Data Products
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Value Creation Through Innovative Data Products
Our Strengths
- Experience in developing successful data products for various industries
- Combination of technological expertise and business acumen
- Comprehensive experience with data architectures, analytics, and product development
- Deep understanding of regulatory requirements and data protection
⚠
Expert Tip
The success of data products depends critically on creating clear value for the customer. Our experience shows that the most valuable data products solve specific business problems or support decisions that have direct financial impact. Particularly successful are data products shaped by deep industry and domain knowledge that seamlessly complement existing business processes.
ADVISORI in Zahlen
11+
Jahre Erfahrung
120+
Mitarbeiter
520+
Projekte
Our proven approach to data product development combines market orientation with technological expertise and considers regulatory requirements and scalability aspects from the outset.
Unser Ansatz:
Phase 1: Potential Analysis - Evaluation of data assets, identification of customer segments, analysis of market potential and competitors
Phase 2: Conception - Development of business models, definition of product features, creation of prototypes, legal assessment
Phase 3: Technical Implementation - Building data architecture, implementing analytics and ML models, developing delivery platform
Phase 4: Market Launch - Piloting with selected customers, iterative product improvement, building sales channels
Phase 5: Scaling and Evolution - Continuous improvement of data products, expansion of product portfolio, opening new markets
"Data products offer companies the opportunity to grow beyond their traditional business models and open new revenue streams. Success lies not only in technical implementation but especially in identifying genuine customer needs and creating measurable added value. Our experience shows that step-by-step development with early customer feedback is the key to success."
Asan Stefanski
Director, ADVISORI DE
Unsere Dienstleistungen
Wir bieten Ihnen maßgeschneiderte Lösungen für Ihre digitale Transformation
Data Product Strategy and Business Models
Development of a comprehensive strategy for monetizing your data and opening new business areas. We support you in identifying opportunities, developing viable business models, and creating a roadmap for implementation.
- Assessment of existing data assets and analytics capabilities
- Development of monetization strategies and pricing models
- Identification of target customers and value propositions
- Creation of a data product roadmap and investment planning
Data Product Design and Development
Design of innovative data products with clear customer value and unique selling points. From initial idea to market-ready product, we accompany you in development, piloting, and continuous improvement of your data-based offerings.
- Creation of data product concepts and user journeys
- Development of prototypes and minimum viable products
- Integration of advanced analytics and machine learning
- Conducting user tests and iterative product optimization
Data Product Platforms and Architecture
Building a scalable, secure, and efficient infrastructure for delivering your data products. We support you in designing and implementing a technical platform that meets your specific requirements.
- Development of a scalable data architecture for data products
- Implementation of APIs and delivery mechanisms
- Integration of security and compliance requirements
- Building self-service portals and customer platforms
Data Monetization and Go-to-Market
Support in successfully launching and monetizing your data products. We help you establish the right sales channels, develop appropriate pricing models, and successfully position your data-based offerings in the market.
- Development and validation of pricing strategies
- Building sales channels and partner ecosystems
- Design of customer contracts and service level agreements
- Development of metrics and KPIs for data-based business models
Häufig gestellte Fragen zur Data Products
What are data products and how do they differ from traditional products?
Data products are specialized offerings where data, analyses, or insights derived from them represent the primary value contribution. Unlike traditional products and services, their core value lies in providing information, supporting decisions, or automating processes through data.
🧩 Core Characteristics of Data Products
•
Data-centricity: Data forms the core of the value proposition, not just a supplement
•
Scalability: Ability to deliver to numerous customers with low marginal costs
•
Continuous improvement: Evolution through usage data and feedback loops
•
Modular structure: Composition of data sources, algorithms, and delivery mechanisms
•
High degree of automation: Minimal manual intervention in ongoing delivery
📊 Typologies of Data Products
•
Raw data services: Provision of processed data for further processing by customers
•
Analytics-as-a-Service: Access to analysis tools and predefined evaluations
•
Insights products: Processed insights and action recommendations
•
Prediction models: Forecasts and simulations for decision support
•
Algorithms and AI modules: Embeddable intelligent components for customer applications
•
Data enrichment services: Enhancement of existing customer data with additional information
🔄 Differences from Traditional Products
•
Value creation: Informational rather than material or direct functional benefit
•
Cost structure: High fixed costs for development, low variable costs for delivery
•
Improvement cycle: Continuous evolution instead of discrete version jumps
•
Customizability: Higher adaptability to specific customer requirements
•
Usage model: Often subscription instead of one-time purchase or licensing
💼 Examples of Successful Data Products
•
Industry reports and analyses with regular updates
•
Risk assessment models for finance and insurance industries
•
Price and demand forecasts for retail and industry
•
Enriched customer data for marketing and sales
•
Preventive maintenance solutions based on machine dataData products represent a fundamental shift in how companies can create value. They enable the monetization of data that was often previously viewed only as an internal operational resource, creating the foundation for innovative digital business models. Through their modular and scalable nature, they offer unique opportunities for continuous innovation and new customer relationships.
What business value can companies achieve through developing data products?
Developing data products opens up diverse opportunities for companies to create value and differentiate in the market. Business value manifests in direct financial effects, strategic advantages, and organizational improvements.
💰 Direct Financial Value Contributions
•
New revenue streams through monetization of existing data assets
•
Diversification of business model beyond traditional products and services
•
Revenue increase through cross-selling and upselling to existing customers
•
Opening new customer segments with data-centric offerings
•
Building recurring revenue through subscription-based business models
🌱 Strategic Competitive Advantages
•
Market differentiation through unique data-based additional offerings
•
Strengthening customer relationships through higher integration depth
•
Building entry barriers for competitors through data advantage
•
Ecosystem expansion and building partner networks
•
Positioning as innovative market leader in digital transformation
🔄 Internal Improvements and Synergies
•
Improvement of own data quality and infrastructure
•
Building analytics competencies with multiple benefits
•
Deeper understanding of customer needs through usage data
•
Acceleration of innovation cycles and time-to-market
•
Strengthening data culture throughout the organization
📊 Measurable Business Success Through Data Products
•
Revenue growth: Typically 5‑15% additional revenue through new data products
•
Customer retention: Reduction of customer churn by 20‑30% through value-added offerings
•
Market expansion: Opening 2‑3 new customer segments or markets
•
Digital revenue share: Increase in digital revenue share of total business
•
Return on data assets: Improvement of return on data engineering investmentsThe following success factors are crucial for realizing these value contributions:
•
Customer-centric approach: Alignment of data products with genuine customer needs
•
Clear product strategy: Integration into overall offering and brand positioning
•
ROI prioritization: Focus on use cases with high revenue potential
•
Iterative development: Fast market launch and continuous improvementBuilding a data product business is not a short-term project but a strategic initiative requiring systematic approach and perseverance. However, companies that invest early in this area can secure significant competitive advantages in increasingly data-driven markets.
How do you identify potential data products within your own company?
Systematic identification of potential data products is the first crucial step toward data monetization. A structured approach helps recognize and prioritize the most promising opportunities.
🔍 Data Potential Assessment
•
Inventory of existing data assets and their characteristics
•
Evaluation of data quality, exclusivity, and completeness
•
Analysis of technical accessibility and processing capabilities
•
Identification of unique data assets with unique selling points
•
Review of legal and regulatory frameworks for usage
🎯 Customer Needs Identification
•
Analysis of customer inquiries and recurring information needs
•
Interviews and workshops with customers on unsolved problems
•
Scouting of market trends and industry developments
•
Analysis of pain points in customer journeys
•
Competitive analysis of existing data-based offerings
🧩 Data Product Concept Ideation
•
Systematic connection of data assets with customer needs
•
Creative workshops with cross-functional teams
•
Development of different use cases and application scenarios
•
Design of various monetization models
•
Prototypical visualization of potential product concepts
⚖ ️ Evaluation and Prioritization
•
Assessment of business value and market potential
•
Evaluation of technical feasibility and implementation effort
•
Analysis of strategic fit with existing business model
•
Estimation of time-to-market and investment needs
•
Risk assessment regarding data protection, reputation, and competitionProven methods for systematic identification include:
•
Data Asset Mapping: Systematic capture and visualization of data assets
•
Value Stream Mapping: Analysis of value chains and data flows
•
Customer Journey Analytics: Identification of data usage opportunities along the customer journey
•
Ideation Workshops: Creative development of data product concepts in interdisciplinary teamsTypical entry points for developing first data products are:1. Extension of existing products with data-based additional services2. Aggregation and anonymization of internal benchmark data3. Development of analytical tools for frequent customer inquiries4. Data-based optimization services for existing customer problemsSuccessful identification of potential data products requires combining market perspective with data expertise. Cross-functional teams with representatives from business development, data science, and customer service have proven particularly effective.
What typical business models exist for data products?
Various business models have been established for data products, which are differently suited depending on the type of data product, target group, and value contribution. Selecting the appropriate model is crucial for commercial success and sustainable value creation.
💼 Subscription-Based Models
•
Time-based subscriptions (monthly, annual) for continuous data access
•
Tiered pricing with different service levels and feature scopes
•
Freemium models with free basic access and premium features
•
Usage-based subscriptions with base fee and usage-dependent components
•
Enterprise licenses with organization-wide access and individual agreements
🔄 Transaction-Based Models
•
Pay-per-use for individual data retrievals or analyses
•
Credit systems with pre-purchased usage quotas
•
API-call-based billing by number and type of requests
•
Revenue sharing models with measurable outcomes
•
Micropayment systems for granular data usage
🌱 Indirect Monetization Models
•
Bundling of data products with traditional products and services
•
Cross-selling models with data-based additional services
•
Lead generation through basic data with upselling to premium insights
•
Free API usage with paid developer services
•
Community models with free and paid components
🤝 Partnership Models and Ecosystems
•
Data marketplaces with revenue sharing between providers and platform operators
•
White-label solutions for integration into partner offerings
•
B2B2C models with indirect monetization through partner channels
•
Open data models with monetization through complementary services
•
Co-creation models with joint value creation and sharingWhen selecting the appropriate business model, the following factors should be considered:
•
Value contribution: Type and scope of customer value created
•
Usage patterns: Regularity and intensity of expected product usage
•
Customer maturity: Willingness and ability of target group to use data
•
Competitive situation: Positioning and pricing models of comparable offeringsSuccessful practice strategies include:1. Hybrid models: Combination of different monetization approaches for different customer segments2. Value-based pricing: Pricing based on customer value created rather than data volume3. Evolutionary models: Start with simpler models and gradual development4. Experimental approaches: A/B testing of different pricing models and continuous optimizationThe choice of business model should not be understood as a one-time decision but as an evolutionary process that develops with market development and data product maturity. Continuous monitoring of customer needs and market trends is therefore essential for long-term success.
What regulatory and data protection aspects must be considered for data products?
Developing and marketing data products is subject to a variety of regulatory and data protection requirements that must be considered from the outset. Compliance-compliant design is not only legally required but also an important trust factor for customers.
⚖ ️ Data Protection Legal Foundations
•
Compliance with GDPR and other relevant data protection laws
•
Lawfulness of data processing (consent, legitimate interest, etc.)
•
Purpose limitation and data minimization in product design
•
Transparency obligations toward data subjects
•
International data transfers and country-specific regulations
🔒 Privacy by Design and Privacy by Default
•
Integration of data protection requirements into development process
•
Implementation of privacy-friendly default settings
•
Conducting data protection impact assessments for high-risk processing
•
Development of pseudonymization and anonymization procedures
•
Building technical and organizational protective measures
📋 Contract Design and Responsibilities
•
Clear definition of responsibilities (controller, processor)
•
Design of legally secure customer contracts and terms of use
•
Creation of data processing agreements with external service providers
•
Regulation of liability issues and warranties
•
Management of subcontractors and data recipients
🌐 Industry-Specific Regulations
•
Financial sector: Compliance with MiFID II, PSD2, Basel regulations, etc.
•
Healthcare: Observance of sector-specific data protection regulations
•
Energy sector: Regulations for smart metering and energy data
•
Telecommunications: Sector-specific requirements for data usage
•
Other industry-specific standards and regulationsFor practical implementation, the following approaches have proven effective:
•
Early involvement: Integration of legal and data protection from project start
•
Data governance framework: Establishment of clear rules and processes for data usage
•
Privacy-enhancing technologies (PETs): Implementation of technical protective measures
•
Continuous compliance: Ongoing review and adaptation to regulatory changesCentral challenges and solution approaches include:1. International data transfers: Implementation of appropriate transfer mechanisms and local data storage2. Consent management: Building flexible consent management platforms for user preferences3. Purpose limitation vs. innovation: Development of governance frameworks for new use cases4. Aggregation and anonymization: Implementation of robust procedures for risk minimizationCompliance-compliant design of data products should not be understood as mere compliance exercise but as an opportunity for differentiation through trustworthiness and transparency. Companies that proactively establish data protection and compliance as quality features of their data products can generate significant competitive advantage from this.
How do you develop a compelling data product concept?
Developing a compelling data product concept requires a systematic approach that connects market needs with technological possibilities. A well-thought-out concept forms the foundation for successful data products with clear added value for customers.Customer-Oriented Concept Development:
•
Identification of specific customer segments and their requirements
•
Definition of clear value propositions for each segment
•
Development of user personas and customer journey maps
•
Validation of assumptions through customer interviews and feedback
•
Prioritization of features based on customer value and implementation effortProduct Components and Architecture:
•
Definition of core functionalities and performance features
•
Design of data sources, models, and processing processes
•
Design of user interfaces and interaction patterns
•
Planning of delivery mechanisms (APIs, web interfaces, mobile apps)
•
Definition of integration interfaces to existing systemsBusiness Model and Value Creation:
•
Development of a viable monetization approach
•
Definition of pricing structures and packages
•
Creation of a roadmap for feature development and market launch
•
Calculation of development and operating costs
•
Estimation of revenue potential and return on investmentRisk Management and Compliance:
•
Identification of potential risks and challenges
•
Review of data protection and regulatory requirements
•
Assessment of technical feasibility and scalability
•
Analysis of competitors and market trends
•
Development of mitigation strategies for identified risksProven methods include Design Thinking, Lean Product Development, Business Model Canvas, and Value Proposition Design. Success factors are clear problem solving, differentiation, scalability, and simplicity.
How can companies effectively monetize their data?
Effective monetization of company data requires a thoughtful strategy based on specific data assets, market conditions, and company goals. Successful data monetization combines innovative business models with technological excellence and compliance conformity.Direct Monetization Models:
•
Data marketplaces: Provision of data on specialized platforms
•
Data-as-a-Service (DaaS): Data delivery via APIs or other access mechanisms
•
Insights-as-a-Service: Provision of processed insights and analyses
•
Predictive models: Licensing of trained machine learning models
•
Benchmarking services: Anonymized comparison data for industries or processesIndirect Monetization Approaches:
•
Product enhancement: Upgrading existing products through data enrichment
•
Customer retention: Data-based additional services to increase customer loyalty
•
Optimization: Data feedback loops to improve own products
•
Co-creation: Joint development of data products with partners
•
Ecosystem building: Creating data-driven platforms and partner networksStrategic Implementation Steps:
•
Data audit: Inventory and evaluation of existing data assets
•
Market research: Analysis of market needs and willingness to pay
•
Proof of concept: Development and validation of first data products
•
Pilot phase: Testing with selected customers and feedback collection
•
Scaling: Expansion to additional customer segments and marketsSuccess factors include value orientation, quality assurance, legal safeguarding, technical excellence, and continuous innovation. The most successful companies understand data monetization as a continuous process requiring systematic testing, learning, and adaptation.
What technical prerequisites are required for developing data products?
Developing successful data products requires a powerful technical infrastructure that supports data collection, processing, analysis, and delivery. The right technical prerequisites form the foundation for scalable, secure, and value-creating data products.Data Infrastructure and Storage:
•
Data lakes for flexible storage of large, heterogeneous data volumes
•
Data warehouses for structured, analysis-oriented data storage
•
NoSQL databases for specific use cases and data types
•
Streaming platforms for real-time data processing
•
Metadata management for documentation and governanceData Integration and Quality:
•
ETL/ELT processes for data extraction, transformation, and loading
•
Data pipeline technologies for automated data flows
•
Data quality tools for validation and cleansing
•
Master data management for consistent master data
•
Change data capture for real-time data updatesAnalytics and Machine Learning:
•
BI platforms for reporting and visualization
•
Advanced analytics tools for complex statistical analyses
•
Machine learning frameworks for predictive models
•
MLOps infrastructure for ML model operation and monitoring
•
Automated feature engineering and model trainingDelivery Mechanisms and Interfaces:
•
API management platforms for data access and distribution
•
Microservices architectures for modular product components
•
Web portals and dashboards for visual data interaction
•
SDK development for client integration
•
Automated documentation toolsSecurity, Compliance, and Monitoring:
•
Identity and access management for access control
•
Encryption for data at rest and in transit
•
Privacy-enhancing technologies for compliant processing
•
Audit logging and monitoring tools
•
Incident response and recovery mechanismsKey principles include scalability, flexibility, modularity, and automation. Proven architecture approaches include cloud-native architectures, data mesh, event-driven architecture, and microservices.
How do you measure the success of data products?
Measuring the success of data products requires a multidimensional approach that considers financial, technical, and customer-related metrics. A well-thought-out metrics system enables continuous optimization and strategic development of the data product portfolio.Financial Metrics:
•
Revenue: Total revenue from data products
•
Average Revenue Per User (ARPU): Average revenue per customer
•
Customer Acquisition Cost (CAC): Costs for acquiring new customers
•
Customer Lifetime Value (CLV): Long-term value of a customer relationship
•
Gross Margin: Gross profit after deducting direct costs
•
Return on Data Assets (RoDA): Return on investments in data resourcesUsage and Customer Metrics:
•
Monthly Active Users (MAU): Number of active users per month
•
User Engagement: Usage intensity and frequency
•
Conversion Rate: Conversion of prospects to paying customers
•
Churn Rate: Cancellation rate of existing customers
•
Net Promoter Score (NPS): Willingness to recommend to others
•
Customer Satisfaction (CSAT): Satisfaction with the data productTechnical and Operational Metrics:
•
Data Quality Metrics: Completeness, accuracy, consistency of data
•
System Performance: Response times, availability, throughput
•
API Usage: Number and type of API calls
•
Error Rates: Frequency and severity of errors and failures
•
SLA Compliance: Adherence to agreed service level agreements
•
Time-to-Market: Speed in product development and updatesProduct-Specific Success Metrics:
•
Feature Adoption: Usage level of different product functions
•
Insight Generation: Number of generated insights or action recommendations
•
Prediction Accuracy: Accuracy of predictive models
•
Business Impact: Measurable business improvements at the customer
•
Data Freshness: Timeliness of provided data
•
Customization Level: Degree of adaptation to individual customer requirementsRecommended approaches include Balanced Scorecard, OKRs, Data Product Analytics, and Voice of Customer. Key principles are alignment with business goals, actionability, transparency, and continuous improvement.
How can data products be effectively marketed to customers?
Successfully marketing data products requires a specific approach that considers both the characteristics of data-based offerings and the needs and buying motives of target groups. A well-thought-out marketing strategy is crucial for effectively communicating the value of data products and convincing potential customers.Target Group-Specific Value Propositions:
•
Identification of relevant stakeholders (e.g., CDOs, CIOs, business units)
•
Development of specific value propositions for different decision-makers
•
Addressing concrete business problems and challenges
•
Quantification of ROI and business impact
•
Adaptation of communication to different maturity levels of data usageProof of Value and Product Demonstrations:
•
Development of convincing demonstrations with real data
•
Offering free trial periods or proof-of-concepts
•
Provision of sample data for value demonstration
•
Interactive product experiences through self-service demos
•
Transparent documentation of methodology and data sourcesContent Marketing and Thought Leadership:
•
Creation of specialist articles, whitepapers, and case studies
•
Webinars and virtual events on relevant data topics
•
Development of benchmarking reports and market analyses
•
Presence at specialist conferences and industry events
•
Publication of use cases and success storiesSales Enablement and Sales Support:
•
Training of sales teams on data-specific topics
•
Development of target group-specific sales materials
•
Building a technically versed pre-sales organization
•
Provision of argumentation guides and objection handling
•
Implementation of sandboxes for customer-side application testsProven marketing strategies for different phases include awareness phase (problem understanding), consideration phase (differentiation demonstration), decision phase (risk minimization), and adoption phase (onboarding and enablement). Critical success factors are transparency, trust building, accessibility, and flexibility.
How do you develop a compelling data product concept?
Developing a compelling data product concept requires a systematic approach that connects market needs with technological possibilities. A well-thought-out concept forms the foundation for successful data products with clear added value for customers.Customer-Oriented Concept Development:
•
Identification of specific customer segments and their requirements
•
Definition of clear value propositions for each segment
•
Development of user personas and customer journey maps
•
Validation of assumptions through customer interviews and feedback
•
Prioritization of features based on customer value and implementation effortProduct Components and Architecture:
•
Definition of core functionalities and performance features
•
Design of data sources, models, and processing processes
•
Design of user interfaces and interaction patterns
•
Planning of delivery mechanisms (APIs, web interfaces, mobile apps)
•
Definition of integration interfaces to existing systemsBusiness Model and Value Creation:
•
Development of a viable monetization approach
•
Definition of pricing structures and packages
•
Creation of a roadmap for feature development and market launch
•
Calculation of development and operating costs
•
Estimation of revenue potential and return on investmentRisk Management and Compliance:
•
Identification of potential risks and challenges
•
Review of data protection and regulatory requirements
•
Assessment of technical feasibility and scalability
•
Analysis of competitors and market trends
•
Development of mitigation strategies for identified risksProven methods include Design Thinking, Lean Product Development, Business Model Canvas, and Value Proposition Design. Success factors are clear problem solving, differentiation, scalability, and simplicity.
How can companies effectively monetize their data?
Effective monetization of company data requires a thoughtful strategy based on specific data assets, market conditions, and company goals. Successful data monetization combines innovative business models with technological excellence and compliance conformity.Direct Monetization Models:
•
Data marketplaces: Provision of data on specialized platforms
•
Data-as-a-Service (DaaS): Data delivery via APIs or other access mechanisms
•
Insights-as-a-Service: Provision of processed insights and analyses
•
Predictive models: Licensing of trained machine learning models
•
Benchmarking services: Anonymized comparison data for industries or processesIndirect Monetization Approaches:
•
Product enhancement: Upgrading existing products through data enrichment
•
Customer retention: Data-based additional services to increase customer loyalty
•
Optimization: Data feedback loops to improve own products
•
Co-creation: Joint development of data products with partners
•
Ecosystem building: Creating data-driven platforms and partner networksStrategic Implementation Steps:
•
Data audit: Inventory and evaluation of existing data assets
•
Market research: Analysis of market needs and willingness to pay
•
Proof of concept: Development and validation of first data products
•
Pilot phase: Testing with selected customers and feedback collection
•
Scaling: Expansion to additional customer segments and marketsSuccess factors include value orientation, quality assurance, legal safeguarding, technical excellence, and continuous innovation. The most successful companies understand data monetization as a continuous process requiring systematic testing, learning, and adaptation.
What technical prerequisites are required for developing data products?
Developing successful data products requires a powerful technical infrastructure that supports data collection, processing, analysis, and delivery. The right technical prerequisites form the foundation for scalable, secure, and value-creating data products.Data Infrastructure and Storage:
•
Data lakes for flexible storage of large, heterogeneous data volumes
•
Data warehouses for structured, analysis-oriented data storage
•
NoSQL databases for specific use cases and data types
•
Streaming platforms for real-time data processing
•
Metadata management for documentation and governanceData Integration and Quality:
•
ETL/ELT processes for data extraction, transformation, and loading
•
Data pipeline technologies for automated data flows
•
Data quality tools for validation and cleansing
•
Master data management for consistent master data
•
Change data capture for real-time data updatesAnalytics and Machine Learning:
•
BI platforms for reporting and visualization
•
Advanced analytics tools for complex statistical analyses
•
Machine learning frameworks for predictive models
•
MLOps infrastructure for ML model operation and monitoring
•
Automated feature engineering and model trainingDelivery Mechanisms and Interfaces:
•
API management platforms for data access and distribution
•
Microservices architectures for modular product components
•
Web portals and dashboards for visual data interaction
•
SDK development for client integration
•
Automated documentation toolsSecurity, Compliance, and Monitoring:
•
Identity and access management for access control
•
Encryption for data at rest and in transit
•
Privacy-enhancing technologies for compliant processing
•
Audit logging and monitoring tools
•
Incident response and recovery mechanismsKey principles include scalability, flexibility, modularity, and automation. Proven architecture approaches include cloud-native architectures, data mesh, event-driven architecture, and microservices.
How do you measure the success of data products?
Measuring the success of data products requires a multidimensional approach that considers financial, technical, and customer-related metrics. A well-thought-out metrics system enables continuous optimization and strategic development of the data product portfolio.Financial Metrics:
•
Revenue: Total revenue from data products
•
Average Revenue Per User (ARPU): Average revenue per customer
•
Customer Acquisition Cost (CAC): Costs for acquiring new customers
•
Customer Lifetime Value (CLV): Long-term value of a customer relationship
•
Gross Margin: Gross profit after deducting direct costs
•
Return on Data Assets (RoDA): Return on investments in data resourcesUsage and Customer Metrics:
•
Monthly Active Users (MAU): Number of active users per month
•
User Engagement: Usage intensity and frequency
•
Conversion Rate: Conversion of prospects to paying customers
•
Churn Rate: Cancellation rate of existing customers
•
Net Promoter Score (NPS): Willingness to recommend to others
•
Customer Satisfaction (CSAT): Satisfaction with the data productTechnical and Operational Metrics:
•
Data Quality Metrics: Completeness, accuracy, consistency of data
•
System Performance: Response times, availability, throughput
•
API Usage: Number and type of API calls
•
Error Rates: Frequency and severity of errors and failures
•
SLA Compliance: Adherence to agreed service level agreements
•
Time-to-Market: Speed in product development and updatesProduct-Specific Success Metrics:
•
Feature Adoption: Usage level of different product functions
•
Insight Generation: Number of generated insights or action recommendations
•
Prediction Accuracy: Accuracy of predictive models
•
Business Impact: Measurable business improvements at the customer
•
Data Freshness: Timeliness of provided data
•
Customization Level: Degree of adaptation to individual customer requirementsRecommended approaches include Balanced Scorecard, OKRs, Data Product Analytics, and Voice of Customer. Key principles are alignment with business goals, actionability, transparency, and continuous improvement.
How can data products be effectively marketed to customers?
Successfully marketing data products requires a specific approach that considers both the characteristics of data-based offerings and the needs and buying motives of target groups. A well-thought-out marketing strategy is crucial for effectively communicating the value of data products and convincing potential customers.Target Group-Specific Value Propositions:
•
Identification of relevant stakeholders (e.g., CDOs, CIOs, business units)
•
Development of specific value propositions for different decision-makers
•
Addressing concrete business problems and challenges
•
Quantification of ROI and business impact
•
Adaptation of communication to different maturity levels of data usageProof of Value and Product Demonstrations:
•
Development of convincing demonstrations with real data
•
Offering free trial periods or proof-of-concepts
•
Provision of sample data for value demonstration
•
Interactive product experiences through self-service demos
•
Transparent documentation of methodology and data sourcesContent Marketing and Thought Leadership:
•
Creation of specialist articles, whitepapers, and case studies
•
Webinars and virtual events on relevant data topics
•
Development of benchmarking reports and market analyses
•
Presence at specialist conferences and industry events
•
Publication of use cases and success storiesSales Enablement and Sales Support:
•
Training of sales teams on data-specific topics
•
Development of target group-specific sales materials
•
Building a technically versed pre-sales organization
•
Provision of argumentation guides and objection handling
•
Implementation of sandboxes for customer-side application testsProven marketing strategies for different phases include awareness phase (problem understanding), consideration phase (differentiation demonstration), decision phase (risk minimization), and adoption phase (onboarding and enablement). Critical success factors are transparency, trust building, accessibility, and flexibility.
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Reduzierung der Implementierungszeit von AI-Anwendungen auf wenige Wochen
Verbesserung der Produktqualität durch frühzeitige Fehlererkennung
Steigerung der Effizienz in der Fertigung durch reduzierte Downtime
AI Automatisierung in der Produktion
Festo
Intelligente Vernetzung für zukunftsfähige Produktionssysteme
Fallstudie
Ergebnisse
Verbesserung der Produktionsgeschwindigkeit und Flexibilität
Reduzierung der Herstellungskosten durch effizientere Ressourcennutzung
Erhöhung der Kundenzufriedenheit durch personalisierte Produkte
KI-gestützte Fertigungsoptimierung
Siemens
Smarte Fertigungslösungen für maximale Wertschöpfung
Fallstudie
Ergebnisse
Erhebliche Steigerung der Produktionsleistung
Reduzierung von Downtime und Produktionskosten
Verbesserung der Nachhaltigkeit durch effizientere Ressourcennutzung
Digitalisierung im Stahlhandel
Klöckner & Co
Digitalisierung im Stahlhandel
Fallstudie
Ergebnisse
Über 2 Milliarden Euro Umsatz jährlich über digitale Kanäle
Ziel, bis 2022 60% des Umsatzes online zu erzielen
Verbesserung der Kundenzufriedenheit durch automatisierte Prozesse
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