Opportunities for AI in Content Marketing Easily Explained

Opportunities for AI in Content Marketing Easily Explained

Chris implemented these examples himself via hands-on coding in the R programming language, using a deep understanding of mathematics, data science, and machine learning. But most marketers don’t have data science and computer programming skills. Machine learning software excels in this case. Chris notes that text mining uses vectorization, which transforms words into numbers. Implementation detail: Similar to the reverse engineering Google example, Chris implemented text mining via the R programming language. For those interested in coding, Chris recommends learning the R and Python languages, which form the basis for a lot of AI tools and libraries. “Staff with data science skills are quantitatively inclined and know how to use the technology properly, so they can be of great help,” Chris said. For text mining or time-series forecasting, in-house data scientists will understand your objectives and goals, build the right models, then implement the necessary codes. If the need is ongoing or more frequent, they can help you build software that runs when you need it to.” Next steps No matter which of the three options makes sense for you, there’s one thing I urge all marketers to do: Learn about AI and understand the role it plays in marketing technology. Cover image by Joseph Kalinowski/Content Marketing Institute Author: Dennis Shiao Dennis is an independent marketing consultant who works with brands on content marketing, product messaging, and social media marketing.

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Until recently, the closest I’ve come to understanding artificial intelligence is knowing that it powered tools in my martech stack (e.g., marketing automation, predictive lead scoring, etc.).

Beyond that, I found the concept hard to grasp until Chris Penn’s presentation at Content Marketing World, How to Use AI to Boost Your Content Marketing Impact.

Chris, co-founder and chief innovator at Trust Insights, covered several real-world applications of AI. His examples helped transform abstract concepts into tangible use cases.

Chris implemented these examples himself via hands-on coding in the R programming language, using a deep understanding of mathematics, data science, and machine learning. But most marketers don’t have data science and computer programming skills. Later in this article, I share Chris’ advice about how marketers can apply these AI concepts.

Here are several of Chris’ experiments.

Driver analysis: What results in profitable action?

When you have a bunch of data but you’re not sure what matters to the outcome you want, driver analysis is an effective tool, Chris says.

Machine learning software excels in this case. You feed in all the data and it tells you what matters in it. Chris explains that the analysis concludes with something like, “Hey, this combination of variables seems to have the strongest mathematical relationship to the objective you want.”

Chris performed driver analysis on the popular PR and marketing blog, Spin Sucks, where the primary business objective is lead generation.

“(It) determined that organic search was the third most powerful driver. The team focused a lot of time and energy on it, and they should, but email was the No. 1 driver,” Chris says.

By understanding better what drives leads, the Spin Sucks team could decide to shift more of their time to email marketing because it was the most effective source.

Whether your objective is page views, social shares, leads, or revenue, a ranked list of drivers can help you plan resources, priorities, and budgets more effectively.

Implementation detail: Chris used the R programing language to implement Markov chain attribution. For a detailed look at one such implementation, read this post by data scientist Sergey Bryl, which will give you a good sense of how much mathematics and data science is involved.

Text mining: Reveal topics, keywords, and hidden problems

Text mining is an application of AI that ingests content (e.g., text) to classify, categorize, and make sense of it.

Chris notes that text mining uses vectorization, which transforms words into numbers. It looks at the mathematical relationship among those numbers and determines how similar those words are. It is a form of deep learning.

Reverse engineer Google to reveal key topics and terms

The Google algorithm, which uses a heavy amount of AI itself, is an example of a deep-learning system. “Google’s search algorithm is so complex now that no one knows how it works, including Google,” Chris said. “They have very little interpretability of their model.”

You can use text mining to reverse engineer the Google algorithm for your targeted topics. “We can deploy our own machine learning models to say, ‘OK, for a search term like content marketing, what words do the top 10 or 20 pages all have in common?’”

Here’s a sample output from reverse engineering Google:

The resulting lists hint at what words or categories to cover when developing new content around your reverse-engineered keyword. Having this set of common words gives you a higher chance of success with organic search than simply saying, “Let’s write a really good article about content marketing.”

Implementation detail: Chris implemented text mining and topic modeling via the R programming language, extracting related topics from a corpus of text (e.g., the contents of articles found in the search engine results pages).

Extract hidden insights via text mining

In 2014, Darden Restaurants, the parent company of Olive Garden, replaced its board. The new group implemented changes, including enforcing its existing but mostly ignored breadstick policy (serving one per person plus one extra).

As Chris explains, employees then spent their time enforcing the policy by counting the number of breadsticks in the basket based on the number of people at the table.

Chris used text mining on 2,500 publicly available reviews written by the company’s employees on Glassdoor. Here’s a glimpse of the results:

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