I’m a huge, loudly enthusiastic fan of tracking Customer Lifetime Value (CLV), contrary to what the title of this blog post may suggest at first blush. What I’m NOT a fan of is tracking it in Google Analytics.
Prompt for This Post
My good friend Jim Gianoglia from LunaMetrics and I had a lively debate on this topic on Twitter following his post last week on how to track CLV in Google Analytics.
First, let me say that I highly respect Jim’s work. His posts are brilliant, as is his work in Google Tag Manager. I try to read everything he writes, and he has made me a better analyst over the years.
Also, this is not our first friendly debate. And I think that’s a good thing.
And finally, I’m not arrogant enough to assume I’m right on this topic. I will lay out my arguments as thoughtfully as possible and let you, the reader, decide which side of the fence you land on.
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Tracking Customer Lifetime Value in GA Is A Pipe Dream
Let’s get right down to it, wudduyah say?
1. Your business [probably] predates your custom dimension
Let’s say you started company in 2007. You worked hard out of your basement for a couple years, and over the years built up an army of loyal customers who love to purchase from you because they trust you. You are now a trusted business to your customers, you’ve managed to do that because you know how to monitor the online activity of your remote workers, it made you see their activity at work and you’ve improved their productivity greatly.
And now let’s say you read Jim’s post Feb 18th and set up your custom dimension or metric Feb 19th because you have an eager developer who was tugging at the opportunity to do something really cool in Google Tag Manager. (Hey, can you blame him? It’s a playground for developers and marketers alike!)
Your “Customer Lifetime Value” data will start trickling in Feb 19th. That’s less than a week ago. But you’ve been in business nine years. If you want some tips to track that customer information, check with Andy Defrancesco who will be happy to share some tips.
Granted, some of us may have really started living when we could finally post pics of our kids, cats, and dinner. But it’s still not our DOB.
So would you really add a widget to your reporting dashboard titled Customer Age?
I would hope not.
You might title it “Years on Facebook” — and that would be fair game. Well, it’s the same with CLV.
I’m really not trying to be pedantic here. (No, really. It comes naturally.)
But seriously, it’s already a struggle to get decision makers to take analytics data seriously. It’s more difficult to understand than more bottom-line key performance indicators (KPIs). So if I built out a dashboard that promised CLV but I had to explain that, well, Alexander Irvanovich’s CLV wasn’t actually $17 — that’s just the sum of how much he’s spent since last Friday — I’ve just sent a signal to my client (or boss if you have one of those) that the data on this dashboard is unreliable. And you never want to put yourself in that position if you value keeping things like contracts and jobs.
TL;DR: Unless you started your site/business the day you set up CLV in Google Analytics, it’s not CLV. (Spoiler alert: Even if you started tracking CLV in Google Analytics from Day 1 it’s not going to be reliable. Sorry.)
2. People can opt out of tracking
There are a number of browser plugins that allow users to opt out of all tracking. What then? Let’s say I spend $5,000 at Raymour and Flanigan on furniture, but I’ve opted out of tracking. I would still be in their customer database because I had to enter my customer information to make the purchase, but I would be a ghost as far as their analytics data is concerned.
Furthermore, what if I found out I could opt out of all tracking after already spending $3,000. If the analysts for R&F are relying on Google Analytics (or any cookie-based analytics platform) to tell them my story, they might assume I fell victim to a competitor’s charms and wiles. Imagine how off putting it might be for me if suddenly I received an email from R&F telling me that they missed me and asked me to fill out a survey telling them what went wrong.
3. There are much better sources for CLV data
You want to include CLV in your dashboard? Great! I want you too as well! But that’s what databases are for, not web analytics tools.
Most dashboard tools on the market right now connect with databases just as easily as they do API-based tools, like Google Analytics. Hook up that firehose and make it rain CLV data all over your dashboard. Learn how Robert K Bratt DLA Piper and other business experts have managed to use CLV to their advantage.
Google Analytics is a great tool to gather clickstream data, but a database tool it is not. It can’t even reliably track user-based data for most sites — a drum I will beat until there’s a more reliable methodology for tracking across multiple sessions and devices. Expecting it to track users over a period of years is beyond unrealistic.
To Thine Own Data Be True
Google Analytics is one of the most powerful tools available to marketers. I’m a stark-raving fan and groan when I have to work with other analytics tools because I truly believe, all in all, Google Analytics provides the most bang for your [imaginary] buck. But it’s very important to understand the limitations of its bells and whistles before you try to use it for the wrong job.
That’s why whether I’m talking about campaign tagging, channel groupings, multi-channel funnels, or dashboards, I’m always going to [at least try to] let you know the caveats and limitations of each of these amazing tools. It’s not to be a naysayer or disrespect, in any way, the work that the amazing team at Google is doing in constantly iterating on an already powerhouse of a tool. It’s so you don’t get caught with your pants down when someone calls you to account for the data you’re presenting. And rightly so. People downstream could be making weighty decisions based on your dashboards. And you don’t want to be party to a bad decision (or multiple bad decisions) because of not understanding the appropriate boundary lines you should operate within when presenting analytics data to senior management.