Artificial intelligence (AI) and marketing automation (MA) are two advanced tools that allow marketers to optimize tasks across the whole marketing spectrum.
For instance, AI can be used for many applications within marketing such as:
- AI can be used as a tool to provide data insights, specifically for marketing teams who do not have access to data engineers: e.g., AI models can identify patterns in customer data and autonomously cluster customers based on those patterns; or AI could support result analysis by helping discover the impact campaigns had on different KPIs by powering analytic models such as marketing mix modeling and multi-touch attribution.
- Personalization is a common use case of AI in marketing, too. Due to their scale and speed, AI models are particularly helpful when needing to identify customer preferences and personalize content in real-time. Common use cases for AI in personalization include models for product recommendations, dynamic websites, self-optimizing campaigns (where the model determines the best treatment from a multivariate test for each individual customer) and personalized messaging via chatbots.
- AI can also be used to recommend content to marketers as they build their campaigns. Use cases around this application focus on tone of voice recommendations as well as reviews and image recommendations.
- Scheduling is one of the most common uses of AI in marketing, a subset of ‘’campaign recommendations’. With each customer preferring to receive communications at their own time, and based on everyone’s open and click patterns, send time optimization algorithms have become a popular capability in marketing automation software.
- Leveraging AI for programmatic advertising provides great benefits for marketers. By analyzing data in real-time, AI models can help predict results of campaigns for different audiences, adjust bidding strategies to maximize ROI and match content to audiences to increase relevancy.
- The most advanced form of AI in marketing is its application for next-best-action decisioning. This refers to the use of AI to determine which campaign is best for each individual customer, instead of abiding to marketer determined automations. In this setup, marketers define the campaigns, and the algorithm determines which to deliver to each customer from all available possibilities.
The fact is that the more sophisticated the logic for sending a campaign and the segment a marketer builds, the more value the marketer will receive from its automation platform. In similar fashion, the more segments and campaigns a marketer creates the more impact a marketing automation software will have for that brand, as it can help scale communications without all the manual work involved. Additionally, marketers who leverage automation platforms to manage multiple channels from a single source will see more returns than those who focus on a single channel. Finally, marketers who use marketing automation tools as part of their reporting practices will greatly improve their performance as they will be creating a closed loop of automation that goes from creation, to execution, to insight discovery and back. (Source: Optimove)