Artificial Intelligence in Marketing
Artificial Intelligence Marketing :
The evolution of big data and advanced analytic solutions have made it possible for marketers to build a clearer picture of their target audiences than ever before; and in this hotbed of advancement lies artificial intelligence (AI) marketing. Using data and customer profiles, AI tools learn how to communicate with your customers, and deliver personalized messaging at the right time without the need for human intervention or assistance from marketing team members.
What is AI Marketing ?
Artificial intelligence marketing (AI Marketing) is a method of leveraging customer data and AI concepts like machine learning to anticipate your customers next move and improve the customer journey.
Armed with big data insights, digital marketers can greatly boost their campaigns performance and ROI, all of which can be achieved with essentially no extra effort on the marketers part. While this definition provides an overview of artificial intelligence marketing at the most basic level, there is obviously much more that goes into it. Well be addressing all of the details in the content to follow.
Core Elements of Artificial Intelligence Marketing :
Artificial intelligence marketing solutions offer ways to bridge the gap between data science and execution. The process of sifting through and analyzing huge dumps of data was once an insurmountable process and is now not only feasible, but its actually easy.
Lets think about all of the new technologies that have hit the market in the past year or so. Weve all heard about the iPhone 7, Googles driverless cars, Snapchat Glasses, and even Elon Musks city on Mars. What about marketing-specific technologies though?
There are a few key elements that make the adoption of AI marketing as important as it is today, including big data, machine learning, and the right solutions.
Big Data :
Big data is a pretty straightforward concept. It refers to a marketers ability to aggregate and segment large sets of data with minimal manual work. Marketing teams can then use this data to ensure the right message is being delivered to the right person at the right time, via their channel of choice.
Machine Learning :
Machine learning platforms come in handy when marketers try to make sense of this huge data repository. They can help identify trends or common occurrences and effectively predict common insights, responses, and reactions so marketers can understand the root cause and likelihood of certain actions repeating.
Powerful Solutions :
Artificial intelligence marketing solutions truly understand the world in the same way a human would. This means that the platforms can identify insightful concepts and themes across huge data sets, incredibly fast. AI solutions also interpret emotion and communication like a human, which makes these platforms able to understand open form content like social media, natural language, and email responses.
AI Marketing Challenges :
Artificial intelligence tools are still in the early stages of development. That means many marketing teams may be unaware of the best way to integrate AI marketing into their operations or how to use the solutions at all. This introduces challenges that marketers must be aware of as they implement these solutions:
Getting Organizational Buy-In :
Stakeholders may be unaware of the value that AI investments could bring the business. Marketing teams must be able to measure qualitative value of AI investments and note performance improvements surrounding customer experience and brand reputation.
Navigating Deployment Issues :
As marketing teams set forth with their AI deployments, it is imperative that they understand deployment best practices for the specific solution they plan to implement. This will require organizations to set aside time for team member training and calls with deployment specialists.
Ensuring Data Privacy :
Organizations must comply with data privacy regulations like GDPR and CCPA or risk considerable penalties. This means marketers need to make sure they practice ethical data usage. These laws can introduce challenges to AI deployments if tools are not programmed correctly to abide by these guidelines, they may risk non-compliance with data privacy laws