Google vs Google: Do you Need Best Practices for Your Best Practices?

Everybody loves best practices.  If you’ve ever worked for a marketing agency, you’ve received plenty of RFPs stating the need for best practices for everything ranging from search engine optimization to user experience to graphical design to source code.  If you’ve worked on the client side, you’ve probably written about the need for these same best practices in your own RFPs, and no doubt your supervisor or Board of Directors is clamoring for a next generation website and/or digital marketing campaign with plenty of best practices baked right in.

Marketing is undergoing a huge paradigm shift to become more scientific, rational, and analytic…

Taken to a logical extreme, one might think that building a personalized website, launching innovative display advertising, or nurturing leads with next generation machine learning was akin to baking a holiday pie:  Perfectly measure the ingredients for the crust, add the right amount of sugar for the filling, sprinkle a few extra seasonings in equally precise quantities, bake in a properly heated oven, and the end result is the ideal lead generation engine.

Of course, the real world doesn’t work quite like that.  Marketing is undergoing a huge paradigm shift to become more scientific, rational, and analytic, but there’s still a lot of art, creativity, and judgement required to develop the best campaigns.  The question remains:  where does that leave us with best practices?

We might start by asking if industry leaders like Google have anything to say on the matter.  Fortunately or unfortunately, they are providing easy answers.  In fact, if you check out Google’s two largest properties, the main search site and YouTube, you see two totally different approaches:

Google Home Page (December 5, 2016)

YouTube Home Page (December 5, 2016)


Of course, the reason for the differences should be obvious to anyone familiar with their respective business strategies.  Google Search makes money when a user clicks on a search ad, therefore the search box is just about the only relevant item on the page.  YouTube makes most of its money from ads embedded in video streams, therefore the page is devoted to a variety of video content with thumbnails to entice clicks.

Your strategic approach, creative, and content is going to be very different if you’re selling Coke or launching a new wearable.

I would suggest that both are adhering best practices in their respective spaces, but that best practices for UX and content presentation are wildly different depending on the nature of the site.  Or, put another way, there is no best way to design a website, but there are some strategies that can address certain desired outcomes.  The adherence and usage of best practices can also depend on what aspect of the project you are considering.  For example, on a website redesign project:

  1. Underlying code:  Almost exclusively driven by best-practices for marketing technology initiatives.  While I don’t want to suggest that there is no art in the coding process, it’s usually reserved for a much higher level (think developing Google’s new Assistant) than websites or marketing automation.  Therefore, your development teams and partners should stick to proven principles and follow published standards.
  2. Implementation and customization:  Highly driven by best practices, but also subject to how a particular application will be used and the skill level of the user.  For example, there are usually many ways to achieve the same goal in a modern Content Management System.  The precise way that is best for your organization can only be determined based on how you see a feature evolving in the future and who in your organization will support it.  Best practices should be considered in the planning stages to identify the ideal solution for your unique needs.
  3. UX + Design:  Partially driven by best practices.  There are some established standards around screen sizes, interface methodologies such as the hamburger menu, and user expectations, but ultimately this is the project phase were the “art” aspect starts becoming more important, as do the specific business goals.  The most important thing is to create something that works for you and achieves your goals.  As your goals are going to be unique to your organization, there is only so much you can learn from everyone else.

Other aspects of your digital transformation are going to align along a similar spectrum.  When it comes to display advertising, for example, your initial ad selection and placement is going to be highly influenced by whether you’re following a demand generation strategy or going straight to capturing leads.  It’s also going to be critical to consider how you integrate retargeting.  There are going to be best practices around the types of ads available, the best performing sizes and placements, and no shortage of research on where to market, but ultimately your strategic approach, creative, and content is going to be very different if you’re selling Coke or launching a new wearable.

Best practices are and will continue to be a critical part of any engagement strategy, they aren’t a substitute for judgement, taste, and…

Of course, you can also use A/B or multivariate testing to experiment with different approaches, and identify the best performing strategies based on real world results; the possibilities are exciting and practically limitless.  We truly are embarking on a brave new era in marketing with a tighter combination of both art and science, but — as in all revolutions in human behavior — it’s important to make sure we don’t lose sight of where we’ve been and how we got here.

The machines aren’t going to do our work for us anymore than someone else is going to write the perfect practices for our businesses.  We can learn from others, and the best practices that result will continue to be a critical part of any engagement strategy, but they shouldn’t be seen as a substitute for judgement, taste, and a well-formed strategy in the first place.

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Salesforce Einstein vs The Real Deal

The AI craze is officially upon us with the majority of marketing and consumer technology providers promoting some form of advanced machine learning, and some of the leading minds in business and academia like Elon Musk and Stephen Hawking warning of an imminent computer revolution right out of James Cameron’s Terminator film-series, or perhaps more topically after the season finale last night and my post last week, Jonathan Nolan and Lisa’s Joy’s Westworld.

One of the latest entries in the rapidly evolving space is Salesforce’s Einstein, an AI that the leading CRM and cloud solution provider claims is “built into the core” of the Salesforce platform.  Einstein promises to be your own personal data scientist and assist with discovering insights, predicting outcomes, determining next best steps, and automating tasks.  The language used to describe Einstein is very human, a far cry from the antiseptic modeling language of earlier incarnations of similar software, and he even has a cute little graphic representation.  Is the revolution already upon us?

Computers are barely scratching the surface of a small portion of human talent…

As usual in the marketing world, one needs to separate fact from fiction, and the truth lies somewhere in between.  It’s a fact that big data, ridiculously fast processing power, and increasingly sophisticated algorithms are completely changing our perception of what computers are capable of, and that some of the latest and greatest software and devices can seem intelligent at times.  It’s also a fact that this new technology has real benefits for marketers as they seek to engage in truly personalized interactions with their visitors.

Thanks to solutions like Einstein, the same types of technologies that power a brief exchange with Siri that resembles a real conversation or Google’s predictions about where you’re headed on a road trip, can be applied to your marketing campaigns.  This offers your organization the benefit of ever more sophisticated and targeted models to uncover new relations among your customers and prospects, and the ability to improve engagement by personalizing interactions in real time.

These are truly amazing developments in the marketing world, and they will change the way we plan campaigns, segment customers, and measure ROI, but, getting back to the original topic, is any of this truly intelligence in the human sense of the term?  Or, more dramatically, do we need to be worried about the future of the human race if we set Einstein loose on our target audiences?

For example, a Presidential election forecasting model that…well, there’s part of the problem in a nutshell…

That’s a far more difficult question to answer, partially because intelligence itself is very difficult to define, and there are a lot of behaviors — memory, creativity, intuition, reasoning, etc. — that are usually blended together when we’re talking about the general subject of human intelligence.  No offense to Mr. Musk, Mr. Hawking, Mr. Cameron, or Mr. Salesforce,  but I believe computers are barely scratching the surface of an exceedingly small portion of human talent, and that a lot of what we are seeing with the AI craze is anthropomorphic language over-selling a certain type of data-driven analysis.

At its most extreme, I think it is fair to say that current technology is capable of (very) limited forms of what we would traditionally call deductive and inductive reasoning, though even that is a stretch given that the algorithms that actually draw conclusions from the data are written in advance by human programmers.  In the deductive case, the appropriate algorithm and associated databases are designed to draw conclusions based on multiple premises.  For example, I often go to my mother’s house on the weekend, therefore if I am traveling on the road I usually take to her house and it is a Saturday afternoon, Google assumes that’s my destination; Google might even give me the time to her house in advance every Saturday morning.

The inductive case is a bit more complicated.  A  large amount of premises that are either true or mostly true are turned into specific predictions.  This is usually done with the assistance of a living and breathing data scientist, where the computer crunches the numbers and the human reviews the output.  For example, a Presidential election forecasting model that…well, there’s part of the problem in a nutshell.  Models are nice and neat; the real world is a lot more messy and difficult to capture in terms of the discreet data points that feed both artificial intelligence approaches.  While a certain marking persona has the propensity to be a new customer, that doesn’t mean that an actual person will be one.

It’s dealing with this very messiness via the human capacity for intuition and ingenuity that has yet to be replicated by a machine, and — in my opinion — remains an essential part of true marketing intelligence, but before we return to the marketing world let’s consider the real Albert Einstein, usually known as one of the most brilliant minds to ever walk the Earth.  How does his Salesforce-branded AI mascot stack up?

While a certain marking persona has the propensity to be a new customer, that doesn’t mean that an actual person will be one…

I think most scientific historians would acknowledge that Einstein’s greatest gift was the ability to make incredible intuitive leaps, to combine completely unconnected data in entirely new ways that ultimately invalidate (some of) the old assumptions and data. In Special Relativity, it was assuming that the ether didn’t exist at all and there was no privileged frame of reference.  In General Relativity, otherwise known as gravity, it was the idea that acceleration and gravity are the same.

Furthermore, the key insights often came from simple thought experiments.  For General Relativity, Einstein imagined an individual in outer space in a windowless elevator, if the elevator was being pulled upward at the same acceleration as Earth’s gravity exerts downward, the individual would have no means to determine if he or she was accelerating or in a gravitational field.  He also imagined that if you were to jump from a very high building along with some office supplies, everything would fall down at the same rate and — for a while at least — it would seem that you weren’t subject to the effects of gravity and that you were in free fall in outer space, therefore acceleration can also cancel gravity’s effects.

This kind of insight and imagination — or dare we call it storytelling — isn’t subject to reason or modeling until after a living, breathing Einstein makes the mental leap.  In Einstein’s case, these leaps lead to assumptions that lead to some of the most powerful ideas in human history.

Identifying and engaging customers without us worrying about the fate of the world…

In short, this isn’t the kind of conclusion Salesforce’s Einstein is going to make anytime soon.  While the revolution will likely be televised, it’s still several generational leaps of technology in the future.  In the meantime, we can safely enjoy the benefits of big data and machine learning as they make our personal and professional lives easier.

In other words, let Google keep helping us avoid traffic, and the new Salesforce Einstein identifying and engaging customers without us worrying about the fate of the world.  Let’s also be sure to remember that marketing is both art and science; machines can help round out the science, but humans are needed to develop truly engaging stories and exercise creative judgment on how best to connect with other humans.  This is true for marketing topics large and small; we can discuss further over my next post, Google vs Google:  Do You Need Best Practices for Your Best Practices?

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