How Machine Learning Will Change Digital Marketing

Machine learning and data science both have significant parts to play in the future of digital marketing. But what does this mean for marketers?

As artificial intelligence (AI) becomes commonplace in advertising, it’s important for marketers to get ahead of the curve by learning to harness machine learning (ML). Let’s look closer at the predicted effect ML and AI will have on the world of digital marketing. 

What is Machine Learning?

It’s important to note that machine learning and artificial intelligence aren’t one and the same. As you may have gathered from sci-fi movies or media coverage of robot-led factories, AI is an attempt to harness certain aspects of the human mind in digital form. 

Machine learning, on the other hand, is a discipline that helps humans solve problems in a more efficient and cost-effective way. Machine learning uses science, data and computer code to predict outcomes based on discovered patterns.

An Example:

Cloud computing is a prime example of machine learning in action. The cloud uses data to offer businesses a high level of scalability and power, driving innovation in almost every sector.

Today, the best marketers are using machine learning to understand, anticipate and act on the needs of the consumer. This discipline allows businesses to deliver advertising to the right audience at the right time. As such, the best digital marketing departments rely on a strong set of analytics and key performance indicators (KPIs) to increase the efficiency and ROI of their campaigns.

Machine Learning and Digital Marketing: What Does The Future Hold?

According to a study by Quantic Mind, 97% of marketers believe that the future of marketing involves humans working alongside machines and AI-led tools. 

By 2020, it’s predicted that 85% of customer interactions will be managed with no human involvement, according to Gartner. The study also reported that real-time personalised advertising and optimised message targeting would accelerate in this timeframe. Marketers also predict that Sales Qualified Lead (SQL) generation will also increase, potentially reducing sales cycles and increasing win rates. 

The combined effect of these marketing technologies will no doubt increase sales effectiveness in retail and B2C-based sectors. 

Why Do We Need Predictive Analytics?

Predictive analytics are important because they give marketers insight into the future. That way, companies can react not just to what is happening currently, but also how a consumer will act in the future. Predictive analytics can also help prevent negative outcomes, reducing churn rate by boosting engagement with sales prospects. 

Predictive analytics also help businesses plan for growth. It means business leaders can distribute their marketing budgets using predictive planning, rather than using historical data. 

Do We Need Machine Learning in Digital Marketing?

Regardless of the projections for the future, the purpose of machine learning and AI isn’t to take over the jobs of digital marketers. The main function of these emerging technologies is to enhance existing strategies, increase automation and make the jobs of digital marketers easier. By utilising machine learning, marketing professionals can streamline and future-proof their digital strategies in a more timely, cost-effective way.

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