Machine learning applies to artificial intelligence (AI) that gives systems the ability to learn and improve without being told to do so. The machine continually and incrementally improves upon itself over time, all without the help of humans. Businesses are finding ways to combine machine learning and marketing as a way to improve their companies.
Although machine learning is a fairly abstract topic, there are some concrete examples of the technology in popular products. For example, Apple's Siri continually understands more about you and can anticipate your needs the longer you actively use your phone. The same is true for Amazon Echo and Google Home—the more you use their services, the better each gets at anticipating your needs.
Applications for Machine Learning and Marketing in Business
Marketers are interested in machine learning. The premise of an algorithm continually learning to serve marketing needs better is an attractive one.
Improving a landing page with testing, tailoring an ad to a person's environmental factors in real-time or searching social media posts for sentiment are all made possible with machine learning.
Here are some more in-depth examples of machine learning and marketing.
1. Natural Language Processing
Aside from the aforementioned consumer products, natural language processing (NLP) has become quite valuable for marketing purposes. NLP uses artificial intelligence to process large amounts of text or speech to gain deeper understanding of the content. If you understand the deeper meaning of text, you can use that text in more interesting ways.
Instead of just being able to parse sets of text for search, machine learning allows us to understand things like sentiment, meaning, categories of text and much more.
For example, NLP could take a product description and extract useful keywords for metadata for search engines or their own cataloging, allowing the products to be searched with better context. Or you could show more accurate related content based on meaning as opposed to just strictly text. The more text is consumed, the better the algorithm becomes at understanding meaning.
The Google Cloud Platform has a service called the Cloud Natural Language. The service takes sets of text, parses them and extracts meaning, taxonomies, sentiment and more.
What could this do for a business?
If you use machine learning to parse transcripts of support calls, you could go so far to understand customer's intent and sentiment, according to Google.
2. Testing
With tons of data to work with, you could use machine learning instead of A/B testing. A/B testing assumes a very limited set of variations, where machine learning uses many forms of predictive analysis to guess the best outcome.
Machine learning can help with testing variations of a website or an ad.
Traditional A/B testing only allows you to test one variable, adapt, test another variable, adapt and so on. With machine learning, the adaption happens with different variables at the same time, and as the machine learns what works, it improves the tests.
3. Better Customer Relationship Management (CRM)
Imagine a CRM application that intelligently sent leads to the right person based on the support request's sentiment from phone conversations, email or text.
Imagine that same CRM also personalized marketing campaigns based on customer data, greatly reducing manpower and cost.
CRMs are starting to have this capability thanks to machine learning.
For example, in 2016 Salesforce launched Einstein AI, which gives developers a way to integrate AI with their CRM. Zoho has a similar offering with Zia.
4. Ads
Right now companies like Facebook tailor ads based on what they already know about you, like interests and demographic data.
IBM launched Watson Advertising in 2017, which fuses large data sets to make highly personalized ads.
Cameron Clayton, IBM's general manager of Watson's Content and IoT Platform, explained in an AdWeek article that you could, for example, get the local weather and use that data to show an ad based on the perceived mood of the web visitor.
Instead of just marketing to the knowledge that we previously had about the customer, we can potentially use real-time data to better target and improve messaging.
Bringing Machine Learning and Marketing to Your Business
With many of the major tech players putting large amounts of resources into building AI programs, it only makes sense that machine learning will one day be part of our everyday marketing offerings.
Instead of just running variations of ads, marketers could one day use machine learning to tailor ads per person based on data that the machine believes the person will click. Using AI to catalog and better understand the relationships between content on a website would be another use case that businesses could use today.
The more development that happens in AI in the future, the more opportunities businesses may have to capitalize on this new technology.
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