Machine learning has been defined by Stanford University as “the science of getting computers to act without being explicitly programmed.” It’s machine learning that is now behind some of the greatest advancements in technology, driving new industries like autonomous vehicles.

From machine learning, a whole new world of concepts has developed, including supervised learning and unsupervised learning, as well as algorithm development to build robots, Internet of Things devices, chatbots, analytics tools, and more. Here are seven ways you can put machine learning to work right now:

1. Analyzing Sales Data

The sales function has benefited from the growth in sales-focused data thanks to the increase in digital interaction. Sales teams can tap into metrics from social media platforms, A/B testing, and website visits. Yet with so much data to sift through, sales teams are often bogged down by the time and analysis it takes to pinpoint the necessary insights.

Fortunately, machine learning can significantly speed up the process of uncovering the most valuable information. Not only does machine learning do a lot of the heavy lifting in the time-consuming process of reviewing all the sales data, but it can also do much of the analysis for your team. For example, Growbots applies machine learning in order to connect sales teams with the best leads for them. In return, sales teams are able to focus only on those leads that have the greatest potential, accelerating their outbound sales process.

2. Real-Time Mobile Personalization

Digital personalization is becoming a more sought-after process to engage prospects and customers, as well as enhance the overall experience so they regularly return to buy your products or services. This has become particularly important in the mobile environment with the advent of tablets, smartphones, and wearables.

Now, mobile marketers and app developers are looking for a way to leverage all the information they can find about each customer’s context so they can develop a highly personalized mobile experience that pleases the consumer and delivers a greater return. Enter machine-learning applications.

Flybits is one company that uses machine learning to enable companies to deliver real-time personalization. This context-as-a-service product allows you to have instant cloud access to internal and external data to develop personalized mobile channels.

Yet as Facebook’s recent experience has shown, companies that fail to protect consumers’ personal data can expect a backlash. According to Hossein Rahnama, founder and CEO of Flybits, “Flybits promotes data transparency and a proactive approach to privacy. Our enterprise customers want to protect the privacy of their customers, and Flybits makes this easy. First, our customers maintain full control over their data — we do not own it. In addition, we follow Privacy by Design to embed security into our software and use tokenization to anonymize all customer data. Our customers have total control over the opt-in choices that they offer.”

3. Fraud Detection

With consumers’ growing preference for shopping online, criminals have gained an enormous opportunity to commit more fraud. Businesses have employed many types of online security measures but are finding that more are needed. The rise in online transactions also means that many of the measures available to check them make each transaction take longer and slow down the purchase experience — and still often don’t work to stop fraud. The result is increased chargebacks that cost money and impact a brand’s reputation.

Luckily, machine learning is available to improve the fraud detection process. For example, PayPal is using machine-learning tools to look for fraudulent transactions (including money laundering) and to help separate these from legitimate transactions. Machine learning assists by examining specific features in a data set and then building models that provide the basis for reviewing every transaction for certain signs it could be fraudulent. This helps stop the fraud in process before the transaction is even completed.

4. Product Recommendations

If you are in the online retail environment, then you know that your customers like having personalized recommendations delivered to them. It improves the shopping experience in their eyes and offers you a way to sell more products. While Amazon was one of the first to introduce an algorithm to improve the product recommendation process, machine-learning tools have ramped up what you can do.

As John Bates, senior product manager for data science and predictive marketing solutions at Adobe, observes: “By leveraging machine learning and predictive analytics, brands can look beyond what customers are searching for and start connecting the dots on what they likely want. It’s cross-selling at scale — matching customers to specific products or content that will nudge them towards more conversions and greater lifetime values.”

Ecommerce giants like Amazon and Alibaba have already jumped on the machine-learning bandwagon. Amazon has improved upon its own product recommendation process with its artificial neural networks machine-learning algorithm, while Alibaba created the “E-commerce Brain.” Its product recommendation machine-learning mechanism has helped the retailer to significantly raise revenues just by populating billions of personalized product recommendation pages.

5. Learning Management Systems

There is greater understanding of the value of ongoing learning opportunities across all learning segments, including virtual training management software. As a result, the global eLearning market is growing by leaps and bounds. In 2010, it was approximately $32 billion. By 2015, it grew to $107 billion. Now, it is projected to reach $325 billion by 2025.

For example, eLearning Industry is an online media and publishing company that was established in 2012 to create a comprehensive knowledge-sharing platform for eLearning professionals. In order to create the most relevant platform for this industry, machine learning became an important differentiating tool. For the tools and platforms that companies create to serve the LMS industry, machine learning is a core competitive advantage because it can generate the most relevant, personalized training management experience possible.

Christopher Pappas, founder of eLearning Industry, writes: “What if you could create eLearning content and then let the system take care of the more tedious tasks, such as reviewing charts and statistics to detect hidden patterns? What if you could provide immediate personalized eLearning feedback and steer online learners in the right direction without any human intervention? Machine Learning and Artificial Intelligence have the potential to automate the behind-the-scenes work that requires a significant amount of time and resources. In the future, AI can help you develop and deploy more meaningful eLearning experiences that bridge undisclosed gaps.”

6. Dynamic Pricing

The travel and retail industries see opportunities to change pricing based on a need or the level of demand. However, incorporating the concept of dynamic pricing can seem impossible across a large enterprise, as there are multiple locations or segments of customers that would need to be taken into account.

That’s where machine learning can make the dynamic pricing model work. For example, both Uber and Airbnb use machine learning to help create dynamic prices for each user on the fly. Plus, Uber uses it to minimize wait time and optimize the ride-sharing aspect of its services. Uber can temporarily change pricing in that area to gain a higher revenue stream. Also, it can reduce rates where demand is much lower.

Machine learning can utilize existing data to predict where demand may occur. In addition, if online companies or app developers can determine where a visitor’s country or city of origin, then they can charge a price based on what that person is comfortable paying in his or her home location.

7. Natural Language Processing

There are so many functions where it would be great to have a stand-in to take care of tedious tasks. These include tech support, help desks, customer service, and many others. Thanks to machine learning’s capability for natural language processing (NLP), computers can take over. That’s because NLP provides an automated translation method between computer and human languages. Machine learning focuses on word choices, context, meaning, slang, jargon, and other subtle nuances within human language. As a result, it becomes “more human” in its responses.

Using this capability, chatbots can step in and serve as communicators in place of humans for various roles. In addition, NLP applies to situations where there is complex information to dissect, including contracts and research reports.

As these examples show, machine learning is ready to step in and make many business areas more efficient, effective, and profitable. The time to implement the technology of tomorrow is today.

Bart Schachter

Bart Schachter

Bart Schachter is an investor, operator, and tech affectionado, with a passion for business operations, team building, product design, finance, and deal making. Bart has been an entrepreneur, VC, corporate executive, and turn-around operator. He lives in San Francisco and all his view are his own.