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How Machine Learning is Making Companies More Efficient

... Preetham Varma

Despite all the talk about machine learning, it would not be an overstatement to say that there is serious gap between the hype and reality. As always is the case, the truth lies somewhere in between. Many companies are already using machine learning in multiple areas and have experience massive changes in the way they do business. Any company which has a context to information is using machine leanring to clear the clutter. According to an article published in Techcrunch, nearly every Fortune 500 company is already using machine learning in one or the other way.

Here are a few of the things where machine learning has found great degree of application and is making serious changes and benefits:

Performance Linked Contracts

Machine learning is helping companies move to completely avant-garde contractual terms which won’t be possible without machine learning. For example, machine learning is changing how airline companies are paying for aircraft engines. Nowadays, airlines are paying for engines based on a time on wing metric, which identifies the operational reliability of an aircraft engine forcing manufacturers to make these engines more dependable. This is where the pattern recognition capabilities of machine learning come into play. These algorithms can isolate vulnerabilities within these implementations and accurately identify what kind of maintenance is required to repair them.

Faster Search & Display

Many companies need fast data analysis and recommendations on what to show to its users in the fastest. Companies like Home Depot, Apple, Intuit all need to know how to prioritse display on their stores, app store or their help page when a user types in a certain tax form. E-commerce startups like Lyst , Trunk Archive, Rich Relevance and Edgecase are employing machine learning to show high-quality content or right products for its browsing customers.

Content Filtering

Machine-learning is increasingly being used by Content platforms that curate user generated content. It’s always a challenge for such platforms as to how to weed out the junk and showcase great content.  Machine Learning models are helping filter out the bad and bring in the good without needing a real person to tag each piece of content. Companies like Pinterest, Yelp, nextDoor and Disqus use machine learning to filter out trashy content and show the good ones.

Spam Filtering

Machine Learning is extensively being used to filter out spams. For example, till not far back spans were a huge nuisance for the emails. Machine learning has helped identify spam and, basically, eradicate it. These days, it’s far more uncommon to see spam in your inbox each morning.

Resource Use/Queuing  Planning

Healthtech companies and hospitals are using a technique called Discrete Event Simulation to predict wait times for patients in emergency department waiting rooms. The models use factors such as staffing levels, patient data, emergency department charts, and even the layout of the emergency room itself to predict wait times.

Customer Engagement

Instead of having users self-select an issue and fill out endless form fields, machine learning is helping companies look at the substance of a request and route it to the right place. Ticket tagging and routing can be a massive expense for big businesses. Machine learning, by helping automate the process is helping customer service save significant time and money, all while making sure issues get prioritized and solved as fast as possible.

Understanding customer behavior

Role of sentiment, mood etc in consumer decision making behavior  is increasingly being recognized as a key factor that drives a lot of big decisions. For example, A game studio recently put out a new title in a popular video game line without a game mode that fans were expecting. When gamers took to social media to complain, the studio was able to monitor and understand the conversation. The company ended up changing their release schedule in order to add the feature, turning detractors into promoters. How did they pull faint signals out of millions of tweets? They used machine learning.

Fact is, machine learning practice has beyond the likes of Google, Facebook and Twitter. Its spreading fast into other areas and companies. Data is more prevalent than ever, and it’s easier to access. So much so that nearly every decent size website you interact with is using machine learning behind the scenes. Big companies are investing in machine learning because they’ve seen positive ROI!

Reference: Techcrunch, Forbes 360

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