9 Revolutionary Ways Machine Learning Is Changing Marketing

AI can help businesses optimize many processes, and the costs for this tech are shrinking.

Artificial intelligence (AI) and machine learning have a lot to offer to marketers. In addition to simplifying several mundane tasks, this technology might finally help marketers achieve relevance at scale and ensure “the most pertinent content reaches the most promising customers at the moments of greatest influence across multiple channels and markets.”

Many marketers have faith in AI’s contribution to their field, but not many are implementing it. Eighty-five percent of executives believe that AI technologies will help their companies gain more competitive advantages, yet only 10 percent are using AI today. In order to have more social media visibility check out Subscriberz and their wide range of social media services.

If you are among the majority of companies not using AI and you’re not sure where to start, here are nine areas where machine learning is making the life of marketers easier.

  1. Better marketing campaigns

One of the biggest benefits of machine learning is that it can recognize patterns and connections that humans can’t. This feature is useful when we want to predict purchase trends and user behavior patterns. With machine learning models to analyze Adobe Analytics or Google analytics data, you can use behavior data from across the web to find and target the audience that is most interested in your product, and, therefore, most likely to convert.

  1. Churn prediction

Predicting customer churn is hard but not impossible. The signs are there if you can spot them, and I certainly can. Based on historical data, machine learning algorithms can analyze behavior, identify customers who are most likely to churn, and even trigger necessary actions to prevent that from happening (special offers, check-up e-mail, etc) – all without human involvement.

A lot of churn prediction revolves around the customer lifetime value score (CLV). Traditionally, it is calculated based on a few simple metrics, but some companies have taken it a step further and built their model using predictive behavior modeling and microsegmentation. The results show which of your customers are most likely to churn in real time.

  1. Improving customer experience

Throughout the customer journey, customers are exponentially more in touch with AI rather than with humans. Apart from marketing automation, chatbots are becoming a staple feature of business interaction. Not only can they answer general questions and troubleshoot problems, they can also help you pick products based on your preferences.

1-800-Flowers is using GWYN, an extension of IBM’s Watson, to help customers pick flower arrangements based on the occasion and intended audience. This alone increased the company’s revenue by 6.3 percent in the first quarter of 2017. North Face has embedded chatbots into their website. Chatbots ask customers where and when they are going and suggest products from the catalog that fits those needs. Hilton Hotels are piloting a new concierge experience using chatbots.

  1. Dynamic pricing

Dynamic pricing allows companies to be more agile and respond more adeptly to supply and demand fluctuations.

Amazon and eBay already tweak their prices daily using algorithms that change the prices of products according to the loyalty level of customer. It’s not only reserved for online stores. Big retailers are slowly embracing dynamic pricing in physical stores. Some cafes and restaurants offer discounted lunch meals for those who want to avoid the midday rush or discounted prices in the evenings to cut food waste.  

  1. AI-generated content

This is a fairly new area for AI. While computers still can’t write an in-depth article about something industry specific, a few companies are offering simple content-generation tools that transform numeric data into coherent narratives or that generate lengthy reports within a matter of minutes instead of weeks.

In a world that is bursting at the seams with fast, meaningless content, this new possibility opens doors to provide quality content that typically takes a long time to generate.

  1. Better customer segmentation

Clustering algorithms can help marketers add more dimensions to the analysis of their customer data, thereby creating a richer picture of clients. Clustering algorithms also adjust constantly as more data becomes available, which makes it more responsive than traditional segmentation techniques.

  1. Personalization

It’s impossible to talk about machine learning in marketing without mentioning what it can do for personalization of content and campaigns. Netflix and Amazon are doing it already. Algorithms can re-engage based on specific customer behavior with incentives like offers and discounts, but with the help of machine learning, ads and campaigns can be customized differently to each customer segment, even down to the pictures that are featured as well as taglines, headers, and formatting.

More companies are using recommendation engines as a part of their personalization strategy, finding that they are a powerful tool for increasing customer loyalty and maximizing customer lifetime value. This wide use of recommendation engines has recently become possible thanks to decreasing prices in the software, data storage and the data itself.

  1. Content optimization

Machine learning has the opportunity to replace A/B testing of content. With A/B testing, there is always a period of time where revenue is lost due to the use of the lower-performing version of the content. With new platforms available, multiple options of content can be tested simultaneously, and traffic from the lowest-performing campaigns is redirected automatically to better-performing options while still providing you with insights.

  1. Monitor media presence

A logo placed on a billboard or a T-shirt is not only seen by bypassers anymore. Thanks to social media, a company’s logo can show up anywhere and have a totally different impact than expected. AI can help companies track and measure brand exposure during commercials, events shown on TV or social media. Companies are applying computer vision to pictures, videos and social media feeds to give a complete picture of traditional media outreach.  

Machine learning applications have the potential to transform the marketing strategies many companies are using. This process doesn’t always have to evolve from inside the firm’s marketing department; there are companies that help marketers solve common problems in a more effective way through constantly improving predictive analytics models with the help of machine learning. This way, the marketing automation trend is only going to grow, providing marketers with more opportunities to be productive, customer-oriented and data-driven.

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