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Most marketers today are aware of machine learning (ML) and how many companies are using it for a wide variety of use cases- from automating business processes to creating highly personalized applications. Some marketers may be concerned that machine learning will leave them without jobs. However, the core tasks of marketing involve creativity- an attribute machine learning does not have. Machine learning can’t take over every marketing task, but it could be used to assist marketers in a wide variety of ways.
According to a recent Workfront survey, U.S. marketers spend 38% each workday on primary job duties. Marketers are spending 62% of their time on tasks many of which are repetitive, monotonous, and aren’t directly related to marketing at all. Emails and wasteful meetings are among the top three things the marketers surveyed said decreased their productivity.
Thousands of marketing technology solutions are available today, and most of them leverage machine learning. With the help of ML-based solutions, companies can automate and streamline a variety of marketing workflows- emails, marketing campaigns, data and documents, and analytics to name a few.
Machine learning allows repetitive marketing tasks to be completed at a speed and accuracy that
humans can’t achieve. When repetitive marketing tasks are automated, marketers can spend more time on brainstorming marketing campaign ideas. They can spend more time on building strategies aimed at finding, attracting, and retaining customers. Marketers can also spend less time on testing their ideas. Some testing solutions leverage machine learning and AI so that marketers can try out all their ideas all at once instead of only a few at a time.
Automating repetitive tasks leaves marketers more time to spend on the core marketing tasks
they were hired to do.
Analytics is a crucial part of marketing. Analytics helps marketing teams find potential customers,
understand customer purchasing patterns, and even predict and prevent churn. Analytics involves many processes including collecting and preparing data, building and training ML models, and creating visualizations and dashboards.
Machine learning along with artificial intelligence (AI) enables platforms to be self-learning and allows many of the processes needed for analytics to be automated. For example, the Zylotech platform features sophisticated self-learning algorithms for embedded analytics. Our platform
is also designed for the whole automation of customer analytics. It features built-in data analytics tools, including predictive analytics, for creating highly personalized 1:1 customer experiences.
Automating repetitive marketing tasks, boosting marketing productivity, and enabling self-learning for analytics are just a few of the ways machine learning can help marketers. Thanks to machine learning, many companies are including in their digital marketing strategies chatbots to engage customers, highly personalized loyalty programs, predictive analytics, and so much more.
Marketers should be enthusiastic about machine learning because it can enable marketing innovation, boost creativity, and increase marketing campaign successes.
Janet Wagner is a Zylotech contributing writer.
If you liked this post, check out our other blog post on how to achieve robust customer data with record linkage.
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