AI marketing tools enable marketers to identify opportunities and ideas rapidly. Mattel, for instance, uses gen AI to scan trends and social media responses to generate new Hot Wheels product concepts that they can test.
Data scientists and engineers don’t typically sit on marketing teams, so many organizations opt for third-party AI marketers to develop and maintain their platforms. They can also provide valuable insights into campaign outcomes.
Personalized Experiences
Personalized customer experiences are essential to building trust and loyalty. However, it can be challenging to deliver them at scale. Ai marketing tools can help by speeding up personalization and improving the accuracy of targeting. They can also be used to identify new segments and predict customer behaviors, which can boost engagement and conversions.
For example, a chatbot can offer product recommendations to retail website visitors based on their browsing and purchasing history. This can increase revenue and improve customer satisfaction by increasing retention.
Other AI personalization applications include image recognition and search engine optimization. For example, a grocer’s website might use image recognition to identify a particular type of food or drink and recommend similar items. Alternatively, it can detect that a user is searching for a certain item and then prompt them with relevant ads on their mobile device. This can boost click-through rates and reduce ad waste. However, marketers must ensure that AI marketing tools are secure and adhere to data privacy laws.
Data-Driven Decision-Making
One of the most powerful uses of AI marketing is its ability to analyze data and provide insights that inform campaign decisions. This can help marketers identify which elements of a campaign are most effective and improve results in the future.
This is a critical area where AI needs to work hand-in-hand with human marketers. It’s important to have strong internal communication between stakeholders and ensure that everyone understands how and why AI is used to make campaign decisions.
Likewise, it’s vital to protect sensitive customer data by using robust security measures and ensuring compliance with data protection regulations. It’s also essential to avoid biases that could inadvertently discriminate against certain demographic groups.
For example, a predictive analytics bot might recommend products to consumers who are more likely to purchase based on previous buying behavior. This can create a more personalized experience for consumers while helping marketers increase sales and revenue. It’s a win-win situation for both businesses.
Programmatic Platforms
Programmatic advertising utilizes AI-powered algorithms and automation to improve targeting, personalize ad messaging, and optimize bidding and ad placement processes. This results in more efficient ad campaigns that drive more effective ROI for advertisers.
Generative AI, or genAI, is another area of ad tech where marketers use tools like ChatGPT and Dall-E to develop text, images and video content for ads. While not an official form of AI, these generative technologies leverage machine learning models and natural language processing to produce creative assets based on a prompt.
Basis Technologies, for instance, is an AI software platform that automatically analyzes and optimizes ad campaigns against specific KPIs. It also recognizes patterns in campaign performance that might indicate fraud and then blocks suspicious inventory from entering the ad stack. This eliminates the need for human oversight while reducing wasteful ad spending. Advertisers can also use AI to identify saturation points, such as when a user is no longer responsive to an ad.
Dashboards
Unlike traditional analytics tools, AI-powered dashboards automatically generate insights to save teams time and increase accuracy. This reduces the risk of human error and allows marketers to make data-driven decisions faster.
Digital marketing agencies need to be able to track and analyze key metrics including lead generation, sales qualified leads and website analytics. They must also understand the impact of their campaigns on brand sentiment. A digital marketing dashboard that incorporates AI can provide valuable insights into the effectiveness of strategies and help them to be more targeted.
The Responsible AI dashboard provides a single pane of glass to operationalize responsible AI, enabling practitioners to flow through different stages of model debugging and decision-making in one place. The tool uses a variety of open-source and Azure Machine Learning capabilities to enable a more customized, responsible AI experience. For example, Model Statistics helps users better understand a model’s performance and informs which are the best targets for early debugging.