The RFM model for calculating customer value and segmentation

Introduction The RFM model is a proven tool in customer relationship management (CRM) that helps companies determine the value of their customers and divide them into different segments. It is based on three key metrics: Recency: When did the customer last shop? Frequency: How often does the customer shop in a certain period of time?

Easy integration of our personalization engine into other systems

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What is personalization? Personalization means tailoring content and experiences on a website to each user. This is done by analyzing user data such as behavior, interests and demographic information. The goal of personalization is to increase the relevance and attractiveness of content in order to strengthen user loyalty, increase conversion rates and ultimately maximize business

The magic of CX personalization with headless CMS systems

In a digital world where the customer is king, personalization is becoming the new goldmine. Companies improving their customer experience (CX) often face the challenge of how to present content effectively and individually. This is where two powerful tools come into play: CX personalization with AI optimization and headless content management systems (CMS). But what

Deep Dive: Data Management Platforms DMP vs Customer Data Platforms CDP

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Data Management Platforms (DMPs): The Power of Data for Personalized Marketing A data management platform (DMP) is a crucial tool in the modern marketing arsenal as it allows companies to collect, organize and activate data from various sources. It creates detailed customer profiles that serve as the basis for targeted advertising and personalization initiatives. Here's

Kundenretention durch prädiktive Analytik: Wie eine CDP Unternehmen dabei hilft, Kundenabwanderung frühzeitig zu erkennen und zu verhindern

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Kundenabwanderung, auch als Churn-Rate bekannt, ist ein bedeutender Faktor für jedes Unternehmen. Die Vermeidung von Kundenverlusten ist ebenso wichtig wie die Neukundengewinnung, wenn nicht sogar wichtiger. Kundenbindung ist daher von entscheidender Bedeutung für langfristigen Erfolg. Traditionell verlassen sich Unternehmen auf historische Daten und Erfahrungen, um potenzielle Abwanderungsmuster zu identifizieren. Allerdings sind diese Ansätze oft reaktiv

Open banking data use to optimize risk scores in banking

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What is Open Banking? Open banking refers to a financial system in which banks and other financial institutions securely share their customer data via standardized APIs (Application Programming Interfaces). This happens based on the customer's consent and enables third-party providers to access banking data and offer innovative financial services. This data may include transaction histories,

Importance of personalization in the financial sector – misunderstandings, challenges and opportunities

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Over the last decade, the rapid growth of digital banks and improving offerings from fintech companies have led to consumers demanding more from their financial institution (FI): a tailored customer experience that meets their evolving needs and preferences - beyond the local branch, at both traditional and new touchpoints. Today, customer experience can either strengthen

20 Efficient Methods to Optimize Conversion Funnels

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Optimizing a conversion funnel is crucial to converting more visitors into paying customers. Here are ten proven methods that are particularly effective for improving your funnel and increasing conversion rates. 1.A/B Testing Efficiency: High Description: Test different versions of your landing pages, call-to-actions (CTAs), and form fields to find out which variants deliver the best

Germany’s data economy: potential and challenges

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Germany is sitting on a treasure trove of data, but hardly uses it. This is shown by a current survey by the digital association Bitkom, which examines the data economy in the country. Of the companies surveyed, only six percent stated that they were exploiting the full potential of their data. Over 42 percent of

Creation and implementation of an AI assistant – flow and processes

Introduction Our AI chatbot assistant uses advanced natural language processing (NLP) to deliver coherent and accurate answers. This guide describes the structured process from needs analysis to continuous optimization to ensure that the chatbot is tailored exactly to your needs. Step 1: Preparation and needs analysis 1.1 Customer advice Aim: Introduction to the possibilities and