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Artificial Intelligence vs. Machine Learning

Artificial intelligence is all the rage right now, and understandably so. Software engineers are making huge strides in everyday applications, and we are seeing the results with much more promise on the near-horizon. However, the more AI is talked about, the more the definition is blurred.

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Evolving Definitions of Artificial intelligence and Machine Learning

Historically, AI referred to software that mimics human behavior and was indistinguishable from a human. One example (apart from Rosie on the old cartoon the Jetsons) is customer service chat bots. Right now, if you need help from a vendor, you can go online and opt to use a chat window to talk to someone to get the answer. Often you are, in fact, working with artificial intelligence – not a real human.

The good definition of  “machine learning” is software that improves its own task performance by continuously analyzing data. A common example is software that identifies which products to stock, how much stock and the best locations. Instead of basing shipping and stocking on last year’s sales, there are many more parameters that machine learning software brings together, including factors such as trends, weather and other information that is new this year. This learning can also be used for customer loyalty initiatives, improving sales and ROI.

However, now, what used to be called “machine learning” is now often branded “artificial intelligence.” machine learning today is incredibly sophisticated, and to an untrained eye, seems indistinguishable from AI. However, machine learning is only a subset or a part of what makes AI. Advances in storage and processing power as well as their decreasing costs are bringing down the costs surrounding machine learning, which is why there’s been an explosion in applications relying on machine learning and their amazing results.


How Machine Learning Helps Brand Managers

There are multiple areas where innovation in machine learning is making huge strides for brand managers in marketing intelligence software. One instance is the ability to gather and process huge quantities of useful information from images and videos. With every pixel, today’s marketing intelligence software can extract large amounts of multidimensional data that can be used in many different ways. Another area of advancement is the utilization of natural learning processing and text mining of customer data across the entire internet to dissect the many dimensions from behavior to performance to psychographic segments and more. Sophisticated marketing intelligence software will analyze all that data and immediately glean useful insights that brand managers can act on. This is just the tip of the iceberg of the advancements that marketing intelligence software can bring to brand managers.

Now, all this innovation in machine learning and even artificial intelligence cannot be leveraged if your marketing strategy isn’t built on a foundation of sound data management. In order to reap the benefits, first data management must be intrinsic to your marketing, and secondly make sure you have a partnership with your technology vendors so your strategy takes advantage of the powerful capabilities you have at your fingertips.

For more on AI and its applications to marketing intelligence: 

 

Photo by William Bout on Unsplash

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