Off-the-shelf vs Custom Machine Learning Models?
When is building better than buying an off-the-shelf solution? Companies can engage in different approaches to model development. From fully...
“Machine learning” is hot. Apple is “building a machine learning system to rule them all.” YouTube uses machine learning to remove objectionable content, while telecom empires apply ML algorithms for predictive maintenance and improving network reliability. And, of course, there are plenty of TedTalks.
For all the hype around machine learning, it still seems like a distant and futuristic concept for many in their everyday work lives. However, this technology — which is defined as a computer learning from experience to improve at a task without explicit programming — can be implemented at your company today regardless of size or industry. The underlying thread of ML for business is the potential to help companies operate more efficiently and competitively.
To illustrate this potential, here’s a brief overview of applications:
The discipline has always been a bit of an art, but machine learning can elevate your creative intuition with a strong scientific foundation. Marketers are already using machine learning to target the right audience at the best time, test different combinations of copy in real-time, and personalize landing pages with optimal product and pricing.
In the knowledge economy, finding and retaining the best employees is more important than ever. Fortunately, machine learning can help. Algorithms can be used to remove bias from the hiring process, rank resumes, and identify candidates similar to your most successful employees. It can create custom experiences that attract applicants, automate feedback throughout the hiring process, and even answer candidate questions in real-time. Post-hire, machine learning enables employers to identify which of their employees are most likely to turnover creating an opportunity to intervene. Here’s an interesting case study on how Blue Orange uses ML to solve problems across the hiring process.
You’ve probably already experienced machine learning applied to customer service — in the form of chatbots. While not all chatbots incorporate machine learning, the ones that do can identify when it’s appropriate to use specific responses, gather required information, and escalate to a human agent. Additionally, natural language processing helps human agents quickly find answers that are buried in heavy text. These applications fundamentally increase customer service speed and customer satisfaction.
Increasingly sophisticated fraud attempts, aided and abetted by new technology, call for increasingly sophisticated fraud prevention and detection. Machine learning’s anomaly detection capabilities make it well suited not only for recognizing old patterns of fraudulent activity, but also detecting new types of activity as they emerge. The resulting reduction in chargeback levels is especially valuable for e-commerce businesses.
Likewise, the constant evolution of cyber-attacks renders machine learning an important tool for cyber defense. Because it does not rely on past attack data as much as conventional approaches, machine learning is able to keep pace with hackers and more accurately predict cyber threats. Additionally, the sheer volume of cyber attacks makes machine learning an important tool for managing staffing expenses.
Machine learning is already an indispensable tool for many industries. If ML is the future of business, then the future is here.
Here’s a helpful framework for understanding machine learning: https://blueorange.digital/machine-learning-an-introduction/
If you want to dig in on specific applications for your business, feel free to reach out to us today.