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.
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,
The gap between a bad and good data visualization is small.
The gap between a good and great data visualization is a vast chasm!
The challenge is that we, and our HiPPOs, bring opinions and feelings and our perceptions of what will go viral to the conversation. This is entirely counter productive to distinguishing between bad, good, and great.
What we need instead is a rock solid understanding of the updraft we face in our quest for greatness, and a standard framework that can help us dispassionately assess quality.
Let’s do that today. Learn how to seperate bad from good and good from great, and do so using examples that we can all relate to instantly.
We’ll start by looking at the two sets of humans who are at the root of the conflict of obsessions and then learn to assess how effective any data visualization is in an entirely new way. If you adopt it, I guarantee the impact on your work will be transformative.
The Conflict of Obsessions.
There are two parties involved in any data visualization.
1. Analyst/Data Visualizer. As I’ve passionately shared frequently on this blog, we, Analysts, are all in the business of persuasion. We work against that desired outcome because when we work on creating a data visualization, here are our top-of-mind concerns/desires/perspectives:
How can I cram as much as I can into the graphic?
What can I include to ensure everyone clearly gets just how much work I did?
How much of my agenda do I need to make overt, and how much can I make covert?
Is there something I can add to increase the chances that this will go viral and result in fame and glory?
Ok. I’m only teasing.
But, as an
Some moments in time are perfect to reflect on where you are, what your priorities are, and then consider what you should start-stop-continue. In those moments, you are not thinking of delivering incremental change… You are driven by a desire to deliver a step change (a large or sudden discontinuous change, especially one that makes things better – I’m borrowing the concept from mathematics and technology, from “step function”).
In those moments – common around new years or new annual planning cycles – the difference between delivering an incremental change vs. a step change is the quality of ideas you are considering. In this post, my hope is to both enrich your consideration set and encourage the breadth of your goals.
My professional areas of interest cover Customer Service, User Experience and Finance, though here on Occam’s Razor my focus is on influencing incredible Marketing through the use of innovative Analytics. To help kick-start your 2019 step change, I’ve written two “Top 10” lists, one for Marketing and one for Analytics – consisting of things I recommend you obsess about.
Each chosen obsession is very much in the spirit of my beloved principle of the aggregation of marginal gains. My recommendation is that you deeply reflect on the impact of the 10 x 2 obsessions in your unique circumstance, and then distill the ten you’ll focus on in the next twelve months. Regardless of the then you choose, I’m confident you’ll end up working on challenging things that will push your professional growth forward and bring new joy from the work you do for your employer.
First… The Analytics top ten things to focus on to elevate your game this year…
The universe of digital analytics is massive and can seem as complex as the cosmic universe.
With such big, complicated subjects, we can get lost in the vast wilderness or become trapped in a silo. We can wander aimlessly, or feel a false sense of either accomplishment or frustration. Consequently, we lose sight of where we are, how we are doing and which direction is true north.
I have experienced these challenges on numerous occasions myself. Even simple questions like “How effective is our analytics strategy?” elicit a complicated set of answers, instead of a simple picture the CxO can internalize. That’s because we have to talk about tools (so many!), work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more.
Soon, your digital analytics strategic framework that you hoped would provide a true north to the analytics strategy question looks like this…
The frameworks above cover just one dimension of the assessment (!). There is another critical framework to figure out how you can take your analytics sophistication from wherever it is at the moment to nirvanaland.
A quick search query will illustrate that that looks something like this…
It is important to stress that none of these frameworks/answers exist in a vacuum.
Both pictures above are frighteningly complex because the analytics world we occupy is complex. Remember, tools, work, processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more.
The Implications of Complexity.
There are two deeply painful outcomes of the approaches you see in the pictures above (in which you’ll also see my work represented as well).
No CxO understands the
Today something complex, advanced, that is most applicable to those who are at the edges of spending money, and thus have an intricate web of internal and external teams to deliver customer engagement and business success.
The Marketing Industrial Empire is made up of number of components.
If you consider the largest pieces, there is the internal (you, the company) and the external (agencies, consultants).
If you consider entities, you’ve got your media agency, your creative agency, your various advertising agencies, your website and retail store teams, your analysts, marketers, advertising experts, the UX teams, campaign analysts, fulfillment folks, the data analysts who are scattered throughout the aforementioned entities, the CMO, CFO, and hopefully your CEO. And I’m only talking about the small portion of your existence that is your marketing and analytics.
Whether you consider the large, simplistic perspective (internal – external) or the more complex entity view, it’s really easy to see how things can become siloed very quickly.
It’s so easy for each little piece (you!) to solve for your little piece and optimize for a local maxima. You win (bonus/promotion/award). It is rare that your company wins in these siloed existence.
That’s simply because silos don’t promote consideration of all the variables at play for the business. They don’t result in taking the entire business strategy or the complete customer journey. Mining a cubic zirconia is celebrated as if it is a diamond.
Heartbreakingly, this is very common at large and extra-large sized companies. (This happens a lot less at small companies because of how easily death comes with a local maxima focus.)
So how can you avoid this? How do you encourage broader, more out-of-the-box thinking?
This might seem simplistic, but sometimes
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
A rare post today. It looks a little further out into the future than I normally tend to. It attempts to simplify a topic that has more than it’s share of coolness, confusion and complexity.
While the phrase Artificial Intelligence has been around since the first human wondered if she could go further if she had access to entities with inorganic intelligence, it truly jumped the shark in 2016. Primarily because we got our first real everyday access to products and services that used some form of AI to delight us. No more theory, we felt it!
I’m going to take a very long walk with you today. This topic has consumed a lot of my thinking over the last year (you’ll see the exact start date below). It’s implications are far and wide, even in the narrow scope that I live in (marketing, analytics, influence). I have so much to tell you, stuff I’m scared about, and so much I’m excited about.
Here are the elements I’ll cover:
+ AI | Now | Local Maxima.
+ AI | Now | Global Maxima.
+ What the heck is Artificial Intelligence?
+ Machine Learning | Marketing.
+ Machine Learning | Analytics.
+ Artificial Intelligence | Future | Kids.
+ Artificial Intelligence | Worry about Humanity.
Through it all, my goal is to make the topic accessible, get you to understand some of the key terms, their implication on our work, our jobs, and in a bonus implications on the future we are responsible for (your kids and mine).
AI | Now | Local Maxima.
AI also seems so out there, so hard to grasp. Let me fix that for you.
A story where data is the hero, followed by two mind-challenging business-shifting ideas.
At a previous employer customer service on the phone was a huge part of the operation. Qualitative surveys were giving the company a read that customers were unhappy with the service being provided. As bad customer service is a massive long-term cost – and short-term pain –, it was decided that the company would undertake a serious re-training effort for all the customer service reps and with that problems would get solved faster. To ensure customer delight was delivered in a timely manner, it was also decided that Average Call Time (ACT) would now be The success metric. It would even be tied to a customer service rep’s compensation creating an overlap between their personal success and the company’s success.
What do you think happened?
There is such a thing as employees that don’t really give a frek about their job or company, they just come to work. You’ll be surprised how small that number is. (Likewise, the number of employees that go well above the call of duty, look to constantly push personal and company boundaries is also quite small.) Most employees work diligently to deliver against set expectations.
Reflecting that, in our story, most customer service reps, re-trained, took the phone calls with the goal of driving down Average Call Time. They worked as quick as they could to resolve issues. But, pretty quickly customers with painful problems became a personally painful problem for an individual customer service rep. They hurt ACT, and comp. Solution? If the rep felt the call was going too long, self-preservation kicked in and they would hang up on the customer. Another
Ten years, and the 944,357 words, are proof that I love purposeful data, collecting it, pouring smart strategies into analyzing it, and using the insights identified to transform organizations.
In the quest for that last important bit, I am insanely obsessive about 1. simplification and 2. pressing the right emotional buttons.
The reasons are that we all like complexity, it gives us energy :), we tend to be logical, and we often treat data output as the end when in reality the data output is just the start of the process that results in actions that deliver business impact.
Very often the output of our work with Big Data or Small Data, Google Analytics or R, will end up in a few cells of a spreadsheet or a table in Word/Keynote/PowerPoint. The stakes for this output are higher when we are in front of the Senior Leadership of any company, we have but a few minutes to communicate what we have to. Hence my two obsessions above.
In this post, with lots of pictures and real-world data examples, I want to share 6 different strategies you can leverage in service of simplification and pressing the right emotional buttons. Along our journey, I’ve also sprinkled in 15 universal truths that will bring you joy.
Here are the sections in this post:
An important assumption.
Death at the last-mile.
1. Rebel against crapification via cluttering.
2. Don’t fragment data, don’t forget higher order bits.
3. Obsess with deleting information provided.
4. Don’t run away, make the tough choices.
5. So what? So What?? So WHAT!
6. Sell smarter,
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