What do push notifications, spam and your latest marketing campaign have in common? They’re among the many things influenced by machine learning.
Ask ten people to describe what a self-learning algorithm is and you’ll get eight blank looks and one lame comeback â€œItâ€™s an algorithm that learns from itself. If the tenth person is a data scientist or engineer, you’ll get a full explanation. But for most of us, even those who know the basics of machine learning and AI, self-learning algorithms are mysterious.
And yet, we all unknowingly expect to see the results of self-learning algorithms. We expect them to inform our purchasing decisions and the stories we read online. We expect them to curate our email inbox content. And we expect them to make number crunching so much easier.
How can we expect so much of something we don’t understand? Because we see its effects. Here are four areas where machine learning strongly impacts marketers and customers alike:
- Ecommerce recommendation engines
- Email spam filtering
- Customized marketing outreach
- Enhanced sales effectiveness
Before we explore these, let?s briefly consider the enigmatic technological wizardry at the base of self-learning algorithms.
Just How Does Self Learning Work?
Essentially, a self-learning algorithm is programmed to refine its own performance. In the context of machine learning, this requires a system powerful enough to process and analyze a ton of information. Into this system you feed requirements (i.e. the desired outcome, such as ?recognize an image of a cat?), parameters (what the machine needs to recognize a cat) and data (images of cats and non-cats). As the system processes more data points, it ?learns? from its performance and begins to get better and better at identifying cats.
For now, though, we have enough information on machine learning to answer our question: How do these self-learning algorithms function?
Ecommerce’s Recommendation Engines
Recommendation engines are close to being entrenched in our culture. We expect Amazon to suggest our next purchase and Netflix to curate our next binge-watching experience. But how many people ever think about the technology powering those suggestions?
In each case, algorithms are looking at our bio data (gender, age, location?), history and our ratings of products. They’re matching that data with what they’ve learned from thousands or millions of similar transactions. Thus, the customer gets a pointer towards something they’re likely to enjoy and the business gets a repeat purchase.
This process isn’t new, but it is improving. For example, self-learning recommendation engines can incorporate geographical and climate data to further customize their offerings. And the more recommendations they make, the more they learn how to make better recommendations.
Email’s Spam Detection Services
Spam email has been annoying people since the 1990s, but it wasn’t until 2001 that any semi-efficacious anti-spam programs appeared. These efforts relied on IP address blocking, content filtering, and other techniques that were only partially successful. Today, we have self-learning algorithms on the job, and email users are happier for it.
Although these smarter spam filters are much more effective than their progenitors, they still rely on human input. Every time you mark an email as â€œspamâ€ or â€œsafeâ€, every time you categorize an email as â€œimportantâ€, you’re providing feedback to the filtering algorithm.
Marketing’s Customized Campaigns
Another thing we expect to get nowadays is hyper personalized marketing messages. Advances in technology have made it possible to customize your marketing appeals to everyone?s preferred channel, product and time of day.
Our old friend the recommendation engine can also be adapted to marketing campaign purposes. And like its product recommendation version, the marketing campaign algorithm continuously learns from the feedback it receives, both from customers and from the marketers who use it.
Sales? Enhanced Performance
Similarly, AI-enhanced sales guidance relies on self-learning algorithms. In this case, they provide information and support to sales personnel, helping them craft their pitches based on the customer’s unique needs and motivations. Once again, there’s a feedback loop that enables the system’s suggestions to get ever more accurate.
These are only four ways that self-learning algorithms are changing what we expect and how we do business. There are numerous other examples of it across all industries. As our ability to utilize technology increases, we are sure to discover even more applications for machine learning.