How to get started with Artificial Intelligence (AI) in Marketing
Refers to the use of advanced machine learning algorithms and techniques to improve various aspects of marketing efforts. This includes analyzing customer data, automating marketing processes, optimizing ad targeting, and more.
One common use of AI in marketing is to analyze customer data and identify patterns and trends that inform marketing strategies. For example, a company might use AI to analyze customer purchase history, demographics, and other data points to identify target audiences for their marketing efforts.
Overall, the use of AI in marketing can help businesses better understand their customers, target their marketing efforts more effectively, and automate marketing tasks, which ultimately lead to better business outcomes.
Pressure on marketing to continually prove value and improve performance? requires marketers to always be looking for the next big thing. Whether it's having a website with the latest bells and whistles (read Web3.0) and being visible on the right devices or platforms or the latest social media craze, the pressure is always on.
The next wave is here: Artificial Intelligence and all its aliases and monikers – AI, Machine Learning (ML), Cognitive, IBM’s Watson, Salesforce’s Einstein and so on. Recently,? ChatGPT stirred things up and made AI more approachable – but where does that leave us in terms of usable applications for Marketing??
Is it real? Sure – but it’s not magic. It does speed up some of the typical ‘thinking’ you need to do, but the basic principle remains – it's only as good as what you put into it – meaning the data on which it learns and what you then want to do based on it.
Data powers AI
So even if AI can do many wondrous things, it still needs good data on which to base any decision. If we go on the adage of – it is only as good as what you put into it – this is even more true with AI. Our human brain catalogs a lifetime of experience and context when we make any decisions, which when working with ML or AI needs that context to be provided in addition to any core data.
This means you need a place (environment), or at least a method for you to aggregate all the data. It also needs an indication of context of what the different pieces of data mean (e.g. consumer data, location data, transactions etc.) and then knowing what is a good or a bad outcome.
By default, the data you have collected helps you explain past behavior. While for many this is a good indicator of how they may behave in the future, the AI needs to understand what drove those behaviors in the first place. What things do we know about these people, or the way they interacted with our brand and the marketing we provided (or not), that made them behave in a certain way.
This then helps us to create more of a predictive model of how they - and people with similar attributes and actions - are likely to behave in the future. Essentially understanding what messages, or creative or suggestions would most likely influence them to act in the way you want them to behave.
The AI recipe
Oversimplifying how AI predominantly works in marketing through analyzing the available data:
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‘Do something’ can be a near infinite combination of tactics, creative, distribution channels, audiences and more. Many of these are the same pieces you need to create in the first place, and will also serve as an input for the AI. However, where AI shines is evaluating variations of the data sources.
‘See reaction’ is critically important to the optimization process. If you have no way to track the effect of the combination of your inputs (either directly or indirectly), neither AI nor human intelligence can realistically improve on that behavior. In digital we typically have close to a real-time response, but in the offline world (e.g. print, direct mail, point-of-sale etc.), especially in certain industries (e.g. CPG, automotive, real-estate etc.), this can be a lot more challenging. IoT and AI video detection is helping to narrow the gap here, but there are limits in terms of privacy and security concerns. As people have been inviting more technology into their lives and have been exposed to what that technology can do, privacy concerns have also escalated which need to be considered in the measurement approaches.
The next step in the evolution of AI is also supporting the creative process and helping you think of potentially new ideas that you can leverage as new inputs to test how well consumers respond to them, but personally I feel these are not yet mature enough to use in an automated way - but definitely worth experimenting with in the back-end with a little human oversight.
Are you ready to take the guardrails off?
Even once? you have a perfectly trained model, the question becomes how much freedom do you give the AI engine to automatically connect with your customers? While focused on the ‘performance’ of your marketing strategies, how much will it prioritize this above your company brand principles, voice or consumer frustration?
A number of key AI experiments have failed – including Microsoft’s Tay, Google’s self-driving cars (Waymo), Facebook’s M and now it seems Amazon Alexa as well. Some have failed due to their voice (data) becoming embroiled in controversy after users trained it to make racist and offensive comments and others due to just not getting enough traction.
What should you be doing about AI
Personally, I feel the underlying potential is real. As illustrated, it is not a quick fix – much of AI’s job is to narrow down what you are already doing, and doing it better, smarter and faster.
That being said, you could start with the following:?
Starting with the above steps will enable a cost-effective and successful outcome when you step into the world of AI with H.A.L at your side (https://en.wikipedia.org/wiki/HAL_9000).