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Not everything a company does works out.

(That is different from everything that a company is doing not working out. :))

If you are in the data business – my bread, butter and tofu – you often carry the burden of being the bearer of bad news.

The conversion rate is down 30% at launch.

The goal was to deliver a 30% increase in revenue, the team delivered 1.7953%.

During 2019, our Net Promotion Score has dropped 15 points.

The average length of our video ads is 30 seconds, less than 10% of the audiences watches beyond 5 seconds and 90% is exposed to less than 1 second.

Our Market Share in the 2-ton truck market shrunk by 1.5% (= -$3 bil).

Negative data.

Accurately collected. Intelligently analyzed. Factually presented.

Sadly still, negative data to the person/team receiving it.

Why be hurtin’?

A decade ago, data people delivered a lot less bad news because so little could be measured with any degree of confidence.

In 2019, we can measure the crap out of so much. Even with the limitations of tools, government regulations, and the astonishing fragmentation of everything (attention, devices, consumption sources, identities and more).

Companies have also evolved to be significantly more complex beings, who have to do so much more than what they did 50 years ago. Think of all P&G had to do to sell soap 50 years ago, think of everything they are doing now. Add on top of that, where P&G could sell soap, the purposes it could sell soap for, and imagine both of those things now. It is a lot of stuff!

When you do that much stuff, and you can measure almost everything… The result is that our ecosystem of data people are returning a lot more negative data when measuring performance of Marketing, Sales, User Experience and Customer Service.

Let me repeat this one more time: It is not that companies have slowly over the last decade started to suck more (well, maybe some). It is that we are able to analyze and identify bad performance with greater accuracy.

While that change has occurred, two things have obstinately stayed the same:

1. Company cultures are rarely open to hearing anything negative.

2. The top leaders in your company grew up, succeeded, and were promoted during the era of no data (and hence a ton less negative news). They are not natively wired to receive data-delivered reality checks.

The combination means no red carpet for negative data. It is not hard to see that a modern large corporation is likely missing out on the benefits of all they should know about their business through data. It also results in a depressing existence for data people.

Short-term, this let’s not listen to the negative data strategy sometimes actually works. No one is telling the Emperor he is naked, and the Emperor is delighted everyone loves his clothes so much.

Long-term…. : (

Four Negative Data Leadership Archetypes.

The solutions to this big opportunity have many dimensions.

I want to focus on the massive “make or break” dimension: #2 above, with Extremely Senior Leaders (ESLs).

Through their words and actions, ESLs can quash data's learn to improve spirit, or they can nurture that spirit and deliver a transformative, positive impact on the company's culture + profit.

In my work with clients around the world (all continents except Antarctica), I’ve encountered a whole host of reactions to when I have shared negative data with ESLs. From the patterns in those reactions, I’ve developed four archetypes of leaders.

While people are never starkly black and white, they typically have a dominant archetype – the one they most frequently demonstrate.

I’ve also observed cultural implications that each leader-type ends up creating from their reactions to negative data. For three of the four, I’ve seen individuals successfully navigate the leader-type – never without scars though – and individuals emotionally burn-out due the environment the leader creates.

Today, I want share the four leader archetypes with negative data as the lens. Included below is my accumulated wisdom, with the hope that you’ll fall in navigate category and not the crushed one.

Ready?

Archetype #1: The Bubble King (/Queen).

Archetype #1: How they react:

This individual lives in a bubble, so their reaction to any data is… Nothing. Data, unless it is super-positive, never makes it to them. Bubble Kings are comfortable making decisions that sound good – decisions just as likely to be informed by their long experience as the quest for shiny objects.

Bubble Kings most commonly reside in organizations where there is little to no accountability (or misplaced accountability, ex: celebration of vanity metrics).

Their most common reaction to negative data, if it makes it through, is to try to discredit it by asking analytically-nonsensical questions: What are the p-values of your multi-channel attribution model applied to performance of my strategy?

Archetype #1: Cultural implications:

In small or medium sized companies, Bubble Kings (/Queens) have short reigns. Not all that hard to imagine why – you don’t listen to data, ignore reality checks, and the transparently oriented accountability loop ensures there is nowhere to hide.

In large companies, or teams with massive budgets, Bubble Kings (/Queens) have long reigns. The accountability loops are larger, less transparent, and the natural large-company multi-layer organization complexity does not help. Typically a change in the C-Suite layer above will transition them out of the company (fresh eyes, sunshine … call it what you will).

However, while they still reign – since feelings matter more than data – sycophantic behavior is common and often encouraged.

Archetype #1: How to deal with them, their org:

Data will never play any impactful role on strategy. Since Bubble Kings live in a, well, bubble, you can often form relationships and influence at lower levels in a Bubble King’s org, and you can have a positive influence on tactics. Absolutely take advantage of it.

If you want to get promoted, give up the quest to identify factual real-world performance and focus instead on proving that the Bubble King's decisions deliver excellent results. Don't compromise on your ethics. But on this blog and in my newsletter I’ve shared enough strategies you should not use to slant data – use them.

[Bonus Reads: A Great Analyst's Best Friends: Skepticism & Wisdom! & TMAI #154: Irrationality, Cognitive Bias, and Us.]

Archetype #2: The Attacker.

Archetype #2: How they react:

They attack.

They attack the data. They attack your knowledge. They attack your intent. They bring up that one time in 2013 when your analysis missed an important assumption. They attack your personal attributes.

In the face of factual negative data related to their decisions, they will counter-attack. At times, harshly. Sometimes they counter-attack, in a twist of irony, by trying to drown you in enormous detail and minutiae.

You will be branded Ms. Bad News or Mr. Not A Team Player or some such ugly moniker.

Archetype #2: Cultural implications:

In extremely senior positions, Attackers fuel the creation of a culture where no bad news ever filters through. When business performance is non-positive, every employee, at every level, will work super-duper hard to look at every dimension of data to find any semblance of good news (no matter how small). Only this good news will make it to the top (Attacker ESL).

A typical example: The entire house is on fire but the analysis of that situation will focus on the one unsinged rose in the font lawn and how beautiful the rose is.

Attackers lead can last for a surprisingly long time in an organization, for a whole host of strategic reasons (as I’m confident you’ve observed as well).

Archetype #2: How to deal with them, their org:

If you are a data person and you are in a small organization lead by an Attacker, you need to update your resume and find a way out. There is no hope for your career (or emotional positivity).

If you are a data person and you are in a large organization lead by an Attacker, also update your resume. If, for any number of valid reasons, you are stuck there my advice is to focus your analytical efforts exclusively on the Attacker's biggest fears. It might take a little bit of effort to discover them, but it is so worth it. Even an Attacker has a point at which their instinct for self-preservation kicks in, in those rare (often hidden) situations they’ll be open to negative data.

And this is key: If you can provide solutions and not just data, you might even become a trusted adviser. This will do nothing to advance your acquired negative branding of Ms. Bad News, nor will this change the broader team/company culture… But…You'll have an impact with data, providing a pretty decent existence in an Attacker created culture while you look for a way out.

Archetype #3: The Rationalizer.

Archetype #3: How they react:

Their trigger instinct in face of factual negative data is to make excuses. To provide context. To identify circumstances to blame. To poke holes in the data/methodology (regardless of the Rationalizer’s analytical competence). To create enough uncertainty to fuzzy up any negative – or remotely negative – data.

If we were stack rank the four types, the Rationalizer would come on top as the most undesirable leader (often corrosive for the institution).

You might think it would be the Attacker. It is the Rationalizer because their approach to dealing with negative data is not as overtly corrosive. A Rationalizer subtly sows doubt. They dilute the analysis with non-facts. They force the inclusion of non-related nonsense in the quest to paint a fuller picture. At their worst, they commonly turn diamonds into coal.

Archetype #3: Cultural implications:

Everything data people do to highlight reality, to bring truth to the fore, to identify positive solutions from negative data, will be discounted, buried, and compete for impact with faith. Questionable analysis and slanted views will have equal footing with the most factual and intelligent analysis.

When people say "this team’s culture runs on BS," they are describing an organizations run by a Rationalizer.

Status quo will rule the day in such organizations, unless there is a big external force that creates change. The operative instinct is to maintain mediocrity with just enough reality massaging flowing upstream to ensure existing mediocrity is not utterly obvious.

One identifying attribute of Rationalizer organizations is the overwhelming abundance of data pukes. Why? Data pukes do nothing to make an organization intelligent, while providing the feeling of competence and productive output.

Archetype #3: How to deal with them, their org:

Rationalizer org’s are hardest to deal with because you are not obviously being ignored (Bubble King) nor are you being openly challenged (Attacker). You are just constantly being undercut to the point where the data represents a watered down version of an adjacent reality.

If you are a data person full of courage and determination, find the largest element of the business' strategy and unpack the power of strategic analysis to present factual data. Lead with as many things as you can find that are going right, then follow that with the most material two things that are factually not going right. Present the collection directly to the Rationalizer if you can.

On that note… Since the Rationalizer is an ESL (Extremely Senior Leader), it is quite possible that you have to work through many layers of people in-between you, my peer data person, and them. In a Rationalizer’s culture, every layer you go through will instinctively take the material two negative news and will try to kill it or fuzzify/massage it. In these cases, if you can make cosmetic changes to pass each layer, do so. Don't give up on the core of the positive and negative stories.

When you are in the presence of a Rationalizer, bring overwhelming analytical competence – there is no better way to deal with their reactions (see above). A Rationalizer never gives up trying to rationalize every small bit of negative data, persistence is a virtue that’ll come in handy.

Keep in close contact with your soul. At some point you’ll find it is sapping, it’ll be your clue that you need to find it a different professional environment.

Archetype #4: The Curious One.

Archetype #4: How they react:

In face of negative data, the Curious One asks you questions to understand the why behind what you are presenting.

If a period has elapsed where the data person has demonstrated competence, the Curious One does not question the analytical approach of data collection methodologies (they trust you to have applied fanatical quality control). The Curious One demonstrates, well, curiosity about what biases might be in the data or what assumptions you might have made.

They have two critical attributes: 1. They demonstrate open mindedness in the face of negative data. 2. Their posture is not to instinctively blame (backwards looking), but rather the posture is to identify and fix (forwards looking).

Archetype #4: Cultural implications:

Due to the demonstrated behavior at the top, open mindedness is usually encouraged in organizations led by Curious Ones.

Negative data is never a delightful experience, but the trust fostered amongst senior leaders results in a lot more truth telling, and is as good of a welcome mat as will ever be provided to the data people.

It might seem odd that in such a positive posture to negative data that there is still accountability, but incredibly in my experience it exists in spades in such organizations. It flows down from the clear measurable goals, an empowered data organization, and a close and direct partnership with different leadership levels (VPs, Directors, Sr. Managers).

Archetype #4: How to deal with them, their org:

Pinch yourself every day.

Don't take your position for granted. Invest in self-learning every week – even couple hours a week – to ensure you can keep pace with the demands for sophisticated analysis which will be expected at an agile pace.

You know my Care-Do-Impact model for analysis and storytelling. Organizations led by the Curious Ones are the very best places for you to slowly migrate your sophistication in Do and Impact. This, in turn, means that your demonstrated sophistication will open up new career options, for example becoming a business line leader or moving on to the strategy side of the house. Joyous outcomes for you, your company, and your company's customers.

Two Inspiring Examples | Curious One Archetype.

Paul Polman.

One person who demonstrated Curious One behavior to me was the recently ex-Global CEO of Unilever, Paul Polman. I had an opportunity to spend time with him and his leadership team. My role was to be a challenger, to share stories about what Unilever did well and focus on the challenges faced by vividly demonstrating things they did not do well. The latter part of the story qualifies as negative data.

It would be normal CEO behavior to be defensive, to pick the story apart, to make excuses. But, no. Through his words and actions to me and his brand CEOs, Mr. Polman demonstrated every attribute of the Curious One. This opened mind share required to re-imagine the future.

As with inspiring leaders, there is a whole ton lot more as to why I admire Mr. Polman.

Alan Mulally.

Mr. Mulally’s stewardship of Boeing and Ford is legendary. I want to share one story that Mr. Mulally shared with us in a meeting (and in his book).

When he got to Ford he instilled the same colored charts approach to identify what's working and what needs more attention. All the charts Mr. Mulally got were color coded green (#everythingisawesome). The problem was that Ford was on track to lose $17 billion dollars that year. Ford's culture was such that business leaders would hide problems, therefore making issues "disappear." Mr. Mulally set a different tone of honesty and looking at negative data as an opportunity to improve/change/fix (classic Curious One approach). In next meetings, things slowly started to turn Red… and Yellow… and some real Green.

There is a lot more to Mr. Mulally's turnaround of Ford. Eleven principles actually (buy the book!). But in this anecdote you can see the central reason that I adore him, and the leadership skills that can turn even the most intractable business problems in some of the largest companies on the planet.

A Plan for Action.

Humans are complicated beings.

No individual is just one black and white type.

Yet, humans, at least professionally, tend to demonstrate a dominant type. It is what they are natively comfortable with.

With that in mind, a suggestion for a plan of action…

Leader.

Introspective is in order. Assume you are doing this only for your own selfish benefit reasons, no one else has to know.*

Take a quiet moment.

Reflect on what your dominant type is: Bubble King or Attacker or Rationalizer or Curious one.

Once you do that, consider the impact that your leadership posture is having on your team, on your data people, on the ability of negative data (or negative anything) to help stop/rethink strategy, and indeed on the corporation.

The global maxima is that you consider a personal shift towards exploring the benefits of evolving to become the Curious One type (if you are not there already).

An incentive is that at some point in a long career, one does tend to reflect on the personal impact of one's professional accomplishments. In that moment, on that day/week/month/rest of your life, realizing the heart-breaking impact you delivered by being a Bubble King, Attacker or Rationalizer does deliver a heavy emotional burden and a personal crisis. So. Not. Worth it.

There is a meme that people can't really change who they are. You'd be surprised how untrue that is.

* Your team already knows what type you are. You might as well be honest with yourself for all the benefits that will come.

Individual.

Self-reflection is in order for you as well.

Set some quiet time aside so that you can consider the how they react and cultural implications demonstrated by the leader who has the biggest influence your personal work. (Sometimes this is your direct manager. Other times it is someone a few levels above yours.)

In your mind only, assign the archetype (BK, A, R, CO) to the leader. That act will bring clarity as you ask yourself these three questions I recommend…

What is your behavior in response to that dominant leader?

Is it as suggested in the how to deal with it section of each leader type?

What will it take for you to change your behavior to optimally deal with the situation you are in?

Make a specific plan.

Act on it. Life is short.

It is always better to be on a path chosen after careful self-reflection and planning, even if you find yourself in an undesirable situation. It might not deliver world peace, but it will reduce your emotional burden.

Bottom-line.

We've used leader reaction to negative data as a vehicle to discuss creating an optimal professional path for ourselves (as leaders or as individuals).

The framework I've shared, how they react, cultural implication, and how to deal with it, can be applied to multiple dimensions of our professional ecosystem. Give it a try.

If you are a leader, if you have a choice, be the Curious One. Here are a recap of the benefits: 1. Lighter personal emotional burden. 2. Cultures where the goal is not blame, it is making progress. 3. People who love you (yes, love, in a workplace!) and will help you deliver transformative results – in good times and bad.

So. Worth. It.

Good luck!

As always, it is your turn now.

What archetype identifies the most influential leader in your organization? If you are a leader, what archetype is reflective of your impact? If you’ve successfully worked inside organizations lead by a Bubble King (/Queen), Attacker, Rationalizer… What worked, what did not work? Have your seen a leader transform into a different archetype – do to an HR-induced or personal induced change? What worked, what did not work? Is there an archetype you would have created, if we are looking through the lens of negative data?

Please share your reflections, critique, culture-shifting strategies, and tips for individuals or leaders via comments.

Thank you.

The post Responses to Negative Data: Four Senior Leadership Archetypes. appeared first on Occam's Razor by Avinash Kaushik.

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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?

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…

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 (!!)…

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..

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The universe of digital analytics is massive and can seem as complex as the cosmic universe.

With such big, complicated subjects, we can get lost in the vast wilderness or become trapped in a silo. We can wander aimlessly, or feel a false sense of either accomplishment or frustration. Consequently, we lose sight of where we are, how we are doing and which direction is true north.

I have experienced these challenges on numerous occasions myself. Even simple questions like “How effective is our analytics strategy?” elicit a complicated set of answers, instead of a simple picture the CxO can internalize. That’s because we have to talk about tools (so many!), work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more.

Soon, your digital analytics strategic framework that you hoped would provide a true north to the analytics strategy question looks like this

The frameworks above cover just one dimension of the assessment (!). There is another critical framework to figure out how you can take your analytics sophistication from wherever it is at the moment to nirvanaland.

A quick search query will illustrate that that looks something like this…

It is important to stress that none of these frameworks/answers exist in a vacuum.

Both pictures above are frighteningly complex because the analytics world we occupy is complex. Remember, tools, work, processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more.

The Implications of Complexity.

There are two deeply painful outcomes of the approaches you see in the pictures above (in which you’ll also see my work represented as well).

1. Obvious:

No CxO understands the story we are trying to tell – or, even the fundamentals of what we do in the world of analytics. Therefore, they are inclined to remain committed to faith-based decision-making and continue to starve analytics of the attention and investment it deserves.

2. Non-obvious:

Leaders of analytics organizations do not truly appreciate the wonderful effectiveness, or gross ineffectiveness, of their analytics practice (people, process, tools). You see… None of the currently recommended frameworks and maturity models aids analytics leaders in truly understanding the bottom line impact of their work. The result is analytical strategies that are uninformed by reality, and driven new tool features, random expert recommendations and shiny objects (OMG we have to get offline attribution!).

When one grasps these two outcomes – blind business leaders, blind analytics leaders – it is simply heartbreaking.

Simplifying Complexity.

The dilemma of how to simplify this complexity, to create sighted business and analytics leaders, has lingered with me for quite some time. I’ve intended to create a simple visual that absorbs the scale, complexity and many moving parts.

On this blog, you’ve seen numerous attempts by me to remedy the dilemma. To name a few: Digital Marketing & Measurement Model | Analytics Ecosystem | Web Analytics 2.0.  Each aimed to solve a particular dimension, yet none solved the heartache completely. Especially for the non-obvious problem #2 above.

The hunger remained.

I wanted to create a visual that would function as a diagnostic tool to determine if you are lost, trapped in a silo or wandering aimlessly. It would help you realize the extent to which analytics impacted the business bottom line today, and what your future analytics plans should accomplish.

Then one day, a magic moment.

During a discussion around planning for measurement, a peer was struggling with a unique collection of challenges. He asked me a couple of questions, and that sparked an idea.

I walked up to the whiteboard, and excitedly sketched something simple that abstracted away the complexity – and yet preserved the power of smarter thinking at the same time.

Here’s the sketch I drew in response:

Yes, it was an ugly birth. But, to me, the proud parent, it was beautiful.

It took a sixteen hour direct flight to Singapore for the squiggly sketch to come to life – where else, in PowerPoint!

The end result was just five slides. As the saying goes: It's not the ink, it's the think.

I want to share the fully fleshed out, put into practice and refined, version of those four slides with you today. Together, they’ll help you fundamentally rethink your analytics practice by, 1. understanding data’s actual impact on your company today and, 2. picking very precise and specific things that should be in your near and long-term analytics plans.

The Impact Matrix.

To paint a simple picture of the big, complicated world of analytics, the whiteboard above shows a 2×2 matrix.

Each cell contains a metric (online, offline, nonline).

The business impact is on the y-axis, illustrated from Super Tactical to Super Strategic.

The time-to-useful is on the x-axis, illustrated from Real-Time to 6-Monthly.

Before we go on… Yes, breaking the x-axis into multiple time segments creates a 2×5 matrix, and not a 2×2. Consider that to be the price I’ve paid in order to make this more actionable for you. :)

Diving a bit deeper into the y-axis… Super Tactical is the smallest possible impact on the business (fractions of pennies). Super Strategic represents the largest possible impact on the business (tens of millions of dollars).

The scale on the y-axis is exponential. You’ll notice the numbers in light font between Super Tactical and Super Strategic go from 4 to 10 to 24 to 68 and onward. This demonstrates that impact is not a step-change – every step up delivers a massively higher impact.

Diving a bit deeper into the x-axis… While most data can be collected in real-time now, not all metrics are useful in real-time.

As an example, Impressions can be collected in real-time and they can also become useful in real-time (if actioned, they can have a super tactical impact – fractions of pennies). Customer Lifetime Value on the other hand takes a long time to become useful, over months and months (if actioned, it can have a super strategic impact on the business – tens of millions of dollars).

Here is a representation of these ideas on the Impact Matrix:

[You can download an Excel version of the Impact Matrix at the end of this post.]

Impressions can be used in real-time for decision-making by your display, video and search platforms (e.g., via automation). You can report Gross Profit in real-time, of course, but doing so is almost entirely useless. It should be deeply analyzed monthly to yield valuable, higher impact actionable insights. Finally, Lifetime Value will require perhaps the toughest strategic analysis, from data accumulated over months, and the action takes time to yield results – but they are magnificent.

Pause. Reflect on the above picture.

If you understand why each metric is where it is, the rest of this post will fill you with euphoric joy rarely experienced without physical contact.

The Impact Matrix: A Joyous Deep Dive.

In all, the Impact Matrix contains 46 of the most commonly used business metrics – with an emphasis on sales and marketing. The metrics span digital, television, retail stores, billboards, and any other presence of a brand you can think of. You see more digital metrics because digital is more measurable.

Some metrics apply across all channels, like Awareness, Consideration and Purchase Intent. You’ll note the most critical bottom line metrics, which might come from your ERP and CRM systems, are also included.

Every metric occupies a place based on business impact and time of course, but also in context of other metrics around it.

Here’s a magnified view that includes the bottom left portion of the matrix:

Let’s continue to internalize impact and time-to-useful by looking at a specific example: Bounce Rate. It’s in the row indicating an impact of four and in the time-to-useful column weekly. While Bounce Rate is available in real-time, it is only useful after you’ve collected a critical amount of data (say, over a week).

On the surface, it might seem odd that a simple metric like Bounce Rate has an impact of four and TV GRPs and % New Visits are lower. My reason for that is the broader influence of Bounce Rates.

Effectively analyzing and acting on Bounce Rates requires the following:

* A deep understanding of owned, earned and paid media strategies.

* The ability to identify any empty promises made to the users who are bouncing.

* Knowing the content, including its emotional and functional value.

* The ability to optimize landing pages.

Imagine the impact of those insights; it is well beyond Bounce Rates. That is why Bounce Rate garners more weight than Impressions, Awareness and other common metrics.

When designating a metric as a KPI, this is your foremost consideration: depth of influence.

With a better understanding of the Impact Matrix, here’s the full version:

[You can download an Excel version of the Impact Matrix at the end of this post.]

As you reflect on the filled out matrix, you’ll note that I’ve layered in subtle incentives.

For example, if you were to compute anything Per Human, you would need to completely revamp your identity platforms (a strategy I’ve always favored: Implications Of Identity Systems On Incentives). Why should you make this extra effort? Notice how high those metrics sits on the business impact scale!

Other hidden features.

The value of voice of customer metrics is evident by their high placement in context of the y-axis. Take a look at where Task Completion Rate by Primary Purpose and Likelihood to Recommend are, as an example. They are high in the hierarchy due to their positive impact on both the business and the company culture – thus delivering a soft and hard advantage.

You’ll also note that most pure digital metrics – Adobe, Google Analytics – sit in the tactical bottom line impact. If all you do day and night is just those metrics, this is a wake-up call to you in context of your actual impact on the company and the impact of that on your career.

At the top-right, you’ll discover my obsession with Profit and Incrementality, which form the basis of competitive advantage in 2018 (and beyond). Analyzing these metrics not only fundamentally changes marketing strategy (think tens of millions of dollars for large companies); their insights can change your company’s product portfolio, your customer engagement strategies and much more.

The matrix also includes what is likely the world’s first widely available machine learning-powered metric: Session Quality, which you’ll  find roughly in the middle. For every session on your desktop or mobile site, Session Quality provides a score between 1 and 100 as an indication of how close the visitor is to converting. The number is computed based on a ML algorithm that has learned from deep analysis of your user behavior and conversion data.

Pause. Download the full resolution version of the picture. Reflect.

It is my hope that the placement of each of the 46 metrics will help you add metrics that might be unique to your work. (Share them in comments below, add to our collective knowledge.)

With a better understanding of the matrix, you are ready to overcome the two problems that broke our hearts at the start of the post – and do something super-cool that you did not think we might.

Action #1: Analytics Program Maturity Diagnostic.

Enough theory, time to some real, sexy, work.

The core driver behind creation of the Impact Matrix was the non-obvious problem #2: How much does your analytics practice matter from a bottom line perspective?

YOU matter if you have a business impact. You’ll have a business impact if your analytics practice is sophisticated enough to produce metrics that matter. See the nice circular reference?

:)

In our case we measure maturity not by evaluating people, process, and layers upon layers of tools, rather we measure maturity by evaluating the output of that entire song and dance.

Answer this simple question: What metrics are most commonly used to make decisions that drive actual actions every week/month/more?

Ignore the metrics produced as an experimental exercise nine months ago. Ignore the metrics whose only purpose is to float along the river of data pukes. Ignore the metrics you wish you were analyzing, but don’t currently.

Reality. Assess, reality. No point in fooling yourself.

Take the subset of metrics that actively drive action, and change the font color for them to green in the Impact Matrix.

For a large European client with a multi-channel existence, here’s what the Impact Matrix looked like after this honest self-reflection:

More of the digital metrics are green, because there are more digital metrics period. You can see the company’s marketing strategy spans television and other offline advertising, including retail.

You’ll likely recognize many of these metrics as the one that your analytics practice outputs every day. They represent the result of a lot of hard work by the company employees, and external analytics partners.

We are trying to answer the how much does the analytics practice matter question. You can see that more sharply now.

For this company most green metrics cluster in the bottom-left quadrant, with most having an impact of ten or under (on a y-axis scale of 1 to a ). There is one clear outlier (Nonline Direct Revenue – a very difficult metric to compute, so hurray!)

As every good consultant know, if you have a 2×2 you can create four thematic quadrants. In our case the four quadrants are called Solid Foundation, Intermediate, and Advanced:

For our company, the maturity of the analytics practice fit mostly in the Solid Foundation quadrant.

Is this a good thing?

It depends on how long the analytics practice has been around, how many Analysts the company has, how much money it has invested in analytics tools, the size of their agency analytics team, so on and so forth.

If they have a team of 50 people spending $18 mil on analytics investment each year, over the last decade, with 12 tools and 25 research studies each year… You can now infer that this is not a good thing.

Regardless, the Impact Matrix now illuminates clearly that highly influential metrics are underutilized. These are the metrics  that facilitate deeper thought, patience and analysis to deliver big bottom line impact.

Recommendation Uno:

Conduct this exercise for your own company. Identify the metrics actively being used for decision-making. Which quadrant reflects the maturity of your analytics program? With the investment in people, process, tools, and consultants, are you in a quadrant where your bottom line impact is super strategic?

Recommendation Dos:

Identify your target quadrant. In this instance the company could move bottom-right and then up. They could also move top-left and then top-right. The choice depends on business strategy and current people, process, tools reality. This should be obvious; you always want the Advanced quadrant lit up. But, you can’t go from Beginner to Advanced directly – evolution works smarter than revolution. (If your Solid Foundation quadrant is not lit up, do that first.)

Recommendation Très:

Create a specific plan for the initiatives you need to undertake to get to your next desired quadrant. You’ll certainly need new talent, you’ll need a stronger strategic leader (less ink, more think), you’ll need to identify specific analytics projects to deliver those metrics, and you’ll most definitely need funding. Divide the plan into six-month segments with milestones for accountability.

The good news is that it is now, finally, clear where you are going AND why you are going there. Congratulations!

Recommendation Cuatro:

Start a cultural shift. Share the results of your assessment, the green and black reflection of the current reality, with the entire company. Inspire each Marketer, Finance Analyst, Logistics Support Staff, Call Center Manager, and every VP to move one step up or one step to the right. If they currently measure AVOC, challenge them to move to Unique Page Views or Click-thru Rate. It will be a small challenge, but it will improve sophistication and, as you can see in the matrix, the impact on the bottom line.

Most companies wait for some Jesus-Krishna hybrid to descend from heaven and deliver a glorious massive revolution project (overnight!). These never happen. Sorry, Jesus-Krishna. Instead, what it takes is each employee moving a little bit up and a little bit to the right while the Analytics team facilitates those shifts. Small changes accumulate big bottom line impact over time.

So. What’s your quadrant? And, what’s your next right or next up move?

Action #2: Aligning Metrics & Leadership Altitude.

When offered data, everyone wants everything.

People commonly believe that more data means better results. Or, that if an Agency is providing a 40 tab, font size 8, spreadsheet full of numbers that they must have done a lot of work – hence better value for money. Or, a VP wants two more histograms that represent seven dimensions squeezed into her one page dashboard.

If more data equaled smarter decisions, they would be peace on earth.

A core part of our job, as owners of the analytics practice, is to ensure that the right data (metric) reaches the right person at the right time.

To do so, we must consider altitude (aka the y-axis).

Altitude dictates the scope and significance of decisions.  It also dictates the frequency at which data is received, along with the depth of insights that need to accompany the data (IABI FTW!). Finally, altitude determines the amount of time allotted to discuss findings.

Managers have a lower altitude, they are required to make tactical decisions – impacting say tens of thousands of dollars. VPs have a higher altitude, they are paid a ton more in salary, bonus and stock, because they carry the responsibility for making super strategic decisions – impacting tens of millions of dollars.

This problem has a beautifully elegant solution if you use the Impact Matrix.

Slice the matrix horizontally to ensure that the metrics delivered to each leader are aligned with their altitude.

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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:

1. Unaided Brand Recall
2. Likelihood to Recommend
3. Lift in Purchase Intent
4. Shift in Brand Perception (negative to neutral, neutral to positive, positive to proactive evangelism)
5. Lifetime Value

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:

1. Recency & Frequency
2. Loyalty
3. Task Completion Rate
4. Assisted Conversions
5. Macro-Outcomes Rate
6. Economic Value

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..

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