Machine Learning in Digital Marketing

Machine Learning in Digital Marketing

Anna Kowalska

Digital marketing is constantly evolving and with it bringing new practices and techniques to help businesses identify, engage with and acquire new customers.

Machine learning may seem like a complicated technological advancement but its core concept is pretty simple: to help find patterns in data and optimise the problem-solving process.

What is Machine Learning?

Machine learning has evolved over time. In its infancy, data sets were used as a spell check tool to help identify grammatical and spelling errors. Although not perfect, it helped minimise the risk of error.

Since then we have seen large leaps in how machine learning is utilised in areas such as retail marketing. Amazon consumers receive personalised product recommendations related to their purchase history.  If you subscribe to Netflix, machine learning applications will promote films based on what you have watched previously.

Retailers achieve personalisation by using algorithms to assess buyer behaviour and intent, then applying rules to help the consumer and make things better.

How Is It Used in Digital Marketing

Brands use machine learning to quickly deliver personalised campaigns with a view to draw better engagement.

SailThru used ML applications to analyse customer behaviour and personalise email delivery. According to Harvard Business Review, the company harnessed insights into customer purchasing behaviour to deploy emails with the right message at the right time. This approach earned the company a double-digit increase in online sales.

Conversion rates can be improved using ML by looking at the Customer Struggle Scores (CS). This helps marketers pinpoint specific areas of the website customers struggle with so that teams can quickly diagnose and fix the problem to make them higher-performing.

Future of Machine Learning

Machine learning won’t be replacing marketers anytime soon but the technology is demanding that we become wiser to its benefits, forcing us to think about data sources and automated insights into consumer behaviour.

According to Rohit Roy, News Editor at MarTech:

“With the already-abundant data expected to double by the end of this decade, there is ample opportunity for marketers to adopt cognitive computing and make the most of all the data to drive marketing success.”

A vast amount of insightful data is difficult for a single person or team to manage. Behavioural and contextual data are vital for the personalisation of marketing campaigns which is why machine learning must be used to help us process, organise and makes sense of the data.

Where to Start

As machine learning grows so do the number of practical applications to help make it work with your marketing strategy.

If you have a team of engineers ready to go, or able to outsource then predictive models can be run on a cognitive system such as IBM Watson or open-sourced machine learning library software such as Google’s TensorFlow.  Alternatively, there are products such as drag and drop editors are user-friendly options that solve integration issues.

With expanding data sets and consumers demanding more personalisation before, simple machine learning gives marketers the power to identify what elements the campaigns works and what doesn’t.