Big Data Fed to Artificial Intelligence Means Great Marketing

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Ross_AndrewIn this special guest feature, Ross Andrew, CEO and Chairman of Maropost, suggests that by embracing AI and machine learning, marketers can turn big data into food that their tools use to accomplish business objectives. Ross is responsible for the direction of Maropost. Since its founding in 2011, he has consistently doubled the growth of the company annually by devising innovative solutions to the industry’s biggest challenges.

Once upon a time, a famous author imagined a great machine called the Multivac. While the name is cheesy, the concept is not: the technological behemoth was an artificial intelligence powered by machine learning that enabled it to process all the data of the universe and answer the last question at the end of time – marketing has more modest goals in mind for their own artificial intelligence platforms. Marketing needs artificial intelligence  capabilities to process the data necessary for marketers to reach the potential customers in their audiences, identify their traits as individuals, talk with them at the moment they’re most receptive to the message and in the way they’ll find most engaging.

While a Marketac AI would be a significant competitive advantage, today’s technology is not capable of that as of yet. Modern AI work best when they have one specific task to accomplish. This does not prevent them from absorbing vast amounts of data, initiating pattern recognition, and performing manifold computations at incredible speeds. It just means that modern AI produce their best results when they have only one job to do. The challenge for marketers is defining that “one job” so AI can correctly analyze the mountains of data companies are sitting on.

Marketing practices are varied and include email marketing, content curation, and semantic analysis, among others. In order to deploy AI to help, it’s necessary to return to the root of the marketing strategy. Each company has its own unique business and specific objectives, so defining these is the first step to creating an AI’s “one job.” Once that foundation is created, marketers need to decide what the AI needs to focus on.

An example is easier to process than theory. AB Co. runs a ten-year-old content business with 1 million unique visitors a month. AB Co. needs readers to see their ads, preferably visitors who make it a habit to come frequently. Their objective is to keep retention rates, high, to turn visitors into habitual consumers. AB Co. has gathered a significant database on their readers, including email addresses, demographics, the kinds of stories certain demographics read, etc. How can marketing use all this data to identify the individuals in their audience and convince them to come back to AB Co. to read more content?

The email addresses AB Co. has in their database means they can exploit email marketing, allowing marketers to directly reach their target audience, an audience that explicitly gave permission to be contacted in the first place. When combined with an AI built with a machine learning core, their email marketing is not limited to automatically sending out pre-programmed templates. Instead, the AI will learn from the data AB Co. will feed it and optimize each message for the individuals in the target audience. Automated mass marketing email blasts will be substituted for machine learning marketing personalized to the individual.

With this, marketing has found the one job it needs an AI to do: build personalized newsletters for the visitors AB Co. can reach. By processing the data to analyze each visitor’s behavior and process the types of stories each individual reads, the AI can begin to calculate the kinds of content it should send to each individual recipient. Once it has calculated all of these factors, the AI can populate the newsletters and send them out, the content for each one unique and tailored to fit the tastes of the visitor. The end result: the visitor receives a newsletter that fits their tastes and is more likely to return to AB Co., improving revenue and user retention.

Today’s digital environment demands that brands adapt to the ever-evolving trends influencing their audiences. This means the acquisition of massive amounts of data combined with efforts to correctly analyze and use that data. Unlike humans, AI do better with big data since it gives them a stronger foundation to draw correct conclusions and execute their one job correctly. By embracing AI, marketers turn big data into food that their tool uses to accomplish business objectives.


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