Machine learning software and pattern recognition have come a long way since their birth during the 1960s, especially during the past decade. The rise in automated and AI-powered marketing, where repetitive tasks are delegated to machines and bots, is allowing many businesses to shift their focus to data-driven marketing, and to better address marketing challenges.
In the absence of machine learning technology, the processing of large amounts of data – something required to predict customer behaviour – is incredibly time-consuming. It also reduces the chances of successful business outcomes. However, when all this data mining is processed by computers, efficient and accurate predictions can be made to determine the “next best action” for each individual customer.
Not only for the big boys
Despite this technology evolution, many small and medium-sized businesses are still lagging behind. For the most part, this is due to the complexity of machine learning, as it requires the involvement of data scientists and developers. However, not all is lost for businesses operating on a modest budget: new AI-driven applications have been created to address specific marketing challenges, and are very easy for marketers to use.
AI needs to be a top priority for businesses that wish to thrive in the current digital environment. These businesses need to capitalise on machine learning, which removes the guesswork involved in the most challenging and valuable aspects of data-driven marketing. Effectively personalising messaging to individual customers results in increased customer engagement, loyalty and spend.
One key way in which a machine learning application can make marketing more effective is automated customer segmentation. Segmenting customers into distinct personas on a daily basis is a highly valuable practice that enables marketers to understand what makes their customers tick, and how to best interact with them. Machine learning customer segmentation models empower marketers to glean a deep understanding of their customers, with its ability to effectively identify small, homogeneous groups of customers who share similar preferences and behaviours. At the heart of this technology, algorithms identify the crucial variables that describe relevant customer segments, thus pushing efficiency and accuracy to an all-time high.
Many marketing technologies are “bolt-on” features, but AI needs to be considered differently. Marketers should look for marketing automation systems where AI is a core capability – software where the rest of the technology is built around a central AI-driven “brain.” This type of system plans, personalises and optimises every customer interaction throughout each customer’s journey, ultimately running thousands of recursive tests to constantly optimise the performance of customer communications.
Racing towards accuracy
Although not perfect, AI-driven predictive analytics makes the most accurate predictions possible today. By analysing past and current customer behaviour patterns, the technology enables marketers to maximise the impact of their efforts in a faster and more efficient way, leaving them with more time to invest in the creative side of their craft.
By Alon Tvina, managing director of EMEA at Optimove
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