This article originally appeared on TechCrunch (this version is slightly different). Most web publishers measure where their traffic is coming from using an analytics package such as Google Analytics, Omniture or Core Metrics.
Today they’re wrong. Terribly wrong. And figuring out who is referring your traffic is a very important part of determining how you allocate your marketing budgets. It is almost certain that Twitter is driving much more of your referrals than you think.
Possibly up to 4x more.
Here’s the awe.sm story of why:
Take a look at the Google Analytics log for BothSidesofTheTable.com for yesterday. I had 8,502 visitors yesterday of which 1,669 are listed as “direct.” Direct traffic are people who typed in my URL directly. As in they weren’t referred by anybody.
But look at the second line. This says “direct – bothsid.es / bothsid.es – twitter” and shows 1,423 referrals. Line 5 says twitter.com / bothsid.es – twitter” for 712 referrals and line 9 shows twitter.com for 170 people.
What does that mean?
I use a product called awe.sm to track all of my social media sharing behavior. I’m an investor in the company. What awe.sm does is it allows publishers to be able to track each individual share behavior to a level of granularity that almost no other campaign tracking tool allows.
If I weren’t using awe.sm then line 2 would have shown up as “direct” traffic and I would have assumed that I was getting a lot more direct traffic than I really was. I would have assumed I was 36% direct and just 10% via Twitter when the reality is that I’m 20% direct and 27% via Twitter.
In fact, the actual Twitter referrals are generally up to 4x as much as people think is happening. And the same is almost certainly the same for most publishers in terms of understating referrals.
This is a problem because publishers might then under invest in Twitter campaigns relative to others because they don’t get “last mile attribution” right.
This happens with other marketing campaigns, too. Often you hear a radio ad, see a TV ad or read an article in a magazine and you type the results into Google to find out more details about the product or service. The problem is that marketers assume that Google drove the traffic. They did not. So you ramp down your TV or print campaigns and suddenly your search volume goes down.
Last mile attribution is very important to understand marketing ROI. For the above problem the best company I know of is called Convertro (I’m not an investor).
In the social media world that tool is awe.sm.
And the problem is even worse than I described. Twitter is an amazing generator of social hooks to websites. Some of that comes from Twitter.com or other Twitter clients. But since many other websites pull in Twitter data, including links, you don’t always know who is referring the traffic to you.
Case in point: LinkedIn. Many Tweets are now being sent to LinkedIn and then the publisher assumes that the source of the referral is LinkedIn. In some ways it is because that’s where your user engaged the content. But get rid of the Tweet and you get rid of the referral traffic in the same way as I described the loss when you cancel your TV commercial.
So when I see MG Siegler announce that LinkedIn is sending more traffic to TechCrunch than Twitter – I’m not so sure. I understand why he would think that – Google Analytics tells him so. But I’ll bet a hefty amount of LinkedIn clicks were originated on Twitter. And I’ll bet a whole lot of TechCrunch “direct” traffic is from Twitter.
Here’s how awe.sm works.
First, we generate a unique URL for EACH share behavior. So if you click on a “Tweet this” button on a website to send an article to your friends, that link is individual to you and to that exact share. If you were to click it again to share the same article we’d generate a new link again.
This allows awe.sm more precision in tracking performance. It allows us to track time of day as well as do things like track which copy converts better if you want to a/b test Tweet copy.
We also cookie users so that we can better track who it was that drove viral adoption of campaigns. It could be that one influential person send a Tweet but he doesn’t have a lot of followers. If Ashton Kutcher follows that person and suddenly shares if with his 7 million followers it would start to snowball.
As a market you still want to know what drove Ashton’s share. It’s the snowball effect, which is why Jonathan Strauss, the founder of awe.sm, actually named the company “Snowball Factory.”
So there you have it. The story is never quite as simple as the data might lead you to believe. It’s why sophisticated marketing tracking programs are important. It’s why clients like GroupOn, Zynga, Gilt Groupe and TopSpin Media are using Awe.sm.
If you want to read a more detailed assessment of the 4x Twitter phenomenon and why it’s likely driving more traffic than you think, please check out Jonathan’s post on the topic.
Image courtesy of Fotolia.