For marketers, attribution is the Holy Grail. For those unfamiliar with the term, attribution means determining what marketing channel or budget was responsible for generating a particular action. Without proper attribution, it’s pretty darn difficult to perform any kind of meaningful ROI calculations on your marketing spend. In fact, I wrote another post about attribution earlier this year or so ago titled “The ‘A’ Word—Learn It, Love It, Live It!,” which pointed out that in today’s marketing world, attribution isn’t always what it’s cracked up to be.
Now it’s no secret that attribution analysis is rather difficult to perform in an age of proliferating media, multichannel customers and, drum roll … Big Data. Think about it, how do you gauge which marketing channel was responsible for generating a sale when a customer was sent and read an email, received a direct mail piece and visited a microsite, Googled the company name and found the homepage, but clicked on a sponsored link leading to a landing page, went to and Liked a Facebook page, became a follower on Twitter, tweeted about it to his friends … and ultimately made a purchase using an App on an iPhone. Which channel gets credit? Email, direct mail, organic SEO, mobile, social? All of them? None of them? Some of them? It’s enough to make your head spin.
Now enter Big Data. In this column, I’ve written extensively about the challenge to marketers posed by Big Data. I know, it’s the meme du jour … seems like you read about it everywhere you go these days. Basically, Big Data is the massive accumulation of information that’s taking place across organizations as they market and engage with their customers and prospects across an ever-expanding proliferation of channels.
As customers and prospects interact with firms across different channels, the data continue to pile up. It’s this deluge of information and how to make sense out of it that is being referred to as Big Data. But, as I’ve written before, Big Data is really the problem—not the solution, per se. The fact that organizations are collecting all of this information is great. It’s what they are doing (or not doing, as you’re about to see) with it that’s most important.
I recently read a study done by the Columbia Business School and the American Marketing Association titled “Marketing ROI in the Era of Big Data.” The study was a survey of 253 corporate marketing decision-makers, director-level and above, at large companies. The results were striking.
They found that 91 percent of senior corporate marketers believe that successful brands use customer data to drive marketing decisions. OK, fair enough … couldn’t agree more. But, among those who are collecting data, a measly 39 percent admit they’re actually unable to turn this information into actionable insight. Pretty surprising, huh?
That’s not all. A whopping 65 percent of marketers admitted that comparing the effectiveness of marketing across different digital media is “a major challenge” for their business. An astounding 57 percent of marketers are not basing their marketing budgets on any ROI analysis whatsoever. And to add insult to injury, 22 percent are using brand awareness as their sole measure to evaluate their marketing spend. That’s right, as their sole measure. A direct marketer by trade, I almost spit out my coffee when I read that last stat.
But the shocking thing is based on my experience, I do not find this to be out of the ordinary. In fact, I met with one client recently and was shocked to learn that the client had basically thrown in the towel when it come to defining attribution, and had created hyper-simplistic ROI analysis by using a control customer group to whom the client didn’t market at all, and compared how much this group bought against the rest. Sounds pretty wonky, right? The crazy part is that even the simplistic model is astronomically better than the 57 percent who don’t even bother with ROI in the first place.
So, what are some solutions to the attribution conundrum? Well, there are several popular models that marketers are experimenting with, and each one of course has its plusses and minuses.
1. First-click attribution—credits the channel where a customer first engaged with the firm. On the plus side, this model actually attempts to discern where the customer journey actually began. The downside is that in today’s environment where marketing is often run in silos, it can be challenging to track customer engagement in a multichannel manner.
2. Last-click attribution—credits the channel where the last action took place (i.e., where the conversion occurred). On the plus side, this model is super easy to track. The downside is that it only measures the channel that’s best at generating the sale itself, and completely disregards how the prospect was initially brought into the fold.
3. Equal-weighting attribution—tracks all of the touchpoints where the customer engaged with the firm, and gives them all equal weight in terms of generating the conversion. The advantage of this model is that it takes a holistic view of the customer-vendor relationship. At the same time, this model overlooks the disproportionate role one channel may play over another.
4. Custom-credit attribution—a hybrid model created by the marketer based on its marketing strategy, customer base, and so on. If done right, a custom model can be highly effective, as it’s designed based on facts on the ground. The only downside is, well, you’ve got to create and test it—which is often easier said than done!
Okay, guess I’m out of room for this post, so I’ll end it here. In any event, I’d love to hear about what if any attribution model you’re been using, how it has worked out, and so on. Let me know in your comments.