Analyzing sales and marketing data and identifying which factors convert leads into customers are two examples of how data collected from your website can help your organization. But did you know that machine learning could help make quick work of both, relieving the need to sift through data manually? Leveraging the latest machine learning technologies is an intelligent way to make better use of your data, as well as to improve other aspects of your company. Clockwise from top left: Dmitri Alperovitch, Pete Kistler, Erik Gustavson, Simon Crosby, Matthew Kolb, Ashley Saddul, Gurpreet Singh. All photos courtesy of the individual members. Below, seven members of Forbes Technology Council offer their best ways to leverage machine learning this year. 1. Improve Cybersecurity Efforts Use machine learning to beef up the security of your organizations. One of the most promising applications of machine learning is in the field of cybersecurity, allowing next-generation providers to build technologies that detect known and unknown threats faster and more effectively than ever before. – Dmitri Alperovitch, CrowdStrike 2. Identify the Factors That Lead Your Customers to Pay Do you have lots of user data? Manually scrutinizing it for relevant insights can be a chore. And while third-party tools like Mixpanel and RJMetrics make it easier, you still have to know what you're looking for. But if you turn machine learning loose on your data set, it may reveal unpredicted patterns of behavior that lead to purchases, which you can use to optimize future conversions. – Pete Kistler, BrandYourself.com 3. Analyze the Buyer's Journey Use machine learning to analyze the buyer's journey. Stats show that most of the buyer's journey happens before they talk to sales. Use machine learning to find and exploit patterns in your sales and marketing data. Do certain sales reps close deals of a certain size? Which content correlates best in a competitive deal? Fix your weaknesses and exploit your strengths to improve win rates. –Erik Gustavson, Bitium 4. Help Your Experts Succeed Machine learning tools are no match for experts. Machine learning tools may help improve security, but they generate vast amounts of data and a nagging suspicion that an attack may have been missed in that volume. Rather than waste money on the unproven promises of machine learning and AI-based security technologies, I recommend that you invest in your experts and in tools that enhance their ability to quickly search for and identify components of a new attack. – Simon Crosby, Bromium Inc. 5. Make Lead Scoring Smarter Some leads are better than others. Your sales team will learn that the hard way -- by calling and getting rejected -- unless you use predictive lead scoring to target the leads most likely to convert. Our in-house algorithm helps my team increase conversion rates and revenue by filtering out unlikely leads. Subscription-based services may work for others as well. Just make sure you track relevant signals. – Gurpreet Singh, TalkLocal 6. Keep Content Relevant It's important to have a good handle on how people are using our services so that we can deliver better content to them. We would like to analyze our user behavior in real time, and have our algorithms learn what they want. We would then be able to serve them with content that's smarter and more relevant over time, thereby increasing engagement and value. – Ashley Saddul, Recruiter.com 7. Make Your Data Easier to Use Machine learning tools do not need to be complex. Linear regression is a common analysis method that can be used for many numeric predictions and is offered by machine learning APIs such as Amazon, Microsoft Azure and Google. However, a tool as simple as Excel will allow you to do multi-variable linear regression with the data analysis add-in, and there are many tutorials already online. – Matthew Kolb, Names.org
The Forbes Technology Report has set out five ways that machine learning can assist and improve a company 's performance : use next - generation cyber - security technology that detects threats faster and more effectively ; analyse the buyer 's journey and datasets that reveal patterns for customer purchases ; maximise the knowledge of in - house experts by equipping them with proven algorithms ; use predictive lead scoring to increase conversion rates and revenues ; analyse user behaviour to provide smarter and more relevant content ; and keep machine learning more easy by using platforms such as Amazon , Microsoft Azure and Google .