Big data is having big effects on content marketing, nowhere more than in its ability to make sense of the complexity of human language. Understanding data science is necessary for agile marketing in 2019. Here’s how marketers can utilize data to hone their craft.
Mining for Meaning
Most web content consists of unstructured text. Mining this text for meaning and relevance is what search engines do. Search engines provide you with what they think are the most relevant and meaningful experiences, based on your search keywords. Understanding those results is the primary clue agile marketers use to better understand audience intent.
How does this work? Search engines are giant natural language processing engines that produce an urban dictionary full of definitions of keywords based on how they are used in digital media. In a sense, search engines “understand” how we use natural language over time. (This is why there’s so much hidden value in onsite search.)
Testing out Answers
The clues from this natural language processing rarely lead to cut-and-dried answers but are analyzed as statistics. Agile marketers use the data to make decisions about information their target audiences are the most likely to need. They provide the information in ways they think will be most useful to them. But, all things considered, multiple versions of the same information are equally likely to serve audience needs. This is where agile marketers are equally likely to serve audience needs. Marketers become data scientists–by serving multiple versions of the same digital experiences randomly to their audiences and testing the results. The experiences that get the best results win.
This technique, known as A/B testing or multivariate testing, yields performance data that provides an ever-more-clear picture of how to better serve the target audience. A/B testing pits one variant against another (A vs. B) while the more powerful multivariate testing allows hundreds of even thousands of possibilities to compete with each other for supremacy. Patterns that tend to yield better results can be used as shortcuts by other digital marketing teams within the organization.
Agile Marketing is Machine Learning
In this way, agile marketing can sometimes be thought of as a form of machine learning, where the inputs of the machine are the practices and the outputs are improved performance. As the machine gets ever better at detecting working patterns for the target audience, it “learns” to optimize digital assets over time.
The machine is not just an optimization engine, however. The machine is a prioritization engine. Every marketer is faced daily with a challenge to do more with less. That means continually examining the mix of activities and weeding out poor performers and building new ones in their place, based on the data.
The market is constantly in flux, and a savvy marketer is continuously adjusting the ix to match shifting audience needs and business priorities. That’s why behavioral data is the next step in the game: incorporating your visitor’s behaviors to truly understand your intent and adapt to it. This kind of agility was not possible prior to digital marketing. Now it is table stakes.
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