Businesses across all sectors will spend over $100 billion per year on artificial intelligence technologies by 2025, up from a mere $2 billion in 2015, according to Constellation Research. The marketing industry will be no exception, as AI holds great promise for making marketing more intelligent, more efficient, more consumer-friendly and, ultimately, more effective.

AI is now simply a requirement for making sense of such vast arrays of data, both structured and unstructured, to extract actionable insights at speeds no human could ever replicate to help marketers make better decisions in near real time. For businesses looking to keep pace with innovation and leverage AI, there are steps they can take today. But first, what are some examples of how AI can help make marketing more effective?

How to put AI into practice

Incorporating AI into your marketing requires technical considerations, choosing the right partners and communicating to consumers how their data is being used.

Infrastructure siloed by channel, or that is not well-integrated, will thwart your ability to bring AI into your marketing activities. There are three areas to consider:

  • Identity: You must know who users are across devices, and be able to leverage their non-personally identifying information to deliver personalised offers, products, services and communications that create value. The first step is to establish a common identifier, usually an alpha-numeric string, across various touch points and data sets to help create a unified view of the customer. Emerging solutions such as DigiTrust are helping marketers tie together their various touchpoints in a safe, respectful, and scalable way.
  • Data gathering: AI can make sense of all your data and extract insights from it, but only if you can collect and normalise it before activating it. Clean and organise your first-party data with the help of a technology provider, onboard it for online use and then choose an integrated technology that lets you centrally manage all your data sources, segment into granular audiences, optimise those audiences and activate in media in real-time.
  • Data end points: Great adtech is key to getting the most out of your paid media. But to make owned, earned and paid media work together, you need great martech, too. Connecting your martech and adtech will help you harness data—1st, 2nd and 3rd-party—across the entire customer journey to form a complete view of your customers and get insights that get smarter over time. Technology such as IBM’s cloud-based Universal Business Exchange helps you integrate data from other systems to provide that holistic view of customer behaviour.

Machine learning uses data to create systems that can help solve problems. AI is a subset of ML where machines replicate the intelligent behaviour of humans. For instance, AI can mimic the creative process in a machine to make creative decisions in marketing, developing, and dynamically switching out, thousands of ad creatives for different audiences. Choose a technology that enables you to extend decisioning across more data by projecting user-level optimisation. This lets you seamlessly leverage always-on data decisioning and unstructured data assets.

As more data on consumers becomes available for AI systems to analyse and decision on, natural questions arise about the consequences for consumer privacy and legality. Educating individuals about how AI technology works and is being applied and how their personal data is being used will create a more consumer-first ecosystem. Armed with knowledge, consumers can make educated choices about which advertising experiences to opt in to.

About the author

Yun Yip is the ANZ country manager of MediaMath which is a member of the Which-50 Digital Intelligence Unit. Members contribute their insights and expertise for the benefit of our readers. Membership fees apply.


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