As machine learning (ML) enters the mainstream, you may have questions about what it is and what it’s used for. Chances are high that you already see it in some of your daily activities. But before you start to evaluate the ways it can assist you, it helps to have a basic understanding of how it works and some possible use cases.
Five Ways Businesses Use Machine Learning
- Targeted Advertising and Marketing — You’ve probably noticed that the ads you see while browsing the web are reflective of your online activities. If you clicked an item on Amazon, you’re likely to see ads for similar items pop up on other sites. If you follow certain brands on Twitter, you might see promoted tweets from similar brands. Salesforce has even introduced a piece of marketing technology that identifies images on social media to help create marketing targets. That’s a lot of internet activity to organize and interpret for a human; no problem for a machine.
- Fraud Detection — Ever receive a call from your bank asking about a charge that doesn’t fit your normal spending habits? It’s quite possible that ML technology is identifying patterns in your spending and flagging any outliers for your bank. PayPal uses ML to detect suspicious transactions that may indicate money laundering.
- User Recommendations — Your music apps will suggest new albums you might enjoy. Your video streaming apps will suggest series and movies you might watch. Again, this is ML using a set of data (your songs and TV shows) to predict other data points that align with that pattern.
- Healthcare — Doctors and medical researchers are using ML to process decades worth of data to find patterns, identify risk factors for diseases, and predict medical outcomes to help determine the best treatment and preventative measures for patients.
- Logistics — Uber is extremely dependent on accurate data tracking and predictions based on that data. That’s how it assigns drivers, predicts arrival times, and directs drivers and riders to low-friction pickup and dropoff areas. It’s way too much information for humans to gather and analyze, so the efficiency of the business is dependent on ML.
Rethinking Service Desk Best Practices
It should be clear that machine learning is all about data. The goal is to collect and analyze it automatically, hopefully producing useful patterns or predictions that you might have otherwise missed.
Service desk software that offers ML capability can reshape the way you serve your customers. Your service desk is a nearly endless supply of data and variables — users, departments, categories, statuses, processes… you could go on forever. All of this data interacts together, and different pieces are dependent on one another. Imagine the possibilities for automation and efficiency if you introduce machine learning to do some of the heavy data-lifting. Imagine the power of a predictive service desk that could alert you to the possibility of potential problems based on trends in the data.
We’re already seeing some of these benefits. Samanage can categorize your tickets automatically, saving your technicians the headache of routing tickets to the appropriate place. Machine learning can discover links between seemingly unrelated incidents and major problems, alerting your team to perform maintenance before a major problem occurs.
Machine learning can also unlock daily, monthly and yearly patterns, helping you to optimize staff scheduling based on volume and needed expertise. It could even identify service gaps within your catalog and spur the creation of new or better services. Today’s best practices are about to be improved (or even restructured) by machine learning.
To learn more about the ML-powered service desk of the future, sign up for our free webinar.
About Chris McManus
Chris McManus is a Senior Content Specialist at Samanage with a variety of media and creative content experience. He works with Samanage customers on case studies, webinars, and spotlight videos.
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