With data-driven insights to measurable results

Data Science is a game-changer for businesses, providing actionable insights, fostering innovation, improving operational efficiency, enabling personalized experiences, and mitigating risks. It enables companies to navigate the complexities of the modern business landscape and unlock the full potential of their data to drive growth and success. 
We'll show you how!

Launch your CX success with data science!

Data science is playing a pivotal role in modern business strategies. By fostering innovation, improving customer experience, and optimizing operations, it helps businesses thrive in an increasingly competitive and data-centric world. 

Our services focus on:

  • Customer Analytics
  • Sales Analytics
  • Data Pipeline Analytics

Application examples include:

  • Customer value management 
  • Customer segmentation
  • Demand forecasting
  • Dynamic pricing and mapping automation

Data science can seem like rocket science, but...


No matter your challenges...

 1 | Data Complexity:
Businesses accumulate vast amount of structured and unstructured data. Data scientists face the challenge of handling this volume and diversity efficiently to extract meaningful insights.

 2 | Model Interpretability:
Developing complex models for predictive analysis is common in data science; however, the interpretability of these models often presents a challenge. Interpretability is crucial for gaining trust in model outputs, explaining predictions to stakeholders, and ensuring compliance with regulatory requirements.

 3 | Data Quality Assurance:
The integrity and reliability of data are paramount for effective Data Science. Ensuring data accuracy, consistency, and completeness is crucial. Poor-quality data, due to errors, inconsistencies, or incompleteness, can severely impact the analysis, leading to incorrect conclusions and flawed decision-making. 

 4 | Infrastructure & Tools:
Data scientists rely on specialized software, algorithms, computing power, and scalable infrastructure to process and analyze large datasets. The challenge lies in selecting the right tools and infrastructure that align with the business requirements, ensuring they are scalable, cost-effective, and compatible with the data analysis needs.


...we can help you 
solve them 

Advanced Data Management Strategies | Implementing robust data governance frameworks, data cleansing techniques, and data integration strategies helps in handling data complexity effectively.

Model Explainability Techniques | That is the core of our work. Our methods range from descriptive and diagnostic analysis to examine the data and gain data-driven insights, to predictive analysis to make predictions, to prescriptive analysis for automated decision support.


Data Quality Control Processes | Data and model quality monitoring is part of our extensive data science offerings. We know Instituting stringent quality control measures, data validation techniques, and regular data audits ensure high-quality, reliable data for analysis. It's vital for our work.


Investment in Your Data Success | Investing in scalable infrastructure, cloud-based solutions, and utilizing the right mix of analytics tools and technologies aligns the infrastructure with the demands of Data Science projects. That's where rpc comes into play!

How data science can support your business

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Driving Insights and Informed Decisions

By effectively analyzing data, you will gain deep insights into consumer behavior, market trends, and operational patterns. These insights serve as a guide for making informed decisions across all aspects of the business.

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Personalized Customer Experiences

Understanding customer preferences, behaviors, and needs enables targeted marketing campaigns, personalized product recommendations, and enhanced customer service, leading to increased customer satisfaction and loyalty.

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Innovation and Competitive Edge

Data science enables companies to identify untapped opportunities, forecast future trends, and innovate products or services aligned with customer needs. Stay ahead in the competitive landscape with rpc!

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Enhanced Operational Efficiency

Streamline workflows, automate repetitive tasks, and identifying areas for improvement with data science. Predictive analytics help you to optimize supply chains and minimize risks, thereby improving overall efficiency and reducing costs.

Future-Proof Your Business with Data Science Expertise from rpc!

Our services aim to perform advanced analytics on customer data, sales data, and data pipelines. We use a combination of descriptive, predictive, and prescriptive analytics to generate data-driven insights and provide automated decision support. We offer:

1 | Consulting

Our consulting services form the cornerstone of our approach, where our seasoned experts collaborate closely with your team. We will: 

  • Assess your business needs, 
  • Identify opportunities, and 
  • Tailor data-driven strategies aligned with your objectives. 

By leveraging our industry insights and expertise, we guide you through the intricacies of data analytics, providing actionable recommendations for optimizing your business processes and decision-making.

2 | Proof of Concept Development

We specialize in building Proof of Concepts (PoCs) that serve as a blueprint for the implementation of data-driven solutions. Our PoC development involves:

  • Creating prototypes
  • Testing hypotheses, and 
  • Validating the feasibility and potential impact of deploying advanced analytics within your specific business context. 

This phase allows you to visualize the possibilities and benefits of adopting data-driven strategies before full-scale implementation.

3 | Model Optimization

We excel in refining and optimizing data models to ensure their accuracy, efficiency, and relevance to your business objectives. By continuously refining and optimizing models, we ensure that they evolve in tandem with your business needs, delivering more precise insights and better decision support.

4 | Data and Model Quality Monitoring

Our commitment to maintaining high data and model quality is paramount. We establish robust monitoring systems that continuously track the quality and integrity of your data and models. Similarly, we implement rigorous checks to verify model performance, ensuring they remain effective and aligned with your business objectives over time.

5 | Our technology stack

Our technologies include AWS, Python-based frameworks/APIs and Excel. We have experience in use cases such as customer value management, customer segmentation, demand forecasting, dynamic pricing, and mapping automation.

Why rpc?

Your partner for digital transformation

At rpc, we pride ourselves on being the ideal partner to help you achieve your technology goals. Our team of highly qualified experts holds in-depth industry knowledge and is always on the cutting edge of technology. Together, we have successfully implemented numerous data-driven projects in over 10 countries with renowned clients.

Our services range from agile methodology, IT change and project management to data engineering, data science, software development and more. We provide customized solutions that meet our clients' unique requirements and work closely with them to help them achieve their business goals.

Our team consists of highly experienced and trained experts who are certified in their respective fields. We dispose of well-founded technology knowledge and use a variety of technology stacks such as AWS, Python, Adobe, Machine Learning, Qlik, Tableau and many more. Our success is based on delivering high-quality solutions that meet our customers' needs and exceed their expectations.

No questions left unanswered

What is data science?

Data science is an interdisciplinary field that analyzes and interprets data to gain insights, patterns and information that help solve complex problems.

What is the difference between data science and data engineering?

Data science focuses on the analysis of data, while data engineering focuses on the preparation and provision of data for analysis.

What are the steps in the data science lifecycle?

The data science life cycle includes data collection, data preparation, modeling, evaluation, and provision of results.

What types of data are used in data science?

Data science can use structured data (e.g. tables), unstructured data (e.g. text or images) and semi-structured data (e.g. JSON).

What significance do ethics have in data science?

Ethics are of great importance as data scientists need to handle data responsibly to ensure that there is no bias or discrimination in the models and that privacy is respected.

What are the application areas of data science?

Data science is used in various industries such as healthcare, finance, marketing, transportation, and many others to improve decision-making and efficiency.

What does the future of data science look like?

The future of data science will be characterized by advances in AI, automation, and data protection. It will have a growing importance in many industries - and rpc is ready for it!

We're proud of our clients


Make the most out of your valuable data.


We’re ready to kick-start your
customer-centric transformation

Bild von Andreas Schuler
Andreas Schuler
Senior Manager