Category: Op-Ed

The rapid pace of innovation and the constantly exploding collection of possibilities is a major contributor to the fun we all have in digital jobs. There is never a boring moment, there is never time when you can’t do something faster or smarter.

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. They never had to worry that they have to be in a persistent forward motion… sometimes just to stay current.

This reality powers my impostor syndrome, and (yet?) it is the reason that I love working in every dimension of digital. We are at an inflection point in humanity’s evolution where in small and big ways, we can actually change the world.

With that context, this post is all about career management in the digital space. Like this blog, it will be particularly relevant for those who are in digital analytics and digital marketing. I would offer that the higher-order-bits in each of the three sections will provide valuable food-for-thought for anyone in a digital role.

The post has three clusters of advice. The first two are from editions of my newsletter, The Marketing – Analytics Intersect (it goes out weekly, and is now my primary publishing channel, sign up!). The third section was sparked by a question a friend who works at a digital agency asked: Will I lose my job to automation soon? (The answer was, yes.)

The Now section provides advice on how investing in growing your Analytical Thinking will contribute to greater success in the role you are in. The Next section provides advice on what you should be doing to invest in yourself to get ready for the depth and breadth change Artificial Intelligence is going to bestow upon us (regardless of your business role). The Long section shares a thought experiment I want you to undertake to figure out your career three years from now.

One more change reflective of the times we live in… Your employer used to be responsible for your career, this is for the most part no longer true. Your employer would send you to trainings to help push your career forward, this is for the most part no longer true. Your employer/manager would help you figure out the skills you can develop, this is for the most part no longer true. It is now all on you. Hence… Take control.

Ready?

The Now Career Plan: Analytics Experience vs. Analytical Thinking

Check the requirements listed in any digital analytics job and you’ll notice a long laundry list looking for analytics experience.

Years of having used tool x. Years and years of practice with R or “Big Data.” Years of proficiency in analyzing m campaigns for n channels resulting in production of z reports.

When you go to the interview, the hiring company will proceed to ask questions that test your competency in the listed job requirements.

This is normal.

Reflecting on my experience, it is not sufficient.

Test for analytics experience AND explore the level of analytical thinking the job candidate possesses.

Analytical thinking is 6,451 times more crucial in the long-term success of the candidate and the value they’ll add to your company.

Analytical Thinking: Skills, Interviewing, Value.

Analytical thinking is a collection of skills.

It is creative problem solving. It is working systematically and logically when dealing with complex tasks. It is exploring alternatives from multiple angles to find a solution. It is a brilliant evaluation of pros and cons, and achieving the balance that is right for that specific moment. It is always knowing that the answer to what’s two plus two is always in what context? It is being able to recognize patterns. It is knowing that every worthy life decision is a multivariate regression equation (hence the quest to identify all the variables in that equation and their weights). It is the possession of critical thinking abilities. And, most of all it is being able to seek and see the higher order bits.

Beautiful, right?

If I have the immense privilege of interviewing you, expect us to spend a lot of time on the elements mentioned above.

One sample strategy: Expect that I’ll ask open-ended questions (If a company has 90% Reach on TV, why the heck do they need digital?). Then, regardless of what you say I’ll politely but forcefully push back, to explore the depth and breadth of analytical thinking you bring to the table.

If you hire strong analytical thinkers, of any background, you are hiring people who will be adaptable, who’ll grow and flex with your organization and needs. They’ll have the mental agility to think smart and move fast. They’ll ask child-like simple questions that’ll lay bare your complex strategic challenges. Hire them. And, if they don’t know tool x… You can teach them which buttons to press.

Caring and Feeding Your Analytical Thinking.

If you are an analytical thinker, there are many ways in which you can keep feeding and stretching the synapses in your brain. There is always more you can learn.

In a business context, request an hour to talk to people three levels above you in the organization. Ask them what they worry about, ask them what they are solving for, ask them how they measure success, ask them what are two things on the horizon that they are excited about. So on and so forth. You’ll see things very differently, and you’ll think very differently when you go back to work.

I’d mentioned being able to look at every situation from multiple angles. (Think of the famous bullet time scene in the Matrix.) Hence, a personal strategy of mine is to look well outside my area of expertise to help me improve my analytical thinking capabilities.

I’m love reading decisions of the US Supreme Court. SCOTUSblog FTW!

The Supreme Court deals with situations that are insanely complex – even when they appear to be stunningly simple on the surface. There are so many lessons to be learned.

My favorites are the ones I massively disagree with. Citizens United is one such example. I could not possibly disagree with it more. Yet reading through the deep details helped me see the multiple facets being explored, the reasoning used by the other side. I learned a lot.

I go in open-minded, and at times have my mind changed. A good example of this Justice Scalia’s opinion in Gonzales v. Raich and the use of the Commerce Clause. And, he was not a man with whom I have overlapping views on anything. I appreciate him stretching my mind in this case.

Optimal Starting SCOTUS Starting Points.

If you would like to pursue my personal strategy, here are a collection of cases to use as starting points.

Some cases are very dear to me, I truly love them, there is a lot to learn from them as you explore the back and forth of the debate, the majority opinion and the dissenting one (or ones).

Loving v. Virginia is close to my heart, it is the reason I can legally marry my wife. It was just 50 years go!

Obergefell v. Hodges brought immense to our family as we celebrated the right of all Americans to marry. Justice Kennedy’s opinion is a thing of beauty. And, it is also useful to read Justices Scalia and Thomas’ strong and powerful dissents.

Texas v. Johnson said that prohibition on desecration of the American flag was a violation of the right to free speech. Of the many wonderful things about America, the First Amendment is at the top and distinctly unique. The court looked beyond the jingoistic distractions the flag always attracts, and protected what’s critical.

As I’d mentioned above, there is much to learn from cases that are heartbreaking

Dred Scott v. Sandford held that African Americans, free or slaves, could not be considered American citizens and undid the Missouri Compromise. It contained the infamous quote “[black men] had no rights which the white man was bound to respect.”

Buck v. Bell is perhaps the one that is a deep, deep source of pain for me, it a decision that still stands. The court upheld forced sterilizations for those with “intellectual disabilities” and contained the despicable phrase “three generations of imbeciles are enough.”

Korematsu v. United States, legalized the shameful internment of American citizens with any Japanese ancestry. It is still on the books, and places extraordinary power in the President of the US to do what they want to people who might not look like “Americans.” People like me.

Each case, regardless of if I agree with the opinion or disagree, helps push my thinking. It makes me a better analyst, a better employee, a better start-up founder.

I’ve added a differentiated collection of links above to take you to sources, I hope they’ll help feed your analytical thinking.

For the Busy Human On The Go, An Alternative.

Given everything above, I absolutely LOVE the More Perfect podcast.

Jad Abumrad and his team are magnificent storytellers. For each episode, they take one case and explore it from multiple directions. They are entertaining, engaging and deeply informative.

Season one covered seven scintillating cases. I found the episodes that shared how SCOTUS was formed and got its power amazing.

Season two kicked of with… Korematsu! I thought I knew all angles of this case. Yet, towards the end you’ll hear two loud silences in a conversation with Judge Richard Posner. Make sure you hear what he says. I have profound respect for Judge Posner, he is brilliant. And, in those two moments, he both made me deeply uncomfortable and appreciate complexity.

More Perfect on iTunes, Stitcher, Google Play.

Bringing It All Back To Analytics.

The latest episode (as of Oct 11th) is “Who’s Gerry and Why Is He So Bad At Drawing Maps.“

The problem is simple. In Wisconsin Republicans in power massively gerrymandered voting districts (something the Democrats also do when in power). Unlike the past where little sophistication was applied, this time sophisticated algorithms and computers were brought into play. Resulting in more effective gerrymandering.

End result: Democrats won 53% of the votes but only 39% of the seats.

You might think: OMG! CRAZY BEANS! What happened to one person one vote!

Well, the case was heard by the Supreme Court last week. And, everything’s quite complicated (analytical thinking!). Listen to the episode for that.

What’s even more material for us is that Justice Kennedy wants to know how can he figure out that a district has been “too” gerrymandered. There is no real standard, nothing the Justices can use.

Math to the rescue!

Nicholas Stephanopoulos and Eric McGhee created an Efficiency Gap formula to assess how bad the gerrymandering was. (More here, PDF.)

I won’t spoil it for you, let Professor Moon Duchin explain it to in the podcast. It is a thing of beauty.

You’ll learn how to create smarter formulas in your job, how to solve complicated and ambiguous challenges with simple assumptions, and how to not to grow too close to your formulas – rather evolve them over time to be smarter.

In 23 mins, it will make you a better Analyst.

If you follow the overall guidance in this section, you’ll continue to invest and grow the one skill you’ll need in every digital career: Sophisticated analytical thinking.

The Next Career Plan: Prepping For An AI-First World

Even with all the hype related to all things Artificial Intelligence, I feel people are not taking the topic seriously enough. That the big, broad implications for the very near future are not causing us to sit up, take notice, and change our strategies (personal and professional).

Or, maybe I’m just too deep into this stuff. 🙂

I had two big ah-ha moments that have changed my view if humans can be competitive in any field compared to what technology will spring forth. I call the two elementsl Collective Continuous Learning and Complete Day One Knowledge, they are harbingers of exciting possibilities for what we can do with AI (and it to us).

For more detail on that, and if humans are doomed (yes, no, yes totally) please read: The Artificial Intelligence Opportunity: A Camel to Cars Moment

The topic of AI is vast, and I’m not even including all the layers and flavors. The more I learn, the more I realize how little I know. My heartfelt recommendation is that every professional should be curious about AI and try to stay abreast with as many new dimensions as they can. After the first few months, you’ll find your own sweetspot that’ll catch your fancy.

Here are the collection of books, videos, people and learning opportunities from my sweetspot…

Books.

I want to recommend three books. None focusses on digital marketing or analytics. Each tackles humans and the possibilities for humans. Hence they’ve had a profound impact on my thinking about humanity’s future (and via that route, my career plans).

1. Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari.

2. Superintelligence: Paths, Dangers, Strategies by Nick Bostrom.

3. Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark

Videos.

Current development of Intelligence is in silos, I’m glad when someone pulls all the experts from around the world in an attempt to guide humanity’s efforts.

The Future of Life Institute hosted a conference in Asilomar in Jan 2017 with just such a purpose. The entire list of videos is well worth watching, prioritize the individual ones: Beneficial AI 2017

If you can only watch one…

1. Science or Fiction?

There is one other video I want you to watch, from the 2015 edition.

2. Robotics, AI, and the Macro-Economy

There is a ton of video content on YouTube. A go to source for me is whoever is curating the Artificial Intelligence AI channel.

People.

In any space that is having the kind of exponential growth like AI, your best bet is to find people who trust and listen to what they are saying/doing.

We are blessed with a ton of experts, practitioners and futurists. I encourage you to curate your own list.

Here are the ones I follow as closely as I can: Sebastian Thrun, Jürgen Schmidhuber, Demis Hassabis, and Andrew Ng.

I watch videos of all their talks on YouTube or tune in to livestreams of their presentations. I read articles they write. I have alerts for them. Luckily they are so darn busy, they pace their public speaking/writing. 🙂

You can follow their work using strategies you currently use for others you stay in touch with.

Learning.

If you are slightly technically oriented and would like to start your journey of acquiring technical knowledge in the space, Udacity is a great place to go.

All three of these courses are free:

If you are deeply technically oriented, you already know where to go and don’t need my pointers!

I’m sure you’ll notice I’ve not given you specific advice for your next career move. One reason: We are in a moment where each of us has to know all the changes coming, all the possibilities arising, and then figure out that answer for ourselves.

The above books, videos, people and lessons will help you discover the right answer for yourself.

The Long Career Plan: Automation & Your Value To A Company

People are scared of automation.

It is logical. The AI revolution will bring a ton of automation that will eliminate current white-collar jobs in large numbers.

Yet, by the end of this thought experiment, you might see that looking out over the nest 25-30 years, we can deal with automation (/elimination of our current jobs).

This thought experiment is for both Marketers and Analysts.

Get in front of a whiteboard. Draw a decent size square box on it.

Today, almost all the work you do is inside that box.

For Digital Marketers, it is finding keywords or websites, setting targeting parameters, building ads, setting bids, adding rules, building landing pages etc. etc.

For Digital Analysts, it is creating data collection mechanisms, writing queries, creating reports, doing segmentation, creating rules, identifying business focus areas based on data etc. etc.

Here’s the thought: If tomorrow everything you currently do, inside that box, is completely automated… What’s your value?

Pause.

Think about it carefully in terms of personal implications.

For the bravest among you, think of what’s the value of your Agency/Company.

If you are anything like me, you are super-scared. Some of you are likely super-excited as well.

Don’t be scared. Take action.

It is not as crazy as you think to envision that you could be completely automated out. In small pieces this has already happened.

Media example: Campaigns to create, target and deliver results for driving app downloads is now almost entirely automated.

Analytics example: There are already buttons in your tools that automate finding of anomalies in your data that your leaders most need to pay attention to. Eliminating the need for the known knowns and automatically providing the known unknowns and unknown unknowns.

An example that combines the both for even more effective automation: With smart creative, smart bidding, and smart targeting there is no need for any human to touch AdWords or soon a whole lot of your Display campaigns. The results of Data Driven Attribution modeling, which use data from *all* digital campaigns, can now be directly plugged into AdWords which means without any reporting/analysis the platform will automatically optimize for the highest profit for your business – with no human involvement. This is not the future, it is Nov 2017.

Back to the whiteboard.

On top of the box with the stuff you do, write the word Automated.

Ponder now what’s your value.

You’ll see there are two areas where you can add value. The area before the box, the area after the box.

If you are a Marketer…

You can shift to taking more ownership of the inputs that go into your current job (which remember is now automated). Shift to a responsibility that requires a deeper understanding of your Prospects and Customers at a human level. Now, because of that beautiful knowledge, take ownership of the entire process of identifying the optimal creative assets required for any great Marketing campaign. Then, step up and move to the other side of the box… Own the use and deployment of large scale machine learning services to understand every human, which results in creating the simplest most meaningful experience across all digital touch-points. And then… I’m taking you so far away from your current box… expand the outcomes you own from just the transactional to building deeper years-long beyond-pimpy relationships with your customers.

And suddenly…

You hate the freaking box you are in as a Marketer today. You want to expand your responsibility to own these deeply meaningful things that Machine Learning and our Deep Neural Networks won’t touch for a while. You want to feel the true joy that comes from doing meaningful things like figuring out how to build relationships or unleash the full and beautiful power of amazing creative (in ads, in apps, on sites, in products), and so many more exciting things that you were born to do.

Now, you are not scared about automated. You can’t wait for your current job to be automated away.

🙂

I have the above scenario and the wonderful possibilities for Analysts as well. It is also very exciting, as you’ll discover when you do the whiteboarding exercise for yourself.

Now. I totally get that your entire job is not getting automated tomorrow. But, I suspect you’ll be surprised though how fast that is coming. For Nurses. For Truck drivers. For Baristas. For… Everyone. Collect a handful of the smartest people you know, draw a box on a whiteboard, have a discussion.

This thought experiment is just one way to think through the implications of what’s ahead of us. In my blog post on the artificial intelligence opportunity, you’ll see another way I framed how to think this through…

The above framing is a bit more in the higher-order-bit spirit.

I recommend the thought experiment. When you’re done: Step one, have a plan. Step two, execute. Step three, joy. Step four, follow the advice in section one (Now) and section two (Next) of this blog post and start investing in the personal growth you’ll need to move to these new more joy-inducing meaningful jobs.

Your career is in your hands, and I deeply believe it is going to be bright. Seize the moment!

As always, it is your turn now.

Considering the Now moment, is there something unique you do to invest in growing your analytical thinking capabilities? How are you preparing for the Next moment, who are you reading, who are you listening to? Considering the next 25 years in our space, how far do you think automation will go? How are you approaching your personal evolution with the Long moment horizon in mind? How about your company’s?

Please share your unique perspective, challenges, and solutions via comments below.

Thanks.

P.S. I’ve touched on the topic of career paths and career management in earlier posts. Here are a couple you’ll find to be of value:

Digital Analytics + Marketing Career Advice: Your Now, Next, Long Plan is a post from: Occam’s Razor by Avinash Kaushik

[Read More …]

Over the last couple years, I’ve spent an increasing amount of time diving into the possibilities Deep Learning (DL) offers in terms of what we can do with Artificial Intelligence (AI). Some of these possibilities have already been realized (more on this later in the post). And, I could not be more excited to see them out in the world.

Through it all, I’ve felt there are a handful of breath-taking realities that most people are not grasping when it comes to an AI-Powered world. Why the implications are far deeper for humanity than we imagine. Why in my areas of expertise, marketing, sales, customer service and analytics, the impact will be deep and wide. Why is this not yet another programmatic moment. Why the scale at which we can (/have to) solve the problems is already well beyond the grasp of the fundamental strategy most companies follow: We have a bigger revenue opportunity, but we don’t know how to take advantage? Let’s buy more hamster wheels, hire more hamsters and train them to spin faster!

Today I want shed some light on these whys, and a bit more. My goal is to try to cause a shift in your thinking, to get you to take a leadership role in taking advantage of this opportunity both at a personal and professional level.

I’ve covered AI earlier: Artificial Intelligence: Implications On Marketing, Analytics, And You. You’ll learn all about the Global Maxima, definitions of AI/ML/DL, and the implications related to the work we do day to day. If you’ve not read that post, I do encourage you to do so as it will have valuable context.

In this post, I’ve organized my thoughts into these six clusters:

There is a deliberate flow to this post, above. If you are going to jump around, it is ok, but please be sure to read the section below first. You won’t regret it.

Ready to have your mind stretched? Let’s go!

What’s the BFD?

I’m really excited about what’s in front of us. When I share that excitement in my keynotes or an intimate discussion with a company’s board of directors, I make sure I stress two especially powerful concepts that I have come to appreciate about the emerging AI solutions: Collective Continuous Learning + Complete Day One Knowledge.

They are crucial in being able to internalize the depth and breadth of the revolution, and why we strengths AI brings are a radical shift beyond what humans are capable of.

The first eye-opening learning for me came from the Google Research team’s post on Learning from Large-Scale Interaction.

Most robots are very robotic because they follow a sense-plan-act paradigm. This limits the types of things they are able to do, and as you might have seen their movements are deliberate. The team at Google adopted the strategy of having a robot learn own its own (rather than programming it with pre-configured models).

The one-handed robots in this case had to learn to pick up objects.

Initially the grasping mechanism was completely random – try to imagine a baby who barely knows they even have a hand at the end of their shoulder. Hence, you’ll see in the video below, they rarely succeed at the task at hand. 😉

At the end of each day, the data was collected and used to train a deep convolutional neural network (CNN), to learn to predict the outcome of each grasping motion. These learnings go back to the robot and improve its chances of success.

Here’s the video…

(Play on YouTube)

It took just 3,000 robot-hours of practice to see the beginnings of intelligent behavior.

What’s intelligent behavior of a CNN powered one-handed robot?

Among other things, being able to isolate one object (a stapler) to successfully pick-up a Lego piece. You’ll see that at 15 seconds in this video…

(Play on YouTube)

Or, learning how to pick up different types of objects (a dish washing soft sponge, a blackboard eraser, or a water glass  etc.).

I felt a genuine tingling sensation just imagining a thing not knowing something and it being able to simply learn. I mean pause. Just think about it. It started from scratch – like a baby – and then just figured it out. Pretty damn fast. It truly is mind-blowing.

There were two lessons here. The first related to pure deep learning and its amazingness, I was familiar with this one. The second was something new (for me). This experiment involved 14 one-handed robot arms. While not a massive number, the 14 were collectively contributing data from the start – with their many failures. The end of day learnings by the convolutional neural network were using all 14. And, the next day, all 14 started again with this new level of collective wisdom.

For a clear way for me to capture this lesson, I call this Collective Learning.

It is very powerful.

Think of 14 humans learning a new task. Peeling an apple. Or, laying down track for a railroad. Or, programming a new and even more frustrating in-flight entertainment menu for Air Canada (who have the worst one known to mankind).

Every human will do it individually as well as they can – there will be the normal bell curve of competency. It is entirely possible, if there are incentives to do so, that the humans who are better in the group will try to teach others. There will be great improvement if the task is repetitive and does not require imagination/creativity/intrinsic intelligence. There might be a smaller improvement if the task is not repetitive and requires imagination/creativity/intrinsic intelligence.

In neither case will there be anything close to Collective Learning when it comes to humans.

Humans also do not posses this continuous closed loop: Do something. Check outcome (success or failure). Actively learn from either, improve self. Do something better the next time.

Collective Continuous Learning. An incredible advantage that I had simply not thought through deeply enough.

Here’s the second BFD.

Machine Learning is already changing lots of fields, the one I’m most excited about is what’s happening in healthcare. From the ability to speed up discovery of new medicines to the unbelievable speed with which Machine Learning techniques are becoming particularly adept at diagnosis (think blood reports, X-rays, cancers etc.). 

An example I love. 415 million diabetic patients worldwide are at risk of Diabetic Retinopathy (DR) – the fastest growing cause of blindness. If caught early, the disease is completely treatable. The problem? Medical specialists capable of detecting DR are rare in many parts of the world where diabetes is prevalent.

Using a dataset of 128,000 images Google’s  Accelerated Science Team trained a deep neural network to detect DR from retinal photographs. The results delivered by the algorithm (black curve) were slightly better than expert ophthalmologists (colored dots)…

Specifically the algorithm has a F-score of 0.95 and the median F-score of the eight expert ophthalmologists was 0.91.

As richer datasets become available for the neural network to learn from, as 3D imaging technology like Optical Coherence Tomography becomes available all over the world to provide more detailed view of the retina, just imagine how transformative the impact will be.

Literally millions upon millions of people at risk of blindness will have access to AI-Powered technology that can create a different outcome for their life  – and their families.

#omg

A recent incredible article on this topic is in my beloved New Yorker magazine: A.I. VERSUS M.D. You *should* read it. I’ll jump to a part of the article that altered my imagination of possibilities.

An algorithm created by Sebastian Thrun, Andre Esteva and Brett Kuprel can detect keratinocyte carcinoma (a type of skin cancer) by looking at images of the skin (acne, a rash, mole etc.). In June 2015 it got the right answer 72% of the time, two board-certified dermatologists got the right answer for the same images 66% of the time.

Since then, as they outlined in their report published in the prestigious journal Nature, the algorithm has gotten smarter across even more skin cancer types – and consistently performs better than dermatologists.

Most cancers are fatal because they are detected too late, just imagine the transformative impact of this algorithm sitting in the cloud easily accessible to all humanity via their five billion smartphones. This dream come true: low-cost universal access to vital diagnostic care.

Oh, and here’s a profoundly under-appreciated facet of all this. These health algorithms (including and beyond the one above), are incredible at corner cases, the rare long-tail anomalies. They don’t forget what they have seen once or “rarely.”

This is just a little bit of context for the key point.

A dermatologist in a full-time practice will see around 200,000 cases during her/his lifetime. With every case she sees, she’ll ideally add to her knowledge and grow her diagnostic skills.

Our very human problem is that every new dermatology resident starts almost from scratch. Some textbooks might be updated (while comfortably remaining a decade of more behind). Some new techniques – machines, analytical strategies – might be accessible to the resident. But, the depth and breadth of knowledge acquired by the dermatologist at the end of her career with 200k cases, is almost completely inaccessible to the new resident. Even if they do a residency at an hospital or with a old dermatologist, a newly minted dermatologist will only be a little better than when the old one left school.

Consider this instead: The algorithm above processed 130,000 cases in three months! And every day it will get smarter as it’ll have access to the latest (and more) data. Here though is the magical bit. Every single new algorithm we bring online will have total access to all knowledge from previous algorithms! It’s starting point will be, what I call, Complete Day One Knowledge.

As it gets more data to learn from, as it has access to more compute power, it will get smarter and build upon that complete knowledge. The next version of the algorithm will start with this new high mark.

There is nothing equivalent to Complete Day One Knowledge when it comes to humans.

Combine having Complete Day One Knowledge with Collective Continuous Learning (networked hardware or software all learning at the same time) and it should take you five seconds to realize that we are in a new time and place.

Whatever form AI takes, it will always have access to complete knowledge and through the network each instance will make all others smarter every single instance/moment of its existence.

Humans simply can’t compete.

That’s the BFD.

Stop. Think. If you disagree even slightly, scroll back up and read the post again.

It is imperative that you get this not because of what will happen in 10 years, but what is happening today to the job you have. If you still disagree, scroll down and post a comment, I would love to hear your perspective and engage in a conversation.

Bonus 1: There is an additional valuable lesson related to open-loop grasp selection and blindly executing it vs. incorporating continuous feedback (50% reduction in failure rates!). The two videos are worth watching to see this in action.

Bonus 2: While we are on the subject of objects… Relational reason is central to human intelligence. Deepmind has had recent success in building a simple neural network module for relational reasoning. This progress is so very cool. Additionally, I was so very excited about the Visual Interaction Network they built to mimic a human’s ability to predict. (If you kick a ball against the wall, your brain predict what will happen when the ball hits the wall.) The article is well worth reading: A neural approach to relational reasoning. Success here holds fantastic possibilities.

Wait. So are we “doomed”?

It depends on what you mean by doomed but: Yes. No. Yes, totally.

Artificial Intelligence will hold a massive advantage over humans in the coming years.

In field after field due to Collective Continuous Learning and Complete Day One Knowledge (not to mention advances in deep learning techniques and hardware :)), AI will be better at frequent high-volume tasks.

Hence, the first yes.

Neuralink at the moment is a concept (implantable brain-computer interface). But many experts (like Ray Kurzweil) believe some type of connection between our human brain and “intelligence, data, compute power in the cloud” will be accessible to humans.

I humbly believe that when that happens, over the next few decades (think 2050), humans could get to parity with AI available at that time. We might even have an advantage for some time (if only because I can’t let go of the thought that our brains are special!).

Hence, the no.

As we head towards the second half of the current century, AI will regain the lead again – and keep it for good. I don’t have the competency to judge if that will be AGI or Superintellignece or some other variation. But, with all other computing factors changing at an exponential rate it is impossible that intelligence will not surpass the limitations of humans and human brains (including the one with a version of Neuralink).

Here’s just one data-point from Jurgen Schmidhuber: Neural networks we are using for Deep Learning at the moment have around a billion neural connections compared with around 100,000 billion in the human cortex. Computers are getting 10 times faster every 5 years, and unless that trend breaks, it will only take 25 years until we have a recurrent neural network comparable with the human brain. Just 25 years.

Hence, the yes totally.

I have a personal theory as to what happens to humans as we look out 150 – 200 years. It is not relevant to this post. But, if you are curious, please ask me next time you see me. (Or, sign up for my weekly newsletter: The Marketing < > Analytics Intersect)

AI: A conversation with a skeptic.

Surely some of you think, to put it politely, that I’m a little bit out there. Some of you’ve heard the “hype” before and are deeply skeptical (AI went through a two decade long tundra where it failed to live up to every promise, until say 2010 or so). Some of you were promised Programmatic was AI and all it did was serve crap more efficiently at scale!

I assure you, skepticism is warranted.

Mitch Joel is the Rock Star of Digital Marketing, brilliant on the topic of media, and a very sweet human being. Amongst his many platforms is a fantastic podcast called Six Pixels of Separation. Our 13th podcast together was on AI. Mitch played the role of the resident skeptic and I played the role of, well, the role you see me play here.

If you can think of a skeptical question on this topic, Mitch asked it. Give the podcast a listen…

(Play at Six Pixels of Separation)

As you’ll hear multiple times, a bunch of this is a matter of thinking differently about the worldview that we’ve brought with us thus far. I share as many examples and metaphors I could to assist you in a journey that requires you to think very differently.

If you are still skeptical about something, please express it via comments below. Within the bounds of my competency, I’ll do my best to provide related context.

Ok, ok, ok, but what about the now? (Professional)

While I look at the future with optimism (even 150 years out for humans), what I’m most excited about is what Machine Learning and Deep Learning can do for us today. There are so many things that are hard to do, opportunities we don’t even know exist, the ability to make work that sucks the life out of you easier, better, smarter, or gone.

In a recent edition of my newsletter, TMAI, I’d shared a story and a call to arms with specific recommendations of what to do now. I’ll share it with you all here with the hope that you’ll jump-start your use of Machine Learning today…

I lived in Saudi Arabia for almost three years. Working at DHL was a deeply formative professional experience. My profound love of exceptional customer service, and outrage at awful customer experiences, can be directly sourced to what I learned there.

Saudi Arabia is a country that saw massively fast modernization. In just a few years, the country went from camels to cars. (I only half-jokingly say that Saudis still ride their cars like camels – and it was scary!).

Think about it for a moment.

From camels to cars. No bicycles. No steam engines. None of the other in-betweens other parts of the world systematically went through to get to cars. They were riding camels, then they were riding cars. Consider all the implications.

We stand at just such a moment in time in the business world. You know just how immersed and obsessed I am with Artificial Intelligence and the implications on marketing and analytics. It truly is a camels to cars type moment in my humble opinion (it might even be a camels to rockets moment, but let me be conservative).

Yet, executives will often give me examples of things they are doing, and they feel satisfied that they are with it, they are doing AI. When I probe a bit, it becomes clear very quickly that all they are doing is making the camels they are riding go a little faster.

That all by itself is not a bad thing – they are certainly moving faster. The problem is they are completely missing the opportunity to get in the car (and their competitors are already in cars).

It is important to know the difference between the two – for the sake of job preservation and company survival.

Here are a handful of examples to help you truly deeply internalize the difference between these two critical strategies…

If you are moving from last-click attribution to experimenting with first-click or time-decay, this is trying to make your camel go faster. Using ML-Powered Data-Driven Attribution and connecting it with your AdWords account so that action can be taking based on DDA recommendations automatically, you are riding a car.

(More on this: Digital Attribution’s Ladder of Awesomeness)

If you are moving to experimenting with every button and dial you can touch in AdWords so that you can understand how everything works and you can prove increase in conversions while narrowly focusing on a few keywords, you are making your camel go faster. Switching to ML-powered Smart Targeting, Smart Creative and Smart Bidding with company Profit as the success criteria, for every relevant keyword identified automatically by the algorithm, you are riding a car.

Staffing up your call center to wait for calls from potential customers is making your camel go faster. Creating a neural-network that analyzes all publicly available data of companies to identify which ones are going to need to raise debt, and proactively calling them to pitch your company’s wonderful debt-financing services is riding a car.

Hand picking sites to show your display ads via a x by x spreadsheet that is lovingly massaged and now has new font and one more column on Viewability, is making your camel go faster. Leveraging Machine Learning to algorithmically figure out where your ad should show by analyzing over 5,000 signals in real time for Every Single Human based on human-level understanding (die cookies die!), is riding a fast car.

(To see a delightful rant on the corrosive outcomes from a Viewability obsession, and what you might be sweeping under the carpet, see TMAI #64 with the story from P&G.)

Asking your Analysts to stop puking data, sorry I mean automate reporting, and send insights by merging various data sets is making the camel go faster. Asking your Analysts to just send you just the Actions and the Business Impact from those Actions is riding a car. Asking them to shift to using ML-powered products like Analytics Intelligence in GA to identify the unknown unkonwns and connecting that to automated actions is riding a rocket.

If you are explicitly programming your chatbot with 100 different use cases and fixed paths to follow for each use case to improve customer service, that is making the camel go faster. If you take the datasets in your company around your products, problems, solutions, past successful services, your competitors products, details around your users, etc. etc. and feed it to a deep learning algorithm that can learn without explicit programming how to solve your customer’s service issues, you are riding a car.

I, literally, have 25 more examples… But, you catch my drift.

I do not for one moment believe that this will be easy, or that you’ll get a welcome reception when you present the answer. But, one of two extremely positive outcomes will happen:

Win-Win.

Hence, be brutally honest. Audit your current cluster of priorities against the bleeding edge of possible. Then answer this question: Are you trying to make your camel go faster, or jumping on to a car?

While Machine Learning has not solved world hunger yet, and AGI is still years away, there are business-altering solutions in the market today waiting for you to use them to create a sustainable competitive advantage.

Ok, ok, ok, but what about the now? (Personal)

If this post has not caused you to freak-out a tiny bit about your professional path, then I would have failed completely. After all, how can the huge amount of change mentioned above be happening, and your job/career not be profoundly impacted?

You and I have a small handful of years when we can create a personal pivot through an active investment of our time, energy and re-thinking. If we miss this small window of opportunity, I feel that the choice will be made for us.

This blog is read by a diverse set of people in a diverse set of roles. It would be difficult to be personal in advice/possibilities for each individual.

Instead, here’s a slide I use to share a collection of distinct thought during my speaking engagements on this topic…

In orange is a summary of what “Machines” and humans will be optimally suited for in the near-future. (Note the for now.) Frequent high-volume tasks vs. tackling novel situations.

In green, I’m quoting Carlos Espinal. I loved how simply and beautifully he framed what I imagine when I say tackle novel situations.

Over the last 24 months, I’ve made an whole collection of conscious choices to move my professional competencies to the right of the blue line. That should give me a decade plus, maybe more if Ray is right about Cloud Accessible Intelligence. Beyond that, everything’s uncertain. 🙂

Summary.

I hope you noticed I ended the above paragraph with a smiley. I’m inspired by the innovation happening all around us, and how far and wide it is being applied. I am genuinely excited about the opportunities in front of us, and the problems we are going to solve for us as individuals, for our businesses, for our fellow humans and for this precious planet.

In my areas of competence, marketing, analytics, service and sales, I can say with some experience that change is already here, and much bigger change is in front of us. (I share with Mitch above how long I think Analysts, as they are today, will be around.) I hope I’ve convinced you to take advantage of it for your personal and professional glory.

(All this also has a huge implication on our children. If you have kids, or play an influencing role in the life of a child, I’d shared my thoughts here: Artificial Intelligence | Future | Kids)

The times they are a changin’.

Carpe diem!

As always, it is your turn now.

Were Collective Learning and Complete Day One Knowledge concepts you’d already considered in your analysis of AI? Are there other concepts you’ve identified? Do you think we are doomed? Is your company taking advantage of Deep Neural Networks for marketing or analytics or to draw new value from your core back-office platforms? What steps have you taken in the last year to change the trajectory of your career?

Please share your insights, action-plans, critique, and outlandish predictions for the future of humanity, :), via comments below.

Thank you.

The Artificial Intelligence Opportunity: A Camel to Cars Moment is a post from: Occam’s Razor by Avinash Kaushik

[Read More …]

I believe deeply in the value of making data accessible.

In service of that belief, there are few things that bring me as much joy as visualizing data (smart segmentation comes close). There is something magical about taking the tons and tons of complexity that lurks in our data, being able to find the core essence, and then illustrate that simply. The result then is both a mind and heart connection that drives action with a sense of urgency. #winning

While I am partial to the simplest of visualizations in a business data context, I love a simple Bar Chart just as much as a Chord or Fisher-Yates Shuffle. As we have all learned, tools matter a lot less than what we do with the tool. 🙂

In this post I want to inspire you to think differently. I’ve curated sixteen extremely diverse visualization examples to do that. By design none of them from the world of digital analytics, though I’ll stay connected to that world from a how could you use this idea perspective. My primary goal is to expand your horizon so that we can peek over and see new possibilities.

To spark your curiosity, the visuals I’ve worked hard to find for you cover the US debt, European politics, lynching and slavery, pandemics, movies, gun control, drugs and health, the Chinese economy, and where we spend our lives (definitely review this one!).

The sixteen examples neatly fall into nine strategies I hope you’ll cultivate in your analytics practice as you create data visualizations:

This post has quite a bit of depth, and loads for you to explore, reflect and internalize. It will take a few visits to absorb all the lessons. In as much, my recommendation is to read one section per day. Take time to really understand what’s going on, go to the site, play, look at the higher resolution versions (click on the images), make notes of what you’ll do for the first time or change about what you already do. Most importantly, practice taking action. Then, come back, read the next one and take action. I promise, the rewards will be rich.

Let’s go make you an even more effective influencer when it comes to data!

Strategy 1: The Simplicity Obsession

One of the reasons so many visuals are so very complex is that the Analyst/Creator is trying to demonstrate how clever they are. Sadly in the process of demonstrating aforementioned cleverness, the visuals ends up being incredibly complex crammed with every little bit of amazesomeness they  are trying to demonstrate…

(Click on the above image for a higher resolution version)

There is absolutely no doubt in my mind that the Creator worked very hard, and, I sincerely mean this, they are very clever.

The problem is that the essence of what they want to communicate is probably only known to them, or to any person willing to take the time to first learn the job of the analyst, dig into the data themselves, create this picture and then understand what is being said.

It breaks my heart.

Go on. Scroll back up. See if you can understand what is being said.

In my humble opinion there is an additional subtle problem. The Creator was asked to plot the data, or perhaps share the insights, but it is unclear whose job it was to answer this simple question at the end: So What?

When you start with that as your destination, so what, as the creator of any visualization you are going to ask for a lot more context, you are going to make sure the visual is in service of the answer, you’ll make sure your cleverness is focused on the outcome the data has to serve.

Please, please, please keep that in mind.

The complicated thing above is trying to highlight an important trend, is missing the context, and is simply not as dramatic as the reality of it actually is!

Here’s a better visual showing the National Debt Burden, with four additional elements of context…

(Source)

Did you get what the point was in zero seconds?

Are you a whale-load more scared as you contemplate the red and the green?

Are you freaked out that if there is one thing both political parties in the US seem to be good at it is the red (!)?

That is what a good data visual does.

For the few of you that are a part of the team I lead, in addition to creating a visual for your analysis that is simple and effective, you know that my expectation is that you’ll come with recommendations on what to do.

To demonstrate that there are many paths to JesusKrishnaAllah… Here is another simple view of the debt, with a different x-axis, a stretched out y-axis, along with a different set of context…

(Source: CBO)

Different questions, different arguments, different outcomes. But, you’ll get to them much, much, much faster than the first visual.

I can’t stress this enough: Don’t try to earn your performance review from the client/audience. Earn it from your boss. Tell your boss how hard you worked, show her how clever you are, earn her praise. Spare your client/audience – show them the simplest manifestation of your brilliant insight, with the NACR criteria applied.

(For more on using NACR to identify out-of-sights, see TMAI #66.)

Strategy 2: If Complex, Focus!

You are going to see my deep bias for simplicity for the rest of this post (or in the 745,540 words written on this blog thus far). I do not want to come across as a simplicity snob.

Deployed well, there are instances where I love complexity.

I thought this was exceptionally well done…

(Source: Michael Paukner  |  His Flickr collection)
(Click on the above image for a higher resolution version)

While it is a little difficult to follow all the arrows back to the original country, the shape of the graphic is an homage to the visual’s topic. The background color could not have been more prefect. And, notice there is just the perfect amount of information about every tree.

There are other more subtle things to admire. I love, love, love that Michael put the US on the right. When we “trip up” our audiences like this,  it gives them a pause and forces them to look at all the other information more carefully.

There is of course data itself that gives you many pauses. Notice the youngest tree in the graphic is older than Jesus Christ. Or, that we should all be so glad that the American West was settled last (by then we were more appreciative of nature as humans).

I am fine with complexity, if the essential makes it through. I am fine with complexity, if someone who’ll spend 1/100th of the time on the visual compared to you get’s it.

Strategy 3: Venn Diagrams FTW!

I love Venn diagrams. Ok, strictly speaking Euler. But, let’s not get pedantic.

I’ve used them to simplify the presentation of complex topics. Ex: Six Visual Solutions To Complex Digital Marketing/Analytics Challenges

I am only slightly kidding but one of humanity’s most complex undertaking is to understand what the heck Europe is. One end’s up ruing even asking, because you hear back EU, EEA, Euro Zone, Schengen, EFTA, and more.

I felt Bloomberg did a wonderful job with, what looks like an amoeba-inspired, Euler diagram…

(Click on the above image for a higher resolution version)

The color schemes are contrasted enough to allow you to follow along nicely.

The context from the sizes of the economy is a nice touch. (This is embarrassing but I was surprised how big Italy is, and how small Sweden is.)

The clusters of countries next to each other, for the sake of cleaner lines, all by itself has a built-in message. Cyprus and Ireland. UK, Romania, Bulgaria and Croatia. So on and so forth.

Overall, this is a topic that has been tackled numerous times, with painful to see results. Bloomberg managed to make it as simple as possible, with valuable built-in context.

Staying in the same geographic area, and my Euler-love, here’s another fantastic visualization of often a very complicated answer: What is each political party in the UK promising?

I adore this as the answer…

(Source: Economist)
(Click on the above image for a higher resolution version)

Would you have believed that the totally out there UKIP would have something on common with Labour? Or that Labour is completely alone in the minimum wage issue?

The visual makes it easier to understand what we might be most interested in from the thousands of pages that form each party’s manifesto. You, the audience, is now empowered to agree more passionately with your party or feel the uncomfortable squirming that comes with realizing what your party is solving for. Both. Fantastic. Outcomes.

Clearly this is a political picture, and someone has to decide what to include and what to exclude because the parties promise the Earth, Moon and the Andromeda galaxy. But that is the life of an Analyst… They have to make tough choices.

Two hopes.

So much to do, so simply, and so little time!

Strategy 4: Interactivity With Insightful End-Points.

There is a common belief that your company’s decision makers would use data more if they could explore it – more efficiently, deeper, etc. This is almost never true, primarily due to the problem outlined in the orange and blue triangles that outline skill/competency and insights/action.

Hence, in a business context I rarely advocate for initiatives whose only purpose is to allow the broad collection of company employees to go on random fishing expeditions.

Exploratory environments can be useful, especially when they are 1. sharply focused 2. have an ability to eliminate dead end-points and 3. allow for smart elements like modeling. Let’s look at the first two below and the third one in the following example.

Here’s a valuable dataset from the Equal Justice Initiative on Lynchings in America.

(Click on the above image for a higher resolution version)

Even at a glance the data is useful, along multiple dimensions.

In this case exploration of the data makes it even more valuable. You hover your mouse over your area of interest, and click…

You get your data drill-down, but what’s of most impactful is that you also get an end-point with a valuable insight providing meaning to the data.

In this case the number 29 for Jefferson County would be an insufficiently valuable end-point. The inclusion of Elizabeth Lawrence’s story on the other hand provides meaning. That is what gives the exploration a purposeful end-point.

You can now zoom out, move on to exploring other areas, continuing to get enriched value from the data.

In a business context when you are working with interactive data visualizations, ask this very valuable question: In a sea of data, whose job is it to include a logical end-point with an insight of value?

Surely, your terabytes of Google Analytics data dumped into a Tableau exploratory thingamagigy won’t magically throw them out there.

Surely, lay business decision makers, even senior ones, won’t have all the context they need to have to convert thingamagigy fishing expeditions, sorry, explorations, into the brilliance you feel the data contains.

Interactive visualization are great, only when packaged with insights for actions at logical end-points in exploration. Tweet that.

[SIDEBAR]
This is a difficult example to share because of the deeply emotional content it contains. But, those who do not learn from history are doomed to repeat it. Beyond the value of the lessons from the visualizations, I encourage you to explore rest of the EJI website. At the very minimum please consider spending five minutes listening to the story of John Hartfield told by Tarabu Kirkland, and six minutes on the story of Thomas Miles Sr told by Shirah Dedman. Thank you.
[/SIDEBAR]

Bonus: Another insightful visualization on this topic is at pudding.cool, The Shape of Slavery…

A bit more complex of a visualization, a function of the depth of data populated.

Follow the story of Louisiana as you reflect on the data.

Lots of data visualization, storytelling and life lessons in this data set as well.

Strategy 5: What-if Analysis Models.

Building on the thought above, if you create exploratory environments it can be exceedingly accretive to decision-making if we build in what-if type models. Rather than stopping at an end-point, provide an option of doing some type of sensitivity analysis with the goal of prodding the audience to take action.

For example… Let’s say they end up looking at Visitors, Conversion Rates, and Revenue. You can easily imagine how you want someone to explore that data by traffic sources or campaigns or geo or myriad valuable dimensions. You can create an environment where they press buttons to get that data.

Necessary, but not sufficient.

Why not build in a model where the decision maker can change Conversion Rates, to see the impact on Revenue? Move it from 1% to 1.5% to 8%. See what happens by traffic sources. Then, make a smarter decision.

Or, empower them to play with discounting strategies. What happens if they offer a 5%, 10% or 18% discount? Show impact on Revenue and Profit.

Even without bundling insights into your prepackaged environment, the what-if models allow your decision makers to play with scenarios, understand impact and make smarter decisions about what to do.

That’s the key. Don’t make visualizations with dead ends.

Here’s a great example of that from Mosaic. The visualization is about outpacing pandemics.

Quoting them: Vaccines are an essential weapon in fighting disease outbreaks. But how does the time taken to develop vaccines compare to the speed and frequency of outbreaks? And how can we do it better?

This is the simple view that greets you, outbreaks from 1890 to 2016 with vaccine development during that same time…

Each element is clickable.

As an illustration, the longest bar is Typhoid fever and the smallest, mercifully, is Measles. For each bar, click on Measles, you’ll see the first big outbreak (1917, 3,000 deaths) and the last (1989, 123 deaths). It is really easy to explore the data.

What I love is the sensitivity analysis.

Click on the yellow dot, and you’ll see that in action. First, you see what actually happened…

Simple exploration. Good reporting. Easy to understand.

The buttons with the number of weeks represent what I wanted to highlight here. Click on them, and it demonstrates what the outcome would have been if action was taken earlier.

I choose 22 weeks…

Even if the vaccine had been introduced after 22 weeks, a long time, we could have saved 1,628 lives!

The team also built in some hypothetical scenarios to help inform decision-making.

You can play with the implications of a fast-moving flu-like pandemic. It would have grievous overall impact, 30 mil deaths in 12 months.

But, what if we restrict 50% of the travel since we don’t have a vaccine yet. That would have an impact…

Not quite as material as one might imagine, but it slows things down.

What if a vaccine was introduced 22 weeks in?

Insanely helpful. 17 mil lives saved.

This type of modeling is rarer than seeing a rhino in the Ngorongoro crater. (We were there last week, you should go, it is pretty awesome.)

As an analyst, as a Big Data person, as a Data Scientist, pouring the right data on humanity is only marginally effective. In this example, in others above, I hope you’ll see the type of additional creativity we can bring to our work to power smarter decision-making. Starting with no dead end-points.

Strategy 6: Turbocharging Data Visuals with Storytelling.

You know this. Even if data is shared in a simple environment, most people are unable to internalize it. As has been hinted in most examples today, the problem is that the Analyst’s brain has not been packaged with the data.

The Global Gender Gap Report is a fabulous example how to solve this problem. The team nor only shares in a simple and beautiful environment, they also include the story they want to tell in that same environment. The output is not the reporting, the output are the conclusions from the Analyst’s brain.

It is very difficult for me to show the beauty of what they have done in static screenshots. You just have to go there and scroll.

Explore how the initial trend in the gender gap morphs into multiple visualizations, note the subtle but important emphasis on trends, and, most importantly, feel joy from how the story is presented with the data (text on the right).

The website and visualization will work on your mobile device (yea!), but it is best admired on the largest screen you can find.

To tempt you, let me just contrast the gender gap performance of the United States (precipitous decline in the last two years!) with… with… inspired by FLOTUS, the 10 year performance of Slovenia…

Play with the histogram and scatterplot options.

Go back and forth a few times (yes, gender parity is an issue I care deeply about), make sure you absorb the many nuances both in the story (why the above stinky performance by the US?) and the way the text (story) and the visualization (data) play together.

When you send data out, is it bundled with a piece of your brain?

Remember, you’ll be the last person with the intelligence and skills to understand the deep layers and nuances in what the data is actually saying (assuming you are an Analysis Ninja!). It is imperative that your brain go with the data.

Bonus 1: Another fantastic example of this type of sequential storytelling is Film Money….

Lars Verspohl takes you along on a wonderful journey through cost and profit structures of movies. Like me, you’ll love the simple and delightful visualizations, how gracefully flow it all flows, and that all the charts and data are primarily there to support the story that emerges from his analysis.

Please also note the thought put into the order in which the story is told, if and when the visualizations switch (from the one above) and the techniques deployed to keep you interested. All excellent, loads to learn.

Bonus 2: This is one subject, storytelling, that I just love, love, love. Indulge me as I pile on and share one more, dramatically different, example of storytelling where data and text go hand in hand.

The team at Reuters Graphics does a fab job of explaining China’s debt problem.

Almost all the visuals are extremely simple. As you scroll through, observe though how they peel back layers of the onion one by one, segment the data, and zero in on the core point they want to make.

Really lovely. Worth emulating.

Strategy 7: The Magic of 2 x 2 Matrices

If you’ve read anything on this blog, you’ve read the importance of seeking why answers to provide critical context to the what answers that you get out of Adobe or Google Analytics. Hence, the amazing value of Surveys, Usability Studies (on or offline), Heuristic Evaluations, shadowing Customer Service calls, and more.

Customers are an amazing source of problems they are having, sometimes they are also a good source of ideas. The challenge is that if you ask people for their opinions you get tons of ideas.

How do you value them? How do you present them? How fast can you get from data to action?

One solution I love is a visualization strategy used by the team at the New York Times. The example illustrates, simply, the ideas related to an emotionally charged topic: Gun Control.

Everyone knows this is a polarizing topic. Friend against friend. Blue vs. Red. Police and minorities and every other combination thrown in. It is a mess.

But. Is it really as fraught with angst as we believe?

No. It turns out if you ask Americans about individual ideas that will reduce gun deaths… A vast majority of us agree!!

The lowest supported idea is “Demonstrate need for a gun.” Support for it is just shy of 50%. A number that simply sounds unbelievable. 

Did you think vast majorities in our countries agree with these common-sense ideas? I have to admit I did not. It is hopeful data.

But, this is not the reason for the inclusion of this visual on our list.

Rather than just share the ideas, the NYT team added incremental value (remember packing the Analyst’s brain?) by asking Experts to opine on the effectiveness of each idea. That’s what you are seeing in the distribution above.

From the 2×2 matrix, here is the slice of ideas American’s support and the ones Experts say are effective…

There are only two ideas rated as ineffective by Expert, but are supported by over 70% of the Americans (national stand your ground law and honor out-of-state conceal and carry permits).

We all basically agree on ideas, and a lot of them will have an impact.

I love the presentation of the ideas and the fact that Experts were brought in to give valuable context. This is what I meant in my above example by not simply taking all the customer ideas and running with them. A wonderful way for you to visualize multiple ideas, and you can combine it with an Expert dimension or a Customer Satisfaction dimension or even a Revenue dimension to give context to the ideas.

One last element of value from NYT.

I’ve said that all data in aggregate is crap. I’m so happy that the NYT team also segmented the data.

What does Mr. Trump support…

What do American law enforcement support…

And, lots more slices that make the data even more meaningful.

Segment. Always, always, always segment!

It is beyond the scope of this humble analytics blog to explore why in the face of such unanimity that nothing actually happens when it comes to reducing gun violence in the US. But, for lovers of data, for believers in the power of data to drive smart decision-making, this is one more reminder on the limitation of data if you can’t tell the story properly.

Strategy 8: Close Contextual Clusters.

Let’s close with examples of work that you’ll normally include in your enterprise analytics efforts.

Usually data we have is lonely. Just the Visits or Assisted Conversions or Order Size. Without other contextual elements, it turns out this data is less useful.

Consider this, conversion rate could go up by a statistically significant percentage… While revenue actually goes down. Or, the overall Visits to the site stay steady… But drop dramatically from your usually second highest source.

The European Monitoring Center for Drugs and Drug Addiction, also known by the gorgeous acronym EMCDDA (!), publishes a ton of data. Their Statistical Bulletin 2017 has a lovely collection of graphs and charts that we all use in some shape or form. The only difference is that we rarely report on Heroin Price and Purity. 🙂

Along with the use of (mostly) simple visuals to illustrate the data, I appreciated the context that they provide. Sometimes using the time dimensions, sometimes using geographic breakdowns, sometimes using two likely interplaying elements (like above), so on and so forth.

This simple strategy is quite effective at delivering insights – or at least causing the audience to ask relevant interesting questions.

I encourage you to take some time and explore the numerous examples on the site…

I’m confident the visualization strategies will spark upgrades to the work you are doing at your company to communicate data more effectively.

Our friends at the EMCDDA mostly avoid two things that I find as poor practices in data visualization. They triggered this in my mind, let me take the opportunity of sharing them with you.

Bonus: In the spirit of government data, I’ll be remiss if I did not share with you three examples of interactive scatter plots from Our World in Data (produced by the University of Oxford).

The second one is timely, it shows how when we look at health spending and life expectancy the United States is a massive outlier (and not the good kind)…

I love fusion charts, the first one on the site, Child Mortality vs. Mean Years of Schooling, is a good example of that as well. And, it shows great news.

Please review all three. Then, consider plotting one for your digital data. Conversion Rates by Discounts for Top Ten Traffic Sources. Time on Site by Visits to site for Content Types. And, more.

Strategy 9: Multi-dimensional Related Line Graphs.

One final example, to cause introspection about the final years of your life.

Wait. Things really got serious.

They did. But, I really do want you to lean into this one.

A small reason is that you are likely creating graphs like these every single day for your dashboards. I hope you’ll find lessons in how to make yours simpler. Notice the use of fonts and colors. Notice the labeling, or not, of the axis. And other little things.

A big reason is that I care for you deeply and I want this data to be a cautionary signal to all of us to possibly start making new choices.

The plots are from the American Time Use Survey, a multi-year study from 2003 to 2015 conducted by the US Bureau of Labor Statistics.

Age on the x-axis and hours we spend per day with on the y-axis…

(Source: halhen on Reddit  |  Github)

In our 20s we’ll spend most time with our friends and our parents. Our partner and co-workers will take over our lives from then on through our 50s.

I’ll let you internalize the rest, and please share via comments what you see as the lessons in this data.

Three things stood out for me, as I consider the larger latter chunk of life. 1. We might be giving an extraordinary amount of importance to our co-workers, perhaps worth a rethink. 2. I love my spouse, regardless of who goes first, I felt very sad after staring at the Partner and Alone graphs. 3. The data demonstrated the value of loving oneself – of being proud of who you are, of being comfortable in one’s own skin. After all each individual will spend huge chunks of a decade plus… alone. You have from now until you are 50 or so to get there. Hurry!

: )

The power of great data visualized simply.

Closing Thoughts.

The sixteen diverse sources and visualization strategies help you think differently about how you are bridging the critical last-mile when it comes to impact from data – from you to the person who’ll take and action of business value. We don’t give enough time and attention to this last-mile.

While some of these clearly take special skills (especially the ones that tell integrated stories), I hope you’ll note that most of them are simple and ones that you can create with just a little more effort.

What’s most important today is that I’ve sparked your commitment to upgrading your personal data visualization skills.

Good luck!

As always, it is your turn now.

Which one or two examples did you like the most? Why? Is there a visualization technique you deploy in your analytics practice that’s not covered in this post? What barriers prevent you from improving your data viz skills? What are your pet peeves when it comes to data visualizations? Do you have go-to sources when it comes to inspiring you?

Please share your tips, best practices, critique, and praise for the people who created the above examples, via comments.

Thank you.

PS: I was not kidding in the opening of this post… I’ve written a lot about data visualization and shared guidance for this type of storytelling in numerous different contexts. To continue your immersion, here’s another collection of knowledge…

I hope you love it, and paint more beautiful pictures with your data.

Create High-Impact Data Visualizations: Nine Effective Strategies is a post from: Occam’s Razor by Avinash Kaushik

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Life is short.

It is time to point out an ugly truth, and to be the brave person that you are, the intelligent rational assessor of reality that you are, and kill all the organic social media activity by your company.

All of it.

Seems radical, but let’s take it one step at a time.

To give you a sense of the depth and breadth of ideas I’ll cover today, here are the sections in this post:

I urge you to have an open mind. My plan is to challenge your critical thinking skills, and share lessons that will apply broadly across the professional effort you put day in and day out. Most of all, I’m excited to frame an important problem, and present solutions that will transform an important part of your marketing strategy.

Let’s go!

The Promise of Marketing Utopia. 

I hate pimping (what marketing has come to be). I adore building meaningful relationships – the kind of long-term connections where a brand truly gives a f about their customers, and gives something of value in exchange for their attention. I LOVE brands that can pull this off, and support them with my un-asked-for evangelism and precious $$$s.

Hence, you can imagine how gosh darn excited I was at the advent of Facebook and Twitter (first real social networks). There were a billion people there, spending a meaningful amount of time on these wonderful platforms. Excitedly, brands could have a presence (a “page”) where they could contribute meaningful updates (info-snacks) in order to be a part of the organic conversations people were already having by the tens of millions.

Daily meaningful brand connections would be converted into brand familiarity, shifts in brand perception, feeding brand loyalty. #orgasmic

If you were a travel company, meaningful would now translate into helping feed wanderlust. The company could contribute info-snacks about where people should go, exposing the coolest places in the world, helping people travel better via tips, pictures, videos… you know… communicating travel love. The one thing a travel company would have in common with travel customers. The most imaginative travel marketers could even extend this opportunity to helping connect the purpose of their existence, selling tickets and hotel rooms, to helping people create moments of happy by crafting day/s of escape from the rough and tumble of life.

Glorious, right? If you work at Expedia or Cathay Pacific, does that not make you want to come to work and, for at least a part of your employment, create meaning? How rare is that!

If you were Cisco, meaningful would mean sharing info-snacks whose entire purpose could be to get Engineers promoted. Share tips, ideas, schematics, usage shortcuts, creative implementations, solutions to top problems that hold Engineers back… you know… understanding your audience deeply and give them something of value in exchange for their attention. The most imaginative B2B marketers could even figure out how to be a part of solving some of the deepest entrenched problems in the industry (STEM education, equal opportunity, + +) and in turn add an entire value-system to their brands.

Amazing, right?

Marketing based on something real, rather than a coupon or company brochure.

The Broken Promise of Marketing Utopia, Implications. 

None of the above transpired on Social platforms.

Businesses of all types, including Google (SMB, Main), got on amazing platforms like Facebook (and Weibo, Instagram, Pintrest etc.) and started pimping. All that their collective imagination could manifest in a Utopia-possible environment was: LOOK ME I AM SO PRETTY!! BUY NOW!!!

Stuff that is a turn off.

Consider the Google’s first FB page above, it is a complete disaster with not a single post in the last six months being of even five seconds of value to any small business. That page, or the main one, is not an overt Buy Now, but if you think critically like the tough Marketer I want you to be you’ll have a hard time finding a single post that’s solving for Google’s human customers. Almost every single one is pimping Google (or pimping random research Google has commissioned – to pimp Google!). The non-value is so transparent, yet they post every single day something that basically is solving for Google (although only God knows what that is). If someone bothers to interact with the post, the posted comment is a spam or totally useless. Yet. They keep posting. Polluting utopia.

Google is not unique in not understanding the promise, checkout your company’s FB page.

This strategy by businesses lead to what I now call the Zuck Death Spiral. ZDS.

Real humans on Social platforms quickly got turned off by these low-grade Social contributions/posts by companies. That meant humans (us!) refused to engage with them. This was noticed by Team Zuck, who started to slowly turn down the presence of company posts in User feeds. This lead to less Reach for brands. Which in turn lead to even fewer customer interactions for content posted by brands. Which was duly noted once more by Team Zuck. Which… you know where this is going, tightened the screws on organic Reach even more. And, here we are in a barren desert for brands on FB.

Most brands get less than 1% Reach via their organic contributions on social platforms. And, less than 1% engagement of any kind from that less than 1% reached (identified using the best social media metrics: Conversation Rate, Amplification Rate, Applause Rate).

ZDS is solving for FB, as FB should, and it is an attempt to solve for FB’s users.

So… If all you can do is overtly or covertly pimp… And, pimping is not cheap (that Google page, and your company’s page, has pictures, videos, an agency deployed, internal company employees with a “social media execution checklist”, senior leadership time committed, and more)… And, all it does is get you 1% Reach, max, with almost no engagement… Why do you still have an active (organic) social media effort?

Why is this reality not smacking some sense into your marketing strategy?

The Broken Promise of Marketing Utopia: Examples. 

Is it difficult to check if your brand is caught up in the Zuck Death Spiral? No.

Do you have access to any data to measure how deeply non-impactful your organic Social Media efforts are? OMG, yes.

Everything you need, data and information, to do an audit is public.

All you have to do is visit your company’s Facebook page (or Instagram, LinkedIn, Pinterest, etc. presence).

Let me show you what to look for. Let’s start with Expedia. They have 6.4 million Likes as of today. Go look at any post on the page if you are an Expedia employee.

First thing you’ll look at is the Applause Rate (likes, other emotions, you’ll see it right under the photo). That number is 75. Divide that by 6,462,977 (potential audience size today).

0.00113%. That’s a painful stab in your heart.

Next Conversation Rate (comments, you’ll see a total at the end of your posts). 7. Divide that by 6,462,977. A sad 0.00011%.

Finally, my favorite sign that you truly added value to a human rather than pimp, Amplification Rate (shares). 3/6,462,977. At this point you are weeping with me: 0.00005%.

To give you some context as to how insanely lame these numbers are, Expedia.com received 59,400,000 Visits in May 2017. This post accomplished 75+7+3. More people walk into the Expedia lobby in Bellevue, WA, every second of every minute.

You might be screaming that is not fair Avinash, the Zuck Death Spiral ensures that a tiny fraction of 6,462,977 are seeing Expedia’s posts! Very fair point. But, is the Social Media Budget at Expedia not justified based on the potential from 6,462,977? Would Expedia commit it’s multi-million-dollar budget to Social Media based on the potential to engage 75+7+3 people on Planet Earth?

One final point. Brand destruction.

Pretty much every single comment on pretty much every single Expedia post is a complaint about how horrible Expedia is (from personal experience I know this is not true). If your Facebook presence is solely to inspire people (see Trish Sayler above) to create clever rhymes about how bad you are… Why are you on Social Media?

Ignore the active smearing of the Expedia brand, let’s go back to data: Is it worth have 75 | 7 | 3 as the value delivered from an organic Social Media strategy for a company with 54,900,000 Visits?

My answer is an emphatic no. Expedia should immediately cease 100% of its organic Social activity.

1/100th of the Social Media budget could be spent on any other random digital strategy to get 75+7+3, and have zero brand destruction!

Oh. And while I’m focusing on Facebook for the sake of simplicity, everything in this post applies to all other Social Media channels. The Utopia failures. The lack of imagination. The small numbers. The uselessness.

Here for example is a post on Twitter by Expedia:

The numbers: 9 | 2 | 2. Divided by 391,000 (followers).

You can do the math and assess dent in the universe this content contribution from Expedia is making.

Almost nothing. Technically, perhaps less than nothing.

I hate making recommendations based on outliers, please know that Expedia is the norm. Hence, the title of this blog post.

Here’s a B2B example, a company I think well of… Cisco.

Go through the same analysis.

Your numbers are 31 | 1 | 3. Divided by 845,921.

Would you spend a single hard-earned Cisco router and switches dollar to get this as the return from a multi-million dollar Social Media budget?

Like my company, your company, and Expedia, Cisco gets no value from their organic Social Media efforts. Technically, Cisco is getting negative returns once you account for the people, process, tools, agency, leadership investments.

Let’s switch gears and look at a B2C company with a massively positive opportunity to leverage the word Social in every way on these platforms… Chick-fil-A.

Better numbers, as you might expect.

1k | 89 | 73. Divided by 7,775,155.

Consider it. Chick-fil-A could buy the most remnant TV inventory on a channel least watched by humans during the middle of the night and get better Reach. And they can also measure how many of them walked into a Chick-fil-A in the next 12 hours.

Does the above number justify custom videos, images, active posting by Click-fil-A on Facebook?

One final example to bring this home.

ProjectManager.com is a lovely tool. It is wonderful that they use folks like Jennifer Bridges, Susanne Madsen and others to create very helpful Project Management videos on YouTube. It seems they are a medium-sized business.

Here’s their Facebook page:

69 | 0 | 25. Divided by 62,951.

Pound for pound, better performance than all three (four including Google) companies above. Shame on them.

Still. Are the resulting Applause Rate, Conversation Rate and Amplification Rate enough for a smaller business to use it’s precious marketing dollars on this Social Media strategy/impact?

Consider this as well for all brands… There is no native discovery model on these Social channels. Your content will live for 20 minutes and then it is dead. Not just because of ZDS, but also because there is no Search behavior by users or a method that would deliver Serendipitous Discovery of content you post.

Unlike say on YouTube, or your Blog, where your Subscribers will see the content right away, and then through Bing and Yandex and YouTube itself people will find your content when relevant and keep viewing it. Your content there has a live beyond 20 minutes.

Win Big: Stop Posting Content for Organic Reach On Social Channels. 

Given the numbers above, and be sure to check any other Social Media channel your company is actively investing in, I hope you have the input you need to apply your critical thinking skills.

Let me give you one final push: You have better alternatives to drive short and long-term Profitability for your company (rather than investing in organic Social Media).

Here’s an example.

I write an insightful newsletter with the singular aim of improving your salary. The Marketing < > Analytics Intersect. You should sign up. It is a companion to this blog, I write once a week there and once a month here.

One year into it’s existence, TMAI has 21,246 Subscribers.

Measuring Open Rates for email is difficult (the tiny pixel ESPs use to track opens are not executed by default for most email programs). Even with that flaw in reporting, TMAI has Open Rates of around 9,000 (9,895 precisely for the last one).  Around 1,000 people (912 for the last one) take an action that is of value to me.

A random person, me, can get 9,000 opens of my content, at least a thousand active engagements with my brand whenever I want. I have over 1,000,000 Social Media followers across the five platforms (Twitter, Facebook, LinkedIn, Google+, Instagram). I can’t even get 1/100th the impact.

My simple unsexy email newsletter strategy crushes the on paper potential of one million Social Media followers.

And, beyond the impact… I also directly own the relationships with my 21,246 Subscribers, I own the data, the relationship exists on my platform, and I can use it as creatively I want to use it with no limitation on type of content (text or video or dancing penguin gifs).

Why should your company be on Social Media 5x per day to get a lousy 20 interactions with your brand? How is that acceptable ROI from your investment in a 5 person Social Media team, a Social Media Agency, a Social Media analytics tool, a Social Media auto-posting tool and more?

Could you not get 100x ROI from the 0.25 person that’s running your email newsletter?

Could you not just take all that Team, Agency, Tool, money, throw it into AdWords or AOL Display Ads and not get massively higher ROI, of any kind, in 10 minutes?

Could you not get better ROI taking all that money and buying remnant inventory on your local Television channel?

Could you not get better ROI if you just took that money and bought free lunch for the employees in your building every other day?

OMG, you most definitely can.

So. Why are you on Social Media?

Is it fun to shout in a vacuum?

Why does it not feel dirty to go waste your shareholder’s money?

Stop it then.

Welcome to the world of higher standards for impact delivered. Feel cleaner and prouder coming to work every day as a Marketer/CMO.

Is the Huge Audience on Social Media Platforms Completely Useless? 

NO!

There are a couple of billion people on Facebook (and billions or hundreds of millions on other Social channels). From an advertising perspective, that’s still an audience that might be of value to your business.

Kill your organic Social strategy completely, switch to a paid Social Media strategy.

Buy advertising from Facebook. I’ll make it easy, click this link!

Buy advertising from Twitter. From Snapchat. LinkedIn. Oh and WeChat and Line.

This simple switch from the fuzzy Organic goals to concrete Paid goals will give the one thing your Social Media Marketing strategy was missing: Purpose.

It is now easy to define why the heck are you spending money on Social Media? To drive short and medium-term brand and performance outcomes.

Fabulous.

Set aside the useless metrics like Impressions and 3-second Video Views. Set aside hard to judge and equally useless Like and Follow counts. Measure the hard stuff that you can show a direct line to company profit.

Define a purpose for the money you are spending.

For the clients I’ve worked with across the world, expressed behavior of the users suggests that the largest cluster of intent is See. There is a little bit of Think and a little bit of Care. (This is why Social marketing strategies that target Do intent yield extremely poor results.)

[Bonus Read: See-Think-Do-Care Business Framework]

If the purpose is to execute See and Care intent marketing strategies (in the old world sometimes incompletely referred to as brand marketing), you can use the following amongst my favorite metrics to deliver accountability:

Humans have measured these using primary and secondary research methods for 3,500 years. Quite easy to do the same for your newly focused paid Social advertising efforts.

[Bonus Read: Brand Measurement: Analytics & Metrics for Branding Campaigns]

If on the other hand the purpose of your paid Social advertising is to target Think and/or Do intent, you should measure the impact using the following across your digital – and pan-digital presence:

We have measured these for a long time on the web. You can use your quantitative tools to measure most of these (Google Analytics, Adobe, True Social Metrics). And. You can measure these for your ecommerce, non-ecommerce, B2B, B2C, pure content, non-profit, or whatever else kind of delicious business you are running.

Now, you’ll hold your agency and employees accountable for delivering business profitability for your Social efforts just as you do for any other advertising effort – Search or TV or Email.

Just as you would do in all those other cases, do more paid Social advertising if the metrics show a business impact and improve/eliminate your paid Social efforts if they don’t.

It will mean a different Social content strategy, different targeting strategy (leveraging rich Social signals), and a different landing page/app strategy. Proper end-to-end user and business optimization. Nirvana, delivered by that magical word… Purpose.

The path to your salary and job promotion is also now crystal-clear. Right?

Is the Idea of Marketing Utopia Permanently Dead? 

I’ve seen the near-future, and I believe we’ll get to Utopia Marketing.

The fact that companies don’t know how to be human, how to take even 20% of their people plus budget and invest optimally in understanding humans and deliver something of value to those humans is deeply heartbreaking.

Yes, I can blame the short-term quarterly focus of the CMOs and the SELL, SELL, SELL MORE incentives they create for you to earn your bonus. But still, how heartbreaking is it that not even 1% of us could convince our CMOs to allow us to do what Social was actually good at? How sad is it that we have such little influence? I blame us.

Still. I am optimistic that Marketing Utopia, as I’ve imagined it at the top of this post, is not dead. I think the solution will be to get rid of the humans from the process!

What? Human marketing by getting rid of humans?

Yes. Hear me out.

I think AI/Machine Learning will solve this problem.

Today, humans and their limited ability to process data, and the finite incentives in place, are the reason we burned Utopia to the ground. We simply can’t process billions of signals across tens of millions of touch points across millions of people, and figure out the best message at every moment and its short, medium, and long-term business value.

Current advances in ML already give me hope that algorithms will understand intent a billion trillion times better than your current employees AND these algorithms will have the inherent capabilities to process billions of data points to truly understand complex patterns of user behavior and a robust understanding across all that to know exactly what delivers business profit.

Companies can then take the equivalent of their Brand and Social budgets and allow smarter algorithms to deliver the right message to the right person at the right time across all clusters of intent. All the while, optimizing for long-term business profitability.

It will help that Machine Learning is not embolden to trivial company politics. 🙂

[Bonus Read: Artificial Intelligence: Implications On Marketing, Analytics, And You]

Bottom-line.

While I’m recommending you stop doing something, hearing no is not super-inspiring, I hope you’ll see that my goal is help you think more critically about where you spend your personal time and your company’s money.

I also hope you’ll see how the shift in strategy I’m recommending brings Social in line with your other advertising efforts, allowing for a ton more focus on your Social efforts and a billion times more accountability.

Finally, I hope you feel optimistic that around the horizon lurk technological solutions that will allow for the manifestation of the beautiful humanity that exists in your company (even if we have to take human employees out of the equation to get there – don’t worry, they’ll still, for now, be responsible for the novel elements required).

Demand more from Social, because Social can deliver more. It just happens to be paid Social.

Oh… And if you’ve chosen to define your professional career as a Social Media Analyst or a Social Media Guru or a Social Media Marketer, I respectfully offer that you should rethink your strategy. You likely already see deep pressure on the possibilities in front of you, and on your compensation growth. This will only get more severe. Figure out how to expand your skill-set, and then scope of influence/impact, so that you can delete the first two words from each of those titles and retain the last one. If you are remotely good at what you do, you’ll be in a recession-proof digital career. The opportunity is there, your career trajectory and compensation growth will be up and to the right.

As always, it is your turn now.

If you’ve achieved sustained success from your organic Social Media content strategy, would you please share your example? If you disagree and believe Marketers should invest in organic Social despite poor Reach, ApR, CoR, and AmR, would you please share how you see value/impact? If you’ve successfully dumped organic and pivoted to paid Social, please share stories of your victory. Are you as optimistic as I am that Machine Learning based intelligence will solve optimally for the Utopia opportunity?

I look forward to hearing your smart perspectives and cogent challenges.

Thank you.

Stop All Social Media Activity (Organic) | Solve For A Profitable Reality is a post from: Occam’s Razor by Avinash Kaushik

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Today, a simple lesson that so many of us miss at great peril. In fact in your role, at this very moment, your company is making a mistake in terms of how it values your impact on the business.

The lesson is about the limitation of optimizing for a local maxima, usually in a silo.

We are going to internalize this lesson by learning from Microsoft. It is a company I love (am typing this on my beloved ThinkPad X1 Carbon Gen 5, using Windows Live Writer blogging software!). I bumped into the lesson thanks to their NFL sponsorship.

If you were watching the Oakland Raiders beating the hapless New York Giants (so sad about Eli) this past Sunday, you surely saw a scene like this one:

Quarterback Geno Smith using his Microsoft Surface tablet to figure out how he added two more fumbles to his career total of 43. Or maybe it was him replaying the 360 degrees view of the three times he was sacked during the game.

The Surface tablet is everywhere in an NFL game. Microsoft paid $400 million for four years for the rights, and just renewed the deal for another year (for an as yet undisclosed sum).

For all this expense, you’ll see players and coaches using them during the game (as above). The Surface branding also gets prominent placement on the sidelines – on benches, on movable trollies and more. It is all quite prominent.

Here’s one more example: Beast mode!

I adore Mr. Lynch’s passion. Oh, and did you notice the Surface branding?

Now, let’s talk analytics and accountability.

NFL ratings are down, but an average game still gets between 15 m – 20 m viewers. That is a lot of pretty locked-in attention, very hard to get anywhere these days.

The question for us, Occam’s Razor readers, is… What does the Surface Marketing team get for all this money?

If the Surface Marketing team is like every other team at every other company engaged in sponsorships and television advertising, it’ll measure the same collection of smart metrics.

First one will be Reach. The Surface team is likely measuring it with deep granularity (by individual games, geo, days, times of days, and a lot more).  I’m confident that their analysis will show they are getting great Reach.

The team will rightly be congratulating itself on this success.

Next on the list, having spent enough of my life with TV buyers, I can comfortably say that the Surface team is also expending copious amounts of effort measuring one or more dimensions of Brand Lift metric. Ad Recall, Brand Interest, Favorability, Consideration etc.

Brand Lift is most frequently measured using surveys.

Given the number of times Microsoft Surface, or its branded presence, shows up in a game (52 times in my count in the OAK – NYC game), I believe the Surface team is getting very positive reads from its post NFL ad-exposure surveys.

After 52 times most people would recall the ad, surely answer the survey with some interest in the brand, and everyone (except Coach Belichick) seems to like using the tablets, a favorability that will surely transfer to a whole lot of viewers.

This would, indeed should, result in more congratulations in the Surface team.

The two-step approach above reflects the most common approach Marketers, and their Agencies, use to measure success. Did we reach a large audience? Do they remember anything?

The answers to these two questions power job promotions, bonuses, and agency contract renewals with higher fees.

I believe this is necessary, but not sufficient.

I believe this approach optimizes for a local maxima (the media buying bubble) and does not create the necessary incentives to solve for the global maxima (short or long-term business success).

Let me illuminate this gap.

Here’s the global maxima question: How many Surface tablets have been sold due to this near-blanket coverage in NFL games via precious undivided attention?

That was the question that crossed my mind during Sunday’s game.

I had one data point handy.

According to TripIt I’ve visited 156 cities across 32 countries in the last few years. During these trips, meetings and meetups, I’ve never seen a Microsoft Surface tablet in the wild. Not one.

That’s not completely true. I have seen one frequently. The one I bought for my dad four years ago.

One data point does not a story make.

To assess a more complete answer, we turn to our trusty search engine Bing…

The picture above starts 12 months after Surface inked the $400 million NFL contract. The Surface’s share of shipments is so small, it does not even show up in a graph.

Not being content with just one view of success, I tried other sources. 

The data from IDC, shows no meaningful Surface anything. Statcounter provides an interesting view as it measures actual use of the Surface when accessing the two million websites that use Statcounter. Surface is at 0.29% share.

This is a bit hyperbolic, but in the grand scheme of things… No one is buying a Surface.

Local maxima view of success: The Surface team’s NFL contract is a smashing success. The team is getting great Reach and great Brand Lift. Contract with NFL renewed for another 12 months.

Global maxima view of success: Microsoft is losing.

[Key caveat: The data Statista and IDC provide capture shipments. It is possible that the Surface is being sold directly in a way that neither of these two sources would capture those sales. Perhaps some kind of B2B sales. To overcome this possible issue I’ve used the Statcounter data to capture usage. Still, there is a possible scenario where none, or not enough, of the Surfaces sold visit those two million sites.]

Sadly, Microsoft is not alone in this local maxima focus. Most companies function in a similar manner. Yours. Mine. Other people’s. Our collective mistake is that we don’t think critically enough about what we really are solving for. Our company’s mistake is the incentive structure they put in place (which almost always rewards the local maxima).

Let me give you two examples of this sad local maxima obsession that crossed my desk just this morning. All in the space of one hour.

Local – Global Maxima Example 2: Gap Inc..

A report has been published on The Age of Social Influence. Its goal is to aggressively recommend the strategy of marketing via Social Influencers. Here’s the publishing company’s intro of themselves: “We are a powerful data intelligence tool that combines the knowledge and insights you need to deliver a successful celebrity and social influencer marketing strategy.”

Their claims of this wonderful Social Influencer strategy is based on a survey of 270 respondents. 270. It seems like an oddly tiny choice by a powerful data intelligence tool company (PDITC).

They have all kinds of numbers from the 270 survey sample showing glory.

The very first example in the report of a brand winning hugely with a Social Influencer strategy is Gap.

Here’s a screenshot from the report…

While we all love Cher, seriously she is special, this is a classic local maxima let’s only look at what will make us look good to pimp stuff we want to strategy.

What would be a global maxima if you are going to use a company as a poster child?

Here’s Gap’s financial performance over the last five years…

Gap Inc. has been struggling for years, flirting with financial disaster recently in every facet of its business.

I invite you to explore other financial data on the eMarketer Retail website. Look at Revenue, Earnings, Margins, Employment… Everything is super sad. For an additional valuable lesson, click on Digital as well. It shows the social performance of Gap (illustrating even the local maxima is quite suspect).

I dearly wish that Gap survives – they make good quality clothes.

I also wish that the powerful data intelligence tool company would have chosen to focus on looking at the global maxima success before using Gap, and the other examples in their 40 page report. That would have made their drum banging for Social Influencers more persuasive. It would also have resulted in fewer clients of powerful data intelligence tool company shuttled in the direction of spending money on something that mostly likely will not produce any business results.

Local – Global Maxima Example 3: Amazon

A celebration was shared with me for 31 custom gifs created by Giphy for the up and coming retailer Amazon.

Here’s a non constantly looped, to ensure you’re not annoyed, sample…

The celebration was based on the fact that the total view count for these 31 custom gifs was 31 million.

[Sidebar: Always, always, always be suspicious of numbers that are that clean. 31 gifs being viewed a clean 31 million times is cosmically impossible. Seek the faq page to understand how views are measured. Identify that there is no clarity. Now, be even more suspicious.]

I’m afraid in my book views don’t even count as a local maxima. Even if they are in yours, I hope you’ll agree they are a million miles away from a global maxima.

I wanted to share this example from Amazon because you can’t use the global maxima of overall business success I’ve used above. Even if Jeff Bezos goes around hitting people with feather dusters, Amazon will keep selling more and more products. They have already reached perpetual motion.

What do you do when it is difficult to identify the global maxima from a super-tactical animated 31 gifs with 31 million views effort?

Try to move four steps up from wherever you are. Global maxima lite.

In this case, here’s a great start: % of Users who shared the gif who are not current Amazon customers.

So much more insightful than Views, right?

We are shooting for a deeper brand connection, by an audience that holds business value for us. Sure these people are annoying their friends, but hey at least as Amazon we can remarket to them – and friends (!) – and convert them to Prime customers!

I’m sure you can think of others that are five, six and eight steps above Views. (Share them in comments, and earn admiration.)

It does not always have to be revenue or profit. But, please don’t pop the champagne on views, impressions and other such primitive signals of nothingness.

On the topic of measurement, let’s go back to Microsoft and brainstorm some strategies for their unique use case.

What should Microsoft have measured?

Purely as an academic exercise I’m leaving aside the possibility that the Surface is simply not a good tablet. That would certainly impact sales – marketing or no marketing. But, since Microsoft went back for year five, it is safe to assume at least they believe it is a good tablet.

Ok? It is a good tablet.

Again as an academic exercise I’m going to ignore the four year horizon. There is no question that at the end of year two Microsoft had overwhelming proof from a multitude of data points that the NFL contract was not selling any Surfaces. They did not need Big Data or Artificial Intelligence to come to that conclusion. If they could not get out of the contract, at the end of year two a better use of $100 mil spend per year would have been to change the covers on the Surfaces to Xbox green, and change the numerous printed brand opportunities on the sidelines to Xbox as well. A great selling product, with a much bigger overlap with the NFL audience than the Surface.

Ok? We are not looking after year two.

During the first and second year, what could we have measured as Microsoft if we wanted to do better than the local maxima? Better than Reach and Brand Lift metrics?

Let me plant three ideas (please add yours via comments).

An enhanced survey would be a good start. Along with measuring ad recall etc., they could also ask how likely are you to choose the Surface over the iPad as your next tablet?

It is a tougher question than do you remember the ad or what tablets can you name. It is going head to head with the thing people usually say when they mean tablet. And, you are looking for switching. A strong behavior shift, a harder yes to get when I’ve done surveys. All this brand exposure, if its working, should shift that key intent signal.

Really easy to do. And, you can easily get thousands upon thousands of responses – you don’t have to settle for 270. It would have given the Marketing team a leading indicator that no one is going to buy the Surface as a result of the NFL partnership. The signal could have been received even a couple months in, and certainly by the end of year one.

Time series correlations would have been a great start right after the first week of the contract. How many people are visiting the Surface website on Sundays? Is that materially significant compared to weeks prior or weeks where there were not as many games? Was there an improvement on Sundays in digital sales? How about retail sales on Mondays?

This is simple stuff. Even visits to the site would have been a nice low level signal.

As the season went on, we could look for test and control opportunities. The NFL always has blackouts in cities/states where the stadiums don’t have enough attendance. This past weekend it was in two states, complete blackout of free broadcast games. Is there a difference in site visits, online conversion rates, offline sales, between states that had one game broadcast on Sunday, two games broadcast on Sunday and no games broadcast on Sunday?

A little more complicated. The site stuff is easy to segment. For store sales Microsoft could easily get data from its stores in malls – and likely also from retailers like Best Buy with a little arm twisting. This data would have shown Microsoft – a few months in – that the global maxima might not be reached.

If you don’t have this type of ubiquity, Matched Market tests are also fabulous in these cases to discern if a specific marketing strategy is having a business impact.

Three ideas that I hope will spark many more in your mind when you shoot to measure the global maxima.

I want to briefly touch on one refrain I often hear about these long term efforts, or short term efforts that are not working but are looking at a longer horizon: So what if the results are not there. This is a long term brand building play, Apple did not become a beloved brand in one year.

There is a kernel of truth there, brand building take time. There is a kernel of BS there as well, Apple is Apple primary because of its innovative products.

Let’s not talk about Microsoft in context of the above statement as even if we assume there was some long term brand building happening, it did not translate into business success.

When you hear a statement like that, after you launch a new underwear, cooking range, VR headset or whatever… Obsessively measure more than the local maxima to discern signals in the short term that illustrate that the long term brand building play is not just an excuse to flush a lot of money. Both the Gap and Amazon examples have ideas to inspire you.

Or consider that even your long term brand building play, in the short term should cause you to take noticeable amounts of market share. It won’t be 80% in the short term, but neither is that statement a reason to spend more money if all you got is 5% in year one and 10% in year two.

Don’t settle for opinion.

Use data.

You have data.

Bonus: The real winner of the Microsoft NFL contract?

The NFL of course.

Microsoft makes great hardware. To make it work for the NFL, Microsoft surely wrote lots of custom software for the NFL’s specific use cases. Microsoft likely invested in tens of millions of dollars of camera equipment, wifi/networking upgrades in every stadium, deployed a small army of Microsoft employees to do on-site tech support before, during and after the games in every single stadium. And, more and more and more.

The NFL should be paying Microsoft $110 million a year to upgrade the ability of its coaches, players and teams to have access to this state of the art technology to compete more effectively every Thursday, Sunday and Monday!

The NFL is slated to make $14 billion in 2017, they can surely afford to give $110 mil a year to Microsoft.

Back to the real world… Even when you measure short term success, please do not be satisfied with a local maxima. Even in the short term you can measure something better. On the long term, you have all the elements you need… Definitely measure the global maxima!

Do this because it is the right and smart thing to do for your company. But, a tiny bit, do it because in my experience (across the world) global maxima solvers progress exponentially faster in their career. Turns out, delivering business results matters. 🙂

As always, it is your turn now.

Do you have a suggestion for what Microsoft or Gap or Amazon should measure as their global maxima? If you’ve been successful getting your CEO to focus on the global maxima, what approach really worked? If you were the role of the Chief Scientist of powerful data intelligence tool company, how would you measure the impact of Social Influencers in a more intelligent manner?

Please add your powerful ideas, brilliant critique and innovative strategies in comments below. I look forward to hearing from you.

Thank you.

Smarter Career Choices #3: Solve for the Global Maxima! is a post from: Occam’s Razor by Avinash Kaushik

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