Mark Stouse

ProofAnalytics.ai

Chairman and CEO

Association of National Advertisers

Member, Brand Valuation Work Group

Marketing-Accountability-Standards-Board

Co-Chair, Modern GTM Organizational Structure

Vaulting Ventures

Vaulting Ventures is a family office that invests in companies that are disrupting current approaches to key corporate functions such as HR, Marketing, Sales and R&D.

Partner

Hard Skill Exchange

Consulting Member

Accelerating Value -- A Weekly Podcast

Host

BMC Software

CCO and Vice President, Solutions Marketing & Management

BMC’s CEO asked me to return to reinvigorate WW Marketing & Communications and to take over Customer Connect, BMC’s customer success and education organization. We used Customer Connect to help begin to align Marketing and Sales in a company where that had never happened. 

Unlike my first tour, I rejoined BMC at the beginning of a very tough period. BMC’s performance had slipped. A powerful activist shareholder was shaking up the company, and all the signs pointed towards privatization. My organization focused on rebuilding trust and confidence across a wide array of BMC stakeholders, including customers, partners, investors, employees and the influencer community.

During this time, innovation remained on the front burner. We deployed a vastly more mature version of the analytics platform that had been such a success at Honeywell and my first tour at BMC. This enabled us to continue to run our operations like an "investment fund,” combining predictive analytics and proven, contemporary approaches to connect with the company's audiences. We drove small gains in deal expansion and sales velocity in 2013 and into 2014, despite the corporate reputation issues that were impacting BMC’s fortunes at that time.

During this period, our analytics platform began to attract a growing amount of attention from the marketing profession and the marketplace. In 2014, it was recognized as the Innovation of the Year by a jury drawn from across the marketing and communications profession. It subsequently won additional honors, including the Holmes Report’s Diamond SABRE.

CCO & Global Vice President, Customer Success

Honeywell

I was recruited by the CEO of Honeywell International to bring profitable change to how the conglomerate reached the marketplace concerning its ~$12 billion aerospace unit, a highly regulated global business with what were very “old school” approaches to go-to-market.

Leadership and Organization are critical to leading big change. My team and I successfully overhauled and globalized a very large U.S.-centric marketing and communications organization to meet business requirements, onboarding more than 20 key, high-talent leaders around the world plus numerous staff and two new global agencies. We forged a large collection of disparate and diffused program budgets into one unified program designed to drive massive sales productivity gains for the Aerospace unit.

In business, success is always about Innovation and Impact. We led Honeywell and the aerospace industry in deploying extensive sales and marketing analytics to calibrate and establish marketing’s contribution to sales productivity.

CMCO, Aerospace

At BMC, I was the top global marketing communications executive in a $2.2 billion global software company.

In many ways, I began to develop my reputation for systems-based innovation at BMC.  Building on my own IP, I deployed and steadily improved a proprietary analytics system that enabled us to both predict and demonstrate exceptional functional performance, operational efficiency and business impact.

The methodology won a BMC Innovation Award in 2009 – the first non-product innovation so honored in the history of the company.  We received that recognition because we were able to accurately calibrate our campaigns and ultimately prove cash-on-cash ROI that exceeded 3000% in 2008, 2009 and 2010. My teams delivered campaigns that were credited with driving 11% gains in deal expansion and a 6-7% acceleration in deal velocity.  Sales and Finance leaders used our data to calibrate their own analytics.

Global Vice President and CCO

Hewlett Packard Enterprise

Created and directed communications for Compaq's Enterprise Storage Group, as well as HP's company-wide, global competitive intelligence and communications operations, directly supporting approximately $70B in new logo and / or competitive deals. Subsequently led all WW communications and marketing communications for the company's huge Personal Systems Group and for the Public Sector, Health and Education segment.

WW Communications Leader, ESG

GTM Economics | 2023 GTM 50 | CMO ➡️ CEO | Plan | Predict | Prove | Pivot | 50 Startups to Watch for 2023 | Ten Most Influential Analytics Leaders

As CEO of ProofAnalytics.ai, I lead the team with the only AI-native, MASB certified platform that enables GTM teams to plan, predict, prove, and pivot their investments in real time. With over 26 years of experience in marketing communications and strategy, I have a passion for transforming GTM performance with data-driven insights and agile decision making.

I also leverage my expertise in GTM analytics and economics to co-chair the Modern GTM Organizational Structure at the Marketing-Accountability-Standards-Board, and to serve as a member of the Brand Valuation Work Group at the Association of National Advertisers. In addition, I have been recognized as an innovator and leader in the analytics field, with multiple awards, patents, and publications. I am committed to advancing the practice and standards of GTM accountability and optimization across industries and markets.

We should remember that almost all of the Corporate world isn't driven by doing things better, but reducing risk.

It's remarkably rarely about driving efficiency, and more often about keeping power.

Next time you're  selling something, you probably need to show how it's a safe option, not (just) a transformative option.

The entire world of Consultancy, is often the insurance business more than the progress business. 

Which is a shame for people like me who dream of how companies can improve how they work, how they can leverage technology for greater impact and how they can transform the way they help and serve customers. 


𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝘇𝗶𝗻𝗴 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗠𝗲𝘁𝗿𝗶𝗰𝘀

This is the inaugural issue of the 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗡𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 and we start by looking at the enormous array of metrics available to modern marketers.

Almost all marketing metrics are useful, but some are more valuable than others, and the key question about any given metric always comes down to:

•	Can Marketers Drive It?
•	Does it Reliably Reflect the Real World?
•	Does It Drive Revenue?

It's useful to consider exactly what information we're attempting to capture to be able to understand which metrics to use and under what use cases. 

If you don't understand the category of information each metric captures, it becomes far too easy to use that metric in nonsensical ways.

👉 Almost all metrics can be categorized along ten dimensions –

1. Efficiency vs. Effectiveness
2. Correlation vs. Causation
3. Forecast vs. Performance
4. State vs. Rate
5. Measured vs. Assigned
6. Modeled vs. Measured
7. Simple vs. Computed
8. Survey vs. Behavioral
9. Leading vs. Lagging
10. KPI vs. Tactical Metric

In this article, I'll examine each of these categories of metrics in more detail, providing specific examples and use cases.

Dale W. Harrison on LinkedIn • 9 min read

Categorizing Marketing Metrics

WEBINAR ANNOUNCEMENT: 

Please join the inestimable Dale W. Harrison, 6sense’s Kerry Cunningham, and me tomorrow (April 8, 9am PT) on Hard Skill Exchange as we debate a hot topic that is sweeping the #B2BMarketing community. 

Does demand creation even exist?

Who creates it?

Who doesn’t create it?

All three of us bring a data and analytics perspective to these questions. If this is a burr under your saddle, please join us!

6sense 
Latané Conant (she/her) 
ProofAnalytics.ai 
Tim Hillison 
Julia Nimchinski 
Michelle Killebrew 
Liam Moroney

I've taken on an advisor role, and I'd love to share a little about who and why.

The company is ProofAnalytics.ai, and it's an incredibly impressive causal analytics platform that uses real defensible data science and statistics to measure and predict the effectiveness of marketing.

It's as defensible as it comes, certified by MASB, an American National Standards Institute (ANSI) and International Organization for Standardization (ISO) affiliated organization, and it's also addressing marketing effectiveness measurement in a way that I think is crucially needed right now.

You've likely heard me talk a lot about the non-linear nature of marketing, and how much marketing works in a way that isn't how we measure marketing today. 

It's for exactly that reason that I'm looking towards the world of causal analytics and MMM, which has for a long time felt like a 'big company' solution, for a number of reasons, both from a data volume and speed of insights perspective.

Proof is addressing this, and I'm really excited to play an advisor with that, and to learn from that.

I met the CEO of Proof, Mark Stouse, about a year ago, and since then we've become friends, but he's also been a major influence in my own reflections on how I think about marketing effectiveness.

So, give him a follow, check out proofanalytics [dot] ai, and expect to hear more from me as I struggle to fully understand exactly what a multivariate regression analysis is.

Let’s look at Brand in a way many have never considered. 

#Brand is whom you say you are. You spend money and do things designed to build that brand. Like advertising, you control this. 

#BrandReputation is what other people believe about you. You don’t control this. Indeed, this is exactly what marketers are talking about when they say, “we don’t own our brand, our customers do.”

In causal models, Brand Reputation is an #external headwind or tailwind. 

How people see you and how much they believe you is directly impacting the effectiveness of what you say and do. 

Brand Reputation plays a huge role in #whether they buy from you or buy into what you’re saying. It determines #howdeeply they buy in, ie how big a #risk they’re willing to take.  And most of all, in a world of low-trust, their trust and confidence in you says a lot about how #quickly they decide to buy. 

This is why the psychographic pillars of Brand Reputation are such huge factors in B2B GTM and B2B Investor Relations. 

#MoreDeals. #BiggerDeals. #FasterDeals. 

#Revenue. #Margin. #Cashflow. 

These are hugely powerful causal relationships with many variables feeding into them. 

It’s not enough to see positive trends in ACV and average deal velocity — we need to better understand the drivers instead of ass-u-me-ing. 

In higher cost higher risk purchase decisions, the role played by Awareness Confidence and Trust psychographics is statistically very significant vis-à-vis Bigger Deals and Faster Deals. 

Equally important is the fact that because Confidence and Trust are not controlled by the vendor but a sentiment of the customer, these participate in causal models as external headwinds or tailwinds. And nothing speeds you up in B2B like #ACT tailwinds, nor slows you down like reputational headwinds. 

This is one reason why 90%+ of startups fail. They haven’t been around long enough to have earned ACT with the marketplace. Indeed, their tendency to want to look like they have sprung fully formed and fully realized like Athena from the head of Zeus actually increases the distrust. 

The amount of money that was spent by startups on Demand Generation that would have been much better spent building sustainable Brand Reputation is IMMENSE. Had they done that, many more would still be alive, all other factors remaining the same as they’ve been. 

Thoughts?  I’d love to hear from you on this.

This is a summary of the major business critiques of Data Science as received from F1000 CFOs and CEOs:

1. Too slow / high latency
2. Totally inscrutable reports 
3. Unscalable systems — “roads to nowhere”
4. What they think is important isn’t important
5. Low business literacy, general and specific
6. Cult of precision in a pragmatic world
7. Academic pov not a business pov
8. Lovers of complexity not simplicity
9. Argumentative in the extreme
10. Inverted priorities due to process mindset

“In general, I find my Data Science teams to be a major pain in the ass.”  Fortune 100 CFO, September 2023

“In my experience, Data Science teams have all the tools to solve big questions and enable big priorities but don’t understand the questions or the priorities well enough to participate, much less contribute. Exhibit A is their focus on data management instead of decision support. It’s like they see data science as like playing Minecraft. Castles in the air, you know?” Fortune 800 CEO, April 2023

Now, let’s play search and replace. Instead of DS, what other functions would likely receive a similar critique? 

Let’s take Marketing. Here’s the list of criticisms I’ve recorded from C-suite interviews:

1. Never admit they’re wrong, ever. 
2. Totally meaningless reports 
3. Unscalable systems, illogical conclusions 
4. What they think is important isn’t important
5. Low business literacy, general and specific
6. Cult of deterministic orchestration in a probablistic world
7. Function-first pov not a business-first pov
8. Lovers of complexity as a validator of self-importance 

Does this sound like IT?  

Does it sound like HR?

It does. 

Does it sound like Sales? Well, no. It’s not that Sales doesn’t have their issues in spades. But they tend to be radical pragmatists on everything because it’s how they make money. 

Thoughts?  Who would have thought that Marketing had strong commonalities with IT and Data Science in these regards?

Below is a very cool watch design generated by AI. Prompts included the use of a Tourbillon and a more modernistic dial design. But what did the AI forget? Hint: it’s all about the entire purpose of a timepiece!

A bit of a metaphor there.

Liam Moroney Michelle Killebrew Bill Schmarzo Malcolm Hawker Mark Ogne Francois Wolf Charles-Antoine D.

This is what happens when the meaning and impact of what you do isn’t as well understood or valued as it should be. 

Many marketing and communications are entering into the “valley of the shadow” in B2B tech. IBM is just the latest, and it won’t be the last.  

The good news is that we are finally at the crux of the arguments that have plagued these professions for a long time. There is much to do, but when it’s done, Marketing and Communications will hockeystick.

cnbc.com • 1 min read

IBM is slashing jobs in marketing and communications

Wow. 

This survey result from a representative yet diverse group of respondents mirrors other studies as well as my book interviews.

When you don’t have a #BusinessStrategy, you’re a captive of inertia, of the status quo. 

When your highest value is satisfying short-term metrics, you are unsustainable. The idea that if “you count your pennies, the dollars take care of themselves” is a statement about savings, not growth.

The closest thing to a Business Strategy that many companies — even big ones — have today is about maximizing EPS. 

Imagine the change that would occur if enough directors and VPs in your company asked their CEO about Business Strategy.

During a summer job at a Ford casting plant (foundry) in my university years, for one week my job was to operate a stamping press for eight hours per day. The force generated by the machine to shape metal into some obscure (to me) car part was impressive and terrifying. What calmed my nerves was knowing that the machine had one huge safety feature and because of it, I'm able to type these words today. 

As in meditation, the operator had to focus with a clear mind. Because they could only activate the machine when two buttons, separated by the length of an arm, were pressed at the same time, they were able to avoid having parts of themselves pulled into the machine. You can imagine the catastrophes that precipitated that feature's invention. I can't recall now what my supervisor at the time told me about the machine's power but a 200 ton force sticks in my memory. A hand wouldn't stand a chance.

Why this post? Because when I think about all the #customersuccess people out there trying to differentiate themselves in a competitive market of job applicants, I want to advise each of them to look in their own history for a story that highlights an example of how they grew from an experience. It might come in handy because it's a depressing reality that applicants need to assume that their competitors have the same qualifications as they do for the job. So what sets one competitor apart from others? I think most interviewers want to see authenticity and individuality from the applicants they are interviewing. You're not going to have ChatGPT available in the moment so you'll have to rely on old fashioned sensibilities like truth and lived experiences.

No one else could make up my story of operating a stamping press. And no one else could convey that specific story with complete honesty and to be able to explain how that experience taught me things about physics and about process flow that I never learned or appreciated in high school classes. That on-the-line experience triggered a lifelong curiosity for trying to understand how things work. And that trait is what you should tap into  

Let’s talk briefly about #Purpose. 

No, it’s not about your purpose.  Or mine. 

It’s about how well whatever you’re offering enables someone else’s purpose. 

Business is Service. This is true regardless of whether you’re a vendor, an employee, a CEO, or a guy driving a van. 

It’s not about you or me. 

It’s about them. Enabling their purpose. That’s why they’re doing business with you.

#Physics is often described as a mathematical understanding of how the world works. Given that, it's become pretty axiomatic that if you're a physicist long enough, you're guaranteed to also become a philosopher because you see the many interlocking causes and effects, and you see what they mean IRL.

Similarly, I'm a a historian of Renaissance-era technology who has been at it long enough to have made a similar transition to become a futurist of sorts.  This is because where physicists study the physical world, History is the study of human behavior, decisions, and outcomes, not just those of famous people but entire societies and systems.  Read the correspondence between famous Renaissance polymaths and their magnate sponsors, and the exchanges mirror those between modern CTOs and CEOs.  

People are reasonably predictable across similar circumstances. When the situations change, the similarities in their reactions don't change as much as the specific ways they're expressed.  This is why we see History as "repeating," despite the fact that the specific circumstances clearly do not.

There's an old maxim that says that if Data Science had come more from the biological sciences than from physics, Data Science would have a very different attitude about uncertainty, aka #probabilistic reality. The physical sciences are more deterministic, the social sciences as probabilistic as humankind itself.  

Here's an example: Gravity is experienced as a highly #deterministic reality. If you drop 1 million rocks from a building, you are guaranteed that not one will be left floating in mid-air.  We know the answer in advance. It's not going to change.

That said, the number of rocks that will shatter on impact into smaller rocks, or God forbid, fall and kill people walking randomly beneath, is entirely probabilistic. It's a matter of odds, of best calculation based on other variables, not deterministic certainty.

Both exist in a single circumstance. Both play a role.  It's like #ClimateChange. Probablistic human behavior mixes with more deterministic laws of Nature to create a very challenging blend of cause and effect.  Yet as we've seen, we are still able to calculate it with growing accuracy.

When we see a focus on #Efficiency in business, it is because of two factors: it reveals that they are assuming the #Effectiveness of their actions, and it reveals that they are seeking a deterministic outcome for their business. Put another way, their business is about themselves, not the customer.

When we prize Effectiveness, we acknowledge that the world is probabilistic, that what is good for some is not the choice of all, that business is about service to others that monetizes, and that Effectiveness is the prerequisite for Efficiency.

This is why -- at so many levels and in so many ways -- my LinkedIn headline begins with the statement that Effectiveness is What Matters Most.

If there’s an absence of an enunciated, calibrated, updatable #BusinessStrategy, that is the responsibility of the CEO and the Board. 

It’s a deadly absence. What exactly are functions supposed to use to align themselves if there’s no Business Strategy?

If functions develop their own “strategy,” it’s human nature to put themselves at the center as the #MVP. This usually has the effect of creating a zero-sum political game, destroying alignment instead of creating it.

Grass roots movements are usually unbeatable.  

One example I see on LinkedIn are posts from people with reach and power and influence that are all about helping 1, 2, 3, sometimes more, people to find work.

Can you imagine if every one of us put our LinkedIn spotlight on just one colleague, shining our light on them and bringing new levels of awareness and opportunity to their door?  Now imagine that everyone did that once a quarter -- that's four times a year.  Not only would you bring light and life to another person, but you'd sow the seeds of the help we all need at some point in our lives.

To that end, I'd like to let you know about Mark Ogne, a top-class marketer and one of a very short list of genuine experts in the connective tissue between #ABM, #ICP, and #GenAI.  His new product, Symplexity.AI, is super hot, but as any bootstrapping entrepreneur knows, it takes time for the cash to begin to flow.  In the meantime, he's still consulting on his big three areas, particularly #ABM and #ICP.  

If you need help, even for a few months, give Mark a ping through LinkedIn.  You'll be very glad you did. As my dad used to say about highly useful people, "he's a slingshot for your stone."

A lot of folks asking how to get started with causal analytics in GTM.  That's the focus of this post.

There is a general assumption that you start with your data. Data is certainly important -- it's the saw material that feeds analytics.  But the reality is that you start at the other end of the problem, which is what do you want to know?

You start by identifying the top 10-20 business questions about causal relationships that you need to answer. At ProofAnalytics.ai we maintain a "greatest hits" list of these questions, and by far these are the top three that most business teams have about GTM:

What's the relative effect of our #GTM investments on #RevenueGrowth, net of external factors and time lag?

What's the relative effect of our GTM investments on #AverageDealVelocity, net of external factors and time lag?

What's the relative effect of our #Brand investment on Sales Performance?  FYI -- this rapidly is transformed into a different question, which is "What is the impact of our #BrandReputation on the effectiveness of our GTM investments in light of other marketplace externalities?"

Next step is to begin to model your marketplace dynamics, aka "#externalities". This is the stuff we don't control but that exert a lot of influence over the performance of what we do control.  GTM teams are very oriented towards the programmatic, the process-driven, and the orchestrated, so the marketplace externalities are the pieces of the causal equation that most ignore.  They are "inconvenient truths." 

So what's included in externalities? Macroeconomic factors, competitor acts and performance, reputational impacts, and whatever other points of context are relevant as headwinds or tailwinds to your business and its GTM. In B2C, the impact of weather is a common external factor that's modeled. 

FYI -- mathematically, about 2/3 of any given modeled scenario is made up of stuff you and I don't control.  It's the #Wave we are seeking to #Surf.  Leaving it out is a sure way to invalidate your conclusions, and ironically it's also the easiest data to get. Much of it is also free.

Once you have a solid model for your relevant externalities, this forms the basis for the specific models you need to answer the list of questions you developed. And that's the subject of another post, so watch out for that! 😁

From the imagination of Mats Georgson, Ph.D., a summary of what Science is. 

#Science is indivisible from #Experience.  

It’s our attempt to explain what we’ve experienced. 

When we are totally ignorant about X, we default not to Science but Myth as a form of explanation. 

The citizens of Pompeii didn’t know about volcanoes or vulcanology. But they experienced the rumblings of Vesuvius, and the occasional small eruptions. 

In the Roman pantheon was a god named Hephaestus, or Vulcan. Among others things Vulcan was the god of blacksmiths. So the explanation for Vesuvius’ tremors and sparks was that he was at his forge inside the mountain.

Today, we rarely ascribe things in #B2B #GTM to a Roman god, but we do have lots of Belief, lots of #Dogma, that the science and math do not support and have never supported. 

It’s time to shed this stuff. 

It’s time to be the cause of Effect-iveness.

Such a great post by Ardath Albee!  A must read!

It indirectly spotlights massive gaps in B2B GTM. 

Tie-in to an enunciated, coherent Business Strategy. 

Lack of customer understanding— we have not because we ask not. B2B GTM outspends B2B on market research so significantly that is not just about a lack of knowledge and empathy but a flaw in our priorities. 

Inability to understand, forecast, prove and adjust the causes-and-effects in GTM net of external headwinds and tailwinds, as well as time lag. The behavior of the people we are attempting to impact is the effect we want to help cause!


marketinginteractions.com • 7 min read

B2B Content Marketing > Going Bold or Fading to Beige? - Marketing Interactions

"Why can't I use the machine learning powering my #MultiTouchAttribution to power #MMM?

It's a fair question.  I'm going to attempt to hit a sweet spot between #TMI and not enough, so here it goes.

1.  Machine Learning, like AI itself, covers a multitude of subsets of intelligence. But the ML that most people are most used to is all about #patternrecognition.  And it's absolutely true that ML can find big and small patterns that would elude the human brain.

The problem is that while we can engineer a pattern of behavior, that doesn't mean the behavior causes the desired effects.  It just means that we succeeded in getting a lot of people to do the same thing.

The Max Planck Institute for Intelligent Systems, Montreal Institute for Learning Algorithms (MILA), and Google Research delved deeply into the challenges related to causal representations in machine learning models, publishing a paper in 2021. "The majority of machine learning algorithms boil down to large scale pattern recognition." That's still the case. 

In short, ML inaccurately conflates patterns with what's important, causally speaking.

2.  Machine learning can't cope with #TimeLag. It doesn't factor in the time that passes between the effects in a pattern and what caused those effects.  Without time lag, you have nothing that enables you to optimize spend, particularly in B2B where the time lags are more significant.

3.  Machine Learning doesn't deliver a multivariable view of what's happening. In #MTA or machine learning-based MMM (not really MMM), the pattern matching is about outcomes, not what caused those outcomes. It involves neither #correlation nor #regression, both mainstream approaches that move us toward better understanding of causal relationships.

4.  #MachineLearning is very vulnerable to what are called "counterfactuals." An example is that you know that when a baseball bat hits a baseball, the player's arm powered the bat. But a ML algorithm can't see it or know it in and of itself. What this means is that the "cause and effect" often gets inverted in a machine learning model, with the bat being credited for making the arm swing.

5. ML doesn't capture the interdependency of factors. It assumes the observations are not interdependent and have a constant probability of occurring. That's called #iid in machine learning parlance. It's a problem.

6. ML operates on the same general premise as #GenAI: it predicts based on statistical regularities, i.e. "Past is Prologue." As soon as things get volatile, ML accuracy is upended as we saw with ML models during Covid. Many ML models began to fail because they had been trained on statistical consistencies instead of variabilities.  This characteristic is also why GenAI does better with established, unchanging fact sets than with speculative ideas.

More to come on this.

Seventy-nine years ago, right about this time, the greatest man-made cataclysm in the history of the world was reaching its climax. 

The countdown in Europe had less than 60 days left to run, and Germany the nation-state shrunk daily as the Allies and the Soviets advanced from West and East. 

Götterdämmerung was almost at the door. 

Some years ago, an archeologist friend of mine was on a dig at the Seelow Heights, an escarpment of rock that is the eastern gateway to the Berlin Plateau. Here the remnants of the Wehrmacht had massed for one last-ditch defense against the Soviet tank armies before the final battle for Berlin. 

By this time, the German Army was bled white, and many of its soldiers were younger than 14 and older than 60. 

As he dug in the remnants of a command trench, my friend found this World War One decoration, a rusting Iron Cross Second Class. The Imperial Crown is still visible on the upper arm, as well as the W initial for Wilhelm, the Kaiser of a very different Germany. 

This award for courage in combat belonged to a "retread" -- a WW1 combat veteran who despite his advanced age had been conscripted yet again to defend the Fatherland. 

Given the ferocity of the Battle for the Seelow Heights, it seems probable that this old soldier left his medal where he breathed his last. He was perhaps a good and brave man but gefallen in a cause so Evil that we still shudder to see its symbols. 

So why am I making this post on LinkedIn?

It illustrates three brands, each with its own reputation: Imperial Germany, the man himself, and the Third Reich. The tremendously evil brand power of the latter swallowed up and overwhelmed the other two to the point where the sight of an Iron Cross from a completely different time makes many people flinch. 

Years ago, I was invited to give a lecture on brand reputation at Stanford. It was in a large lecture hall with a very large (for the time) screen. I started my lecture by asking people to nominate the most powerful brands of the post-war period. Coke, Chevrolet, Toyota, all and more were mentioned. 

After defining brand reputation, I then suggested that the most powerful brand of the 20th C sadly wasn’t a commercial brand but a political one. 

Instantly, the all-too-familiar black, red and white flag of Nazi Germany flashed hugely on the screen. 

The giant collective gasp from the audience proved my point. The physicality, the instinctiveness, of their response showed that decades later, that symbol still retained all of the power that the Nazis gave it. 

My archeologist friend gave the Iron Cross to me. It sits on my desk today, a relic of a nameless, faceless man from two very different wars, with the grime and grimness of both Somme and Seelow forever part of its corroded surface. 

Whenever it catches my eye, it gives me pause. It a powerful reminder of the importance of what we become associated with, the decisions we make, particularly in light of the times in which we live.

Some people see #causalanalytics as a judge in black robes.

I think the reality is that analytics are a #GPS and an #insurancepolicy.

A hallmark of ProofAnalytics.ai is that model recalculation is automated, so it can keep up with the tempo of decision-making in your business.  It operates and is used very much like a GPS, including the adjustment of your "route to value" to account for changes in the marketplace.

It's an insurance policy because, as one #CMO said, "Proof means that you're never wrong."  I button-holed him after he came off-stage and asked him to explain such an extraordinary statement. "Well, just like with my GPS on my phone, I always get to my destination, either on time or with an explainable reason for being late. But I always arrive. That's what it means to never be wrong anymore."

Personally, I prefer to see #causalanalytics as a way to understand the odds of success or failure, very similar to the way sports bookies calculate the odds of winning and losing. The odds dictate the bet and the likely payouts. But I appreciated his confidence! 💪

Speaking of #confidence, Proof announced today that our Proof Causal.ai software had been evaluated and certified by the Marketing-Accountability-Standards-Board, or MASB.  Proof is the only causal analytics system on the market today that has received such a certification credential.

If you're not familiar with MASB, it is part of a confederation with the ANSI - American National Standards Institute (ANSI) and the International Organization for Standardization (ISO), both eminent in their own right. If you've heard of ISO-9000 series quality certifications, that is an example of ISO at work. 

Taken together with the thumbs-up that Proof has received from leading data scientists like Bill Schmarzo, certifications like this one from MASB contribute to more and more companies adopting causal analytics. Together with the Association of National Advertisers and the American Marketing Association, MASB is one of three high-credibility research organizations that serve B2B and B2C marketers.

https://lnkd.in/ghT8Jy6v

Taplio

Mark Stouse's Linkedin Analytics

Mark Stouse

What is Mark talking about?

Mark Stouse's Best Posts (last 30 days)

Want to drive more opportunities from LinkedIn?

Taplio

.css-1jgreyo{background-image:linear-gradient(284deg, rgba(39,43,72,1) 0%, rgba(33,47,183,1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Mark Stouse's Linkedin Analytics

Mark Stouse

What is Mark talking about?

Mark Stouse's Best Posts .css-qqfgvy{font-size:var(--chakra-fontSizes-md);}(last 30 days)

Want to drive more opportunities from LinkedIn?

Mark Stouse's Linkedin Analytics

Mark Stouse's Best Posts (last 30 days)