There is, almost literally, an unlimited number of things you could focus on to create a high impact data-influenced organization.
And, as if unlimited is not enough, nearly every month your analytics vendors release new features, you discover new analytics solutions, and as your business is more successful (hurray!) there is a new mobile app to track or a new digital experience to problem-solve or a crazy online to offline campaign that upends everything unleashes a new layer of tactical activity.
In a world when your work will never be done, how do you assess that the core things necessary are present? How do you ensure that your can zig-zag with business strategy? What guarantees that agility and innovation are present in your analytics practice?
I believe there are five elements that have to be persistently present in the primordial soup at any company that expects amazing life to spring forth.
You’ll be surprised, there’s only one tool in that mix. It is not even an analytics tool. My reason for that is simple… At this point, it honestly does not matter which web analytics tool you use as long as it is a tool that is under active development by your vendor. Yes, some tools can dance on their left foot and others can only do so with their right foot. Not as important as you might think.
My recommended five elements are much more primal, their presence powers brilliant life to constantly evolve.
Here’s a little back story.
I was asked a few weeks back: “What companies should we proactively help with analytics, for free, so that they can make smarter data-influenced decisions?” I think the answer expected was my view related to
Culture is a stronger determinant of success with data than anything else. Including data.
[People + Process + Structure] > [Data + Technology]
It seems hard to believe. Yet, it is so fantastically true. At least for now. At least until AGI takes over.
Why is this formula material?
The first part of the equation, for better or for worse, improves in an evolutionary manner. The second part of the equation most frequently improves in a revolutionary manner.
The challenge for Senior Leaders is that revolutions seem a lot more attractive and hence they charge full speed ahead. This results in frustration, derailed careers and a massive amount of money flushed down sad places.
Revolutions in our context, almost always fail. Evolution works. Hence, it is dangerous to overlook the super critical importance of P+P+S.
You want to win big with data, with marketing, with transformative digital yada yada and blah blah, evolve. Do so at the fastest pace you can put in place for transformation of the left-side of the above equation, and use the same pace to evolve the right-side of the above equation.
This will ensure that the people, process and structure will be smart enough to take advantage of the smart and wizbang tech.
Maybe this metaphor will help make this real.
You can’t give a toddler a Harley Davidson motor cycle. The moment your start the motorcycle, the toddler is going to start crying. It is not the mistake of the toddler, she is just a toddler after all. It is not the mistake of the Harley, it is a very cool motorcycle. The mistake is yours.
The toddler needs something to steady her, something she can push, something to exercise her legs to
Today’s post comes from a source of deep pain. Analysis Ninjas are valued less than I would prefer for them to be.
The post is also sourced from a recent edition of my newsletter, The Marketing – Analytics Intersect. I send it once a week, and it contains my insights and recommendations on those two topics. Like this blog, the newsletter is geared towards being instantly actionable (rather than just theory-smart, which is pretty cool too). Do sign up if you want to deliver a small electric shock of simulation to your brain each week.
TMAI #41 covered a graph that resulted from a survey done by Econsultancy and Lynchpin. I received a ton of responses for it, and great discussion ensued. It prompted me to write this post, essentially an expanded version of TMAI #41. I’ve added new insights, recommendations, and two bonus lessons on how to do surveys better and a direct challenge to your company’s current analytics strategy.
If your heart is weak, you can stop reading now. I promise, I won’t mind one bit. I heart you. If you are open to being challenged… then here are the short-stories inside this post…
The World Needs Reporting Squirrels. Wait. What!
Three thoughts that explain the Econsultancy/Lynchpin graph.
Bonus #1: Lessons from Econsultancy/Lynchpin Survey Strategy.
Bonus #2: The Askers-Pukers Business Model.
Let’s go and challenge our collective thinking!
The World Needs Reporting Squirrels. Wait. What!
Some of you know that I created the phrases Reporting Squirrels and Analysis Ninjas to emphasize the difference between those that puke data and those that puke insights with actions attached to them.
Here is my slide the first time I presented the concept in a
Being book smart is good. The outcome of book smart is rarely better for analytics practitioners then folks trying to learn how to fly an airplane from how-to books.
Hence, I have been obsessed with encouraging you to get actual data to learn from. This is all the way from Aug 2009: Web Analytics Career Advice: Play In The Real World! Or a subsequent post about how to build a successful career: Web Analytics Career Guide: From Zero To Hero In Five Steps. Or compressing my experience into custom reports and advanced segments I’ve shared.
The problem for many new or experienced analysts has been that they either don’t have access to any dataset (newbies) or the data they have access to is finite or from an incomplete or incorrect implementation (experienced). For our Market Motive Analytics training course, we provide students with access to one ecommerce and one non-ecommerce site because they simply can’t learn well enough from my magnificent videos. The problem of course is that not everyone is enrolling our course! 🙂
All this context is the reason that I am really, really excited the team at Google has decided to make a real-world dataset available to everyone on planet Earth (and to all intelligent life forms in the universe that would like to learn digital analytics).
The data belongs to the Google Merchandise Store, where incredibly people buy Google branded stuff for large sums of money (average order value: $115.67, eat your heart out Amazon!). And, happily, it has almost all of the Google Analytics features implemented correctly. This gives Earth’s residents almost all the reports we would like to look at, and hence do almost all
You don’t use an ad blocker, right? Of course not! You would never want to take away the opportunity a content creator has online to monetize their work via ads.
I know that at least some of you think I’m being sarcastic. I am not, and this post is all about getting the data to show you that I am indeed not being sarcastic.
I am insanely excited that we can track ad blocking behavior in Google Analytics, so easily. This post covers these key elements:
Here’s how this post unfolds…
1. Ad block: #wth
2. Technical how-to implement enhanced code guidance (Google Tag Manager or direct)
3. Setting Google Analytics front end elements (custom dimensions, advanced segments)
4. Five Reports and KPIs that deliver critical insights from ad blocking behavior
While you could call on your favorite IT BFF to do this for you, let me encourage you by saying that if I can do this all by myself…. You can do it too! Honestly, it is that easy.
Excited? Let’s go!
1. Ad block: #wth
The reason you might think I was being sarcastic above is that there is such venom in the media (of course the media!) about people who use ad blockers, and an incredible amount of hoopla around how the only reason media is dying is the awful people using ad blockers in their web browsers.
The reality is not quite that cut and dry.
First, plant me firmly in the column of people who believe that using an ad blocker is a personal choice, each person makes the moral decision they are most comfortable with. Second, I believe that the let me make cheap money by
Here’s something important I’ve observed in my experience in working with data, and changing organizations with ideas: Great Analysts are always skeptical. Deeply so.
This was always true, of course. But, it has become mission critical over the last few years as the depth, breadth, quantity and every other dimension you could apply to data has simply exploded. There is too much data. There are too many tables/charts/”insights” being rammed down your throat. There has been an explosion of “experts.”
If you are not skeptical, you are going to die (from a professional perspective).
And, yet… You can’t be paralyzed by skepticism. At some point, you have to jump. Or, you are dead (again, professionally).
Let’s do this post in two pieces.
First, a plea to be skeptical, of everything and everybody, illustrated using an example from one of the most respected sources of data out there. Followed, by advice on getting to a decision rather than what happens to poor analysts: paralysis.
Second, as we are on the topic of great analysts, I want to share how to recognize that you might be one, from a macro perspective, and, if you are, or are not, what’s your value to your company.
Surely, you are intrigued!
#1A: Skepticism is your BFF.
I saw these two numbers presented the other day: 42% of online shoppers use video for pre-purchase research. 64% use YouTube to find products.
As soon as I heard them, I knew they were horse-manure.
The source of skepticism was simple, neither number is true for me – and I’m in a place, with people, who are the most connected people on the planet with more devices to do this type of research if it was true.
If you don’t have goals, you are not doing digital analytics. You are doing i am wasting earth’s precious oxygenalytics.
Let’s back up. Let me start with a story.
We were brain storming about the next cluster of coolness for Analytics, the conversation quickly went to what Analysts need to look at on a daily, weekly and monthly basis. I started to outline a simple framework that stated that no one should look at anything daily (that should all be automated and run off automated or custom set thresholds – things don’t really change materially on a daily basis), weekly should be based on stuff that borders reporting squirrel work and pinches of analysis ninja work, and monthly…. well super analysis ninja stuff. And, then I started to redefine what daily, weekly and monthly even means. From there, it is only a hop, skip and jump to the most deadly question in analytics….
What’s the business solving for?
Everything came to a screeching halt. This beautiful daily, weekly, monthly blog post I was drafting in my head to share my excitement with you about thinking analysis differently went poof.
It pains me how critical it is to know what the heck we are solving for with our analytics, and how few people identify goals for their website (mobile or desktop). The reason is simple: If you don’t know where you are going, you’ll get somewhere and you’ll be miserable.
We see this everyday. “Analysts” spewing data out left right and center, after spending so much time tagging and re-tagging and Google Tag Managering. Yet, few Marketers or executives take them seriously (because they don’t know what the heck all that means to the business
For the last decade (#omg!), I’ve consistently complained about a fundamental flaw in Web Analytics tools: They incentivize one night stands, rather than engagements matching customer-intent.
This leads to owners of digital experiences (insanely) expecting all visitors to their websites to convert right away – anything less than that is a failure. Damn the intent the customer is expressing.
It also results in Marketers obsess about awful things like last-click conversions (die last-click attribution die!). They make silly user experience decisions (Searching for car insurance options? We will remove every single thing from the page except a GET QUOTE button. Ha! Sucks to be you Visitor!). They never consider Think or Care intent, all they obsess about is Do intent (See-Think-Do-Care business framework). Not even all of the Do, just the strongest of commercial intent. The very bottom of the Do! It really is quite crazy.
You’ll agree all of this sounds quite insane. Not just insane, so visibly insane that everyone should see through it and fix their minds/reports/strategies. So, why are we still so obviously wrong and still on the insane path?
Simple. It is just how all of the Digital Analytics tools are configured at their very core.
Every standard report in every standard tool is configured off Visits (or in Google Analytics language, Sessions), rather than Visitors (GA language, Users). The specific metric I’ve been mad about since day one of this blog (May 14th, 2006!) is Conversion Rate. It is measured as Orders/Visits. [Or, its variation Outcomes/Sessions]
Built into that is the mental model that if you visit a website, then every Visit has to result in money for the site owner. Else, it is a failed visit. Scroll
The difference between a Reporting Squirrel and Analysis Ninja? Insights.
As in, the former is in the business of providing data, the latter in the business of understanding the performance implied by the data. That understanding leads to insights about why the performance occurred, which leads to so what we should do.
Do you see how far away a Reporting Squirrel’s job is from that of an Analysis Ninja?
For one, I hope you see the massive investment in self-development of business skills required to have the foundation required to get to the why and, even more, the so what.
Pause. Reflect on the implication of that why and so what on your current skills/career.
I’m sure you came up with a set of actions you can take to evolve from a squirrel to a ninja, or, if you are already a ninja, how to become even more awesome at ninja’ness.
One of the actions that both clusters will come up with is the ability to communicate the insights you discover. Even if you have really amazing why and so what, I’ve observed many Analysts die at the last mile: Presenting their whys and the so whats, in the form of stories.
In fact 86.4% of all Analyst careers fail due to a lack of this critical last mile skill!
Ok, ok. I kid. I kid.
It is really 88%. : )
Tom Fishburne’s wonderful cartoon is here for another purpose.
We send out our multi-tab spreadsheets, our best Google Analytics custom reports, our great dashboards full of data , and more to the tactical layer of data clients. The Directors, the Marketers, the Optimization employees and our resident social media gurus. The valiant hope is that they will
Standard reports stink. Custom reports rock!
If you are a regular reader of this blog, you are quite familiar with this sentiment. I’ve expressed it often. 🙂
The primary reason is simple: You are unique. Your business is unique. Why would a report created for everyone work for the special someone that you are?
There are other great reasons as well.
Custom reports allow you to deeply focus (by eliminating the rif-raf metrics and dimensions, they save time and show just what you want). When shared, custom reports allow you to deliver deeper relevance. Custom reports allow you to package up entire datasets for deeper analysis.
I’ve shared a whole bunch of custom reports in the past. You can download them into your Google Analytics account via one click (along with some lovely Advanced Segments and a Dashboard). Just go to the GA Solutions Gallery and click Import: Occam’s Razor Awesomeness.
You can download a bunch more, that are not yet in the bundle above, by following the links at the end of this post. Seven more! The include single custom reports that replace all/most current standard reports in GA on Mobile, Content, Paid Search and Acquisition. Your life will be simpler. Grab the above, then grab the ones at the end of this post.
Today, I want to share a few of my recent favorites that solve day-to-day challenges in clever ways.
But, before we go there I want to share an important concept. Many custom reports are wrong because we mess up the fundamental data model in analytics. We mis-align metrics and dimensions across Users, Session, Hits. If you want to create accurate custom reports (or apply advanced segments), this post is mandatory reading: