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How to make the most of marketing insights with machine learning
By our News Team | 2023
Use it to make sense of vast amounts of data to better understand buyer behaviour, identify patterns and make more accurate predictions.
It’s been almost a year since OpenAI’s ChatGPT was released for public use. Since then, the world has become enamoured with artificial intelligence (AI).
But, says Charl Fourie, Head of Business Intelligence at digital agency Sprout Performance Partners, while many people believe this is our first chance to experience what AI has to offer, the truth is that AI – including subsets of this technology like machine learning – has been around for some time.
Photo by Alex Knight from Pexels
Machine learning is what powers the chatbots we interact with when placing customer service calls and enables the predictive text that corrects our typing errors, for example.
For modern marketers, machine learning can be used to make sense of vast amounts of customer data so that they can better understand buyer behaviour, identify patterns and make more accurate predictions, emphasises Fourie.
Unfortunately, many marketers are not using the full power of these algorithms, which means that they aren’t tapping into the insights hiding in the vast amounts of consumer data they collect.
There are many reasons for this, Fourie believes. For example, if the data isn’t of the right quality, if you don’t know how it is structured and stored, and if you don’t understand what data you do and don’t have, your machine learning investments won’t bear much fruit.
“If you want [your] data to deliver any real value, it needs to be cleaned up and consolidated so that it actually tells you something useful,” he says.
For marketers experimenting with machine learning, Fourie provides some suggestions:
Understand your data
“This may sound fairly obvious but if you don’t know the ‘who’, ‘where’, ‘why’, ‘what’ – who has access to it, where it is, why are you keeping it, and what it is – your data won’t add any value to your business,” notes Fourie.
Before you approach an agency or third-party provider to help you on your machine learning journey, take the time to audit your data so that they don’t have to waste time searching for information and tidying everything up.
Identify use cases
“It’s a bad idea to bring in machine learning just because of the hype. Conversely, if you understand what machine learning offers and what it can do, you’re better positioned to use it for your unique needs and challenges,” he states.
For example, if you use third-party cookies frequently, it makes sense to get your own data in order before this data-mining resource is no longer available. Similarly, if you’re looking to deliver more personalised experiences, machine learning enables you to provide more relevant and customised content, deals and recommendations.
Because many businesses still don’t fully grasp the potential, they are afraid to get started. But it’s important to experiment so that you can figure out how machine learning can add value and how to incorporate it into the work you do.
“These tools aren’t going anywhere,” he emphasises. “So, if you don’t get stuck in soon, you really do run the risk of being left behind.”
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