Smart Data Visualizations: Quality Assessment Algorithm

The gap between a bad and good data visualization is small. The gap between a good and great data visualization is a vast chasm! The challenge is that we, and our HiPPOs, bring opinions and feelings and our perceptions of what will go viral to the conversation. This is entirely counter productive to distinguishing between […]

The post Smart Data Visualizations: Quality Assessment Algorithm appeared first on Occam’s Razor by Avinash Kaushik.

The gap between a bad and good data visualization is small.

The gap between a good and great data visualization is a vast chasm!

The challenge is that we, and our HiPPOs, bring opinions and feelings and our perceptions of what will go viral to the conversation. This is entirely counter productive to distinguishing between bad, good, and great.

What we need instead is a rock solid understanding of the updraft we face in our quest for greatness, and a standard framework that can help us dispassionately assess quality.

Let’s do that today. Learn how to seperate bad from good and good from great, and do so using examples that we can all relate to instantly.

We’ll start by looking at the two sets of humans who are at the root of the conflict of obsessions and then learn to assess how effective any data visualization is in an entirely new way. If you adopt it, I guarantee the impact on your work will be transformative.

The Conflict of Obsessions.

There are two parties involved in any data visualization.

1. Analyst/Data Visualizer.

As I’ve passionately shared frequently on this blog, we, Analysts, are all in the business of persuasion. We work against that desired outcome because when we work on creating a data visualization, here are our top-of-mind concerns/desires/perspectives:

How can I cram as much as I can into the graphic?

What can I include to ensure everyone clearly gets just how much work I did?

How much of my agenda do I need to make overt, and how much can I make covert?

Is there something I can add to increase the chances that this will go viral and result in fame and glory?

Ok. I’m only teasing.

But, as an Analyst, a Data Visualizer, I can’t say that these thoughts don’t cross my mind. :)

I’m sharing the above primarily to ensure that you know these motivations exist – and, like me, you should try to fight and resist!

The very best Data Visualizers, obsess about:

1. known and unknown variables
2. causality
3. nuance
4. visualization techniques
5. rank-ordering messages
6. simplicity, simplicity, simplicity, simplicity, simplicity, simplicity, and, just to be safe one last time, simplicity.

These are the six things that matter supremely in my work, and they should be what matter in yours.

Simplicity matters more than the rest because if I can’t distill complexity, I might as well not do the work because that is only a snowball’s chance on the sun that the audience will understand my complex visual.

Let’s look at the other set of humans involved in a data visualization equation.

2. Data Consumer.

Here are the concerns/desires/perspectives that a consumer of data visualizations has top of mind when they are presented with a set of analysis:

What’s in it for me?

How easy is it  to grasp the most important point?

What’s in it for me?

How much effort do I need to put in to understand the whole infographic?

What’s in it for me?

How can I trust that this message is from a credible Analyst/source/using sound methodology?

(Never underestimate the staggering selfishness that a Data Consumer brings with them to the table when you are showing them a table of data or a data visual. And, it is understandable because they have difficult jobs and 71 other things to worry about.)

Notice there is very little overlap between the obsessions of the Data Consumer and Data Visualizer.

If you have a choice (and you do!), let the needs of the Data Consumer drive your data visualization efforts. The only exception is when you are trying to push propaganda, then go with your agenda.

If an infographic sucks, it is usually due to the conflict between the Visualizer and the Consumer along the above dimensions.

You’ll see it vividly on display when you look at any graphic through the Consumer lens with an eye on simplicity (the Analyst dimension).

The Data Visualization Assessment Algorithm.

Algorithm might perhaps be a tad bit pompous, as applied here. I’ve developed a set of filters and lenses through which you can look at any data visualization in order to quickly assess quality.

Perhaps someone reading this blog post is going to help us all out by building a Machine Learning algorithm to assess if a Data Viz is bad, good, or great. :)

Reflecting on the aforementioned Consumer vs. Visualizer conflict of obsessions has helped me distill the evaluation of data visualizations to eight dimensions. They influence each other and the entire portfolio, yet they stand on their own.

In the format of “Obsession | [ratings scale],” here’s the data viz assessment algorithm:

1. Time to the most important insight. [Scale: Fast. Slow. KMN!]

2. The effort to understand the whole graphic. [Low. Medium. No Thank You.]

3. Trust marks. [Clear. Non-Obvious. None.]

4. Rank-ordering of key messages. [Yes. Partial. WTH!]

5. Explaining the key logic powering the graphic. [Super clear. Cloudy. Invisible.]

6. Exposing nuance. [Sweet. Some. Sour.]

7. Visualizer trying to be too clever. [No, and thank god. Yes, but it is harmless. Yes, sadly.]

8. Likely to recommend to influential leaders. [Yes! No. No way.]

I want you to explicitly notice:

I’ve put the Data Consumer first

Incentivized good behavior by the Data Visualizers, and …

… Included an outcome in the end because activity is well and dandy but it is outcomes are what matter.

My hope is to share a very specific algorithm that gets your critical thinking juices flowing. I invite your critique and suggestions on how I can make it even smarter. Please reply.

The best way to learn is to practice via real-world examples. So.. Let’s do that!

COVID What Should I be Afraid of (!) Data Visualizations.

A few weeks ago, perhaps not coincidentally, a number of different entities published visuals to help us understand what we can do safely and what’ll cause grievous harm.

I’ve collected four of these efforts – each a really different way to visualize nearly identical information. This gives us an ideal data set to apply our algorithm, and learn discerning skills along the way.

Data Visualization #1

The first graphic is from the inimitable Randall Munroe (I’m a very big xkcd fan!).
Randall has a unique way to communicate complex information (buy Thing Explainer!), and this graphic is no different. It combines seriousness, fun, and scientific accuracy.

As an approach, 2x2s work really well. They force simplicity. The color clustering above helps, you can jump to the safest or riskiest activities faster.

On the downside, it is hard to take in the whole thing. You can get lost.

I’m treating this as a very serious example, but it is important to remember that the intent above includes the goal of making us smile.

Let’s apply our algorithm and see how this graphic does with our tough, but with love, lens.

1. Time to the most important insight. [Fast. Slow. KMN!]

2. The effort to understand the whole graphic. [Low. Medium. No Thank You.]

3. Trust marks. [Clear. Non-Obvious. None.]

4. Rank-ordering of key messages. [Yes. Partial. WTH!]

5. Explaining the key logic powering the graphic. [Super clear. Cloudy. Invisible.]

6. Exposing nuance. [Sweet. Some. Sour.]

7. Visualizer trying to be too clever. [No, and thank god. Yes, but it is harmless. Yes, sadly.]

8. Likely to recommend to influential leaders. [Yes! No. No way.]

The graphic should technically get a pass on #3 as it is for fun, and possibly #5 as well. But, I’ve still graded it seriously so that all of us can practice scoring.

If the phrase big miss applies here it is perhaps #2, the effort to understand the whole graphic (or more precisely, cartoon).

Based on the algorithm’s assessment, it earns a score of 23/66.

Oh, I totally forgot to tell you… I made a little scoring system to help you truly internalize the key messages. Those who know me will not be surprised that my system has a steep grading curve (#highstandardsFTW!).

The scoring system uses a multiplier across each rating in the scale above. Additionally, since each dimension does not carry the same level of importance, there’s a multiplier for each dimension – to effectively communicate my values.

Here’s the math…

It is all fun and games until you realize there’s a score involved! :)

Important: My intent in creating the data viz assessment algorithm, and scoring sheet, is not to have you entirely agree with how I’m grading each visualization. My intent is to teach a systematic approach you can bring to these difficult and complex tasks.

I do hope you see why I’m scoring the way I am, I hope you’ll agree. But, that desire is tertiary.

Data Visualization #2

The second graphic is from the world-famous Information is Beautiful (IiB). They have some of the world’s most famous data visualizations. (The simple and effective: When Sea Levels Attack)

IiB tends to make graphics for large screens, I need to be on my beloved 27” ThinkVision monitor to read it optimally.

In this instance, you’ll notice the color palette works against the ability to read the text (teal on dark gray or slightly lighter gray on dark gray).

The spectrum from light yellow to blood red of the circles, with internal gradations, is trying to add a layer of cleverness that possibly satiates a Data Visualizer, at the cost of the Data Consumer.

Once you zoom into one part of the visual, things become readable. You do lose the full picture of any section. In this view, perhaps you’ll agree that there is a sense of randomness to what’s in the bubble (check for this in the two visuals below as well).

It was a lovely touch to add the “risk factors to consider” on the top left of the visualization which explains the logic powering the graphic.. (You can see it more clearly in the higher resolution view, the blue font on gray makes it hard above.)

I do like the subtle helpful tips like the one about condiments, below.

Let’s apply our algorithm and see how this graphic does with our tough, but with love, lens:

1. Time to the most important insight. [Fast. Slow. KMN!]

2. The effort to understand the whole graphic. [Low. Medium. No Thank You.]

3. Trust marks. [Clear. Non-Obvious. None.]

4. Rank-ordering of key messages. [Yes. Partial. WTH!]

5. Explaining the key logic powering the graphic. [Super clear. Cloudy. Invisible.]

6. Exposing nuance. [Sweet. Some. Sour.]

7. Visualizer trying to be too clever. [No, and thank god. Yes, but it is harmless. Yes, sadly.]

8. Likely to recommend to influential leaders. [Yes! No. No way.]

I was this close to choosing no way in terms of recommending this graphic to others (because I never will). In the end, IiB is such a huge entity and so famous and so many people love them… no way seemed too much against the grain.

I've come to understand that IiB has a very specific design language, texture, and philosophy that has come to define them. It possibly acts as a constraint now.

Based on the algorithm’s assessment, it earns a score of 7/66.

Here’s the math:

It is important that data this critical – for this wide a consumption (whole planet) – needs to figure out how to hit an extraordinarily high simplicity and effective comms standard.  Else, it remains an exercise in self-satisfaction by the Data Visualizer.

Data Visualization #3

The third graphic is by Professor Saskia Popescu, Dr. James P. Phillips, and Dr. Ezekiel Emanuel.

I’m a huge fan of Dr. Emanuel. He was the special advisor for health policy in the Obama administration and played an instrumental role in passing the Patient Protection and Affordable Care Act (aka. Obamacare). For this, he has my eternal gratitude on behalf of those who society and politicians don’t usually listen to in the United States.

The Covid-19 Risk Index clearly identifies the logic powering the graphic: enclosed space, crowds, duration of interaction, and forceful exhalation.

Note that IiB also had some of these factors, forceful exhalation is an addition here (unsurprising that the doctors brought that to the fore).

The colors in the graphic are related to the intensity of the risk, green is low and red is high. Simple, direct, effective.

I’m not a huge fan of a giant company logo on graphics as you see below in the "hexagon art." I believe: More white space = more peace.

Given the heartbreaking debate in the US, I did appreciate the bonus call to action up top to wear a mask.

Did you notice the trust marks at the bottom? Really nice.

As in the case with the IiB graphic, this one is meant for the large screen display. I applaud the team for making sure each segment is readable – no fancy font colors and fancy background as a demonstration of the Visualizer's smartness.

Folks in my teams know I hold a special hatred for icons. They add clutter. In this case, I do support the decision to include icons.

For example, without needing to read any text I know that working in the office carries medium/high risk, and participating in group religious services is in the recommend you please avoid category – even in the small version above and certainly in the zoomed-in version below.

Let’s apply our algorithm and see how this graphic does with our tough, but with love, lens.

1. Time to the most important insight. [Fast. Slow. KMN!]

2. The effort to understand the whole graphic. [Low. Medium. No Thank You.]

3. Trust marks. [Clear. Non-Obvious. None.]

4. Rank-ordering of key messages. [Yes. Partial. WTH!]

5. Explaining the key logic powering the graphic. [Super clear. Cloudy. Invisible.]

6. Exposing nuance. [Sweet. Some. Sour.]

7. Visualizer trying to be too clever. [No, and thank god. Yes, but it is harmless. Yes, sadly.]

8. Likely to recommend to influential leaders. [Yes! No. No way.]

This graphic went viral on the socials, and deservedly so. With CV-19 flaring up in multiple countries (sadly, we in the US are still making our way through wave one), I hope that you will use the graphic above to stay safe – and share it with your friends and family so that they can stay safe as well.

Based on the algorithm’s assessment, it earns a score of 50/66.

Here’s the math:

Clearly a graphic the Data Visualizer can be proud of, reaching a level of obsessions overlap with Data Consumer obsessions that is rare.

Data Visualization #4

The last graphic was developed by the physicians on the Texas Medical Association COVID-19 Task Force and TMA Committee on Infectious Diseases.

I love it.

It is simple. It is easy to digest. There is absolutely nothing cute about it (hurrah!). There are no circles to jump through. No expensive Data Visualizer Specialist In Fonts was hired. The graphic is not trying too hard.

It was probably designed by the Doctors in TMA. It is insanely boring. All it is is… Effective.

Just about the only lite criticism I can make is that perhaps in keeping with the (ironically) liberal posture of the state of Texas when it comes to dealing with Covid, this graphic lowers the bar for what’s risky compared to all other sources. I share that as a small red flag, but it is adjacent to the technical analysis of the data viz that we are undertaking today.

The logic powering the graphic is integrated into the core of the graphic, as becomes clear below. There is little to no effort necessary to understand the visual. Start at the top, keep going. The colors and bars help you along.

Even in this small size, it is fairly readable…

When information is laid out so clearly other things jump out at you that makes you think (an excellent trait of a great data visualization).

All of the below items are an 8 or a 9 – but consider the staggering differences.

Attending a bar is just as risky as a religious service with 500+ worshipers! And, both are a tiny bit riskier than eating a buffet!!  You were leaned-in questioning the data, being curious. A good sign.

TMA COVID Highest Risks

Let’s apply our algorithm and see how this graphic does with our tough, but with love lens:

1. Time to the most important insight. [Fast. Slow. KMN!]

2. The effort to understand the whole graphic. [Low. Medium. No Thank You.]

3. Trust marks. [Clear. Non-Obvious. None.]

4. Rank-ordering of key messages. [Yes. Partial. WTH!]

5. Explaining the key logic powering the graphic. [Super clear. Cloudy. Invisible.]

6. Exposing nuance. [Sweet. Some. Sour.]

7. Visualizer trying to be too clever. [No, and thank god. Yes, but it is harmless. Yes, sadly.]

8. Likely to recommend to influential leaders. [Yes! No. No way.]

Based on the algorithm’s assessment, it earns a score of 64/66.

Here’s the math:

The TMA graphic was the spark to write this newsletter.

The world needed a simple way to communicate effectively, in this case literally, information that can save lives.

While things are rarely that high-stakes in a business environment, I hope the TMA inspires you to ensure that you don’t lose sight of what’s important when you work on data visualizations: The understanding of data.

Bottom line.

How do you handle the conflict between your goals as a Data Visualizer (and incentives your employer creates for you) and the Data Consumer? While the answer seems obvious, it is incredibly difficult to execute. I hope you’ll use the data visualization assessment to ensure you, your team, solve for the Data Consumer first, yourself second.

If you have graphics that score above 60, I would love to see them! (If they are shareable.)

All the best.

PS: Bonus Life Lesson:

A small number would surely have noticed that the perfect score from the algorithm is 66 (all Great), and the score for it was good enough is 22 (all Could Be Optimized). That massive chasm reflects life (and my philosophy).

There are thousands of Analysts who’ll stop at good, after all it is good. Perhaps a hundred, or less, will do the hard work required to get to great. They’ll rule the (biz) world.

#nowyouknow

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Deliver Step Change Impact: Marketing & Analytics Obsessions

Some moments in time are perfect to reflect on where you are, what your priorities are, and then consider what you should start-stop-continue. In those moments, you are not thinking of delivering incremental change… You are driven by a desire to deliver a step change (a large or sudden discontinuous change, especially one that makes […]

The post Deliver Step Change Impact: Marketing & Analytics Obsessions appeared first on Occam’s Razor by Avinash Kaushik.

Some moments in time are perfect to reflect on where you are, what your priorities are, and then consider what you should start-stop-continue. In those moments, you are not thinking of delivering incremental change… You are driven by a desire to deliver a step change (a large or sudden discontinuous change, especially one that makes things better – I’m borrowing the concept from mathematics and technology, from “step function”).

In those moments – common around new years or new annual planning cycles – the difference between delivering an incremental change vs. a step change is the quality of ideas you are considering. In this post, my hope is to both enrich your consideration set and encourage the breadth of your goals.

My professional areas of interest cover Customer Service, User Experience and Finance, though here on Occam’s Razor my focus is on influencing incredible Marketing through the use of innovative Analytics. To help kick-start your 2019 step change, I’ve written two “Top 10” lists, one for Marketing and one for Analytics – consisting of things I recommend you obsess about.

Each chosen obsession is very much in the spirit of my beloved principle of the aggregation of marginal gains. My recommendation is that you deeply reflect on the impact of the 10 x 2 obsessions in your unique circumstance, and then distill the ten you’ll focus on in the next twelve months. Regardless of the then you choose, I’m confident you’ll end up working on challenging things that will push your professional growth forward and bring new joy from the work you do for your employer.

Ready?

First… The Analytics top ten things to focus on to elevate your game this year…

The Step Change Analytics Obsessions List.

A1. Improve the Bounce Rate of your top 10 landing pages by 50%.

(Improving Bounce Rate results in reducing it. :))

You'll be surprised by the steep drop in Cost per Acquisition.

Google Optimize will be one of your BFFs in this quest. You’ll know you’ve moved beyond basic improvements when you start setting Custom Objectives – they require deeper thinking, which is a good sign.

A2. Eliminate 40% of the numbers from your dashboard.

Take the newly-created white space to explain what to do based on performance of 60% of the numbers that remain.

What your boss wants most this year, more than love, is to be told what the data wants her to do. Don't leave her guessing.

(Bonus, with actionable ideas: Smart Dashboard Modules.)

A3. Take your first steps towards unlocking smart algorithms.

Learn what Session Quality is in Google Analytics, then learn how to use it in your campaigns to improve conversions. In the Audiences section, go to the Behavior folder.

Learn what Smart Bidding is in Google Ads, then learn how to use it in your campaigns to improve outcomes.

Machine Learning algorithms will make our data smarter in unparalleled ways; Session Quality and Smart Bidding offer early clues about the scale and type of intellect. In both instances, it is immensely valuable to really understand how a smart algorithm uses billions of data signals to calculate likelihood of a conversion.

Across all your analytics data, algorithms will take you places humans simply can't. This should be the year you invest in an expansion in skills and practice to take advantage of these possibilities.

A4. Take a class in data visualization. It will save your life.

Anyone can make a complicated visual, it takes someone very special (you!) to draw out the essence of the story data is trying to tell.

My recommendations:

Free Courses: Data Visualization and D3.js and Data Analysis and Visualization at Udacity.
Affordable: Data Analysis and Presentation Skills at Coursera.
Occam’s Razor: Start with this one: Closing Data's Last-Mile Gap: Visualizing For Impact. And, there are five more linked to here.
 

Through all these courses remember the most important thing about data visualization: It’s not the ink, it’s the think. Obsess about improving the think, just as much as I’m encouraging you to improve the ink.

A5. Obsess about what happens after campaigns end.

In our analytics practice we tend to celebrate victory too early (at the end of the campaign) or with insufficient breadth (the full scope of impact).

Did you get customers with high lifetime value? How long did the brand lift – say Awareness – last? What was the average order value of the second purchase by people you acquire via Search, compared to those via Retail?

Is there a difference in behavior between people who signed up for email over the last year vs those who did not? What the cost of getting a retail customer to make subsequent purchases over mobile apps lower?

A6. Understand your personal impact, obsess about improving it.

Grab the revenue number for the company. Now work out how much of it is influenced by you directly. Make a note of what it is (likely to be a couple percentage max).

Double that number this year.

What are the first five things on your list?

None of them will be easy, but converting insights into action via influence rarely is. But, you don't have to stretch too far to see how amazing it would be for you (and data too!) if you double your impact.

A7. Run one super-large controlled experiment.

To prove what your Executives believe purely from their gut. Or, to disprove it.

Does Facebook advertising really work better than TV? Can you create premiumness for your brand using digital? Is a 15% coupon now better than 20% off the next purchase? Does swapping out male model posters for cute animals triple sales?

Does sponsoring a fashion show lead to an increase in brand equity? Does free pickup in store result in higher attach rates?

A8. Identify four relevant micro-outcomes to focus on in 2019

(In addition to the macro-outcome of revenue).

Businesses win when you optimize for a portfolio, because at any given time only a tiny fraction of people want to buy. Solving for micro and macro-outcomes is directly connected to the holy grail of solving for short-term AND long-term success.

Employees also become smarter when they have to optimize for more than one thing. :)

A9. Throw away your custom attribution model. Embrace data-driven attribution.

For some things, humans are already less smart than machines. Trying to guess what might be happening across millions of touchpoints on and off site, on and offline, is one of those things.

Skip the first five steps of attribution’s ladder of awesomeness, jump to DDA. From the tens of hours saved per week, figure out how to feed offline data into your data driven attribution model.

With an obsession with data-driven attribution, you are also solving for a portfolio rather than a silo. Super cool, super profitable.

A10. Hire an experienced statistician to be a part of your analytics team.

There is too much goodness in modeling that you are not taking advantage of. From segmentation models to identifying incrementality to predictive modeling to survival analysis to clustering to time series to… I could keep going on and on.

2019's the year you get serious about serious analytics.

A11. Bonus: Reporting kills, analysis thrills.

If that is true, and it is, :), then what % of time are you personally spending between Data Capture – Data Reporting – Data Analysis?

data_capture_data_reporting_data_analysis

Outsource or eliminate half of your data capture and data reporting responsibilities, and allocate it to data analysis and driving action.

You'll be surprised at the increase in your salary and bonus (oh, and the company will benefit too!).

In context of Analytics are you aiming for something special in 2019 that I've not covered above? Will you please share that with me by adding a comment? Thank you.


Switching gears, here are ten things to obsess about to collectively deliver a step change via your Marketing game this year…

The Step Change Marketing Obsessions List.

M1. Improve the Bounce Rate of your top 10 landing pages by 50%.

(Improving Bounce Rate results in reducing it. :))

Same as the #1 on the Analytics list. :) Far too many Marketers ignore this simple strategy to make lots more money. You work so very hard to earn attention, why then let your ads write checks your website can’t cash?

An additional delightful benefit: I find that getting Marketers to obsess about landing pages forces them to audit the user experience, something worth its weight in gold.

M2. Put up or shut up time for your social media strategy.

99.999% of corporate social media participation yields nothing.

Your CMO wants people to love your brand and organically amplify its goodness. It genuinely is a good thought. Except, a cursory glance at your social contributions show nothing of that sort over the last three years.

So, why are you spending all that money?

I recommend using that money to buying your team iPhones every Friday, I assure you that'll have a positive ROI.

Or. Focus on social media primarily as a paid media strategy. Bring the same discipline to the application of accountability to social media ads that you bring to your Display or Video ads anywhere on the web.

Here are five brand and five performance metrics that'll be your BFFs in 2019, as you social strategy lives up to that now famous mantra: Show me the money!

M3. Keep control of creativity, give up control of the creative.

Machines are much better at optimizing the latter for short or long term.

(For now) You are still better at the former – do lots of it, then hand it over to smart algorithms.

It is hard, especially for creative types who confuse creativity with creative. But, with every passing day you are harming your bottom-line more if you don’t follow the formula above.

Also consider the Machine Learning opportunities for Marketing beyond creative.

Aim to shift 25% of your marketing budgets in 2019 to opportunities that are powered by ML algorithms and rejoice at the boost in profits that results.

M4. TV works, solve for each factor that drives success.

Most TV campaigns are sold and bought based on reach (GRPs FTW!).

In my experience you should optimize for reach AND one overarching story AND creative consistency AND ensure each successfully tested creative has enough frequency to wear-in.

And, if you can't solve for three ANDs… Shift money to max out the Performance Digital opportunity, then with the left over money buy every person in your team – and at your agency – a new car. Your TV budget is big enough , and trust me when I say that giving out a new car will have very high motivational and bottom-line ROI.

M5. Seek to understand the customer journey.

What drives the first purchase? What drives the second? What drives the support calls in between? What does using the product really, really feel like? What drives advocacy?

All advertising that fails does so because the Marketer behind it understands only one sliver of the experience, then solves for that sliver with heart-breaking short-term focus.

When the Marketer understands the answers to the above questions, it influences the creative, it influences targeting, it influences retail store displays, it influences frequency, it influences product design, it influences…. it changes everything. Including profits.

Journeys are better than tinder dates.

6. Solve for intent. It is more possible and more critical with every passing day.

See-Think-Do-Care is a great intent-centric business framework, if I may say so myself, for challenging your current marketing strategy.

What intent is your current marketing content (tv, digital, ads, emails) targeting? What happens once your ads meet that intent? What meaningful content are you publishing, on and offline, to engage audiences before and after the BUY NOW (!) moment? Is your measurement aligned with the intent your marketing is targeting, or are you judging a fish by its ability to climb a tree? How do you know?

Shifting to See-Think-Do-Care is the single biggest force multiplier when it comes to your marketing. Help shift your organizational thinking to the current century in 2019.

M7. Your marketing budget allocation can be improved anywhere from 50% to 50,000%.

Allocating budgets is the hardest decision a Senior Marketer will make. Most will use strategies like Digital had 27% of budget last year, this year we should do between 28 and 30%. History, gut-feel, inter-company-politics, etc. are primary reasons why this silly mindset is pervasive across companies.

A better way? Profitable opportunity size.

I don't think you can argue with the first part: Invest where you make more profit. The second part takes a bit more work. It comes from plotting diminishing margin curves with confidence intervals. In English: How high can the investment goes before every $1 you invest returns less?

You are a Marketer, so it's unlikely that you'll plot these curves. Make it a priority for your Analytics team to do so; without them massive chunks of your budget is being flushed.

(Also, see obsession #10 on the Analytics list.)

M8. A grandmother's Marketing strategy for grandmothers only.

A bit provocative, but I want to challenge how most Marketers just make little tweaks to their strategy. The bigger the company, the more that this pernicious problem exists. Don't let that be you, and allow me to share two views that'll challenge your reality.

Here's the average time spent per day by US adults with media devices…

average_time_spent_media_devices_age

My humble description of a "grandmother's marketing strategy" is the bar on the right (65+).

It is eminently sensible for our marketing for our fellow 65+ aged Earthlings to be reflective of the implications of that right-most bar.

The problem arises when our entire marketing strategy is an extension of that right-most bar. For our entire marketing strategy to be structured on that 6:55 you see above, when our products and services are not 65+ centric is… A bit silly. Perhaps even reflective of failing our fiduciary duty.

Note the difference in total media consumption (time, place, device, more). Note the products and services your company currently offers. Reflect on this: How misaligned is your current marketing strategy?

I get really excited about something super-cool, but subtle, in the data above: The implication of the difference between active vs. passive consumption!

The difference between leaning-back and letting content wash over us vs. leaning-in and pulling content you desire is huge. It dramatically changes what your marketing should be solving for (beyond the obvious investment alignment by platforms issue).

One more reality-check for your 2019 Marketing strategy: Here's a helpful deep drive into the shifts in consumption of TV across US adults – in just six years (!!)…

us_time_spent_watching_tv

This possibly explains why Toyota's entire Marketing strategy seems to be TV-centric (with the incredible frequency of 48 per day per person here in the bay area!). It seems Toyota is only trying to sell cars to 65+ (whose TV watching has actually increased).

In 2019, resolve to align your marketing strategy with your 1. products 2. goals 3. audience, and 4. amount of expressed intent on the platform.

Credits: Originally created by Sara Fischer of Axios, the first graph is via my buddy Thomas Baekdal's newsletter. 100% of you need to sign up for it. The second chart is from the lovely team at The Economist.

M9. Suck less more.

Every campaign you are currently executing can be made to suck less – especially if you think end-to-end experience.

Ex: Expedia's emails are so long they always trigger "[Message clipped] View entire message." Suck less and maybe use my past behavior to send shorter emails so I know you care about me?

Ex: Nordstrom sends me one email a day with exclusive deals – how many clothes do they think I need? Suck less and maybe send me one a month? Or, base it on shopping patterns in store to deliver delight and not just a deal?

Ex: Macy's email I just received (titled "Resolution #1: get an extra 20% off before it ends") has promotions for Women, Men, Shoes, Bed & Bath, Kids, Juniors, Jewelry, Plus Sizes, Handbags, Home, Kitchen, Beauty. All above the fold. Below the fold: Large pictures with promotions for White Bedding, Biggest Underwear, Biggest Mattress (yes again), Best Face Forward, 25% off Adidas, Macy's presents the Edit, Fresh Pastels (the image does not make clear what this is), Free, Fast Pickup. PHEW! This can be unsucked at so many levels, with just a little bit of love and focus.

Ex: Even really good programs can use sucking less. Companies like Google and Microsoft have so many divisions. Each team/department optimizes for itself, emails are pretty good, hence each thinks they are doing really well. But, if you flip the lens to me – the recipient – I get a lot of email from each company. I wish someone at G/M would track Emails Sent/Humans Sent To, and reflect on the sad reality. It would create a culture of Marketing with me at the center instead of a company department – you can imagine the benefits.

I'm using email marketing as an example of activating the power of suck less because I love email marketing. It is an effective and profitable strategy. It has loads of behavioral data available. It needs a comparatively small team to execute well. Yet see how much opportunity there is to suck less at even the largest companies.

Substantially bigger opportunities to suck less exist in all other Marketing you are doing. TV. Print. Radio. Display (omg, sooooo much opportunity!). Video. Website. Mobile app. Everything else.

All you need to do is take a quick peek under the covers.

Your 10x goal for 2019: For every $1 invested in chasing a shiny object (VR ads! Influencer marketing!!!), invest $10 in sucking less in existing large clusters of your Marketing.

Profits that follow will also be that lopsided.

One last bit, culture eats strategy for breakfast. Create a quarterly Most Unsucked Team award, and celebrate this dimension of success. Incentives matter.

M10. Bring your great taste and expectations to work.

You can easily recognize when something is mediocre – even when others put lipstick on the pig and run it around the organization as the greatest success of the month.

You know what exceptional looks and feels like – you are not just a Marketer, you are an intelligent customer.

Yet, my experience is that most Marketers stay in their lane. Often, company cultures encourage that non-beneficial behavior.

In 2019, speak up.

You have great taste. Don't leave it at home when you leave for work.

Speak up.

When you see low quality work being pushed out by your Marketing organization… Create alternative mocks. Push for your version of the brand's tag line (not the generic MBA buzzword puke-fest). Ask for a better balance between Earned-Owned-Paid marketing. Politely challenge your Leader's assertion that creative x is better because he feels like it will be. Recommend experimenting with reckless ideas, instead of directly putting 30% of the budget on them. If you see lipsticked pigs being paraded around as exceptional examples, humbly, privately, flag the corrosive implication on culture to the most senior leader who'll listen to you.

Speak up.

You deserve to be heard.

When you speak, it'll give others around you the courage to speak up as well. Smart people tend to run in packs.

That’s it. :)

A slight repetition: Reflect deeply on the impact of the 10 x 2 obsessions in your unique business environment. Then, distill down to a total of ten you’ll focus on in the next twelve months. Finally, put a start and expected end date for each item. If you get through the list, you would have contributed a step change to your company’s bottom-line, and discovered unexpected personal joy.

As always, it is your turn now.

If you had already identified obsessions for Analytics and/or Marketing for the next twelve months for yourself, what obsessions did you choose? I’m super curious. Are there a couple in my lists above that would be particularly impactful in your company? Some of my recommendations are quite straight-forward, what do you think get’s in the way of focusing on them?

Please share your obsessions, tips, culture-shifting strategies, and critique via comments below.

Thank you.

The post Deliver Step Change Impact: Marketing & Analytics Obsessions appeared first on Occam's Razor by Avinash Kaushik.