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August 7, 2016

Why CMO’s are becoming CIO’s: Extreme Complexity

by Tom De Baere


As one frustrated CMO exclaimed at the Forrester CIO-CMO Summit in 2013, “I feel more like a CIO than a CMO! I have marketing automation, CRM, listening platforms. I’m up to my eyeballs in technology.”

Just 3 years later, this overwhelming feeling is still very much alive. In virtually any market or type of business, the speed in which technology is advancing, is adding extreme complexity to the  world of CMO’s.

Those that do not react will become obsolete.

This is the new reality of CMO’s, which demands his attention and demands changes to his organization.

The drivers for this  extreme complexity, from a buyers perspective, are that buyers today have louder voices and new powers, new customer expectations, combined with a fast trend towards more technology when buying products and services.

In my previous post on this subject I’ve talked about this particular future of marketing, and about the six megatrends that are changing the future of marketing.

In this blog post, I want to cover this topic again, but now from CMO perspective:

What kind of complexity is heading towards the position of the CMO?

If you want to check out the other blog posts in this series:


3 complexity trends entering the world of CMO’s

The pressure is on. Not only are the responsibilities of CMO’s increasing, the environment required to deal with these new responsibilities is becoming more complex. Massively more complex.

I’ve  summarized this new world of CMO’s into 3…well… for lack of better wording I call them “EVERYTHING’s”.


Behavioral … EVERYTHING

behavioral targeting_look-alike modellingCustomer experience and personalization is the key to modern marketing.

Admitted, those are last years’ buzzwords. But more and more, also in my own practice, I see these concepts coming to live. They have become realities that need to be tackled head-on by CMO’s.

Prepare for a world in which a mix of strategic thinking and technology will drive the following marketing tactics:

  • Targeted Advertising & Retargeting:  The marketing function can now spend their marketing budget on the right customers. By using use ever more intelligent targeting solutions, using data cloud solutions and Data as a Service (DaaS) (from Oracle Bluekai, Alliant, etc), and obviously through targeting on Facebook and Google. The challenge will be how to engage customers in a world of ad blocking and mobile phones.
  • Automatic segmentation & Look-alike Modeling:  Customer segmentation is hardly a new concept for online marketers. New is that we now have access to powerful segmentation engines built into marketing solutions from major software players like Adobe, Oracle, IBM and others. These, but also point solutions from companies like Thirdshelf , can use online behavior and customer purchasing patterns to automatically create segments. Look-alike modeling on the other hand can identify high-value segments from larger anonymous or identified audiences, based on so called seed audiences , which are smaller and selected audiences that have been identified as a high-value audience.
  • Advanced Recommendation Engines: Today we have personalization software that is capable of doing the typical e-commerce type of recommendations (those that viewed this, also viewed that / you previously bought this, and might like that). These recommendations engines use CRM, online & website behavior,  and transactional data. Contextual behavior is also becoming to be used in recommendation engines. The usual vendors such as Adobe Target, IBM, Selligent, … but also Episerver , Sitecore and Kentico have added tools to personalize content based on various rules, such as geo-location, search terms, referrers, lead scores and also provide more advanced personalization based on user behavior and profiles. Further in this same corner, vendors now also try to include data from sensory data, chat data, email data, social behavior and voice recordings from customer interactions.
  • New Chatbots apps: I’ll cover chatbots a bit further down, but to be complete, chatbots also serve as product recommendation engines. Nice examples are  Kik and Mezi , which are apps on mobile phones that serve as personal shopping assistant and even digital concierges.


An excellent example that demonstrates a company which is handling this complexity is Stitch Fix.

This San Francisco-based startup has developed a service that shops for women, matching clients with boutique-brand clothes, shoes and accessories on recommendations powered by a combination of data science and human stylists.


Personalization using micro-data

Stitch-Fix recommendations are powered by a combination of data science and human stylists.


Customers pay a $20 “styling fee” to receive a box of five personally curated items either on demand or by subscription at regular intervals. They try on the clothes at home, keep what they want and return what they don’t. Clients pay the full retail price of any clothes they hold on to, less the $20 fee, which is applied as a credit.

From a recent interview article with the CEO of Stitch Fix on I’ve curated these interesting bits-n-pieces that demonstrate my point:

  • “With a team of 80 data scientists–among them astrophysicists and computational neurologists (with 49 Ph.D.s)–Stitch Fix believes that computers, with the help of humans, can pick your clothes better than you can. The company’s algorithms use a customer’s data to predict how likely she is to keep a given item based on parameters that range from the woman’s style to her occupation to her Zip code (which is used to predict weather). A stylist then reviews the information, ultimately picking out five items to include in a customer’s fix.”
  • “The clothes are not exclusive. We don’t price them better than anyone else. We don’t do fast shipping. We’ve just got to be more relevant.”
  • One hundred percent of things that are bought on Stitch Fix are recommended.” Even returns are treated as valuable data points , with Stitch Fix’s stylists absorbing the negative feedback in a customer’s comments to better calibrate the customer’s style or an item’s description.”



Google Home

Facebook M, KIK, Siri, Line, Amazon Alexa, Google Now, Google Home, iVee, the list goes on. All examples of consumer technologies, software and platforms that drive artificial intelligence chat-bots and sometimes voice controlled robotic assistants.

My second EVERYTHING, or mega-trend if you want, is INSTANT EVERYTHING.

Humans are hardwired to want things — right now. It’s called instant gratification, and it’s a powerful force. It can make people convert, as long as you’re doing the right things.

Today, instant gratification is expected in many ways by consumers and businesses. We gain instant feedback from our devices, because we’re constantly plugged in and turned on. Social media gives us instant ability to upload videos, photos and status updates. We receive instant feedback from our social followers. We respond in near-real-time to emails and tweets. We have the ability to make things happen without having to wait.


These higher customer expectations are putting organizations under pressure to invest into:

  • ever faster delivery times;
  • real-time customer services;
  • real-time content;
  • chat bots with artificial intelligence and machine learning.


What is the impact of this on marketing? These new customer expectations introduce the following digital dynamics into our world:

  • Live video streamingBlab, Periscope, Facebook Live, 360° & choice-point video. All examples of the tech world, and consumers, adopting live video streaming.
  • Instant contentFacebook Notes, Google AMP, Medium, and Podcasts. These are examples of platforms and technology designed to provide faster access to content.
  • Self-Expression & Conversational chat – FB Messenger, WhatsApp, Snapchat, Line, Slack. All examples of platforms that are riding on the real-time communication desire of consumers and businesses.
  • Chat-bots, Artificial Intelligence with Voice ControlFacebook M, KIK, Siri, Line, Amazon Alexa, Google Now, Google Home, iVee, the list goes on. All examples of consumer technologies, software and platforms that drive artificial intelligence chatbots and sometimes voice controlled robotic assistants.
  • Enhanced & Virtual Reality and Wearable tech: Samsung Gear VR, Facebook Oculus, HTC Vive, Sony PlayStation VR, but also even the brandnew Pokemon Go and the not so brand new anymore Microsoft Hololens. All examples of wearable tech, VR, and enhance reality.


How are CMO’s going to add these content formats to their marketing mix?

How are CMO’s going to add these technologies and platforms into their strategy to engage customers and keep them engaged?


Predictive … EVERYTHING

The third “EVERYTHING” I want to discuss with you is about prediction. Predicting content, predicting leads, predicting churn, predicting what you want.

Large and historical investments in fiber optic & last-mile cables created connectivity that facilitated the early Internet growth. Today we have access to unlimited connectivity AND storage to collect / aggregate / correlate / interpret all of the data we generate and collect.

Not so long ago we had to use complex tools, operated by data scientists, in a chaos of data silos created across the organization. This data is now moved to the cloud because of cheap storage and unlimited connectivity.

A whole new wave of data platforms is appearing that provide new data instruments to the business. In a previous blog post on “How to leverage big data in your marketing strategy” I talked about big data “in a box”, which makes big data easier to implement and use.

Related to this “big-data-in-a-box” thinking, are the observations of Mary Meeker, in her Internet Trends of 2016 report. In that report, she talks about Data as a Platform. She states that a whole new wave of data platforms are abound, which provide departmental applications, analytics platforms and security solutions that become much more accessible and easier to implement. Much similar to the “in-the-box” thinking of my article on big data strategies.

This increased accessibility and ease of use of data platform applications will drive marketing departments to become more advanced in the way they use data to:

  • Find new customers
  • Predict content
  • Predictive & prescriptive action / next best action
  • Predictive lead analytics & revenue analytics
  • Churn & loyalty models
  • Look-alike modeling & intelligent retargeting


Connecting the dots – 3 strategic pillars of change

marketing extreme complexity_how to react


This is my thinking of what kind of complexity is entering the world of CMO’s. Whether you like it or not, and the future will tell if I’m right, some or all of these changes are coming your way.

In this blog series I have covered where this complexity is coming from, from a buyers perspective, and in this blog post I now have covered it from a CMO perspective.

In the next post in this series I  discuss how CMO’s can react to this new reality.

Thanks for reading.

Warm regards from sunny Belgium,

Tom De Baere


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