Machine Learning: what is it and why is it so important?

Just a few years ago, conversations surrounding machine learning were a lot different than they are now. We went from confused and uncertain to trusting and hopeful in the blink of an eye. It is, after all, a technology we use every day. Things like Google Maps guide us to our destination, Netflix shows us what content we might enjoy, and Spotify delivers playlists of tunes we like.

Now that we’re aware of machine learning, we want to make sure that it’s giving us the best chance to succeed. This article will walk you through the basics of machine learning, then highlight the ways you can use machine learning to improve your campaigns, optimize your data analysis and more.

What is machine learning?

Machine learning is a form of artificial intelligence that uses data analytics to create superior systematic structures. It is when a computer system is able to adapt to data by learning from its historical patterns, then deciding the best course of action. Machine learning is meant to be employed with negligible human involvement.

Why is it important?

Machine learning shines new light onto important business insights, giving companies and their teams a chance to better understand and adapt to customer behavior, as well as analyze behavioral data. Machine learning can be the foundation to build campaigns and the key to improving or creating products and services.

The digital age has elevated machine learning as one of the most important factors for data analysis. Data is created at a surging rate, making machine learning a priority to all companies looking for ways to maintain effective, accurate analysis without overloading their workflows.

Different machine learning types

1. Reinforcement

Reinforcement learning gives systems good or bad feedback, essentially teaching it how to best perform. Think of reinforcement like a game of ‘hot’ and ‘cold’. As the machine navigates a task, the administrator tells it it’s getting hotter or colder. This guides it in the right direction, but the system ultimately makes decisions as to the optimal route and/or solution.

2. Supervised

Supervised learning is where administrators give systems specified data, variables, settings and expected outputs. The machine then draws from information it already has in order to navigate the exercise and forecast potential future actions. A supervised model generally makes better adjustments long term.

3. Unsupervised

Unsupervised, as the name would indicate, allows computer systems and algorithms to work on data that isn’t adjusted, labeled or signaled to in any way by administrators. The systems use this unlabelled information to make unique suggestions and conclusions.

4. Semi-supervised

Semi-supervised learning is a combination of the supervised and unsupervised types. The information given to the program in this case is identified for the machine, but the machine is given room to navigate through it freely, creating mostly uninfluenced results.

Now that you’re aware of machine learning’s value and its different types, let’s learn the different ways  to use it in business settings.

Machine learning for marketers

Machine learning helps marketers by guiding, adapting and improving customer journeys and personalization. It’s no longer necessary (or optimal) to build a complex journey relying on an arbitrary algorithm, such as ‘send email A after three days, send email B after 7 days if A is unopened’. It reduces the work this would take, as well as restructuring the overall formula, building a more dynamic approach for superior customer experiences.

Machine learning also empowers marketers to provide personalized experiences. The algorithms created allow teams to adjust their projects and campaigns based on detailed data analysis. This brings real-time personalization into the mix without tying up marketers.

Practical machine learning examples

Chatbots

Chatbots are popping up more and more lately, bringing a machine learning flavor to all things customer engagement. Thanks to machine learning, chatbots are able to answer important questions, make recommendations and solve problems with services or products.

Image recognition

No one has time to comb through thousands of images within a database. Fortunately, machine learning does it quickly and effectively. These processes and their application extend much further than organizing a digital photo album as well, powering important solutions for potential real-world problems.

Digital recommendations

When we think of machine learning in terms of digital recommendations, things like Netflix and Amazon’s recommendation algorithms come to mind, but it’s more than that. The processes can build upon data to understand new trends and make recommendations that have impacts on a much bigger level of importance in the future.

Final Thoughts

Instead of resisting the power of machine learning, industries have embraced its potential, reducing manual tasks, improving decision-making, changing data analysis, and empowering customers.

Lastly, this technology is improving, as researchers pour hours of work into making its processes stronger and more adaptable. What this will ultimately bring about, no one knows. What we do know is the drive to improve it is in full gear. That should excite everyone, from marketers to customers alike.