Three years ago, Gartner predicted that by 2017, CMOs would spend more on IT than their counterpart CIOs. Fast-forward to 2015; we are more than halfway there and can already bear witness to the shift in focus to the Chief Marketing Officer as the advocate and purchaser of technology of the future.
The role of the CMO has evolved from a traditional and tactical approach based on simple data capture and inefficient targeted campaigns to performance-led strategies based on rich data insights and measurement of business impact.
However, is there too much technology out there for marketers to handle? And how best should companies balance the adoption of technology with human capital?
Machine Intelligence And Automated Learning
Marketing today is very labour-intensive, often requiring marketers to dig through too much data that may not even be giving them the bigger picture they need to make impactful business decisions.
By 2020, the digital universe (source EMC) will grow by a factor of 300, from 130exabytes to 40,000exabytes, or 40tn gigabytes. However, the human brain can only hold the equivalent of 1m gigabytes of memory. With too much data for humans to sift through, machine learning is the act of a machine producing insights without being told what to do.
“[Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed.” - Arthur Samuel, 1959
Many people say ‘data is everything’. It’s not; what you learn from the data and what you do with it is what matters most to the CMO.
Art And Science
Balancing what insights we can automate and what additional value human capital can provide is the key to marketing success. Marketing is often described as part art and part science, and in this case data provides the science and marketers provide the art. Machine learning helps marketers solve complex data-rich challenges, that are beyond the capacity and capability of the human brain, and use algorithms to initiate actions based on data. This type of machine learning is often referred to as ‘unsupervised’, as technology finds patterns, builds insights and automatically acts on those insights.
Machine learning has been part of a marketer’s everyday life for decades, without many realising it. I am using automatic spell check as I type this. Modern day examples are found with Google, Apple’s Siri, IBM’s Watson, Facebook recommendations, Quora and (related questions) and any technology that says ‘suggestions’.
In fact, only recently Google CEO Larry Page told shareholders on a webcast how “Google is using machine learning in a growing number of products and services, including automatic translation, voice-based searching, self-driving cars and the Nest connected thermostat”.
In B2B environments machine learning helps CMOs connect with consumers by providing recommendations based on insights about their interests, emotions and interactions with the development of ‘knowledge graph’ technology. Machine learning can help businesses with content marketing. Content performance marketing platform BrightEdge, for example, recently launched a technology that integrates with Adobe Experience manager and leverages machine learning to automate decisions on the content battleground (Adobe is CMO.com’s parent company).
US home furnishings retailer Pier 1 imports is another example of a company using the power of machine learning--in this case predictive analytics--to allow for more personalised interactions with its customers. And over in Australia and New Zealand a new project called Radiant is using machine learning to identify brand behaviours and practices that irritate customers the most in the banking sector.
Other forms of machine learning still require human capital. ‘Supervised’ machine learning requires that programmers input a pre-defined set of rules that the machine can analyse and use to produce recommendations. Date scientists and campaign marketers can then make informed, impactful decisions based on new insights generated from the data.
Human Creativity And Capital
Machine learning is not about eliminating human input. It requires talented data scientists to build the technology and infrastructure; it also needs the human hand to extract knowledge from data. Only humans can create great content that evokes emotions in consumers, encouraging them to interact and deriving added value from insights provided by data. Technology can provide sophisticated relevancy but it cannot replace creativity. As Dietmar Dahman mentions in his CMO article titled The Automation Of Relevance - “attention, attraction, desire are even more rare than relevance”.
As machine intelligence and artificial intelligence worlds converge, human input will shift from people ‘teaching computers via code’ to teaching with examples (such as with speech recognition). The more ‘curious’ a machine becomes the more it becomes truly artificially intelligent.
The Evolution Of “Do It For Me”
Technology also has other limitations, such as the struggle to provide a service. In a recent article on Techcrunch, Anthony Lee wrote a fascinating piece about the combination of technology automation and ‘specialised labor’ to deliver complete solutions to business problems.
Lee describes this as a ‘Do-It-For-Me revolution’ based on a combination of the Software-as-a-Service (SaaS) technology and value-based labour services for consumers. Consumers are the key drivers of innovation and DIFM is a principle already deeply embedded in the consumer world. It is an example of a perfect balance of machine learning, intelligence and human capital.
OutboundEngine is an example (that Lee also points out) of a company that designs, test and automates targeted marketing campaigns for client business. They describe it as “Your Marketing On Autopilot”.
Power To The CMO
With over 2000 marketing technology companies competing every year for corporate attention it is becoming increasing difficult for marketers to navigate through the fragmented technology vendor landscape. What’s more, many of these technologies focus only on the infrastructure of process and distribution but little on marketing efficiency and outcome. Machine learning technology and DIFM based principles will fill this void.
However, some important business decisions are beyond machine learning algorithms. Balancing technological investment and managing human capital to maximise business outcomes provides great challenge and unlimited opportunity.
And the person who makes that final decision is the CMO.