Many companies – and the marketing teams that support them – are rapidly adopting intelligent technology solutions to encourage operational efficiency while improving the customer experience. Through these platforms, marketers are able to gain a more nuanced, comprehensive understanding of their target audiences. The insights gathered through this process can then be used to drive conversions while simultaneously easing the workload for marketing teams.
What is Artificial Intelligence (AI) Marketing?
AI marketing uses artificial intelligence technologies to make automated decisions based on data collection, data analysis, and additional observations of audience or economic trends that may impact marketing efforts. AI is often used in marketing efforts where speed is essential. AI tools use data and customer profiles to learn how to best communicate with customers, then serve them tailored messages at the right time without intervention from marketing team members, ensuring maximum efficiency. For many of today’s marketers, AI is used to augment marketing teams or to perform more tactical tasks that require less human nuance.
AI marketing use cases include:
- data analysis
- natural language processing
- media buying
- automated decision making
- content generation
- real-time personalization
Components of AI in Marketing
It’s clear that artificial intelligence holds a vital role in helping marketers connect with consumers. The following components of AI marketing make up today’s leading solutions that are helping to bridge the gap between the expansive amounts of customer data being collected and the actionable next steps that can be applied to future campaigns:
Machine learning is driven by artificial intelligence, and it involves computer algorithms that can analyze information and improve automatically through experience. Devices that leverage machine learning analyze new information in the context of relevant historical data that can inform decisions based on what has or hasn’t worked in the past.
Big Data and Analytics
The emergence of digital media has brought on an influx of big data, which has provided opportunities for marketers to understand their efforts and accurately attribute value across channels. This has also led to an over saturation of data, as many marketers struggle to determine which data sets are worth collecting.
AI Platform Solutions
Effective AI-powered solutions provide marketers with a central platform for managing the expansive amounts of data being collected. These platforms have the ability to derive insightful marketing intelligence into your target audience so you can make data-driven decisions about how to best reach them. For example, frameworks such as Bayesian Learning and Forgetting can help marketers gain a clearer understanding of how receptive a customer is to a specific marketing effort.
Challenges for AI Marketing
Modern marketing relies on an in-depth understanding of customer needs and preferences, and then the ability to act on that knowledge quickly and effectively. The ability to make real-time, data-driven decisions has brought AI to the forefront for marketing stakeholders. However, marketing teams must be discerning when deciding how to best integrate AI into their campaigns and operations. The development and use of AI tools are still in early stages. Therefore, there are a few challenges to be aware of when implementing AI in marketing.
Training Time and Data Quality
AI tools do not automatically know which actions to take to achieve marketing goals. They require time and training to learn organizational goals, customer preferences, historical trends, understand overall context, and establish expertise. Not only does this require time, it also requires data quality assurances. If the AI tools are not trained with high quality data that is accurate, timely, and representative, the tool will make less than optimal decisions that do not reflect consumer desires, thereby reducing the value of the tool.
Consumers and regulating bodies alike are cracking down on how organizations use their data. Marketing teams need to ensure they are using consumer data ethically and in compliance with standards such as GDPR, or risk heavy penalties and reputation damage. This is a challenge where AI is concerned. Unless the tools are specifically programmed to observe specific legal guidelines, they may overstep in what is considered acceptable in terms of using consumer data for personalization.