3 Ways to Better Manage Marketing Automation So the ‘Shiny Object’ Doesn’t Stab You

I presented at the All About Marketing Tech Virtual Conference & Expo on the topic of targeting and automation. One of the themes I hit upon was about how companies are hindering their marketing automation success with needless complexity.

On Thursday, I will be presenting at the All About Marketing Tech Virtual Conference & Expo on the topic of targeting and automation. One of the themes I plan to hit upon is about how companies are hindering their marketing automation success with needless complexity. This topic falls squarely in the “land of shiny objects,” which is a recurring theme in many of my posts.

This theme in my posts and the 1:10 p.m. ET session, “Using Automation + Targeting to Engage and Convert,” focuses on how tempting technology can be to the marketing practitioner and how it can lead to the desire to do too many things — to detrimental effect. However, there are three things you can do to manage automation better.

Step 1 in Marketing Automation

First, make sure you have a customer strategy. If you do not have a solid strategy, then you will be automating a bunch of tactics. Unless these tactics sit under a cohesive strategy, they may work against each other.

For example, a price-focused customer acquisition program may hurt long-term brand development or pricing power. When you add automation to this scenario, it will supercharge the tactic and potentially cause greater harm.

Step 2

Second, make sure you have a test-and-learn agenda. Automation is a very data and metrics-driven process and it is managed by humans, using those same data points and metrics.

Successful marketing automation involves iterative learning to drive growth. Therefore, knowing what you are trying to achieve through automation and running multiple tests to better understand the underlying dynamics is critical.

What tends to happen, however, is that too many objectives are pushed through the automation system and the ability to learn is muddled by an excess of data and a dearth of focus.

The advice I often give is:

“Because you can do something through automation, it does not mean you should.”

Creating a learning agenda you can manage and identifying the critical metrics needed for evaluation are critical first steps before automating a marketing function.

Step 3

Third, make sure you have a pivot plan. A pivot plan anticipates how you will modify your automation program and lists the levers at your disposal.

For example, if results are not coming in as expected, you may alternate content, alternate segments or redefine the automation goals.

Doing all three at once will most likely leave you as clueless as when you began. While this seems like marketing management 101, it is easy to lose sight of this with automation. Automation generally promises rapid decision-making over volumes of interactions and self-learning capabilities.

As a result, it is tempting to get out of the way and let it do its magic. In the near to mid-term, despite automation’s usefulness, this will not substitute for strategic and management thinking.


I am in no way discouraging the use of marketing automation. It is not only the future, but it is also the present and is driving positive results.

Successful marketers need to start experimenting with the technology now.

However, marketing automation is also not so wonderous and awe-inspiring that we forget that it needs management and strategy. That, in turn, means balancing lofty automation goals with what you can managerially digest.

Automation — With a Little Help From Good Machines

Some claim that human behaviors are just algorithmic responses developed over past 70,000 years or so. Now, armed with data that we are casually scattering around, machine-based algorithms outperform human brains in most areas already, and such evolution will continue.

We should be mindful when dropping buzzwords (refer to “Why Buzzwords Suck”). As more and more people jump on the bandwagon of a buzzword, it tends to gain magical power. Eventually, some may even believe that buying into a “word” will solve all their problems.

But does it ever work out that way? Did anyone make a fortune buying into the Big Data hype yet? I know some companies did; but, ironically, the winners do not even utter such words. I’ve never seen any news release from Google or Amazon that they are investing in “Big Data.” For them, playing with large amounts of data have been just part of their businesses all along.

Now the new buzz is about AI, machine learning and automation, in general; and it will be a little different from buzzwords from the past. Whether we like it or not, that is the direction that we are already headed in the world where each decision will be increasingly more dependent on deterministic algorithms.

Some even claim that human behaviors are just algorithmic responses developed over past 70,000 years or so. Now, armed with data that we are casually scattering around, machine-based algorithms outperform human brains in most areas already, and such evolution will continue until most humans will become largely irreverent in terms of economic value, they say. Not that it would happen overnight, but the next generation may look at our archaic way of things the way we look at our ancestors who were without computers.

First, the Marketing Case for AI

If such is our fate, why are contemporary humans so willingly jumping onto this automation bandwagon where machines will make decisions for us? Because they are smarter than average humans? What does “smart” even mean when we are talking about machines? I think people generally mean to say that machines remember details better than us, and calculate a complex series of algorithms faster and more accurately than us.

Some may say that humans with experiences are wiser with visions to see through things that are not seemingly related. But I dare to say that I’ve seen machines from decades ago finding patterns that humans would never find on their own. When machines start learning without our coaching or supervision — the very definition of AI — at a continuously increasing rate, no, we won’t be able to claim that we are wiser than machines, either. In the near future, if not already.

So, before we casually say that AI-based automation is the future of marketing, let’s ask ourselves why we are so eager to give more power to machines. For what purpose?

The answer to that philosophical question in the business world is rather simple; decision-makers are jumping onto the automation bandwagon to save money. Period.

Specifically, by reducing the number of people who perform tasks that machines can do. As a bonus, AI saves time by performing the tasks faster than ever. In some cases — mostly, for small operations — machines will perform duties that have been neglected due to high labor costs, but even in such situations, automation will not be considered a job-creating force.

Making the Marketing Case for Humans Using Data

Some may ask why I am stating the obvious here. My intention here is to emphasize that automation, all by itself, doesn’t have the magic power to reveal new secrets, as the technology is primarily a replacement option for human labor. If the result of machine-based analytics look new to you, it’s because humans in your organization never looked at the data the same way before, not because it was an impossible task to begin with. And that is a good thing as, in that case, we may be talking about using machine power to do the things that you never had human resources for. But in most cases, automation is about automating things that people know how to do already in the name of time and cost savings.

Like any other data or analytics endeavors, we must embark on marketing automation projects with clear purposes. What would be the expected outcome? What are you trying to achieve? For what types of tasks? What parts of the process are we automating? In what sequence?

Just remember that anyone who would say “just automate everything” is the type of person who would be replaced by machines first.

At the end of that automation rainbow, there lie far less people employed for given tasks, and only the logical ones who see through the machines would remain relevant in the new world.

Nonetheless, providing purposes for machines is still a uniquely human function, for now. And project goals would look like those of any other tasks, if we come back to the world of marketing here. Examples are:

  • Consolidate unorganized free-form data into intelligent information — for further analyses, or for “more” automation of related tasks. For instance, there are thousands of reasons why consumers call customer service lines. Machines can categorically sort those inquiries out, so that finding proper answers to them — the very next logical step — can also be automated. Or, at least make the job easier for the operator on the call (for now). Deciphering image files would be another example, as there has been no serious effort to classify them with sheer manpower in a large scale. But then again, is it really impossible for humans to classify large numbers of images? How about crowdsourcing? Or let an authoritarian government force a stadium-full of North Koreans to do it manually? We’d use machines, because it would be just cheaper and faster to do it with machine learning. But who do you think corrected wrong categorization done by machines to make them better?
  • Find the next, best product for the buyer. This one is quite a popular task for machines, but even a simple “If you bought this, you would like that, too” type of product recommendation would work far better if input data (i.e., product descriptions and product categories) were well-organized — by machines. Machines work better in steps, too.
  • Predict responsiveness to channel promotions and future value of a customer. These are age-old tasks for analytics teams, but with sets of usable data, machines can update algorithms and apply scores, real-time, as new information enters the system. Call that AI, if algorithms are updated automatically, all on its own. Actually, this would be easier for a machine to pick up than fixing messy data. Not that they will know the difference between easy and difficult, but I’m talking about in terms of ease of delegation, from our point of view.
  • Then ultimately, personalize every interaction with every customer through every touch channel. I guess that would be the new frontier for marketers, as approaching personalization on such massive scale can’t be done without some help from good machines. But I still stand by my argument that each component of personalization efforts is something that we know how to do (refer to “Key Elements of Complete Personalization”). By performing each step much faster with machines, though, we can soon reach that ultimate level of personalization through consolidation of services and tasks. And the grand design of such a process will be set up by humans — at least initially.

This Human’s Final Thoughts on AI

These are just some examples in marketing.

If we dive into the operational side, there will be an even richer list of candidates for automation.

In any case, how do marketers stay a step ahead of machines, and remain commanders of them?

Ironically, we must be as logical as a Vulcan to control them effectively. Machines do not understand illogical commands, and will ignore them without any prejudice (but it would “feel” like disrespect to us).

Teaching Humans to Automate

I heard that some overzealous parents started teaching computer programming to 4- or 5-year old children, in addition to a foreign language and piano lessons. That sounds all Cool and the Gang to me, but I wondered how they would teach such young kids how to code.

Obviously, they wouldn’t teach them JavaScript or Python from Day 1. Instead, they first teach the kids how to break down simple tasks into smaller steps. For example, if I ask you to make a grilled cheese sandwich, you — as a human being — will go at it with minimal instruction. Try to order an imaginary machine to do the same. For the machine’s sake, it won’t even know what a grilled cheese sandwich is, or understand why carbon-based lifeforms (especially gluttonous humans) must consume such large quantities of organic materials on a regular basis.

Teaching Machines to Human

If you try it, you will find that the task of writing a spec for a machine is surprisingly tedious.

Just for a little grilled cheese sandwich, you have to:

human automation, the grilled cheese story
Photo by: Christoher Del Rosario (www.christopherdelrosario.com) | Credit: Getty Images by Christopher Del Rosario / EyeEm
  • instruct it on how to get to the breadbox,
  • how to open it,
  • how many slices of bread should be taken out,
  • how to take them out without flattening them (applying the right amount of pressure),
  • how to open the refrigerator,
  • how to locate butter and cheese in the mix of many food items,
  • how to peel off two slices of cheese without tearing them,
  • how to ignite a stove burner,
  • how to find a suitable pan (try to explain “suitable,” in terms measurements and shape),
  • how to preheat the pan to a designated temperature (who’d design and develop the heat censor?),
  • how to melt butter on the pan without burning it,
  • how to constantly measure and monitor the temperature,
  • how to judge the right degree of “brown” color of grilled cheese,
  • etc. etc..

If you feel sick reading all of this, well, I didn’t even get to the part about serving the damn sandwich on a nice plate yet.

Anyway, Human Marketers, Here’s the Conclusion

I am not at all saying that all decision-makers must be coders. What I am trying to emphasize is the importance of breaking down a large task into smaller “logical” steps. Smart machines will not need all of these details to perform “known” tasks (i.e., someone else taught it already). And that is how they get smarter. But they would still work better in clear logical steps.

For humans to command machines effectively, we must think like machines — at least a little bit. Yes, automation is mostly about automating things we already know how to do. We use machines to perform those tasks much faster than humans. To achieve overall organizational effectiveness, break down the processes into smaller bits, where each step becomes the stepping stones for the next. Then prioritize which part would be the best candidate for automation, and which part would still be best served by human brains and hands.

For now, that would be the fastest route to full automation. As a result of it, many humans may be demoted to jobs like reading machine-made scripts to other humans on the phone, or delivering items that machines picked for human consumers in the name of personalization. If that is the direction where human collectives are headed, let’s try to be the ones who provide purposes for machines. Until they don’t even need such instructions from us anymore.

5 Trends in Customer Experience Software for 2019

I asked people who use customer experience software to share their thoughts on how the software, and its use, will evolve in 2019. Here are five trends to look for this year.

I asked marketers using customer experience (CX) software to share their thoughts on how the technology and its use, will evolve in 2019. Based on this research, I expect CX technologies will evolve in 2019 to support greater system and data connectivity, improve customer insights, and increase message relevance and process automation.

Here are five trends to look for this year.


M&A activity reflects a trend toward unified customer data platforms and all-in-one solutions for marketing, sales, support, analytics and CX. That’s great for businesses, because soon they won’t need to spend millions on integrations and IT for a single, holistic view of the customer.

We’ll see the introduction of tools and improvement in design to help vertical markets perform integrations more easily, which can find ways to transfer customer information from one application to the other.


Improved integration will help collate disparate customer data to provide a holistic view of customer activity across all departments — sales, marketing, customer support, etc. As CX software improves, organizations will start valuing CX data more than actual goods or services sold, because CX data will have a stronger correlation with long-term revenue generation and profitability.

Customer experience software users say 2018 was the year of data for CX software — from GDPR-mandated data cleanups to a wave of new data from relational and transactional customer interactions, CX software companies focused on data collection. From real-time data collection facilitated by chatbots and AI, 2018 saw a new way for the CX world to gather, store and leverage customer data for more customized engagement. This focus on data will be the foundation for what’s to come in 2019 — a greater focus on data collection, data analysis, and acting on data to increase customer retention and better connect with customers.

CX software will get a chance to show what it can do. We can expect the use of AI to grow and enable companies to sort and evaluate the data collected faster than in previous years. As CX software continues to gather more information, it will also continue to improve the software’s processes and provide better analysis.


We see the embedding of more “marketing-like” approaches — more analytics of customer behavior and more automation of responses and customer outreach. CX pros are starting to do this as well, moving away from working only with survey responses from a small number of customers. This is parallel to the development that started some 20 years ago in marketing automation when businesses moved on from small-scale surveys in market research to using all their business data to better understand customers. CX solutions are looking at all data on customers, using it to understand their needs and wants, and building automatic processes to meet those needs.

Businesses are realizing that customers today rate their experience based on the sum of all the interactions with business — not just on call wait time, or Internet ease of use. All of these things come together as customers move seamlessly from one channel to another — they see this as one overall experience. As such, successful CX solutions are embedding tools that can work with the entire customer journey — from its “discovery,” based on journey analytics, to the orchestration of a better overall CX through customer journey management.


We’ll see organizations leveraging technology to make customer journeys frictionless, personalized — and ultimately, more profitable. Companies will be able to better target customers with more in-depth information, personalized messaging and tailored recommendations that better align with needs. At the end of the day, it will all come down to how well companies use CX software to learn about their customers and better serve them.

Customers will become more skeptical of companies that fail to personalize emails and content. Consumers respond better when they feel like they’re people, not just another number on a list. The future of successful CX software implementations is those that take the time to focus on personalized, relevant information of value that helps makes customers’ lives simpler and easier.

Artificial Intelligence/Machine Learning

The shiny toys of Augmented Reality (AR), Artificial Intelligence (AI), and data analytics only account for the aesthetic aspects of CX. Many companies are looking at the sexy components of a well-built website while overlooking why customers fell in love with brands like Netflix and Amazon. These top sites and companies gave the people exactly what they wanted from the onset. The next step for companies in 2019 is to find the right balance to effectively blend UX and CX to suit the customers’ needs like never before.

With that being said, the automation side of CX is incredibly powerful. We’ve seen improvements in AI software within the past year and it will be fun to see how much it develops this year. The next couple years will revolutionize CX. Now that companies have built the technology, the only thing left is to fine-tune it. Build on the useful technology put in place.

Machine learning (ML) and AI are already being used to identify data points with the most impact. We will see this translate into providing meaningful action points which leverage the data points.

We will see CX software using AI to predict CX for new products, based on past data with greater accuracy.

Lastly, we will see marketers leveraging AI to learn about their target customers and prompt them to take action to meet their needs.

The Triple-A Approach to Succeeding at Digital Marketing in 2019

While it’s impossible to predict the new innovations advertising platforms will release in the year ahead, there are three trends and tactics marketers should be aware of that made a big enough impact in 2018 that are likely to be the cornerstones of digital marketing in 2019, and they all begin with “A.”

While it’s impossible to predict the new innovations advertising platforms will release in the year ahead, there are three trends and tactics marketers should be aware of that made a big enough impact in 2018 that are likely to be the cornerstones of digital marketing in 2019, and they all begin with “A.”

The triple A approach to succeeding at digital marketing in 2019: Amazon, Audiences and Automation.

Amazon Advertising Reaches the Big Time

Rumors circulated for years that the online retail giant could become one of the biggest media companies, yet advertisers just recently started taking these rumors seriously and jumping on the bandwagon. After an initial uproar when Amazon began showing up in Google Shopping results in Q4 of 2016 causing competing advertisers’ costs-per-click (CPCs) to spike, many retails shifted gears, choosing to partner with Amazon rather than compete against it, making Amazon’s growth inevitable. In fact, Amazon has already become the third largest digital ad publisher behind Google and Facebook, and according to a recent survey, 80% of advertisers plan to increase their budgets on Amazon in 2019.

For those advertisers who haven’t yet dipped their toes into Amazon advertising, it’s time to explore the self-service Amazon Marketing Services. Sponsored Products are usually a good place to start since the ads go live immediately, appear organic, and don’t require images or custom copy. Automatic targeting that adapts dynamically to trends and seasonality is available, eliminating the need to manually select keywords. Combined, these options make for a relatively simple process. Of course, an agency can help fine-tune an Amazon advertising program; especially if they completed Amazon’s power training which grants them assigned dedicated support.

Audiences Become the Priority

Having been a hot topic for some time now, in 2019 audiences will become unavoidable. While many advertisers already applied audiences to their advertising programs, not many have done so strategically. Imagine if step one of any digital program was building an audience to target with your program rather than building a list of keywords. Keywords would be supplanted as the most important aspect for paid search, replaced by audience targeting to positively impacting a client’s bottom line with a much more refined approach.

Refining an audience approach will be the single most effective tactic to win at customer-centric marketing; by focusing on the right person at the right time, with the right messaging, marketers set themselves up for victory. By focusing on audiences, marketers can move beyond the all-too-common channel-approach that limits cohesiveness in campaigns and limits effectiveness. The audience approach enables refined targeting, eliminating waste and delivering deeper, more meaningful insights.

Both Google and Bing have already made advancements in this area. Google expanded its audiences beyond website visitors and customer lists to in-market, custom intent and detailed demographics. Bing followed suit and introduced its new Audience Network which uses artificial intelligence to connect Microsoft data insights with LinkedIn’s to deliver the right message to the consumer. Advertisers can then target these audiences on Microsoft’s premium properties such as MSN, Outlook and Microsoft Edge.

Automation Enables Marketers to Get Strategic

Marketing automation tools maximize efficiency and increase revenues; they also eliminate tedious tasks, freeing up marketers to focus on strategic growth for their clients. While bidding automation has been adopted by most marketers at this point, Google has taken automation a step farther, introducing multiple fully-automated campaign types that require very little, if any, intervention during its Google Marketing Live in July 2018. The first campaign type was automated feeds for Shopping campaigns that get products online faster and simplifies maintenance; second was Hotel campaigns that simplify management and optimization; third was smart campaigns for small businesses, that are designed for local and small businesses that might not have the staff to focus on management and need an easy solution instead.

This continues a trend for Google, which already utilized automation in Universal App Campaigns and Universal Shopping Campaigns; these campaigns do not allow for marketer intervention beyond creative asset upload and budget changes. With the continued deployment of new fully-automated campaigns that rely on machine learning for optimization and management, Google has established a clear direction. It seems possible, if not likely, that Google will eliminate its manual offerings for Shopping campaigns and text-based campaigns the way it did with App campaigns. After all, Google is already testing responsive ads, which dynamically combine headlines and descriptions based on performance and the advertiser’s goal. There’s a possibility that completely automated campaigns will be the new normal for advertisers sooner than we think, once Google works out the kinks.

By understanding and adopting these trends and tactics, digital marketers should be able to deliver an A across the board on their campaigns, keeping clients happy and budgets flowing.

The ‘Algorithmification‘ of Everything

If I had asked any of my schoolmates what an “algorithm” was, their eyes would have glazed over and they would probably have asked me what I had been smoking. Fast-forward a few decades and we’ve got the algorithmification of everything, including marketing.

If I had asked any of my schoolmates what an “algorithm” was, their eyes would have glazed over and they would probably have asked me what I had been smoking. Fast-forward a few decades and we’ve got the algorithmification of everything, including marketing.

Those glazed looks would’ve happened a long time ago, long before Facebook was a glimmer in Mark Zuckerberg’s eye and he had started to bring together the more than 2 billion people who log in at least once a month. That Facebook population is now what Evan Osnos of the New Yorker says, were it a country; “ … would have the largest population on Earth … [and] as many adherents as Christianity.” When they log in, they are shaking hands with unnumbered algorithms and putting into those invisible fingers their faith and their data to be parsed, analyzed and manipulated, and hopefully not stolen.

What is an algorithm? Programmers like to say it is a word used by them when they don’t want to bother explaining what they do.

And because algorithms have become so ubiquitous, we seldom give them a thought — except when our IT colleagues start telling us why making any small change in our marketing program will take weeks or months and cost a bundle, or until something goes badly wrong as Facebook and others have discovered about their hacked data.

Our legislators, not usually well-versed on technology matters, have now started making a lot of noise about regulations: They are closing the server door after the data has bolted — an unlikely way to solve the essential problems.

Automation has always been the Holy Grail for marketers; not surprising when the ability, speed and relatively low cost of using artificial intelligence (AI) to number-crunch and manage segmentation of media and analysis of data gets better and better every year. eMarketer reports that: “About four in 10 of the worldwide advertisers surveyed by MediaMath and Econsultancy said they use AI for media spend optimization. This is another application of AI that is increasing among marketers as their demand-side platforms add AI features to increase the probability that a given programmatic bid will win its auction.”

Where is it headed? No one knows for sure. It’s all in the hands of the algorithms and they appear to be multiplying like rabbits. If you revere Darwin, as I do, you’ll expect them to get better and better. But before you totally buy into that, you would do well to read Melanie Mitchell’s thoughtful New York Times article “Artificial Intelligence Hits the Barrier of Meaning.”

There are more and more times when we applaud the use of the algorithms and can see that if properly created, they offer many benefits for almost every area of our marketing practice, as well as other areas of our lives. We really don’t have to panic (yet) about the machines and their algorithms taking over. As Neil Hughes wrote here last month; “The reality is that machines learn from systems and processes that are programmed by humans, so our destiny is still very much in our own hands.”

Machines screw up just like we do; and all the more so, because they are doing just what we told them to do.

All this machine thinking doesn’t come without dangerous side effects. Sometimes, when we try to communicate with inflexible AI systems supposedly designed to simplify and ease customer interactions, the “I” in “AI” becomes an “S,” replacing “A-Intelligence” with “A-stupidity.”

If, as defined, “an algorithm is a procedure or formula for solving a problem, based on conducting a sequence of specified actions,” we can only optimistically hope that the specified actions will take into account individual customer differences and make allowances for them. The moments when they don’t are when we start screaming and swearing; especially if we are on the customer end of the transactions.

As The New York Times wrote in a recent article: “The truth is that nobody knows what algorithmification of the human experience will bring.”

“It’s telling that companies like Facebook are only beginning to understand, much less manage, any harm caused by their decision to divert an ever-growing share of human social relations through algorithms. Whether they set out to or not, these companies are conducting what is arguably the largest social re-engineering experiment in human history, and no one has the slightest clue what the consequences are.”

However important algorithmification may seem to us, our marketing efforts and our use of AI and its algorithms are not very significant in the greater scheme of things outside of our limited business perspective. But don’t dismiss their growing impact on every facet of our future lives. As data guru Stephen H. Yu opined in his recent piece “Replacing Unskilled Data Marketers With AI”:

“In the future, people who can wield machines will be in secure places — whether they are coders or not — while new breeds of logically illiterate people will be replaced by the machines, one-by-one.”

You had better start to develop a meaningful relationship with your algorithms — while there is still time.

The Marketer’s Job in an AI Future

Whether you’re talking about cognitive computing, machine learning, artificial intelligence or its more common acronym, AI, the real topic is machines doing jobs humans used to. What does that mean for marketers in an AI-dominated future? How will the human role change? Are robots going to steal marketing jobs, or elevate them?

Whether you’re talking about cognitive computing, machine learning, artificial intelligence or its more common acronym, AI, the real topic is machines doing jobs humans used to. What does that mean for marketers in an AI-dominated future? How will the human role change? Are robots going to steal marketing jobs, or elevate them?

Let’s think it through.

Luddites and Automation

Automation has always been seen as a threat to human employment. In fact, one of the first uses of sabotage against automation happened back in the 1810s. “Luddite” textile workers destroyed weaving machines that were poised to take their jobs. (Yes, that is where the term “Luddite” comes from.)

Today the alarm may be less destructive, but it’s still ringing. For example, a few months ago, PWC projected that the U.S. stands to lose 38 percent of its jobs to automation in the next 15 years. And the New York Times’s Claire Cain Miller has built her column on cataloging the negative impacts automation will have on jobs.

But these analyses focus just on job losses, and that’s not the best way to think about automation. After all, the Luddite movement was 100 years ago. While hand-weaving may not be a growth field today, the textile industry employs far more people now than it did then.

While automation changes the tasks employers will pay people to do, in the past it has not put populations truly out of work. The jobs change, but they’re still there.

Analysts are starting to see hope in the AI future on our horizon as well.

USA Today recently ran a special report on the impact of automation across the U.S. economy. And while some of the stats in it are eye-popping — PWC believes 45 percent of work activities can be automated (PDF), potentially “saving” $2 trillion in labor costs; McKinsey identified 70 jobs that could have 90 percent of their tasks handled by automation — the overall takeaway is that the economy is not collapsing, it’s changing.

How Jobs Will Change With AI

Quartz is one publication that’s taken a positive view of the impact AI will have on humans and our careers. A recent Quartz article by Dennis R. Mortensen argued that AI will elevate our jobs and “restore our humanity.”

“Each time technology ate one type of jobs, new ones appeared to take their place,” says Mortensen. “Human ingenuity did its thing, we adapted, and we survived to live (and work) another century.”

His big takeaway: “Automation will take away the parts of our jobs we don’t like and leave room for more meaningful work.

Why Content Marketers Fail

Without the right foundation, attempting to produce curated, personalized content can quickly become overwhelming and ineffective. Why? In this issue of The Pulse, we’ll address two common problems and how you can overcome them.

Check out even more about personalization and artificial intelligence with FUSE Enterprise.

With 75 percent of marketers reporting an increased investment on content marketing in 2016 and about 70 percent reporting they expect to produce even more content in 2017, it’s clear that content is king. But, without the right foundation, attempting to produce curated, personalized content can quickly become overwhelming and ineffective. Why? According to a 2017 Content Marketing Institute survey, the top two reasons cited for sub-par content programs are strategy issues (49 percent) and a lack of time (48 percent).

In this issue of The Pulse, we’ll address these two common problems and how you can overcome them.

Get To Know <Sally Sample>

Twenty years ago, personalized marketing was as simple as adding a variable name field to a direct mail piece. Today, however, customers expect more. They know we’re tracking their every move on and offline. And, thanks to successful disruptors like Amazon, Uber and Netflix, these customers also know that this data can be used to create VIP-level, curated, customized experiences.

That’s a lot of pressure on marketers.

The good news is that you don’t have to create bespoke content for every single one of your customers. You just need to create an experience that makes them feel like you did.

Done right, personalization can reduce acquisition costs by as much as 50 percent, lift revenues by 5 to 15 percent, and increase the efficiency of marketing spend by 10 to 30 percent.

To get results like these, most marketers have the technology and tools needed. But when it comes to integrating and orchestrating the technology stack to unlock the full value of personalization, many marketers get stuck.

Don’t Just ‘Set It and Forget It’

With about 5,000 Marketing Technology companies out there, it’s easy to get swept off your feet with promises of automated success. But, as much as we’d like to believe it, long-term success doesn’t come from flipping a switch and walking away.

Currently, an average of 49 percent of companies use marketing automation technology to help plan, manage and measure content. However, many of these same companies latched onto a technology solution before determining their content marketing strategy — and now they’re out of money and time.

It’s helpful to think about marketing automation technology in the same way you think about a car’s cruise control. Whether you’re driving a campaign or a car, you can tell your machine to maintain a certain speed. However, both scenarios require someone in the driver’s seat to steer and, occasionally, to pump the breaks.

Even the biggest brands have fallen victim to the siren song of automation.

In May 2017, UK-based Walkers Crisps put their #WalkersWave campaign on cruise control. The crisps company featured Twitter-submitted selfies in a video where FIFA World Cup record holder Gary Lineker held up their portrait, showed them performing the Mexican wave and wished them luck in a sweepstakes for Champions League tickets. Unfortunately, trolls overloaded the system with photos of murderers, dictators and other ne’er-do-wells causing Walkers to pull the campaign and issue a public apology.

In 2012, Target automatically mailed promotions to women based on their “pregnancy scores” — a proprietary equation triggered by Target purchases commonly made by women in their first trimester. For those public about their pregnancies, this mail was timely, effective and helpful. However, for a teenage girl who hadn’t revealed her dilemma to her parents, it was disastrous.

In both of these examples, extreme personalization backfired. That’s not to say that hyper-targeted marketing materials should be avoided, but it is to point out that hyper-targeting and personalization isn’t always the right answer. The more important moral to this story is recognizing how quickly things can spiral out of control when you don’t have a contingency plan in place. And, while you can’t predict everything, you can build a solid strategy that can be adjusted along the way.

Building a Foundation for Marketing Automation

According to the 2017 B2C Content Marketing Trends report from CMI and MarketingProfs, the number one factor contributing to B2C marketers’ stagnant success over the last year is strategy issues (49 percent). This is followed by not having enough time devoted to content marketing (48 percent) and the challenge of content creation (37 percent).

In order to avoid the pitfalls of content marketing and customization, it’s critical to partner with someone that understands the value of and is experienced in developing content marketing strategies to unlock personalization at scale.

At HackerAgency, we’ve developed an agile process designed to help us predict the future for your brand and develop the content your customers need before they know they need it. Although we customize our approach for every client, three common elements include:

Plan Ahead to Pivot Quickly

Although a solid piece of content can quickly go viral, the best content marketing programs aren’t built from fly-by-night strategies. Determining the right strategy takes time. Developing the right creative takes time. Measuring results and pivoting the approach takes time. And, although automated marketing technology makes things move more quickly than managing everything manually, it still takes time.

When you’re scrambling to keep up with the content marketing Joneses, the last thing you want to hear is someone telling you to slow down. But the world of content marketing and personalization requires the ability to analyze the past, tune into the present and predict the future — and that definitely takes time.

The return on your investment in the initial content marketing strategy will pay off when you’re about to hit the new fiscal and know you don’t have to create a new content strategy from scratch. Instead, this malleable foundation is designed for year-round testing, analyzing, fine-tuning and scaling. So, although it’s a hefty investment of brainpower up front, the long-term results far outweigh alternate short-term solutions.

Learn even more about the convergence of technology and branded content at the FUSE Enterprise summit. Artificial intelligence and personalization will be featured among many other techniques and technologies.

Automation Beating Humans at CX

What’s more important to customer experience? Your people or your automation? Recent evidence from the fast food sector isn’t so good for the humans. If you’re looking to build a great CX, it may be time to stop training your humans, and start building a better robot.

A Chinese maid service robot: Is this the CX customer actually want?
A Chinese maid service robot: Is this the CX customer actually want?

What’s more important to customer experience (CX)? Your people or your automation?

Recent evidence from the fast food sector isn’t so good for the humans.

Andy Puzder, CEO of Hardee’s and Carl’s Jr., talked to Business Insider about his experience putting order kiosks in their restaurants to supplement human order takers. Puzder commented to Business Insider: “I’ve been inside restaurants where we’ve installed ordering kiosks … and I’ve actually seen young people waiting in line to use the kiosk where there’s a person standing behind the counter, waiting on nobody.”

Puzder is even considering opening a new restaurant that wouldn’t require human interaction, similar to Eatsa in San Francisco.

Such ideas are gaining traction across the industry. Not just because it could mean labor savings, but because there appears to be a customer base that prefers automation to human interactions.

And the reason seems to be … Millennials hate dealing with people.

That doesn’t just come from Puzder. Frischer restaurants in the Midwest U.S. did a study on their drive through traffic, and found that a third of 18 to 24 year-olds use the drive through because, “they don’t feel like dealing with people”

They prefer a process that, although not automated yet, is as close to automated as possible.

Human interaction isn’t helping the CX for them. Humans are ruining it.

So apply that finding beyond the fast food space. Where does that leave us?

For years we’ve been thinking good people are the key to good service. But what if the real key is automation?

After all, we already know customers don’t want a relationship with their cough medicine, they just want to stop coughing. If that’s the CX they want, why not let the robots do it?

I’ve long heard readers and contributors bemoan the loss of the human touch. … Maybe they only notice the lack of touch because they’re not getting good robots?

This isn’t just about machines replacing humans for productivity or financial reasons. It’s about an intuitive CX. If you know what your customers want, why do they need to ask a human for it? Why not just set it up automatically? Or on demand at the push of a button?

The Robot CX Uprising Has Already Begun

We can already see several very successful businesses that were built simply on improving CX by letting machines do what humans may not be very good at:

  • Uber automated your taxi dispatch and hiring.
  • GrubHub automated restaurant order taking and delivery.
  • Facebook automated friendship.
  • Amazon automated … well, everything about shopping.

Jeff Bezos and the robot uprisingSo if you’re looking to build a great CX, it may be time to stop training your humans, and start building a better robot.

Strategy and Technology: Which Is Chicken and Which Is Egg?

The average marketing stack includes up to 17 distinct technologies, according to Signal. Yowsa. No wonder integration is a big pain point for most marketing teams. No wonder martech is a hot investment area. No wonder our heads hurt.

The average marketing stack includes up to 17 distinct technologies, according to Signal. Yowsa. No wonder integration is a big pain point for most marketing teams. No wonder martech is a hot investment area. No wonder our heads hurt.

Technology is taking over marketing. Let me hear you say, AUTO! Let me hear you say, “AUTO-MATE.” I’m a devoted fan of technology that improves marketers’ productivity while enabling a more fabulous and satisfying user experience. But let’s not let all this technology go to our heads.

Technology is still a tool. And so strategy and content and creativity still need to be in the driver’s seat. You can’t buy the right technology if you don’t have a clear idea of what you need to accomplish. I had this conversation with a client this week — where we kept getting distracted by bright shiny objects (AKA: sales pitches from technology vendors), instead of focusing on what experiences we wanted to create for the customer. Once we hunkered down and really focused on the customer experience, the technology needs became obvious and much more attainable.

And yes, we did agree that we’d set aside some budget to test some wild and crazy ideas. Because that is often where the best new experiences come from — ideas that customers didn’t know to ask about.

Once you have that customer-focused objective in mind, the fun begins. Learning about all of the technologies available to modern marketing leaders is a feat in itself, but understanding how each technology maps to the goals of the marketing organization is even more challenging. It’s a little bit like trying to solve a jigsaw puzzle with a pure white (blank) picture. There are very few visual cues as to what fits together when and how … you need a deep understanding of technology — or an internal or external advisor you really trust to be looking out for you.

Your business strategy will set the expectation about what is being built, and a marketing technology strategy will provide clear outlines of the necessary steps to accomplish the work.

I find the chicken and egg problem appears at two levels.

  • Which Comes First? First, is the strategy borne of what technology is already in house? That could be a good starting point, and usually the best place to start for a proof of concept. Of course, buying technology is also a poor way to figure out your strategy. Yet, too often, we hear about a new feature or tool, and we want to test the channel without doing the work of considering content development and design, brand alignment, staffing resources and, most importantly, the customer need.
  • Best vs. Possible? Second, is the solution we select the best for the customer, or only the best that can be accomplished by the tools we have today? This is always the question when we get suggestions from a technology vendor. I know most will honestly aim to solve the problem in the best way for your business. It’s just that they only have their own tools to work with. It’s a caution for all marketers to watch.
Scott Brinker graphic
Scott Brinker graphic

As marketing technologist Scott Brinker aptly says in his ChiefMarTec blog, “No technology is a strategy-in-a box, and no strategy comes with a defined technology bundle.” He visualizes this concept in a Venn diagram, detailing the relationship between marketing, technology and strategy. He claims that where the three meet is the “Most interesting intersection in the world.”

I tend to agree. What are your most pressing challenges in getting your marketing technology to work for you? Please share your thoughts in the comments below.