Digital transformation is a never-ending process. As soon as you've integrated the latest device or marketing trend into your strategy, the next one appears on the horizon.
Many companies allocate a percentage of their marketing spend for testing new technologies and programs, integrating those that prove beneficial into the mainstream marketing plan and sending those that don’t to the “Trash Stack.”
In most cases this is relatively straightforward: in some cases software has no clear use, but may hold potential for the future, for example augmented reality. While I see no clear application for us as a B2B business, we'll set this into a "future stack" rather than relegating it to the trash.
But one growing technology area is not as straightforward: artificial intelligence (AI). It isn’t clear yet where AI fits in today’s marketing mix.
Sounds Like AI, But Is it AI?
Part of the confusion with AI is that it's an enabling technology, not a product in itself.
Part of the confusion lies in the use of the term itself. Many vendors throw the term AI around regardless of whether any AI is being leveraged in the platform. As fellow marketers, you know what happens if you give a marketer a buzzword — it suddenly shows up everywhere (solutions not products, integrated, optimized, scalable, intelligent and now AI algorithms).
The truth is many vendors are blurring the lines of machine learning and artificial intelligence and in many cases their products are leveraging machine learning rather than artificial intelligence. My colleague Sarah Fay of Glasswing Ventures has written extensively about machine learning and AI, and her definitions are very clear:
- Machine learning generally relates to a software algorithm that leverages specific data sets and rules programmed by humans that become faster and smarter as they iterate in performing the tasks for which they were created
- Artificial intelligence exists when software can make decisions outside of the data strategy and rules set by humans, to form new solutions.
A marketing technology vendor was pitching his product at a recent conference. The first bullet on his graphic was “AI delivers ROI.” When I asked him to explain how the platform leveraged AI technology, he told me that his platform — a predictive analytics product — could crunch and analyze data faster than any human to determine which of several campaigns would be most appropriate for a particular user.
To me, that’s machine learning. If the platform were able to suggest a new campaign that didn’t exist, that would be artificial intelligence. Machine learning is immensely valuable, don't get me wrong, but AI sounds sexier. So while it might appear that AI is suddenly everywhere, in reality it’s not.
Where AI Fits in Marketing Strategies
So where does AI fit in your marketing strategy? I think about AI using two approaches — opportunistic and strategic — neither of which are mutually exclusive.
You're likely to start tripping over products that claim to be AI-enabled as you search for a product to address a specific need. We’ve seen AI referenced in chatbot, BI, personalization, design, content and many other product categories.
As you evaluate products with those claims, find out how their AI (or machine learning) algorithms enhance the performance and outcome of using the product, and whether that in itself is enough of a differentiating feature to justify purchase. Just because a product is enabled by AI, doesn’t necessarily mean it’s the best product choice in its category.
Strategically, AI offers the potential to deliver on the personalized marketing we are all striving for in our digital transformation efforts. I imagine a day when an intelligent agent will reach out to my customers proactively to recommend products and content without my having to create endless rules around trigger emails.
A marketing technology strategy will have many applications for AI, such as:
- Prospect targeting and advertising: Using AI to define and target micro-demographic segments
- Customer experience: Using AI to enhance the personalized experience each customer
- Data insights to action: Translating analytics insights into action at the micro and macro customer level.
Start by focusing on your marketing and business objectives, then evaluate how AI-enabled products might support those objectives, not the other way around. Also think ahead and outside the box, dream of what “could be” without the constraints and limitations of current technology (remember my "future stack" above?).
Use What's Available Today, Dream of the Future
One of the things that makes marketing so exciting today is being on the cutting edge of technology. AI as it relates to marketing (and just about everything else) is in its infancy: we suspect it will profoundly change the way we interact with prospects and customers, but don’t know exactly how. The important thing is to leverage what’s available now to the benefit of the business while remaining open-minded and aware of the potential ahead.
Author's note: This article was written by my digital assistant, Alexa. Just kidding, so far Alexa is proving useful at giving me the weather forecast, playing music and setting a timer, but I look forward to the day she tells me my article is due and asks me if I want her to write it for me.
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