Despite a proven track record of successful application in the field of marketing, many marketers are still not utilizing Machine Learning. According to a recent MIT study, 73% of companies believe that machine learning can increase customer satisfaction. Yet, the same study found that only 23% of businesses have adopted the technology – with even less (5%) actively using it. Meanwhile, the companies that are using data and analytics are reaping the benefits, with a reported 19% increase in operating margins over a five-year period.
This begs the question: Why aren’t more companies embracing machine learning? Perhaps the reason is as simple as a lack of understanding, or even fear of the unknown. But with demonstrable increases in accuracy, profit margins, and customer experience, marketers can no longer afford to ignore machine learning.
At a base level, machine learning is an algorithm or Artificial Intelligence (AI) application that enables systems to automatically learn and improve from experience – without explicitly being programmed to do so. The application can be given a specific data set to focus on, and the more it’s used, the smarter and more accurate the results. This provides a unique opportunity for marketers, as it opens the door for an elevated level of marketing customization.
Consider Netflix, whose business model was built around machine learning and data. These insights allowed them to identify a range of offerings that made the streaming service a household staple. Seasons are released all at once, episodes are auto-played, and recommendations are given based on what viewers are most likely to enjoy. These features make the service more relevant and enjoyable – and for that, you can thank data.
The amazing thing about machine learning is that it takes out the potential for human error. When humans try to combine and analyze data, they risk potentially misinterpreting orders, requests or timing. Machine learning allows all information to be processed simultaneously – exploring what has happened and what is happening – and then uses that data to predict what will happen next. These predictions and background data allow companies to personalize and enhance the customer journey.
Are you looking to amp up your customer experience with machine learning? Here are a few of my favorite ways this technology can make a difference.
Developing More Accurate Customer Personas
Companies develop personas based on transaction data, and usually have a decent idea of who their customers are and why they are ordering a certain product. However, they often don’t have a good understanding of the motivators (price, quality, cause, loyalty, personality, engagement) that led to the purchase. The ability to layer data and discover more about customers helps to create more accurate customer personas, and provides insight into the motivators that drive action.
Consider value, for example. If machine learning is able to distinguish the customers that are motivated by price, you’re able to see exactly who to market deals to and who it won’t resonate with. Thanks to data, marketers have the ability to not only know when to send promotions, but also when to develop customized promotions that match past orders. This knowledge creates a better understanding of customer personas and motivators, and lays the foundation for successful 1:1 marketing. Similarly, this data can be used to develop better photos and imagery that appeal to taste motivators or more convenience-driven options. Backed by data, the possibilities are endless.
Forecasting with Data Instead of Intuition
Beyond the customer service touchpoints that customers see, there are many ways machine learning can enhance the customer experience behind the scenes. For example, my marketing team can give the R&D team data on the most popular pizza toppings to help determine new menu items. Additionally, layering data to explore online conversations, requests and social followings allows us to discover optimal locations for new stores that already have a dedicated audience.
Machine learning can also be used to predict supply, inventory and even shipping/delivery opportunities. After all, who is unhappy when their products are delivered on a faster timeline and are never sold out?
Assisting Customer Service by Pinpointing Unsatisfied Customers
Finally, machine learning can tap into data to inform customer service teams of potentially upset customers. This helps to retain current customers and attract new ones. Whether it’s knowing a customer’s typical order, contacting them with an apology or immediately seeing reviews, data can inform several service scenarios. When you consider that 67% of online users are influenced by online reviews when making purchases, you can see why it pays to prioritize top-notch service.
To explore how machine learning is impacting today’s sales organizations, and how top-performing sales teams are already reaping the technology’s rewards, download this comprehensive report: Foundations for Harnessing Machine Learning in Sales.
Kevin Myers is Chief Marketing and Information Officer at Donatos Pizza. With previous executive experience in B2B and B2C companies, he specializes in implementing innovative branding/marketing and technology programs that fuel sales.
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