge by finding prospects who have already shown the propensity to be interested in your brand? To put it simply, the techniques we have been using are not strong enough proxies for interest in a given brand with a specific appeal at a specific time online.

Our behaviors have changed dramatically as we have become more comfortable with this all-access anytime to anything world. Shouldn’t we adjust to those new behaviors and look for new ways to find our customers?

“It’s systemic and there’s an evolution that we’re going through and it takes time…The whole media consumption pattern is totally different than what we’ve had in the past”
-Tom Phillips, m6d CEO
Part One:

Part Two: