The most innovative marketers routinely pick up new things, try them out, and succeed or fail. Why are marketers struggling so much to adapt to artificial intelligence and machine learning? In this series, we’ll explore machine learning and artificial intelligence to build a foundation for understanding the field – and how it applies to marketing.
AI Applications in Marketing: AI-Powered Conversation
One of the greatest challenges we have faced in marketing in years past is true customization and personalization. Early attempts to do customization with software often left customers feeling unsatisfied at best. We’ve all received that email from a fellow marketer that starts with "Hello %FirstName%" and had a chuckle and a bit of vicarious embarrassment.
The reason the promise of personalization has not panned out is because we treat people in clusters and groups that we can mentally manage. Our marketing capabilities simply cannot create true personalization for thousands or millions of people. Could you imagine even trying to build a website with thousands of variations for every possible customer? It’s an insurmountable task.
As a result, we pulled back from personalization. We started creating artificial constructs like customer personas to cluster types of customers together. However, we know that personas are lies. There is no Sally Soccer Mom, no Tony Technologist. These aggregated people don’t exist; every person is a unique individual. Creating marketing based on these gross generalizations has led to unimpactful campaigns, causing customers to feel like abstract afterthoughts rather than valued individuals.
Talking 1:1 to Millions of Customers
All that has changed with the advent of artificial intelligence and conversations powered by machines. Unlike humans, machines create new instances of themselves for every customer. Every time our machines talk to a customer via a chatbot, for example, they are speaking in a true one-to-one interactions, individual machine instance to individual human.
In one example, IBM has developed and published a Watson tone analyzer for customer service:
The Watson tone analyzer service lets us know the emotional state of a customer when an issue is escalated from automated machine service to human interaction. We know how the customer feels, what issues they have, and we’re able to respond accurately to the customer’s needs. We integrate technology like this into our websites, our messaging apps, our chatbots and soon, even our call centers.
Beyond customer service, however, we are entering the age of conversational advertising.
Customers Talking to Ads
Here’s an example of an advertisement in which we, the audience, have a conversation with an ad. We tell the ad our favorite ingredient or dish:
…and IBM Watson creates a unique recipe just for us based on our favorite ingredient:
Naturally, the created recipe uses the advertiser’s product. What an innovation: the ability for us to not only have 1:1 interactions with our customers at scale, but to provide them unique content that is only for them. What better way to help someone feel valued for their uniqueness than to make something just for them.
Building AI Integrations
You might be saying, “well, that’s great if you’re IBM or can afford IBM’s services, but what about the rest of us?”. The good news is that these technologies can be licensed from any of the major tech giants. Amazon AWS, Google Cloud, IBM Bluemix, Microsoft Cognitive, and many other tech companies offer developers the ability to use their AI platforms on a pay-as-you-go basis, starting at fees as low as tenths of a penny per use.
Here’s an example of a service I built. I wrote a URL scanner that tells me how many times a piece of content has been shared, and on what social networks it was shared on most.
That’s useful stuff, but I was able to use IBM Watson through IBM’s developer portal to add Watson’s language analysis capabilities to my software. Now, for any given URL, I not only know what was shared, but I can run statistical analysis to determine if particular tones, sentiment, or emotions engender more sharing. (If you’re interested in using this technology, I offer it through my employer, SHIFT Communications)
Even individual programmers or developers have access to artificial intelligence tools for true mass personalization and conversation today. If you haven’t already tried tools from the major vendors, get started.
Next: AI-Powered Analytics
In the next post in this series, we’ll look at how artificial intelligence and machine learning help us to improve our analytics. Stay tuned!
The AI Primer for Marketers Series
- Part 1: Why AI and Machine Learning are harder for marketers
- Part 2: Algorithms and Machine Learning basics
- Part 3: Types of Machine Learning
- Part 4: The AI landscape
- Part 5: AI-Powered Content Marketing
- Part 6: AI-Powered Conversation
- Part 7: AI-Powered Analytics
- Part 8: AI-Powered Distribution
- Part 9: Preparing Your Career for an AI Future
Be sure to catch my keynote talk about this topic as well.
You might also enjoy:
- The Power of Analogy in Marketing Communications
- Branded Organic Search: The One PR Metric Almost No One Uses
- How to Think About Conversion Efficiency in Content Marketing
- You Ask, I Answer: Best Language for Marketing Data Science, R or Python?
- Why I Stopped Curating Content on LinkedIn
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