Machine learning or “artificial intelligence”, has undoubtedly grown in popularity and prevalence within the past few years, especially in the marketing world. Whereas before, artificial intelligence was assumed to be the rise of Terminator-like machines, the term is now much more familiar. With the majority of people having had experience with being sent personalised promotional offers or having them show up on their feed, website chatbots and spam filters in their email service, the term has more positive connotations than before.

Machine learning is a form of artificial intelligence that allows software applications to predict outcomes using existing data and analysis. When collecting data manually, marketers previously have to go through the lengthy process of creating hypotheses and then having to test, evaluate and analyse them. On the other hand, machine learning can process hundreds of requests, organise them and then provide the answer to the hypothesis in a much shorter amount of time. However, when setting up these conversions, it is vital that the data is correct and relevant. For example, the significant similarities in a group of 10,000 are going to generate a much better outcome than in a group of 100 people. 

Is it helpful to have automation on sites like Google and Facebook?

In the past, native tools such as Google Analytics and Facebook Business Manager were solely used to understand and evaluate the quality of marketing campaigns, especially for paid ads. But, the death of third-party cookies and privacy updates has meant that analysing user metrics for optimisation is becoming harder. 

Automated bidding strategies are another form of machine learning. Google uses machine learning to achieve your goals based on your bidding strategy in order to optimise conversions. This means while maintaining control of your keywords and ad copy, Google shows your ads to the people most likely to carry out your business goals, based on existing data. Providing Google with as much data as possible gives you the best chance of attracting valuable and qualified users. 

Meanwhile, social media site, Facebook uses machine learning by creating custom audiences. Social platforms analyse these custom audience lists and use key markers to identify what the users have in common. Look-a-like audiences are then added to the custom audience lists as they are additional users who also possess the key markers. 

Therefore, these audiences are targeted for re-marketing, as they have previously engaged with similar content or have key markers which make them more likely to close a sale, or they are look-a-like audiences who have significant similarities to those who have previously engaged. 

What are the benefits?

• Machine learning improves the quality of data analysis
• It enables you to analyse more data in less time
• It can quickly adapt to changes and new data, allowing marketers more time to create hypotheses rather than carrying out routine actions
• It allows you to automate marketing processes

Should we fully rely on automation?

While the team at Distract think machine learning is very beneficial, we also think it’s best to find the right balance between manual work and automation. The need for a human to go through the accounts alongside using automation is still there. This is because computers cannot fully understand the meaning or intent behind a search term. Whereas when both are used, humans can use their intuition to guide the results from automation to get the best overall outcomes. 

If you’d like to know more about how we use automation to get the best results for our clients and how we can help you, get in touch.