Why Machine Learning Moves the Needle for Marketers

In recent years, the ubiquity of the internet and a state-of-the-art technology changed everything. Customers became prosumers, well informed about the product before the purchase.

They’re less loyal, more bargain-seeking. And what is even more important, customers frequently use a variety of channels: online and traditional stores, mobile apps, online auctions, price comparison websites, social media and more.

Today, in spite of all the available technologies, life is more challenging for both marketers and salespeople.

Last year Hubspot asked thousands of marketers about their top challenges and prepared the State of Inbound Report. The not so surprising results prove marketers struggle to:

The takeaway: marketers struggle with data and technology.

They’re creative people, but even a brilliant campaign won’t attract many new customers if it’s based on wrong assumptions and too much guesswork. Above all, marketers need their activities to be predictable, measurable and scalable.

Older, but still up-to-date research by Econsultancy and SmartFocus also shows the biggest pain points are related to technology and data processing.

Marketers cited moving data between systems (74 percent), gaining a single customer view (69 percent) and turning data into insights (65 percent) as their major pain points. They also indicated budget (41 percent) and workforce (54 percent) as major bottlenecks.

But the right technology, especially machine learning, can mitigate most if not all of these problems.

Have you ever wondered how the spam filter works in Gmail?

How about your “Discover weekly” playlist on Spotify and recommendations on Amazon and other ecommerce sites?

How does Netflix know what series you’re probably going to binge-watch this weekend?

It’s a vast field, which plays a key role in expert systems, natural language processing, image recognition and data mining.

Machine learning is simply defined as “making computers work without being explicitly programmed.”

For example, if you want to predict how your customers will respond to your marketing actions, you can use machine learning to determine it based on their past actions.

This way, data that you’ve gathered in the past will be turned into valuable insights that you will be able to use in the future. No analyst could possibly achieve that, as there is just too much data and there is always too little time to analyze it.

Marketers realize it’s crucial to collect and process data from many different sources, both online and offline — website and mobile app usage, purchase behavior, responses to the previous campaigns etc.

Algorithms cluster that data into segments and allow marketers to create personalized offers in a right time.

Machine learning can also decrease churn rate — the number of customers who have stopped using a service during given time period. How? By discovering patterns in behavior that precede churning and proposing measures that can prevent it.

If machine learning is so useful why are only 49 percent of marketers using it?

Probably because the rest think it’s too complex and that they cannot afford to hire an IT team to support it. It’s also a common view that the marketing clouds are difficult to use, expensive and suitable only for big companies with separate teams dedicated to marketing automation.

It’s not necessarily true, as there are many different tools with various pricing plans, from those affordable for startups to advanced corporate plans allowing to work on huge amounts of data.

Marketing and sales jobs used to require a lot of guesswork and machine learning can remove most of it, if not all.

Marketers like to quote a pioneer in their field, John Wanamaker (1838–1922), who said: “Half of the money spent on marketing is wasted, I just don’t know which half.” It’s a funny and straight to the point summary of their struggles, which haven’t changed much for almost a century. Machine learning in marketing is a true game-changer.

Thanks to the possibility of gathering enormous amounts of data and processing them by algorithms, marketing is becoming predictable.

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