5 examples that show Machine Learning can be useful for your business

5 examples that show Machine Learning can be useful for your business

Machine learning can help organizations get better business decisions. If you are thinking  on taking advantage of this technology, here we show you some benefits ML has to offer.

5 Wow examples that show Machine Learning can be useful for your businessAfter years of promises, prototypes, and wild speculation about how Artificial Intelligence is going to change the world, it is finally starting to deliver real benefits.

Many stem from the application of Machine Learning (ML), a type of AI that is particularly well-suited for use in business operations and the management of core systems.

Content related: Benefit your business with Machine Learning


To take a closer look at this, here are five examples of how some of ML’s early adopters are extracting real value – right now – with Machine Learning.

1. It identifies important clusters in customer data for highly targeted marketing campaigns

When Orbitz put their ML algorithms to work on customer data, they discovered that people who used Macs were willing to pay more for their hotel stays. That prompted them to create marketing strategies to target customers who paid, on average, $30 more per night for their hotel rooms. Bottom line benefits? You bet.

Machine Learning works by analyzing data and looking for patterns – in this case, patterns in customer attributes and behavior. Data clusters like that can be hard to find using traditional, human-centered data analyzation techniques but ML offers more computing power and more speed – exactly what is needed to enable precise customer segmentation at scale.

2. It offers enterprises a fighting chance against cyber threats

Cyber attackers are not missing a beat when it comes to developing new strategies for their cyber crime initiatives. Whether it is using big data to pinpoint exactly which companies are most likely to pay up when affected by ransomware or creating subversive threats that enter systems undetected until ready to deploy, they are getting more sophisticated every day.

Enterprise systems, especially those who have increasingly complex cloud environments, must be able to identify threats as quickly as possible. Then, once detected, they must be able to react quickly to mitigate the risk. As cyber criminals digitize their operations and speed up their attacks, it is becoming apparent that human interaction is the weak link in enterprise security strategies.

Example machine learning in your business

Machine learning algorithms can learn “normal” behavior in enterprise systems and then look for anomalies that could represent cyber risks. They can also learn what types of actions human-powered security teams usually take when reacting to threats. Eventually, the ML program will identify attacks and handle them before the team even knows there was an event. And the icing on the cake – ML can quickly process vast amounts of incoming threat data from in other systems across industries, making threat prediction possible as well.

Although ML-enabled digital security is becoming an imperative for enterprises, it is the future of cyber security for smaller organizations, too. Smaller and Medium Enterprises are increasingly becoming targeted by sophisticated cyber criminals on the lookout for common vulnerabilities—exactly the type that ML can easily discover and handle for them.

Take a look at this: Is your company ready for Machine Learning?

3. It keeps sales teams from wasting time on dead-end leads

A seasoned salesperson can tell right away if someone who walks into their store will end up buying anything. Likewise, an experienced call center staffer can tell if there is going to be a commitment from the lead they have been nurturing, just from the way their conversations have been progressing.

Historically, this has been an area of sales that has been tough to automate, simply because of the myriad of nuances in the way that language is used, both in written and spoken forms.

Now, however, ML systems can now learn to make these types of predictions, too. First, they will need to be fed enough high-quality data about customer behavior from past conversations and emails in the sales department. Then, after analyzing the way the brand handles leads and how they respond to sales techniques, it can pinpoint leads will convert and which ones are more likely to become dead ends.

Some of the red flags that ML can look for are signifiers in the language that indicates the salesperson is more excited about the product/service/offer than the lead. Or it can learn the type of language that indicates someone is too busy to learn about an offer. Or when a prospect is constantly coming up with ways to delay their commitment for no apparent reason, ML algorithms can recognize the patterns quickly and flag them as probable dead ends. The sooner the sales team knows about these red flags, the sooner they can direct their time elsewhere and start using their resources far more efficiently than before they had ML on their team.

4. It can help manufacturers ramp up production

Machine Learning systems are hungry for data, which makes them the ideal tool for manufacturers who have enabled their systems with IoT devices. Sensors placed on equipment on the factory floor can collect the type of information that leads to significant improvements in production speeds. If the ML is trained properly, it will learn how to configure and tweak your equipment and your processes for the types of efficiency gains that make production managers sing its praises.

Furthermore, ML can assist production not just on the factory floor but also within the entire supply chain. Even small price changes from a supplier can lead to ML-enabled optimizations that eventually lead to big savings.

5. It can help managers and leaders make better decisions

Example machine learning in your business

So far, we have seen how a mature ML system makes tiny decisions all the time to optimize systems, segment customers, and analyze language. But it can help with higher-level strategic decisions, too. Using data from multiple sources like the company website, its e-commerce store, or social media accounts, ML algorithms can deliver fast, automated insights that managers and leaders need in order to make better decisions about how to steer the company toward success.

Do not miss this Blog Post: Clues to succeed in a Machine Learning Project

To Sum Up

Machine Learning offers businesses many opportunities for a brighter future. But all said and done, it does take a certain degree of digital maturity before leaders can even begin to consider harnessing benefits like these. And when you combine digital capability with proactive strategies and a culture of innovation, there is no telling what the future may bring. Are you interested in trying some of this in your business?

Look at the Machine Learning solutions area of our site and let us know what you are looking for.

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