It’s 2019, and certain aspects of artificial intelligence (AI) have permeated some of the world’s largest companies. They’ve been part of our lives long enough for us to have forgotten about them.
Data giants like Google, IBM, Amazon, and Verizon have integrated marketing AI into their offerings, making a business crunching numbers and providing more and more relevant predictions and recommendations.
We’ll examine the current, real-world applications of AI in marketing and business in 2019.
Do you remember how search used to be? It technically found some pages containing your search terms, but the results weren’t always relevant. Sometimes you’d keep hitting next and scrolling and still didn’t find what you were looking for.
Fast forward to 2019 and search is predictive and responsive, using more than just keywords to search and rank for what you’re looking for.
Google’s major search algorithm, uses what the company calls “signals” which pages should rank highest for a search query.
The entire practice of search engine optimization (SEO) is the practice of ensuring your content ranks highly for relevant queries. That is, working within what is known about Google’s AI, and trying to get your website rank higher as a result.
Targeted digital advertising is becoming easier than ever. AI uses predictive analytics and modeling to predict behaviors in users. AI can be used to predict the best time of day to serve an ad or the likelihood that an ad’s locational placement will convert a user.
On the other side, AI can be used to predict the value of a customer and can adjust ad bidding rates accordingly.
Demand-based pricing is reasonably common in industries like hospitality and air travel.
Hotel rooms and plane tickets go up or down based on who is looking at them and how often. In the past, there were just seasonal prices for rooms, but with AI, there are many more factors that can affect pricing.
Predictive analytics can be used to determine when customers might make a repeat purchase and incentivize them (or charge them more) based on what is most effective.
There’s a lot of copy that needs to be written by a person. But there is also a sort of repetitive nature many business communications, where often responses are predictable and a less human touch is acceptable.
An example of this is in chatbots. Have you noticed how many more organizations have 24/7 live chat?
We all know that live chat isn’t always live; in most cases, it’s not reasonable to have a person on the other end of the system at all hours of the day.
Chatbots can communicate to answer simple, common questions. As the algorithms improve, the questions that AI can handle will become increasingly complex.
Another example of where AI is taking over the written word is in the case of content creation.
At one point, AP Sports was using a platform called Automated Insights to generate articles based on sports data points. This works for sports and other statistic-oriented areas with millions of data points.
Breaking news, stat-based posts, and more service-oriented micro-copy like chat messages and are all great fits for AI. They are simple, short, and easy to derive based on data.
AI’s main application is in predictive analytics and sales forecasting.
An interesting application of this technology is in the form of IBM Weather Signals. IBM purchased both Weather Underground and weather.com and has wrapped the technologies associated with both to create predictive analytics forecasting business related to weather.
Here, AI is used to indicate changes in demand based on season, current temperatures and weather forecasts, and more.
This technology can be applied to other factors, like buying rate, changes in season, and more.
Voice is growing in marketing today. As products like Amazon’s Alexa creep into more homes, voice search and other AI applications are getting more prevalent.
We’ve seen voice recognition move from inaccurate, challenging to the use, to something that the day to day user might use to navigate, send messages, or set meetings.
More and more users are embracing voice technology. As many as 65% of 25 to 49-year-olds speak to their voice-enabled devices at least once per day.
AI is leading the charge for computers that are aware of what they are created and can learn from it.
Google Photos is a great example of this. Never before have you been able to search for keywords in a set of images without descriptions or metadata. Now you can type increasingly more complex search terms into your Google Photos and find that elusive picture from your beach vacation.
Recently, 3D printer company Markforged released its AI technology, Blacksmith. This technology allows the Markforged printer to see what it’s making and learn from its experiences. This sort of self-aware manufacturing technology is going to become more common in both the business and marketing world.
Imagine automated content creation that measured its own success and would tweak the way it posted to increase engagement? That’s a very likely possibility in the future.
Recently, sent my friend a picture I took of multiple Thomas English Muffin trucks through Facebook Messenger.
I swear to you; I still get ads on both Facebook and Instagram from Thomas English Muffins. I can’t even eat English muffins. An algorithm read the picture I sent and identified the logo, which then must have triggered Thomas to start trying to sell to me.
They’re a little off, but they’re a work in progress, so I’m understanding. Humans are still in the process of teaching AI how and what to learn so that eventually they’ll be set off on their own.
Currently, these AI need humans to guide them in what they need to learn and how they should learn it. In the coming years, more and more jobs will be teaching AI how to do the job and giving it little nudges in the right direction.
Humans still strategize and innovate, but they do it based on data. The menial tasks are increasingly being swept up by AI doing things humans need, but no one wants to do.
