I’m excited – are you excited? Maybe you don’t know everything that’s going on with Universal Analytics, but that’s OK because we’re going to tell you – woo!
I told you I was excited. But seriously – there’s good reason to be all jazzed up about Universal Analytics, but because I’ve probably already lost some of you, I’ll start getting right into it:
- Reason #1 – Two words – Measurement Protocol. Measurement protocol gives you the ability to track users across devices, among numerous other things.
- Reason #2 – You can easily set Custom Metrics and Dimensions. Things like the name of the blog article author, what the weather is that day, whether or not someone has visited a particular section of the site – like a store locator.
Ok, I still might not be impressing you, so I guess I will need to provide some real-life examples of universal analytics.
Universal Analytics Example #1
You have a client or boss who is in the Travel Industry. That’s right – big T, big I. These are high-ticket items. I need to let you in on a secret here – people are much more likely to convert over multiple sessions for an expensive item, when compared to a similar experience for an inexpensive product. Think about the amount of time you spent researching your last TV purchase, compared to the amount of time spent researching that audiobook of “50 Shades of Grey.”
So the problem here is when you are marketing across media. What do I mean by media? I mean you are advertising in newspapers, you have an (expensive) email campaign, you have TV spots, you have ads on buses, on the radio, or even mobile devices. Don’t you want to know how all of these are performing? Sadly, you can’t look at either one in vacuum by itself, but you can see how they perform working together. We’ll get back to those other mediums later, but let’s focus on mobile devices for a second.
Let’s say you have a $100,000 monthly budget on mobile advertising and email, among others listed above. Mobile is hot right now, right? Everyone in the boardroom can agree on that. You are spending a healthy chunk of change on mobile, but not many people will buy a $5,000 vacation while they’re commuting to work (actually, they are most likely to purchase a vacation while at work.) People will, however, start their search process on their smartphone:
Let’s say we’ve got this guy – Stan, we’ll call him. Stan has been working his butt off lately, and really wants to take his family on a nice vacation. Poor Stan’s got an hour-long commute, but luckily he’s got a sweet tablet to play with. He’d checked out Fun Paradise Vacationland earlier that weekend, and at that moment a retargeting cookie was set.
On Monday, it’s raining, and Stan is taking his hour-long train ride into work. Stan is exhausted and doesn’t want to go to work. He goes to YouTube to watch videos about cats playing with lasers to cheer himself up. Before or during the video, he sees an ad for Fun Paradise Vacationland, and at this moment Stan is 90% sure he’s going to go through with this. He creates an account, and begins playing around with the different options, but his stop has arrived and it’s time for him to get to work.
Stan’s not having a great day. There was a huge problem with one of his clients and he’s getting an earful of from his boss. He’s thinking about that vacation on his lunch-break. He re-visits the Fun Paradise Vacationland site and plans for tickets and rooms, spending over $8,000.
As long-winded as you’ve probably found this story, it’s actually a greatly condensed version of what often happens and a great example of why you need universal analytics. When someone is spending a significant amount of money, they want to do their research.
The whole point of this story is that without Measurement Protocol, you wouldn’t be able to track Stan as one user. In your current reports, he probably looks like 2 different people – one who visited twice on his tablet, and another guy who decided to visit out of the blue and make a purchase from his office IP.
So maybe they saw your ad while watching Hulu or YouTube on the train, or maybe they got one of your emails, or maybe they’ve seen every single bit of marketing you’ve thrown at them. You want to know which ones they’ve seen and which ones have contributed towards a sale so you can reinvest into those campaigns. Again, you’ll want to turn to universal analytics to find your answer.
Let’s say we’ve got another visitor to use in this same example; her name is Sheila. Sheila comes across an advertisement for a new promotion Fun Paradise Vacationland is running. I’ll be frank here – this promotion sucks. It was a bad idea, and who knows how it was approved, but that’s not our department. The point is that although Sheila also had a great time, she’s not about to return to Fun Paradise Vacationland unless there’s a similar deal for her. There are 20,000 people just like Sheila throughout the lifetime of that campaign. That was a huge waste of your budget that could have been avoided! That campaign needs to die. Time to try something else!
Stan however, had the time of his life at Fun Paradise Vacationland and he wants to plan a return trip. Stan actually becomes a customer of Fun Paradise Vacationland for life, and is the most profitable type of customer for Fun Paradise Vacationland. Throughout the fiscal year, there are 12,000 other people that behave just like Stan, and this accounts for 8% of all of their customers.
As crazy as it sounds, it is possible to discover this type of customer. Analyzing data to discover this holy grail of a metric is called Customer Lifetime Value or LTV. How did you discover this? By setting up a client ID and using it within the Measurement Protocol for Stan when he registered for his first vacation. Three years down the line, you could see that user #1285423 is one of your most frequently returning customers. After that, you would then want to analyze the channel that first brought that customer to make a purchase, and what drove them to make subsequent purchases. Once you’ve discovered which type of customer has the highest LTV, you want to bend over backwards catering to them and increase spending on campaigns that have worked in the past for those high-value customers. Another analysis you can do would be to compare what it costs to acquire a new customer (by taking averages of your marketing expenditures and comparing that to customer acquisition rates) against what it could potentially cost to lose a current customer – especially a high-value lifetime customer. Sadly, this is an analysis that many major corporations could benefit from.
Universal Analytics Example #2
Remember when we were talking about Stan’s crappy Monday morning commute earlier? Remember when I said it was raining? I have a theory that people are more likely to purchase vacations when the weather is miserable versus when it’s comfortable out. Malarkey, you say! Hogwash even! Well, what if I wanted to bet on it, how would we prove it? Enter Custom Metrics & Dimensions.
Please keep in mind that this is a highly customized example, and much more simple implementations of this information will prove to be just as, if not more helpful to you. With this new feature, you can define a custom dimension for weather type, a custom metric for temperature, use an open-source API to define these variables, and then port them into Universal Analytics for analysis. Does that sound super complicated? It can’t be that complicated, because someone else has already figured out a concise set of instructions for just that – “Universal Analytics to Segment By Weather.”
This comes from a colleague of mine across the pond, in the UK. I’m not sure why weather matters so much to them because I hear it rains all the time over there, but that’s beside the point. What if my earlier theory on the effect that weather has on conversions was correct? If we were to prove that, the custom metrics would allow us to measure the temperature at the time of purchase, and the reports could show how a deviation from room temperature correlate to an increase in transactions. You could also set a custom dimension when transactions are made to see whether it was cloudy or rainy at the time of purchase and see how that correlates to conversion.
*Since this article was originally published, we have been able to prove this theory! Check out our case study to find out how we increased CTR 500% for one of our clients through geo-targeted and script-based copy.
These are examples of actionable data. In the above example, you would likely increase your ad spend during days with inclement weather. Combining this data with timing trends of purchasing habits, you can do day-parting from 11:00am to 2:00pm – the times people are most likely taking their lunch break. Lunch break during a rainy Monday – that’s when I want to be targeting people to take a vacation. These two factors combined together could have a conversion rate much, much higher than the average paid search visitor.
Information you collect through Universal Analytics can and should be used to make decisions which will improve the performance of your site. Again, most of these examples were far-out there, but the whole point of custom dimensions and metrics – like custom variables before them – is that they are ways of porting-in customizable data to analytics so you can ultimately attribute this data to conversions, allowing you to make decisions on actionable data.
This post was written by Eric Erlebacher.