My previous two posts (I, II) have attempted to lend structure to the inherently elusive measurement model for cross-channel campaigns. As much as I underscore on the ‘qualify over quantify’ model, people love numbers. Particularly clients.

Hey, go figure.

Luckily, many of our applications love the numerical spotlight. Take Jumbli, for example. For our cross-channel campaign with AT&T, the average interaction time for the word game was 76.77 minutes for web players and 4.15 minutes for mobile players. We have players that have accumulated more than 5 million points- that requires literally months of play.

However, as I discussed in yesterday’s post, the Vans Be Here campaign follows an entirely different user reward model. For this campaign, we want the actual user interaction time to be fairly short (because the UI is clear and the submission process is efficient), and for the meat of the interaction (the ‘share’) to carry on long after the site has been closed (or after the user walks away from the billboard).

The metrics we can track numerically (total # of users, # of unique users, # of submissions) pale in importance to the number of minutes that users spend sharing their snapshot, replaying their Times Square webcam video, encouraging their friends to send in their photos.

And therein lies the rub.

Because we’ve outsourced the ‘reward’ to the user (in allowing them to use our platform in a very personal, natural way), we’ve made the richest metrics harder to quantify. Does this make the campaign less valuable than a Jumbli campaign that has a much more quantifiable user experience? Of course not.

But it does require an understanding within the industry that a shift is happening– one that humiliates your calculator under a pile of Facebook status updates.

Yesterday, I addressed the difficulty in pinning down metrics for place-based cross-channel campaigns like Vans Be Here.

We can all agree that we’re scrabbling on slippery ground when it comes to crunching numbers. So what do we do about it?

First, locate the reward.

For this campaign, the real payoff for the user is grabbing a snapshot of their photo (or text message) displayed on Viacom’s iconic Jumbotron in Times Square and sharing it with friends.

Now make this step as simple as possible for the user. (In this example, we send them an email with a direct link to their ‘moment,’ and we allow them to spread the photo with a single click.)

Direct Link:

Upon clicking the “Snapshot” button:

But, what is this interaction exactly?
How do we account for this?; rather, how do we ‘count’ this?

Well, the tricky thing is that this interaction starts with a unique user’s single click, but the real fruit falls when the snapshot is shared. For simplicity’s sake, let’s say the average # of connections for this one user is 126 (the current average # of followers for Twitter users, which seems fair in respect to The Dunbar Number).

So does this count as 1 click and 126 impressions?
Not quite.

What about the out-of-home component? How many eyeballs saw this photo in Times Square? How many people did it affect (passively, subconsciously)?; How many were actually stirred to effect (actively sending in their photo, too)?

Well, if we take into consideration that 1.6 million people pass through Times Square each day (and 500,000 gather there on NYE), then we’ve clearly thrown an exponent into the mix here (though actual computations here are flimsy at best). [src]

A campaign like this must be understood as tracking ‘interaction bundles’ rather than simply impressions or clicks (at the risk of muddying the already murky waters of digital out-of-home vocabulary). The only way to give meaning to numbers here is to qualify rather than quantify:

* Define your verbs (click, txt, view, visit, watch, write, submit, photograph, playback, share, embed)
* Assign worth (find the ‘fruit’)
* Construct goals around collecting as much of this fruit as possible (in this case, getting as many users as possible to share their image with their friends).

Not done yet.

Now calculate the out-of-home spreadability and brand identification piece that’s happening here on a much larger scale than any subset of active users could ever proliferate (no matter how much you subscribe to The Law of the Few).

We end up with a results overview that should remain focused on the brand awareness component, but should also give due credence to the rich ‘interaction bundles’ of the superuser (one who actually employs at least three of these verbs- e.g. visits the site, sees the billboard, sends in content, gets a snapshot, and shares among his social graph).

Have I made you nostalgic for the days of banner ads yet?

A brief introduction: Hi there. I’m Gabi Schaffzin, Director, Creative Development at LocaModa. I’ll be posting alongside LocaJayne on The Web Outside. Feel free to bug me with questions or comments about anything; I’ll gladly talk digital, creative, dooh, sports, food, movies. Whatever you’d like.

Enough of about me, though:

Now that the highly touted Arbitron Out-of-Home Digital Video Display Study has been out for over a week, we here at LocaModa thought it was about time to chime in.

If you haven’t seen it yet, the aforementioned study can be found, free, online [pdf link]. It gives a great overview of the number of American adults who recall seeing a digital out of home display in the last month (spoiler alert: 67%, or more than 155 million). While these figures have lead to an overwhelmingly positive industry-wide reaction (and deservedly so), we think it’s time to start asking some harder questions. Bill Gerba begins to scratch the surface when he notes that this study will not “put to rest any lingering doubts over our medium’s viability as a communication channel.”

Over 155 million seeing your screen is great. But do they see your content? Do you even know who is seeing it and when? And if so, do they find it relevant? Neither a large study, nor a recorded stream of viewers looking at your screen could provide this level of granular insight. Instead, it is making sure your screens are dynamic and interactive that will get you there.

Digital out of home provides us with the greatest mix of television’s attention-grab and the web’s contextual relevance and measurability. We know that. But are we taking advantage of it? Arbitron’s study was a great first step to understanding who may be looking at DOOH screens. As Mr. Gerba puts it, “it’s solid research from a reputable firm.” But it’s up to the networks and content developers to bring the granular – and extremely necessary – analytics to their screens, be it through mobile, physical, or online interactivity.