Have you defined some business outcomes?Is there an urgency and a clear time frame to achieve those results?Is there a dataset to model?Did my team participate in the project?Have you evaluated your build and purchase decisions?Have you created a short list of vendors?Is their system pre-trained or is there a long training process?Definition of important termsIt’s important that everyone is based on the same definition, as they are really beginning to take advantage of AI and predictive marketing. Here’s a quick primer: Artificial intelligence (AI) is the science of building machines that do what humans consider to be intelligent.Machine learning is a subset of AI that allows computers to learn without being explicitly programmed. Typical machine learning use cases are optimization (selecting the best option to achieve a set goal over time), identification (extracting meaning from an image or text), and anomaly detection (over time).

Content Intelligence Is the Application

Content intelligence is the application of AI to content management, especially the understanding and classification of content to improve targeting and measure performance.Predictive marketing is the application of AI to marketing. Typically, you identify prospects, anticipate what you’re interested in, and recommend suboptimal content and product information.ConclusionWith some tips on how to understand and get started with AI, it’s time to turn “almost implemented” into the reality of AI, improve enterprise marketing, and truly understand and connect with your customers.A version of this article was originally published in the June issue of the Chief Content Officer. Sign up to receive a free subscription to the bi-monthly print magazine.

Cover Image by Joseph Kalinowski

Cover image by Joseph Kalinowski  Content Marketing Institute– Many times, clear executive sponsorship for the overall concept is at the top of the list. Mid-sized marketers may succeed in purchasing loyalty solutions, but large organizations will ultimately take a more automated approach to open the right datasets and increase overall business value. You will need an executive sponsor to support you.Clear Results – Early innovators needed to leap trust without a known purpose. However, as the vendor situation matures and client examples are documented, every project can and should have goals linked to valuable and measurable business outcomes.Available Datasets – Most experts agree that a mediocre algorithm with a large dataset is always better than a good algorithm with a small dataset. Dive into the available options, clean up what’s possible, integrate new data sources, run tests and see the results.

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