7 Ways That Marketers are Using AI To Make Money

Artificial intelligence is becoming an incredible tool in modern marketing. Beyond the robotic assistance of Siri and Cortana, AI is the wave for the future for companies looking to process big data and engage customers in more dynamic, efficient ways. Yes, machine learning is a workhorse.

It makes data analysis and pattern recognition a cinch. The idea of scaling and utilizing massive data sets is becoming a legitimate reality for companies. As a result, AI platforms boost productivity via automated processes, cut costs, and deliver superior customer experiences with minimal effort.

AI is not a stand-alone entity, yet. Machine learning is still most powerful when manned, but employees accomplish more by interacting with the insights gleaned en mass from these platformed algorithms.

Here are seven outstanding ways you can employ AI in your own marketing department.

1. Content creation

Gartner predicts that as of 2018, 20% of business content will be written by machines. Viewers are already unable to tell the difference between human and digital authors, provided the article is limited to straightforward and factual ideas.

The Associated Press is making good use of AI for this automated system to report financial news. Articles written by the AI contain the tag “This story was generated by Automated Insights.” The Automated Insights technology is currently at work for Comcast and Samsung, able to author 2,000 articles per second.

2. Programmatic advertising

Machine learning is able to assess and react to big data in an automated way. By better directing ad buys, it increases the likelihood that users will click on programmatic advertising (formerly gone the way of banner-ad blindness). Automating the planning, buying, and optimization of ads across social media, mobile, and display ads offers simplicity, efficiency, and improved targeting across demographics.

By detecting more indicators and surveying a wider network of trends, advertisement servicing runs via a “recommendation engine.” The platform aggregates behaviors on an individual level, from geopositioning,  point-of-sale providers, and search engine results.

Companies collect this wide-source data to better interpret how consumers react to certain stimulus. These programs also use algorithms to price bids according to CPA, advertiser, and available inventory.

3. Information transfers

Beyond speech recognition software, language recognition can be used to assimilate unstructured information to better access prospects and customers. Say you have third party information you need to manually input for use in an app or a software platform, such as sales lead outreach into a CRM.

IBM’s Watson technology makes short work of this labor-intensive chore formerly reserved for employees. Facebook and Microsoft also offer machine learning platforms that make transcription and data transfer an automated instead of a human process.

4. Virtual assistants

To improve customer experience, many companies hire teams virtual assistants to help customers better interact with their brand. Apple’s Siri is the longstanding example of the friendly virtual assistant while Microsoft's Cortana is the fresh iteration.

Facebook debuted “M” in 2015 as part of its messenger platform. “M” functions to save users time by booking restaurants, arranging travel, and purchasing items when prompted.

Website chatbots have shown us for years now that brand-related assistance needn’t always come from a human. BMW uses iGenius technology to field ongoing questions related to new products from customer service staff and facilitate dealership training. The iGenius smart-learning platform assimilates previous questions to provide more meaningful answers in the future.

5. Customer segmentation

To better sort customer groups for targeting, brands are turning to clustering algorithms for machine-acquired insights. It would take countless man-hours to figure each prospect's position in the sales pipeline. Instead, predictive AI pores over emails, previous conversions, and all past behavior to return an accurate image of lead quality.

Delivering the perfect message to the right person via the appropriate platform is not easy. But AI excels here, ramping up traditional A/B test efforts by collecting data and acting upon it in future adaptations. Though not pinpoint, machine learning makes short work of filtering through all your sales lists, allowing improved pipeline forecasting.

6. Customer service

When big data mixes with CMS and CRM systems, predictive customer service is almost possible. But it still requires a lot of inter-platform communication and employee effort to make useful. Saffron, now an Intel company, operates AI capable of analyzing thousands of discrete factors to synthesis customer behavior patterns.

It’s valuable to know why, when, and how customers will want to reach out with issues - and especially about which products. USAA is reportedly able to guess customers concerns with 88% accuracy with Saffron’s smart-learning tech, up from 50% before.

7. Image recognition

Many traditional newspapers have transitioned into cross-platform content distribution networks. As part of increasingly busy days, journalists are now creating multiple blog posts, championing social networks, and sourcing images.

But as part of these companies’ new content management system, AI delivers readers a more-personalized experience without adding hassle to overworked employees. Based upon existing user information, the content and images displayed are all the left to a program. This way, viewers get the most relevant content and the journalists can to get back to work.

Conclusion

Hugely powerful for marketers, artificial intelligence still requires a human touch to function optimally. The widespread interpretation of data and the subsequent return of readable information assists with customer service, customer segmentation, and programmatic advertising.

AI is also helpful in assuming traditionally human roles. Websites with chatbots and platforms with virtual assistants reveal that machines are very helpful to humans in the right setting. Algorithms can transfer data and take orders, but also create content as well, making them valuable assets for SEO and content strategies in the future. And this future is getting nearer all the time.

Which area of your business would benefit the most from Artificial Intelligence?