Your analysis provides clear data that the campaign was a (glorious) failure.
It could not be clearer.
The KPI you chose for your brand campaign was Trust, it had a pre-set target of +5. The post-campaign analysis that compares performance across Test & Control cells shows that Trust did not move at all. (Suspiciously, there are indications that in a handful of Test DMAs it might have gone down!)
Every so often, the story is just as simple as that.
You do the best you can with a marketing campaign (creative, audience, targeting, channels, media plan elements like duration, reach, frequency, media delivery quality elements like AVOC, Viewability, etc.), and sometimes the dice does not roll your way when you measure impact.
You would be surprised to know just how frequently the cause for failure is things that have nothing to do with the elements I mentioned above. In future Premium editions we’ll cover a bunch of these causes, today I want to cover one cause that is in your control but often a root cause of failure:
Judging a fish by its ability to climb a tree!
AKA: You picked the wrong KPI for the campaign.
[Note 1: I’m going to use the phrase Success KPI a lot. To ensure clear focus, clear postmortems and clear accountability, I recommend identifying one single solitary metric as the Success KPI for the initiative. You can measure seven additional metrics – say for diagnostic purposes -, but there has to be just one Success KPI. Close accountability escape hatches.]
[Note 2: Although the guidance in this article applies to companies/analytics teams of all sizes, it applies in particular to larger companies and large agencies. It is there that the
Category: Web Metrics
Increase Analytics Influence: Leverage Predictive Metrics!
Almost all metrics you currently use have one common thread: They are almost all backward-looking.
If you want to deepen the influence of data in your organization – and your personal influence – 30% of your analytics efforts should be centered around the use of forward-looking metrics.
Predictive metrics!
But first, let’s take a small step back. What is a metric?
Here’s the definition of a metric from my first book:
A metric is a number.
Simple enough.
Conversion Rate. Number of Users. Bounce Rate. All metrics.
[Note: Bounce Rate has been banished from Google Analytics 4 and replaced with a compound metric called Engaged Sessions – the number of sessions that lasted 10 seconds or longer, or had 1 or more conversion events or 2 or more page views.]
The three metrics above are backward-looking. They are telling us what happened in the past. You’ll recognize now that that is true for almost everything you are reporting (if not everything).
But, who does not want to see the future?
Yes. I see your hand up.
The problem is that the future is hard to predict. What’s the quote… No one went broke predicting the past. 🙂
Why use Predictive Metrics? As Analysts, we convert data into insights every day. Awesome. Only some of those insights get transformed into action – for any number of reasons (your influence, quality of insights, incomplete stories, etc. etc.). Sad face.
One of the most effective ways of ensuring your insights will be converted into high-impact business actions is to predict the future.
Consider this insight derived from data:
The Conversion Rate from our Email campaigns is 4.5%, 2x of Google Search.
Now consider this one:
The Conversion Rate from our Email campaign is
Simple Tricks To Up-level Your Analytics Reports.
Like you, I consume a whole lot of reports every day – company data, public data.
Many are acceptable, some are very good and all the rest leave me extremely frustrated with both the ink and the think.
People make so many obvious mistakes. Sometimes repeatedly.
Just yesterday I was quietly seething because none of visuals included in the report contained any context to understand if the performance I was looking at was good or bad.
Rookie mistake.
The graph could be going up, down, all around and I as a consumer had the job of figuring if something was good, bad or worth ignoring.
The heartbreaking part is that most executives will take a look, realize the difficulty in interpretation in 15 – 20 seconds, and go back to shooting from the gut. Even if the report has hidden gold.
In a move that might not surprise you, I sat down with the person for 90 minutes going visual by visual, table by table, directing changes that would ensure everything had context.
A report usually has a hard time explaining why something is going awry or going really well. (That is why you have job security as an Analyst!)
A report can usually be very good at clearly highlighting what is going well or badly.
Your #1 job is to make sure your reports don’t fail at this straightforward responsibility.
So today a simple collection of tips that you can use to up-level your reports – to allow them to speak with a clear, and influential, voice.
For many of you a reminder of what you might have let slip, for others a set of new things to implement as you aim for your next promotion.
#1. Context, Context,
Five Strategies for Slaying the Data Puking Dragon.
If you bring sharp focus, you increase chances of attention being diverted to the right places. That in turn will drive smarter questions, which will elicit thoughtful answers from available data. The result will be data-influenced actions that result in a long-term strategic advantage.
It all starts with sharp focus.
Consider these three scenarios…
Your boss is waiting for you to present results on quarterly marketing performance, and you have 75 dense slides. In your heart you know this is crazy; she won’t understand a fraction of it. What do you do?
Your recent audit of the output of your analytics organization found that 160 analytics reports are delivered every month. You know this is way too many, way too often. How do you cull?
Your digital performance dashboard has 16 metrics along 9 dimensions, and you know that the font-size 6 text and sparkline sized charts make them incomprehensible. What’s the way forward?
If you find yourself in any of these scenarios, and your inner analysis ninja feels more like a reporting squirrel, it is ok. The first step is realizing that data is being used only to resolve the fear that not enough data is available. It’s not being selected strategically for the most meaningful and actionable insights.
As you accumulate more experience in your career, you’ll discover there are a cluster of simple strategies you can follow to pretty ruthlessly eliminate the riffraff and focus on the critical view. Here are are five that I tend to use a lot, they are easy to internalize, take sustained passion to execute, but always yield delightful results…
1. Focus only on KPIs, eliminate metrics.
Here are the definitions you’ll find in my books:
Metric: A metric
The Very Best Digital Metrics For 15 Different Companies!
The very best analysts distill, rather than dilute. The very best analysts focus, when most will tend to gather. The very best analysts are display critical thinking, rather than giving into what’s asked. The very best analysts are comfortable operating with ambiguity and incompleteness, while all others chase perfection in implementation / processing / reports. The very best analysts are know what matter’s the most are not the insights from big data but clear actions and compelling business impact from usually a smaller subset of key data.
The very best analysts practice the above principles every day in every dimension of their jobs. It is that practice that I try to discern when I do job interviews. When I see evidence of them in any candidate, my heart is filled with joy (and the candidate’s inbox is filled with a delightful job offer).
This post shares one application of the above skills. People ask me this seemingly simple question all the time: What Key Performance Indicators should we use for our business?
I usually ask them back: What are you trying to get done with your digital strategies?
There is no golden metric for everyone, we are all unique snowflakes! 🙂
That then takes us down the very best way to answer that question, to use the five-step process to build out the Digital Marketing and Measurement Model.
But, what if we did not have that opportunity? What if I was pushed to answer that question with just a cursory glance at their digital existence?
While it is a million times less than ideal, I can still come up with something good based on my distillation skills, application of critical thinking, comfort in operating
Be Real-World Smart: A Beginner's Advanced Google Analytics Guide
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
Ad Block Tracking With Google Analytics: Code, Metrics, Reports
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
Rock Analytics More: Obsess About Goals And Goal Values!
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
Excellent Analytics Tip #27: Chase Smart Calculated Metrics!
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
Great Storytelling With Data: Visualize Simply And Focus Obsessively
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