3 Ways machine learning is improving real-time marketing

Machine learning is vital to facilitating real-time marketing through analysing data to anticipate consumer behaviour and deliver relevant messages.

Real-time marketing delivers dynamic, personalised content to the right audience, at the right time and on the right platform by:

Producing relevant audience insights and segmentation

Machine learning can synthesise and analyse large data sets to create unique customer profiles which can be grouped based on specific characteristics. This data includes the websites a customer visits, the emails they open, the social media accounts they interact with, and the ads they click on. This allows marketers to identify the optimal point in a customer’s journey to target them, which channel the customer is most engaged with, and what type of content the customer is most responsive to, enabling marketers to improve both lead generation and conversion rates.

Increased personalisation

Facebook, Amazon, Netflix – even news sites – all curate personalised content to enhance the user experience. When two people log onto Netflix, their home screens look very different depending on the content they interact with. Machine learning could facilitate similar personalisation on brand’s websites, EDMs and online ads. Algorithms can predict which type of content would be the most popular with each unique customer, meaning a website may look very different for a visual customer who engages more with emotive video content, than a customer who is more responsive to a logical presentation of written information. This level of personalisation could restore trust in brands and increase customers’ brand affinity.

Reducing churn rate and improving the post-purchase experience

The customer journey isn’t over once a customer purchases your product or service. In fact, there are crucial points along the customer’s post-purchase journey. For example, customers may exhibit certain behaviours in the lead up to switching service providers. This behaviour can be identified and modelled by a machine learning discovery model, so when a customer begins to display switching behaviour, the model sends a personalised email to the customer, notifies a customer service representative, or alters other personalised content for that customer, to intervene before they decide to leave.

Consumers are becoming more and more selective in the content they interact with. With consumer trust in social media companies at a low, brands using social media must reconsider how they segment and target customers. It is vital for marketers to understand how machine learning can improve the customer experience and trust in brands through real-time marketing.