Event-Driven Marketing and AI: strategies to increase conversions and loyalty

Event-driven marketing (EDM) is a strategy that enables companies to respond immediately to specific user behaviors, such as visiting a page or opening an email, in order to improve the relevance of communications and drive conversion. This real-time responsiveness capability is particularly relevant for personalizing the user experience, increasing engagement and return on marketing investment.

A turning point for EDM was provided by Google Analytics 4, which abandoned the traditional session-based approach to adopt an event-centric structure. In the previous version, Universal Analytics, the focus was on sessions and page views in a single visit, limiting the ability to track detailed interactions. With GA4, every relevant user action is considered an “event,” allowing a much more granular view of behavior, unconstrained by session, device, or content.

The need to adopt an event-driven approach emerged with the spread of mobile and the increase in the number of digital devices and channels used during the customer journey. Monitoring models centered on linear sessions had become outdated and poorly able to collect all interaction signals from the various touchpoints. Today the user experience is even more fragmented and multichannel: moving rapidly from digital to physical environments, from an interactive totem to a mobile device, from an out-of-home sign to a wearable, and finally to a desktop site. There are numerous micro-moments of interaction, such as watching a video, clicking on social ads, or saving content.

Another factor driving EDM adoption is the evolving expectations of consumers, who now want immediate and highly personalized interactions. Customers expect companies to understand their specific needs and intent by tailoring communications accordingly. An event-based system allows these interactions to be tracked and responded to quickly with customized content or offers.

The event-driven approach naturally leads to an increase in the amount of data collected, but thanks to artificial intelligence and machine learning technologies, this amount of information is not a hindrance but a huge opportunity. Artificial intelligence allows event data to be processed to extract additional and predictive insights by segmenting data in real time and predicting future behaviors. This allows complexity to be turned into value, enabling proactive and targeted response and improving understanding of the customer base.

Events: what they are and how to identify them

An event can be defined as any action taken by the user in interacting with a company touchpoint. Examples of events include playing a video, clicking on a link, visiting a physical store tracked through beacons, swiping a loyalty card or moving a shopping cart within a store. All of these actions, when tracked and collected properly, allow companies to get a more complete picture of the customer journey, especially through accurate and orderly data collection.

To optimize the collection of this data, tools such as GA4 allow events to be exported to a centralized database. This approach allows data to be aggregated and organized efficiently, facilitating event analysis and integration with other business systems, such as CRM, e-commerce platforms, ERP and others.

Centralized Database: the foundation for Event-Driven Marketing

The implementation of a centralized database is critical to the success of Event-Driven Marketing, as it enables the efficient collection, management and analysis of large volumes of data from multiple touchpoints. Cloud computing is essential to this architecture: in addition to scalability, cloud database platforms offer speed, security and flexibility in data processing.

One of the leading companies in this area, Snowflake, introduced the concept of Marketing Data Foundation, arguing that the marketing department needs a dedicated cloud database specifically designed for data processing and integration. Unlike siloed storage systems, which fragment information, a centralized database provides a holistic view of the customer, which is essential for accurate predictive analytics and a personalized event-driven approach.

In addition to Snowflake, leading cloud database platforms include BigQuery (Google Cloud Platform), Redshift (Amazon Web Services), Databricks, and Microsoft Azure.

In an EDM context, a centralized database enables companies not only to respond quickly to events, but also to extract key insights from the data in a fluid and integrated manner, optimizing marketing campaigns with a direct impact on performance and customer satisfaction.

Event-based approach: smarter algorithms and better results

With the introduction of artificial intelligence, event-based marketing has reached new levels of effectiveness, revolutionizing the way companies interpret and respond to consumer behavior. Event-based tracking, in fact, is distinguished by its ability to capture an extraordinary amount of granular signals, offering a detailed and dynamic overview of user interactions that would otherwise be inaccessible. This approach provides algorithms with an extremely rich database, which enhances their ability to learn and adapt and allows them to increase the statistical confidence of their analyses.

Companies can leverage three main categories of algorithms to extract value from collected events:

• Clustering algorithms: They segment users into homogeneous groups based on similar behaviors and characteristics, facilitating personalization strategies. Algorithms such as k-means or DBSCAN benefit from the breadth and granularity of event data to identify complex behavioral patterns and create audiences with greater precision.
• Detection Algorithms: Designed to analyze time series, they detect recurring patterns and anomalies in the data. Event-based tracking amplifies their effectiveness by providing rich, contextual details, enabling early identification of changes in user behavior and rapid response to unexpected events.
• Prediction Algorithms: They use collected data to accurately predict future behaviors, such as the probability of purchase, the risk of abandonment, or the potential value of each user. Advanced models such as neural networks, decision trees and regressions become more reliable and perform better due to the quality and quantity of signals provided by the event-based approach, which allows them to develop predictive models with high confidence.

These families of algorithms, powered by event data, transform complexity into strategic value, making marketing increasingly personalized, proactive, and results-oriented.

Practical applications for marketing

The synergy between event-based tracking and artificial intelligence algorithms thus paves the way for many practical applications such as the detection of user recency, a parameter that measures the time elapsed since the last purchase. Artificial intelligence algorithms effectively monitor this data and can trigger an alert whenever recency reaches certain thresholds. This alert becomes a trigger for marketing automation tools such as Marketo, facilitating specific and timely interventions.

Clustering algorithms, on the other hand, allow customers to be segmented through metrics such as RFM (Recency, Frequency, Monetary), identifying, for example, top clients. This data enables specific actions such as retargeting campaigns dedicated to the most valuable users when they perform meaningful actions.

In the area of prediction, algorithms can estimate, for example, the lifetime value of customers, allowing marketing strategies to be targeted toward those with high potential.

Prediction of purchase intent, on the other hand, when it reaches thresholds around 65-70%, can support useful strategies to encourage conversion. On the other hand, when the 90% probability of purchase is exceeded, it is effective to adopt exclusionary campaigns, thus optimizing the budget and reducing the investment on users who are already very likely to buy.

These are just a few examples of what can be done through the accuracy and speed of AI-powered Event-Driven Marketing.

Case study: Bytek Prediction Platform at the heart of event-driven marketing strategies

An online fitness course platform used Bytek Prediction Platform to address a crucial challenge: detecting and preventing imminent abandonment of the service.

Using artificial intelligence, Bytek Prediction Platform analyzed numerous signals such as declining frequency of logins, reduced time spent on the platform, sudden discontinuation of a workout routine, absence of interactions with new content offerings, and so on. The AI automatically segmented at-risk users and calculated predictive probabilities of abandonment for each profile. This allowed the client to implement targeted and timely interventions, including:

• Remarketing campaigns on Google Ads and Meta, with creative optimized for each segment. Ads promoted targeted offers, such as discounts on annual and semi-annual subscriptions, to incentivize returning users.
• Personalized push notifications and motivational emails sent at the most strategic times to maximize impact. These messages included suggestions for adopting a healthy lifestyle, reminders to complete courses started but not completed, and personalized feedback on progress achieved.

Through a combination of event-based tracking, predictive analytics, ads campaigns, and marketing automation, the platform achieved significant results:

• Increased access frequency: Users have resumed regular access to the platform, with a 35 percent increase in average weekly frequency.
• Reduced risk of abandonment: Proactive action reduced the percentage of users at risk by 25%, significantly improving retention.
• Increased customer lifetime value: Personalization of interventions strengthened the connection between the platform and users, with a 20% increase in the overall average value of each subscriber.

This case demonstrates how granular tracking, combined with artificial intelligence, is key to identifying critical issues and implementing automated, rapid and specific responses, while improving user experience and business performance.

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