AI Consulting

AI-driven Customer Data Analysis

Turn customer data into decision support in five steps. Expand each step to explore its methods and outputs.

1. 1. Data Collection

   Gather comprehensive customer data from various channels.

   Explore methods and outputs

   Sources

   • CRM systems
   • Website and mobile app interactions
   • Social media, customer feedback,
   • surveys

   Output: Raw Data turns into structured data.

2. 2. Data Preparation

   Clean and structure data for AI algorithms.

   Explore methods and outputs

   Sources

   • Handle missing or duplicate data
   • Standardize formats (e.g., date formats)
   • Apply feature engineering for better model performance

   Output: Clean and structured data is ready for modeling and analysis

3. 3. AI Modeling

   Select AI algorithms and train models.

   Explore methods and outputs

   Machine Learning Models

   • Supervised Learning: Customer segmentation, purchase prediction
   • Unsupervised Learning: Behavior clustering analysis
   • Deep Learning: Image and text data analysis

   Output: Predicts future patterns and customer behaviors.

4. 4. Analysis & Insights

   Extract meaningful insights from model outputs.

   Explore methods and outputs

   Example Analyses

   • Which customers to target for increased sales?
   • Which customer groups are likely to churn?
   • Which product combinations drive campaign success?

   Output: Reports, visualizations, and predictive tables.

5. 5. Decision Making

   Use insights for strategic decision-making.

   Explore methods and outputs

   Example Actions

   • Targeted marketing campaigns
   • Training AI-chatbots for enhanced customer support
   • Cross-selling strategies to boost revenue

   Output: Lower costs, more efficient decisions and increasing customer satisfaction and market image.

Problem Solving: User Interface

Solutions

Development of a smart failure management system that systematically leverages database-stored failure knowledge and continuously learns through employee interaction to replicate human cognitive problem-solving skills.

1. Tasks of the failure management system:

• Prediction of future failures
• Early detection of failure correlation in the production
• Identification of possible causes of problems
• Proposal of suitable measures

2. Outcomes

• Development of an e-learning platform for the application of quality science and lean management methods using gamification
• Dialogue-based implementation of the problem-solving process (PSP) (chatbots support employees in carrying out the method and interpreting results)
• Prediction of future failures
• Discovery of unknown anomaly correlations
• Detection of failure hotspots
• Derivation of failure causes / corrective measures in real time
• Independent solution of unknown problems
• Implementation of a demonstrator for user-centered provision of decision support in a quality assistance system
• Use of new possibilities for process monitoring and promotion of the use of self-learning algorithms in the production

Questions about this service

What does AI consulting cover?

The service scope includes data analytics, AI-based web applications, cyber-physical systems and chatbots. This page explains the stages of customer data analysis alongside AI-supported problem solving and smart failure management.

How does customer data analysis progress?

The approach has five stages: data collection, data preparation, AI modelling, analysis and insights, and decision making. CRM systems, website and mobile app interactions, customer feedback and surveys are examples of data sources.

What applications of data analysis are described?

Customer segmentation, purchase prediction, behaviour clustering, and image or text analysis are modelling examples. Reports, visualisations and predictive tables can support decisions such as targeted campaigns and customer support.