Taking a look at not only interactions, but intent.
4 min read
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Marketing automation has come a long way — from merely being a tool for marketers to automate repetitive tasks to becoming a fortune ball to understand the high demanding customers of today. The technology advancements in this field have empowered marketers to use customer data to unearth valuable insights and create highly personalized marketing communication, sent at the right time and in the right channel. No wonder it’s slated to become a $5.5 billion industry by 2019.
Here are three examples of how deep learning in marketing automation is making marketers’ life easier.
1. Identifying deeper patterns in data for hyper-personalization.
“Personalization” is a widely used term, but it has started to wear down its significance. Don’t get me wrong; I am not suggesting that you should stop personalizing your online shopping experience and marketing messages. What I am proposing is focusing on hyper-personalization.
Marketing automation with machine learning (ML) allows you to personalize customer experience based on their history of interactions, like purchasing habits, behavioral traits and digital preferences. But it does not take the intent of the customer into consideration.
Deep learning technology, on the other hand, will not just rely on the interaction history of the customer but will consider their intent. For example, a customer comes to your site and buys a dress. During their second visit, the same customer starts checking out footwear. In this scenario, the equation will not rely on transactional and interaction data to personalize the experience, but will consider the intent.
Deep learning is much better than other ML and AI techniques in understanding what customers want as it has the potential to find patterns inside of patterns. According to Michio Kaku, AI is only as smart as a “lobotomized, mentally challenged cockroach.” Deep learning techniques identify and analyze patterns so it can predict real-life outcomes. But the technology is still in its infancy, and what it’s capable of is yet to be seen.
2. Use deep learning to drive customer retention.
Every business knows that retaining customers costs less than acquiring them. And, customer retention can increase company profit by manifolds. According to Bain and Co., a 5 percent increase in customer retention can increase a company’s profitability by 75 percent. When it comes to improving customer retention, deep leaning can help. How? By giving customers what they need, when they need it. According to a Trendspotter report, 82 percent of people are likely to shop at a retailer that provides them with personalized offers.
Marketing automation powered with AI does the same thing by ensuring the right message gets to the right person at just the right time. But deep learning can take it a notch higher. It takes into account customer taste, personal preferences, spending patterns and even micro preferences combined with external factors, like weather, to send highly customized and more relevant suggestions to their customers.
3. Customer behavior is a science: Big data analytics and deep learning.
Prescriptive analytics is another technique that uses deep learning from customer data to predict future trends and behavior patterns. Marketing automation platforms have become powerful enough to anticipate predictions — like when a customer will make his next purchase, what’s the LTV of a customer, who are the most valuable customers, what time is a customer is most likely to buy, and what’s the right discount to offer to a customer segment.
Deep learning has already been used in the advertising industry to make activities up to 50 percent more efficient. That’s why many marketers are super excited about it. But, deep learning is not as easy as it sounds. Still as a marketer, it’s important to know how it works and how you can use it to your advantage. Marketers who strive to be relevant to their consumer need to pay attention to this technology.