5 Use Cases For Machine Learning in Business

One of the most powerful technologies a business can use is machine learning. This is a branch of artificial intelligence that uses algorithms and data to make predictions, forecasts, and suggestions.

While more and more businesses are adopting it, some are left thinking “How can I apply it?” and we have the answer. Keep reading to learn five uses case for machine learning in business.

1. Anomaly detection and marketing performance

One form of machine learning is anomaly detection. This is an AI that takes real-time information and historical data about a company or individual campaign. It then uses those to find data-sets that are abnormal.

For example, if you are running a PPC campaign, machine learning may find sales copy, audiences, or individual ads that are outperforming others. These are anomalies. A business could then double-down on these high-performing assets to enhance ROI.

Alternatively, marketers would have to create experiments and perform A/B split tests to conclude the same findings. Thus, anomaly detection is capable of both improving marketing performance and saving company resources.

2. Cybersecurity and fraud detection

Cybersecurity is a rising concern for modern companies. As technology and tools become more sophisticated, so do vulnerabilities and security flaws. This means that customer data and other sensitive information is at risk. That’s where machine learning comes into the picture.

It is capable of being integrated into a brand’s security to predict and identify problems before humans can. This results in a business being able to fix flaws before they are exploited and see attacks in real-time. Cybersecurity becomes automated, leaving marketers to focus on their strong points versus constantly monitoring web assets.

It’s not uncommon for security breaches to go unnoticed until they’ve caused damage either. Machine learning empowers companies to be one step ahead of attackers and vulnerabilities.

3. Predictive analytics

Predictive analytics is a field of artificial intelligence that combines traditional data reporting with machine learning. The result is live data that trains itself to continually offer accurate forecasts and predictions. Predictive analytics can be integrated with anything including marketing, advertising, financing, and security.

Organizations can use predictive analytics to implement optimizations ahead of time and prevent poor-performing campaigns from running too long. This is thanks to the patterns and trends machine learning is capable of recognizing. Similar to other forms of AI, predictive analytics becomes more accurate and effective the more it’s used.

4. Improving ad targeting

If you’ve ever run PPC ads, you know that improving targeting is one of the greatest focuses. This is because a hyper-segment tends to engage the best with ads while providing a higher click-through rate and conversions. Nonetheless, it can take a hefty amount of capital and time to test your way to success.

Machine learning solutions, on the other hand, are capable of learning how your paid ad campaigns perform along with what customer profiles offer the best ROI. Over time, you will be able to narrow down the best audiences and traits to continually target for high performance.

This is achieved by using data mining, predictive modeling, and other techniques that teach the AI how your ads typically operate. It is then able to discover the exact characteristics that compose your best audiences and what to avoid at the same time.

5. Optimizing budgets and finances

Balancing ad spend, marketing budgets, and other finances are key to longevity in business. That’s why machine learning models have the potential to optimize budgets of all sorts.

For example, machine learning software can determine if specific audiences, channels, or campaigns are spending too much while underdelivering. This can be minimized to save capital while the good performing assets are focused on.

Similarly, machine learning can be deployed to find bugs and human error. It’s normal to forget to set limits for PPC campaigns or turn on campaigns to begin with, for instance.

Conclusion

Machine learning is a powerful technology for companies to use in 2020. It’s capable of improving efficiency, marketing, finances, and more.

In particular, anomaly detection can be used to find outlier campaign assets that offer higher performance than others. Similarly, predictive analytics is capable of forecasting marketing, sales, and other reports to make faster decisions.

Security can be integrated with AI to prevent breaches and handle them sooner, as well. Brands may want to consider using machine learning for finding high ROI audiences if they invest in PPC, too.

Lastly, machine learning is a great compliment to a financial department as it can help optimize budgets and spending.

Leave a Reply

Your email address will not be published. Required fields are marked *