Marketing Humans & Machines: Balancing Technology And Human Capital

Jim Yu on
  • Categories: Channel: Martech: Management, Martech Column

  • There has been an incredible explosion of content on the Web. By 2020, the digital universe will grow by a factor of 300, from 130 exabytes to 40,000 exabytes, according to IDC (International Data Corporation). The wealth of information on the Web will be incredible, as will the potential for effective marketing.

    There is one chief problem that will only be exacerbated by this incredible growth: The human brain can only hold roughly a million gigabytes of memory.

    The sheer size of the impending online world will be too much for the human brain to process. Marketers will find themselves with the information necessary to reach their customers but will be unable to comb through the data to find what they need.

    For systems to be efficient, they must be able to scale to the enormous impending size of the digital universe. To understand the problem, think about the early internet classification systems.

    Classifying The Internet

    Yahoo initially became successful by tagging sites into specific categories and using this information to help users sort through websites. As the internet began to grow, however, this system quickly showed its inefficiencies. It simply was not possible to maintain this manual approach.

    By contrast, Google arose to prominence because of its creativity in understanding websites. Through the development of its algorithm, it created a system for gaining insight into the intentions and value of websites.

    It looked at factors such as how many people linked from different websites and the types of vocabulary on the site to “understand” the page.

    As the Web continues to grow, machine learning offers marketers and site owners similar opportunities to use technology to read articles, grasp their intent and match them to users. The prospect of machine learning is no longer a question of if — it is more of a question of when, but that can feel intimidating to some marketers.

    It is important to understand the role of machine learning within marketing. This will not be a cure-all that leaves marketing and advertising in the hands of a computer and its algorithm. Instead, it will be a powerful tool in the creation of a personalized experience for the Web user.

    Machine Learning Vs. Human Capital

    No matter how technologically advanced a system becomes, it will never be able to take the place of humans.

    Machines have the power to find patterns in customer behavior, determine likely intent and forecast behavior, but they cannot write an article so moving that it motivates people to buy. It can tell you what your audience wants to read, but it cannot create the content itself.

    The role of machine learning is to provide marketers with the insights they need to optimize their content production, but the creative realm will always belong to people.

    Using Data To Make Intelligent Marketing Decisions

    Machine learning has already become increasingly prominent in many areas of technology. Did you write your business article using a spell checker? Did you set up your inbox to automatically sort out your spam?

    Machine learning can be seen in Facebook recommendations and websites that make suggestions or predictions about your interests.

    In the marketing world, machine learning will give us a chance to better understand the customer journey. It will help tame the enormous amount of data that is now available for marketers and help them make sense of the different touch points and customer behaviors.

    Instead of trying to manually determine customers’ intentions, marketers can analyze the data to produce a clear picture of what a particular consumer is interested in and what he or she wants to see.

    Natural Language Processing (NLP) is a form of machine learning that will be particularly helpful for marketing. NLP analyzes content based upon themes and topics, which can then be used to categorize the types of content and understand the people who are interested in them.

    For example, content might be categorized as either B2B or B2C. It might be further broken down into different topics such as “personalization,” “technology” and “machine learning.”

    As customers visit the website and begin to show signs of interest in different types of content, the machine-learning system will be able to understand what this customer is interested in and the types of marketing materials to which he or she will likely respond the best.

    The customer might be the prime target for certain types of segmented emails, advertising promotions, downloads or coupons.

    Content Marketing And Machine Learning

    As this machine-learning technology begins to show its value, it’s quickly being incorporated into marketing tools. My own company, BrightEdge, recently began rolling out a landing page optimizer that will work hand-in-hand with the Adobe Experience Manager to help marketers maximize their landing page potential.

    DataSift’s Vedo Intent technology uses machine learning to help marketers ascertain the intent of their audiences on social networks. These new products and services help brands transform their content production from a system of continuously guessing and estimating to one that is efficient and productive.

    As machine learning and NLP begin to understand users’ intent and what they want to see from your website, your content production system can identify and target content that will keep customers interested in moving down the sales funnel.

    Use the insights to create segmented emails and customized CTAs (calls to action) that will speak directly to the user.

    You will also be able to watch and respond as customers’ needs grow and evolve.

    Software customers might need basic Web security software when they first make a purchase for their new startup. Five years later, they might need a more involved system to handle their growing organizations.

    A system that can recognize changing needs can help make targeted efforts more productive.

    Machine learning can help brands gauge their content quality and relevance by looking at key signals such as:

    • How often customers reference the content.
    • The freshness of the content.
    • Trends in the usage of the content over time that can help pinpoint when others might be interested in reviewing the information.

    As brands gain more insight into the usage of their content, they will be able to make the production of the material and their targeted messaging more efficient and helpful, improving the user experience and increasing personalization.

    Machine Learning For Content In Action

    As machine learning continues to grow as a tool within the marketing community, some innovators have begun to show the power of machine learning in their content production.


    The popular Kraft brand implemented machine learning to track more than 22,000 different characteristics of its audience based upon how they interacted with the brand’s online content, according to MarketingProfs. Their efforts were immensely successful.

    Kraft now receives the equivalent of 1.1 billion ad impressions a year. It also began to see its content marketing produce 4x better ROI than its advertising. Its content was matched to the right customer, which allowed it to interest new people.

    Pier 1

    Pier 1 began using machine learning in the form of predictive analytics, according to a case study released by Microsoft. It used data to gain a better understanding of the behaviors customers will likely display in the future based upon their actions in the past.

    This allows Pier 1 to create a more personalized experience for customers, who feel as though their unique needs are recognized and catered to. The new technology also allows the company to run real-time campaigns, producing analytics of its efforts within minutes.

    A Global Tire Manufacturer

    A leading global tire manufacturer recently tested these ideas in its advertising. Upon implementing the machine-learning ideas, it saw a drastic increase in its CTR (click-through rate) on display ads, from 0.07 percent to 0.32 percent, according to MarketingProfs.

    The new ads were more precisely targeted and more likely to be shown to people who would respond favorably, and the response was incredible.

    Key Takeaways

    Machine learning is quickly earning a place in the world of marketing. Businesses have already begun to see the value of this way of processing data and understanding user intent.

    Here are a few key takeaways you should note about where this technology can go and how it can help you.

    1. The sheer amount of content and data to be produced in the next few years will far exceed the human mind’s ability to synthesize and compute.
    2. Machine learning will give brands the power to put their Big Data to use and leverage their analysis instead of letting the information slip through their fingers.
    3. Machine learning will help create a more personalized experience for the user, improving his or her impression of the brand.
    4. It will also help brands make their marketing efforts more efficient by better targeting customers.
    5. Machine learning will never replace humans as the main source of creativity; it will only help make that content production more efficient.

    As the digital universe continues to explode, businesses will be faced with the challenge of not only getting their voice heard above the noise, but also leveraging the enormous amounts of data now available.

    Machine learning gives brands the power to do both. By recognizing the power of this technology, you can begin to see how it can work in your own organization to create a powerful, personalized user experience.

    About The Author

    Jim Yu
    Jim Yu is the founder and CEO of BrightEdge, the leading enterprise content performance and SEO Platform. He combines in-depth expertise in developing and marketing large on-demand software platforms with hands-on experience in advanced digital, content and SEO practices