Self-Learning AI will Transform the Marketing Industry as we know it


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Self-learning AI is becoming a crucial component of automated customer data platforms (Automated CDPs). Self-learning AI enables Automated CDPs to gather insights about customers at each point of their journey automatically. It allows Automated CDPs to understand the behaviors that drive each customer, reduce churn and deliver highly relevant and personalized content to each customer at precisely the right time.

Self-learning AI will not only become a key component of every CDP but will also transform the marketing industry as we know it.

What is Self-Learning AI?
Self-learning AI is an umbrella term for technologies that enable a system to become more intelligent over time. Self-learning AI involves machine learning (ML) technologies which allow systems to learn and gain insights from collected data automatically. A system driven by self-learning AI adapts automatically based on the data it collects and is distinguished in this regard from other AI systems which rely on humans training and retraining ML models.

Self-Learning AI Poised to Transform Marketing
Traditional self-service CDPs have dominated many areas of marketing for quite some time. However, a CDP that leverages self-learning AI to automate and enhance the entire customer experience (CX) will be more helpful to marketers than a traditional self-service CDP. Businesses are now poised to leverage self-learning AI which has the potential to transform nearly every aspect of marketing.

Automating Marketing Orchestration
Customer journeys often start on one device and end on another. For example, a customer may begin their journey by viewing a website on a laptop but end up engaging with an application on a smartphone. Marketers spend a great deal of time planning and coordinating marketing campaigns across multiple channels. Orchestrating all the components of a marketing campaign to achieve a specific goal is difficult to do without automation. With self-learning AI, most of the tasks required to orchestrate a marketing campaign across multiple channels can not only be automated but also enabled to learn and adapt based on real-time customer behavior and data.

Maximizing Recommendations and Personalization
Most e-commerce systems provide recommendations based on historical customer data or use collaborative filtering which often results in recommendations that are not personalized enough or not relevant. Self-learning AI enables a recommendation system to learn from the behavior of users automatically. With self-learning AI businesses reach customers on their preferred channels with highly personalized marketing campaigns at precisely the right time. Add computer vision, and a recommendation system could provide personalized recommendations based on the product images the user is viewing in real time along with other customer data. Visual search and image-based recommendations are especially beneficial for applications on smartphones and tablets. Mobile interfaces tend to emphasize visual components instead of textual elements.

Enabling Intelligent Marketing Virtual Assistants
Chatbots are all the rage these days. However, 90% of the chatbots available today are rules-based which means they have limited conversational capabilities. Using self-learning AI along with natural language processing, companies today can build intelligent virtual assistants capable of complex, natural sounding conversations. Intelligent virtual assistants can also be implemented for a variety of marketing use cases. For example, a home improvement company can build an intelligent virtual assistant that engages consumers in lengthy conversations about choosing the best interior decorating products. A travel business can develop a self-learning virtual assistant that helps consumers plan vacations based on the data collected from conversations in real time. And reservations can be booked directly through the intelligent assistant.

Enabling Predictive Analytics
Predictive analytics is a powerful tool for marketers. Marketers can use predictive analytics to analyze customer purchasing patterns and reduce churn. Churn is a significant problem for every business selling products or services. For example, Walmart does not want customers deciding to buy products at a competitor like Target or Costco. Amazon does not want Prime customers canceling their subscriptions. And Netflix certainly does not want subscribers canceling their accounts and moving to Hulu. With self-learning AI, predictive analytics capabilities can be enabled to analyze customer data faster and in greater detail. The key to preventing churn is understanding and predicting customer behavior and then engaging those customers at the right time using the most appropriate marketing channel.

Replacing Traditional Self-Service Analytic Tools
Self-service analytics allows business professionals to perform queries on data without the need for extensive IT support. Traditional self-service analytics tools generate basic reports and visualizations from data that has been painstakingly cleaned and prepped by business analysts. Preparing data for analysis takes time, so the data used for self-analytics is not always as up-to-date and accurate as it should be.

Every minute customers are generating massive amounts of data from numerous sources such as social media sites, smartphone apps, and online marketplaces. Most customer data platforms (CDP) are configured with DIY self-service options that limit marketers as to the speed and accuracy in which customer data can be analyzed. But marketers must be able to analyze customer data from numerous sources in real time. A CDP that leverages self-learning AI can handle massive volumes of real-time customer data and provide more precise insights than traditional self-service analytics tools. As more customer data is collected in real time, marketers will use traditional self-learning analytics tools far less, if at all.

Compensating for Shortage of AI Talent
More than 7,000 martech tools are available today and the majority of them are all using machine learning. Machine learning allows martech companies to build tools that automate marketing processes, enable hyper-personalized marketing campaigns, and extract valuable insights from customer data. Martech companies that want to incorporate machine learning into products need AI talent to do so, and AI talent is in short supply. However, with self-learning AI, companies can make the most out of the talent they currently have. For example, data scientists spend nearly 80% of their time performing repetitive tasks such as deduping data, connecting related records, and combining records. And many data scientists spend a great amount of time building machine learning models. Using self-learning AI along with AutoML, repetitive tasks can be automated and ML models trained to build new models automatically – freeing data scientists to focus on finding new ways to use machine learning to enhance martech platforms.

Only the Beginning
When it comes to transforming marketing with self-learning AI, we are only at the beginning. Customer data and analytics platforms will eventually evolve to include self-learning AI as a key feature. Marketing orchestration and ML model building will eventually become automated thanks to self-learning AI. Self-learning AI will allow businesses to enable predictive analytics for marketing campaigns. And machine learning will become a must-have marketing tool for every business, especially businesses that want to enable self-learning AI for intelligent marketing capabilities. Self-learning AI is destined to transform nearly every aspect of marketing, and the good news is that transformation is already underway.

Abhi Yadav
Abhi Yadav, Co-Founder and CEO of Zylotech is a passionate AI/ML technologist who loves to solve problems and build products that sit at the intersection of data, decision-making, and marketing. He has worked with numerous enterprise brands across the retail, technology and financial industries over the last decade to solve their complex Customer 360 category problems while building products and teams. He is an engineer with an MBA from MIT Sloan School of Management. A frequent speaker and writer on AI/ML, Customer Tech and Agile Marketing, follow him on Twitter at @abhishekyd.


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