The Best & Worst Examples Of AI Use In Digital Marketing

AI for marketing is the process of leveraging data and emerging technologies to better understand your customer’s behavior and to deliver personalized and relevant content. AI and machine learning optimize and streamline digital campaigns, as smart marketers are already using AI every day to make decisions, predictions, and assessments.

But not every use of AI is effective, as the following examples show.

Best Examples

Programmatic Ad Targeting

Programmatic advertising uses AI to help automate decisions about what ads to show to which people, so advertisers can save time and money on the process. AI can target customers whose behavior and demographic information matches based on information collected through cookies or other processes.

Speech Recognition

Marketers are using speech recognition for lead generation to quickly analyze call conversion data and identify operator training opportunities. Speech recognition is used at scale to, for example, identify calls where salespeople have closed the sale, and to pass that information back to the ad platform to improve campaign performance. 



Predictive Analytics

Predictive analytics is a branch of statistics that relies on data analysis and computer algorithms to predict outcomes. Predictive analytics is typically used in marketing companies to gain insight into customer behavior and to optimize marketing campaigns. Marketers are intelligently using predictive analysis to determine a customer's likelihood to take action.

Worst Examples

Using Neural Language Generation To Make Content

Every brand has become a media publisher, and every marketer is a content creator. There are a variety of neural language generation tools that promise to create compelling AI articles and written content that marketers could use for blog posts, articles, or other copywriting endeavors. None of these platforms can currently deliver any meaningful, reliable, or even usable content. There is a lot of promise with neural language generation technology, but so far, it has yet to deliver.

AI Chatbots In Regulated / Sensitive Industries

Chatbots are computer programs that are designed to simulate human conversations with an automated system. They use algorithms to analyze data and come up with the best response for a given situation.

Most chatbots are being used (and some very effectively) for repetitive tasks, such as sales, customer service, and customer data entry. However, as Chatbot technology matures, marketers are implementing them for more novel tasks. This presents a significant problem for finance, legal, health and other heavily regulated companies.

Chatbots are expected to parse complex datasets, and then provide insights, replies, or advice that adheres to regulatory standards. IBM's Watson failed at such a task in 2012, providing incorrect medical advice to cancer patients. 

Changing the Industry

As AI matures, marketers will grow more effective. Marketers will be able to easily use data to target consumers with the right message at the right time, and will be able to optimize and personalize ads to the individual. AI will help us to analyze data, learn from it to provide users with a better experience. We stand at the precipice of change.

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