Marketing

Customer anxiety/fear around product adoption could hinder marketing success, even if you have a high-quality product at hand. Healthcare giants often face the challenge that parents face several apprehensions when it came to vaccinations. By applying machine learning and text-analytics to online conversations, and performing an analysis on keywords such as ‘safety’ and ‘comfort’, ‘happiness’ or ‘unhappiness’, these companies arrived at an understanding of why parents were putting off vaccinations. Consequently, a targeted communications strategy was designed to address these concerns.

As a marketer, you could apply this example to any disruptive or unique offering where ML adoption might be uncertain. By applying a combination of text-analytics and machine learning to your data (support tickets, call center transcripts, emails, etc.) you can unearth useful insights and train the analytics model to constantly improve based on new information.

Email marketing offers a ready repository of data where you can apply analytics and machine learning. Using AI and machine learning based tools, companies are able to optimize their mailer copy for each segment. Based on customer behavior, a personalized message could be auto-inserted into the content, enabling a higher chance of click-through and opens.

This is among the most relevant machine learning examples for marketers. Being informed about click-through patterns can help optimize your future campaigns, and machine learning makes that possible with minimal efforts and maximum accuracy.