Use of Machine Learning In Marketing: Saving Cost of Customer Acquisition

Use of Machine Learning In Marketing: Saving Cost of Customer Acquisition

Marketers are increasingly finding machine learning (ML) an ally in their work. Although a new entrant here as compared to other fields, the use of commercial machine learning in digital marketing has started to reduce wasteful expenditure and resources considerably. Know more about the use of machine learning in marketing and the saving cost of customer acquisition.

There’s little doubt that the discipline of ML is growing by leaps and bounds. The machine learning market itself is poised to grow by US $11.16 billion during 2020-2024, progressing at a CAGR of almost 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 the content, customer servicing, and improving 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, a headline, or images, digital marketers can now avail of help from machine learning tools to decide what best connects with their audiences.

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Use Of Machine Learning In MarketingSome of the common use cases where ML can be of use to personalize the experience for the customer are as follows:

  1. Customer segmentation
  2. Targeted messaging
  3. Recommendation engines
  4. Marketing Mix Modeling

For a long, 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 in serving up 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 helps in customer segmentation based on a client’s behavior or characteristics which, in turn, helps serve them targeted content. The collaboration between men and machines allows digital marketers to serve up more optimized content.

Before the entry of machine learning, customer segmentation was done manually and was time-consuming. Now that’s an old story because ML-based algorithms can search for similarities within a customer base and group certain customers together.

A popular technique for marketing spend optimization is Marketing Mix Modeling which involves the use of 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 does optimal allocation of 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:

To improve personalization: Customer personalization must become part of a company’s business strategy. For personalization to be effective, it requires a systemic and sustained effort on part of the marketing team.

Past surveys show that consumers will switch brands if they don’t feel a company is not doing enough in personalization. That is where machine learning can shore up the efforts put in by digital marketers.

AI-powered content: Machines have started writing content; not just some incoherent stuff but the text that seems it is written by a human. There are tools out there, for example, that can rank your content, ad copy, or headline, even simplify them.

Automated email marketing: The deployment of machine learning in marketing can help marketing teams hyper-personalize their campaigns. There are a variety of automated email marketing programs available that are powered by machine learning.

Social Listening: Machine learning-powered social listening tools can help in following brand mentions and hashtags across many social media channels. These can be used by digital marketers to understand how audiences react to specific content products and help create content that finds an echo with their audience.


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