Suppliers of marketing automation software such as HubSpot or Eloqua are currently working on new applications and products with integrated AI. The goal is to better take the needs and preferences of customers and leads into account.
Providers such as HubSpot or Oracle Eloqua are currently working on new applications and products with integrated AI applications. Combining automation with intelligent data analysis is the logical next step on the way to intelligent data evaluation and campaign management for personalized customer experiences. It will be possible to create meaningful customer profiles in 1:1 marketing in order to retain or win back customers.
The difference to the previous systems is that the campaigns are adapted and played out in real time to the user behavior. Connecting AI to marketing automation tools can help to convert existing data into strategic and tactical insights and thus automatically and precisely optimize thousands of user experiences.
Artificial intelligence brings higher precision and efficiency to customer communications, improving sales opportunities for the enterprise. However, the algorithms depend on a solid, sufficient database. If this prerequisite is met, AI algorithms can predict many times more accurately when the right time to interact with the lead or customer is and what tactical marketing action is required to make a customer buy, stay or return.
Campaigns are leaving static grounds. They evolve dynamically by updating all knowledge about purchases, interests and user behavior and using it for a significantly better customer experience. Machine learning makes this possible on a large scale.
AI models for customer segmentation
Whether finance, NGOs or classic retail – some AI applications show outstanding performance in personalized customer approach across all industries. If these are connected to HubSpot, Eloqua or Mautic, the recipient groups for campaigns are compiled even more accurately and differentiated according to needs, leading to higher conversions and more efficient marketing. Here is a selection:
Create customer segments
Machine learning algorithms connected to marketing automation systems analyze user data in depth and search for patterns in similar values and behaviors. Common features and trends determine the target groups generated and stored in the system (behavioral clustering). Customers who buy similar products can be combined in promotions (product-based clusters).
Prevent customer churn
Machine Learning calculates probabilities of who will buy or who is no longer interested in the products and services. Thanks to churn analysis of customer data, it is possible to start marketing measures at an early stage if there are signs of migration.
Playing out personalized content
AI significantly improves the way in which personalized content is addressedand played out on preferred channels compared to already dynamic content.
CONTINUOUSLY INCREASING EFFICIENCY
Even if the use of intelligent algorithms in the initial phase of the changeover means an additional effort for the preparation and storage of data, a significant increase in efficiency can be achieved simply by using the Marketing Automation Software (without AI). We have presented 10 examples of how Marketing Automation can be used to streamline marketing processes and transfer routine, previously manual activities to the systems.