Data layer standards

I just listened to the latest episode of Rudi and Adam's Beyond Web Analytics podcast, all about data layer standards. This may sound like an esoteric subject but it's going to become really important in the future, and is key to out industry moving to the next level.

As technologies mature, there is always a tendency to standardize so that we can move to the next layer of abstraction. It means we've worked out the details of things that practitioners have embedded in their practices and we can move on to bigger and better things at higher levels.

So what is a data layer? In "traditional" web analytics implementations, you push information to your web analytics platform using its own platform-specific mechanisms. To record a "Newsletter signup" event in Google Analytics you might use:

_gaq.push(['_trackEvent', 'Newsletter', 'Subscribe', 'Customer list']);

in Omniture you might set:

s.events="event34";

If you wanted to switch between vendors or have something like an ad server conversion beacon inserted on the page, you have to write yet more platform-specific code. More code means more scope for error and for the events to fire on subtly different criteria, so your numbers never line up across systems.

A standardized data layer means instead you'll record things in a common manner and if you're using tag management you can very easily set up whatever analytics, ad server or other tools you want to fire on the same criteria. If well adopted we'll see platforms like Shopify, BigCommerce, Magento, CMSes and the like all supporting it and having turnkey web analytics implementations for 80-90% of use cases. Now that's a good thing so we practitioners can start working on more cool stuff and less tedious implementations and reimplementations.

This is a fantastic initiative and incredibly important. Check out the W3C community: http://www.w3.org/community/custexpdata/wiki/Main_Page

I'll be giving a talk on this next month at Web Analytics Wednesday Sydney.

Web analytics in the real world

(Gmail messed up the original post. Fixed manually.)

Over the break a news story bubbled up about Euclid's retail analytics product. These kinds of tools are pretty damn exciting, and scary too.

The premise is this: track individuals as they move through a retail store using the unique MAC address their smartphone's wireless gives out. Euclid explains that by tracking signal levels, they can triangulate the individual's location within the store. Visitors don't need to have their WiFi actually connected to the store's network, just have it switched on.

Another company doing this is Helsinki-based RapidBlue, who illustrate it with this diagram:

This is pretty amazing stuff and brings the kind of analytics and optimization we regularly do online into the retail environment. Conversion rates, dwell times, split tests, the whole lot.

How many individuals walked past your store? How many then went in? Then how many bought?

Thinking about this further, you could get even better at it:
  • Directional antennae to isolate specific areas of the store
  • Highly directional antennae pointed at the checkouts to record sales
  • Match sales using credit card number or loyalty cards, suddenly you've matched the MAC address to a CRM identifier
  • Give visitors an app with some kind of discount and you can automatically match MAC to CRM identifier
  • Free in-store WiFi and you can see what sites they're looking at as they browse through the store
I can think of some further ways retailers might track people beyond smartphone MACs:
  • Long-distance reads of RFID devices (public transport cards, contactless credit cards, security passes)
  • Partner with mobile telcos to bring mobile coverage into the store, in exchange for sharing anonymized identity information
  • Bluetooth MACs

Web Analytics Wednesday in Sydney

Some of you have probably heard me talk about this for some time. Well I've finally got around to setting up a WebAnalytics Wednesday in Sydney. In 2013, this will run every month on the second Wednesday of the month.

The first event will be:

Wednesday, January 09, 2013 at 6:30 PM
at the City Hotel on Kent Street in the Sydney CBD.

What's Web Analytics Wednesday?
It's a casual social meetup of like-minded web analytics, digital marketing and optimization types. We're a reasonably small community, so it's worth getting together and learning from each other.

What format will it take?
We'll have a couple of short presentations, but the focus is on networking. If you've got something you'd like to present, please get in touch with me!

What does it cost?
It's free! I'm looking for sponsors willing to cover food, drinks and venue hire. Please contact me.

Iframeception

I'm integrating Omniture with a (very poorly done) web chat system. The solution involves their iframe pointing at their domain sitting on our site which creates a popup for the chat session. The popup contains an iframe pointing at our site with parameters passed into the querystring. My code reads the querystring parameters and inserts the analytics beacon. Iframeception. Apparently it's not possible to do this in any remotely sane way, according to the guy I'm dealing with anyway.

Amazingly it actually works. Even in IE. Without any frigging around with P3P headers. It seems IE sends the cookies in the header for third-party cookies if they were set earlier in the session, so it's all okay. I was surprised.

Optimizely throws down the gauntlet to Adobe

This piece on TechCrunch talks about how Optimizely is rapidly catching up on industry leader Test&Target.

website_optimization_platform_adoption_10k

In the comments, an Adobe product mangler points to a blog where they discuss unnamed competitors. Optimizely follow up by throwing down a challenge: try Optimizely and Test&Target side by side and decide.

If you haven't tried the Optimizely demo, well worth a go. It's awesome. Love the product. Be warned though, the pricing isn't the Enterprise Grade you're used to ;)

Real time: you probably don't need it, but you're going to have it anyway

A big buzz in web analytics for the last while has been real-time. It's one of those really cool features that everyone gets all tingly about it. But you don't need it. Seriously, you don't.

Real-time is popular because managers have a Napoleon complex. We all see ourselves as generals sitting on top of the hill, directing our minions into battle. "Send reinforcements down the left flank." War happens in real-time and important decisions need to be made based on real-time outcomes. Marketing and business don't work this way.

The geeks among us all want to feel like we're in the control room of a nuclear power plant, or the War Room from Doctor Strangelove. Those real-time graphs tickle something deep in our consciousness, make us feel like we're alive with fresh data, completely up-to-date. But it's an illusion.

If you're making decisions based on real-time data, you're probably doing it wrong and spending a lot of time spinning your wheels. You really shouldn't be wasting time implementing the kinds of changes you'd make on real-time results. It's just a waste of time. Spend time doing quality analysis of a decent length of time's data, then make a decision.

That's not to say there's absolutely no valid use cases for this stuff. I'm sure in a digital newsroom it's be great to see the results of a breaking story in real-time, tweaking headlines and allocating resources to popular stories. Tools like Chartbeat pretty much have that market nailed.

Google Analytics has a pretty half-arsed attempt at real-time. It's pretty crappy though. No conversions makes it a complete non-starter for any ecommerce business, and the design is completely wrong for the standard use case, sticking it up on a big screen hanging on the wall.

So there's plenty of reasons not to use real-time. You'll probably will end up using it anyway. The pull of a real-time dashboard is just too great. Your boss's Napoleon complex will be stroked by a Big Board dashboard. It'll raise your team's profile. There'll be people standing in front of it, mesmerized, watching the data come in. Particularly when there's a big launch.

The challenge for us analysts is to ensure we do the minimum possible, and keep the real-time data as unactionable as possible. Reducing to a bare minimum the number of "why'd that go down in the last hour" questions we waste time answering.