{"id":56772,"date":"2025-02-18T23:34:27","date_gmt":"2025-02-18T23:34:27","guid":{"rendered":"https:\/\/www.bloomreach.com\/?post_type=library&p=56772"},"modified":"2025-02-19T22:22:34","modified_gmt":"2025-02-19T22:22:34","slug":"how-bloomreach-builds-customer-loyalty-and-engagement-with-ai","status":"publish","type":"library","link":"https:\/\/www.bloomreach.com\/en\/blog\/how-bloomreach-builds-customer-loyalty-and-engagement-with-ai","title":{"rendered":"How We Build Customer Loyalty and Engagement With AI"},"content":{"rendered":"\n

We have heard from many of our clients that customer loyalty is paramount, especially in today\u2019s marketplace where customers are quick to explore alternative options if their expectations are not met.<\/p>\n\n\n\n

This is why Bloomreach Engagement is engineered to bolster customer loyalty by delivering truly personalized experiences at every touchpoint. To achieve this level of personalization, we have trained advanced AI models and integrated them into a sophisticated platform capable of activating insights in real time.<\/p>\n\n\n\n

In this post, I will outline the technology that empowers marketers to enhance customer loyalty and engagement across every channel.<\/p>\n\n\n\n

Scenarios \u2014 The Key to Personalized Omnichannel Marketing <\/h2>\n\n\n\n

One critical method we employ for personalized marketing automation is the creation and deployment of scenarios<\/a>. Within Engagement, scenarios enable real-time journey orchestration. Brands can leverage customer data to define logical flows that connect with customers through multiple channels, including email, SMS, push notifications, webhooks, and more.<\/p>\n\n\n\n

Our scenario builder functions as a no-code platform with an intuitive drag-and-drop interface. The core of initiating personalized automation through scenarios lies in its triggers. For instance, a trigger can be configured to dispatch a personalized follow-up email if a customer does not open an initial campaign within two days.<\/p>\n\n\n\n

\"Using<\/figure>\n\n\n\n

However, like any program, as scenarios become more complex \u2014 incorporating segmentation, multiple communication touchpoints, and layered triggers and filters \u2014 they can demand greater time and effort from marketers. This is where our multi-agentic AI approach<\/a> comes into play. Our next iteration of Loomi AI will automate and streamline the creation and maintenance of scenarios by leveraging large-scale reasoning models.<\/p>\n\n\n\n

Yet, this represents only one layer of AI at work \u2014 the layer that constructs the “program” (scenarios) using human-level knowledge, reasoning, and intuition. Complementing this, we deploy another layer of AI to integrate specific machine learning models within the scenarios. These are functional AI components such as 1:1 personalized recommendations, predictions, contextual personalization, and more \u2014 capabilities that are instrumental in delivering personalized experiences that enhance customer loyalty and engagement.<\/p>\n\n\n\n

Let\u2019s dive deeper into each of these functions and the technology we\u2019re using to support them. <\/p>\n\n\n\n

Recommendations <\/h2>\n\n\n\n

Product recommendations are fundamental to any effective marketing campaign. Showing the right products to the right customers not only drives revenue but also deepens customer engagement and loyalty.<\/p>\n\n\n\n

\"AI<\/figure>\n\n\n\n

We use different models depending on the type of recommendations<\/a> you\u2019re offering. To deliver personalized recommendations, we turn to a mix of: <\/p>\n\n\n\n