Marketers are increasingly finding machine learning (ML) an ally in their work. Although a new entrant in this field compared to others, the use of commercial machine learning in digital marketing has begun to reduce wasteful expenditure and resources significantly. Know more about the use of machine learning in marketing and the cost of customer acquisition.
There’s little doubt that the discipline of ML is growing by leaps and bounds. The machine learning market is poised to grow by US$11.16 billion from 2020 to 2024, progressing at a CAGR of nearly 39% during the forecast period, according to this study.
The benefits of using machine learning in marketing are more visible in enterprises that make high-velocity data-driven decisions. Specifically, ML is currently being used in content creation, customer service, and enhancing customers’ online experiences. So be it, A/B testing of marketing campaigns, smart form flows, or even A/B testing content, such as email subject lines, headlines, or images. Digital marketers can now leverage machine learning tools to determine what best resonates with their audiences.
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Use Of Machine Learning In Marketing
Some of the everyday use cases where ML can be of use to personalize the experience for the customer are as follows:
- Customer segmentation
- Targeted messaging
- Recommendation engines
- Marketing Mix Modeling
For a long time, we’ve heard of how Amazon used machine learning techniques to shore up its revenue through personalized product recommendations. Netflix and Amazon Prime, with their recommender systems, are other examples of ML helping to serve the right content to the targeted customer base.
What machine learning in marketing essentially does for now, at least, is to reduce the cost per customer acquisition. This is possible because the technology enables customer segmentation based on a client’s behavior or characteristics, which in turn helps serve them targeted content. The collaboration between humans and machines enables digital marketers to deliver more optimized content.
Before the entry of machine learning, customer segmentation was done manually and was time-consuming. That’s an old story, because ML-based algorithms can search for similarities within a customer base and group specific customers together.
A popular technique for optimizing marketing spend is Marketing Mix Modeling, which utilizes machine learning to decompose sales into incremental sales influenced by each marketing channel/activity. This model can be used to create a scenario builder that allows marketers to compare the impact of different marketing plans and an optimizer that optimally allocates the marketing budget, subject to constraints specified by the marketer.
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Some Of The Other Ways That Machine Learning is Used in Marketing:
Improve personalization: customer personalization must become an integral part of a company’s business strategy. For personalization to be effective, it requires a systemic and sustained effort on the part of the marketing team.
Past surveys have shown that consumers are likely to switch brands if they don’t feel a company is doing enough in personalization. That is where machine learning can augment the efforts of digital marketers.
AI-powered content: Machines have begun writing content, not just incoherent text, but text that appears to be written by a human. There are tools available, for example, that can rank your content, ad copy, or headline, and even simplify them.
Automated email marketing: The deployment of machine learning in marketing can help marketing teams hyper-personalize their campaigns. There are various automated email marketing programs available that utilize machine learning.
Social Listening: Machine learning-powered social listening tools can help track brand mentions and hashtags across multiple social media channels. Digital marketers can use these to understand how audiences react to specific content products and help create content that resonates with their audience.
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