Data: the "new oil" of the digital age. This metaphor, despite its weaknesses, highlights the value creation potential that data offers. It is now at the heart of business models that were once the silent backbone of companies. In this context, data-driven business models in particular are emerging as a real opportunity for companies today.
While tech giants like Google and Apple top the value creation list, established giants like Siemens and Shell have also recognized the power of data. But despite the hype, the conceptual underpinnings of data-driven business models are often misunderstood, smirkingly ignored, or yet misunderstood.
Here, we dive deeper and shed light on what's really behind these models.
In doing so, this article not only highlights the benefits of data-integrated products, but also explains concrete steps to successfully implement them.
To this end, we explain, among other things, the basics of data-based business models and how they can look in practice.
So if you're looking for a guide to help you navigate the complex digital terrain and show you how to not only reach your customers, but retain them for the long term, you've come to the right place.
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The most Important in a Nutshell
- Data-driven business models enable companies to develop innovative products and thus create new business value.
- Examples of successful data-based business models include connected motorcycles from Harley-Davidson and the streaming offering from Disney+.
- With Konfuzio at their side, companies fully exploit the potential of a future-proof, data-driven strategy. Contact Konfuzio now!
What are data-based Business Models?
Data-based business models use data to generate economic value. To this end, they analyze and interpret data to gain insights for the development or further development of innovative products and services based on the needs of the market. In other words, companies that use data-based business models turn data into a product and thus into the value driver of their business.
In the course of the article, we illustrate how this can work with 8 practical examples of successfully implemented data-based business models.
Data-based business models vs. digital business models
Both business models are closely linked, but they emphasize different aspects of the business. Data-based business models use digital data to develop new products and services and thus generate business value. To do this, they analyze large volumes of data to identify patterns and thus identify new potential.
Digital business models, on the other hand, integrate digital technologies into all aspects of the business, from communication to sales. They use digital platforms to provide customers with products and services. In other words, value creation in digital business models is purely digital. Classic examples are comparison platforms such as CHECK24 and idealo as well as streaming services such as Netflix or Spotify.
Data-based business models vs. data-driven business models
Data-based and data-driven business models are closely related, but they have subtle differences. While data-based business models use data as a primary function to create new value for a company, data-driven business models use data only as a supporting element to improve their existing business processes. In other words, data-based models emphasize the immediate dependence on data and the goal of creating new value, while data-driven models focus on data as an additional resource for optimizing existing approaches.
An example of a data-driven business model is the introduction of an app that adds a new digital channel to the existing business.
In practice, data-based models are often more disruptive as they enable innovative approaches, while data-driven models aim for more incremental improvements. However, both approaches are critical in today's digital economy as they help companies improve their competitiveness.
What are collaborative data-driven Business Models?
Collaborative data-driven business models emphasize the cooperation of different actors sharing and using data together. In doing so, companies combine their data sources to gain insights and develop innovative solutions.
Collaborative data-driven business models foster data sharing between partners, create multiple perspectives, and drive innovation. In practice, for example, companies share data with suppliers to optimize inventory. Platforms such as car sharing services use collaborative data to optimize vehicle usage and ensure availability.
Fundamentals of a Business Model
In order to develop a data-based business model, companies should first define what fundamentally characterizes a successful business model. A business model is the basic framework on which every company is built. It forms the blueprint for success. It usually answers the following 5 questions:
1. Who are the customers?
Identifying target groups is of crucial importance. Companies must therefore know exactly who they are addressing. These can be end consumers, other companies or specific market segments. Through a deep understanding of customer needs, companies are able to develop products or services that are precisely tailored to these needs.
2. What value do companies offer customers?
The value that companies provide results from the products or services they offer. This value comes in the form of quality, innovation, convenience, or other factors. Customers often choose a company based on the benefits or value they derive from what the company offers.
3. How do companies generate and deliver this value?
This is where operational aspects come into play. This includes product development, manufacturing, marketing, sales and customer service. It also includes the resources needed for these activities, such as personnel, technology, raw materials and financial resources. Basically, efficient processes and resource utilization are critical to delivering value with high quality and profitability.
4. How do companies capture value?
Value is expressed in the financial revenue streams of companies. These include, for example, the sale of physical products, digital downloads, subscription fees, and license fees.
5. What are the costs associated with the generation and delivery of this value?
Every business model entails costs. These are, for example, production costs, marketing expenses, personnel costs, logistics and distribution costs. Companies keep track of all costs to ensure that the value generated exceeds the costs and thus generates a profit.
A well-designed business model takes all these aspects into account and helps position the company in the market niche.
It also enables flexibility to adapt to changing market requirements - crucial in today's fast-moving business world. This means that companies that continuously review and adapt their business models are in a better position to secure long-term success.
3 possible Roles for Companies in a data-driven World
Following the Research to data-driven business models, companies typically take on 3 roles:
1. Data user: pull value from existing data
Data users use internal or external data to develop or enhance new products based on data analytics.
2. Data suppliers: Provision of relevant data
Data providers focus on providing relevant data products to data users, creating value for their own business. Their main focus is on providing data for other business models.
3. Data enabler: provide supporting data services
Data enablers provide supporting data services or data infrastructure solutions. They help data users by giving them access to relevant data or providing technical support to use data effectively.
These 3 types are not always strictly separable in practice. For example, data suppliers often offer additional services to provide data in the required form.
7 Variants of data-based Business Models
In practice, data-based business models appear in different variants - depending on the industry and market niche. The most important variants include these:
|Provides free services to users, funded by advertisers placing targeted ads.
|Google offers free services such as Search and Google Drive, financed by personalized advertising.
|Companies offer the basic service free of charge, while premium features are chargeable.
|Spotify provides a free version with ads, paying customers get premium features.
|Usage-based / on-demand model
|Customers pay for the services they actually use.
|Uber charges customers based on distance traveled and time. Streaming services like Amazon Prime charge for specific "on-demand" content.
|Retailers sell physical products online.
|Companies like IKEA sell products directly to consumers through their online store.
|Platform brings buyers and sellers together and charges fees for transactions.
|Amazon and eBay allow third parties to sell their goods on their platforms.
|Consumers have temporary access to goods and services without owning them.
|Airbnb allows renting apartments for short periods of time. Rent the Runway rents out designer clothing.
|Customers pay a regular fee for access to a product or service.
|Netflix charges a monthly fee for unlimited content streaming.
Opportunities of data-based Business Models
Before we dive deeper and take an in-depth look at how to implement data-driven business models, we'll first disclose the opportunities that companies have by properly evaluating big data:
Customer satisfaction and personalization
Data-driven business models enable the analysis of customer behavior and preferences to offer personalized services and recommendations. For example, Netflix uses data-based analytics to understand viewer behavior. Based on the content viewed, the streaming provider suggests personalized movie and series suggestions to its customers. This personalized recommendation significantly increases customer satisfaction.
Risk management and forecasting
Detailed data evaluation enables more accurate risk assessment and prediction of future developments. Insurance companies like Geico, for example, use data analytics to better assess risks. It does this by analyzing accident data, driving behavior and other factors to accurately calculate insurance premiums. This precise assessment lowers risk while granting policyholders fair rates.
Product innovation and market research
Data enables informed decision-making in the development of new products and services. For example, the toy manufacturer LEGO uses data analytics, in order to better understand customer behavior. By analyzing sales data and customer feedback, LEGO develops new products that meet customers' needs. This results in innovative toys that are successful in the marketplace because they are based on customers' real needs and preferences.
Cost efficiency and resource optimization
Data analytics help reduce costs and make better use of resources. Airlines such as Delta Airlines, for example, use data-based analytics to optimize aircraft maintenance. By analyzing machine data, they are able to perform preventive maintenance before costly breakdowns occur. This not only leads to cost savings, but also to safer and more reliable flight operations.
Risks of data-based Business Models
Collecting and storing large amounts of data brings with it many opportunities for companies. At the same time, however, data-based business models also harbor some risks and dangers of which companies should be aware. Only then will they be able to take appropriate security measures. The following risks are particularly common:
Data protection breaches
Companies store large amounts of sensitive data for the implementation of data-based business models. In this way, they are more vulnerable to data breaches. An example of this is the case of Equifax in 2017. The credit reporting provider had to admit to a massive data breach in which personal information of 147 million people was stolen.
Large-scale data storage makes companies attractive targets for Cyber attacks. One example is the 2017 ransomware attack on the UK's National Health Service (NHS), where attackers encrypted patient data and demanded a ransom for its release.
The use of large amounts of data - if not adequately secured - leads to legal problems. This is especially true when companies use data without appropriate consent. In 2012, for example, Google was fined $22.5 million by the Federal Trade Commission (FTC) for placing cookies on Safari users without their consent.
In the event of a data leak or misuse, a company's reputation suffers greatly. In 2013, for example, data from 40 million credit cards was stolen from Target. This led to a significant loss of trust and cost the company millions of dollars in compensation and expenses to restore its reputation.
What Legal Requirements data-based Business Models must meet
As the examples just mentioned show, the risks of data-based business models lie primarily in the lack of protection for collected data. Legal requirements in Europe are set primarily by the General Data Protection Regulation (GDPR), the Digital Markets Act (DMA), the Data Governance Act (DGA) and the Digital Services Act (DSA), which will apply from February 17, 2024. In addition, currently the Data Act is under development which is expected to take effect in the fall of 2025. We show in detail which regulations the individual laws for the collection, use and storage of data bring with them and which goals the EU is pursuing with them in our article on the European data strategy.
In 8 Steps: Strategic Data Integration for sustainable Business Models
In an era when data is the "new oil," implementing a data-driven business strategy is becoming a critical success factor. It is essential not only to collect data, but also to use it intelligently and integrate it into the business core. The following practical guide shows companies the 7 steps they can take to do this:
- Alignment with corporate vision
This step is the foundation for any data-driven strategy. Companies define their long-term goals and understand what challenges currently exist. A clear vision makes it possible to target data-based activities to steer the company in the desired direction.
- Holistic data integration
Data is often scattered across different departments and systems. By identifying and merging these data sources into one central system, companies gain a comprehensive 360-degree view. This not only enables better decision making, but also leverages synergies between different business units.
- Data analysis with advanced technologies
Data-driven business models require advanced technologies such as Big Data analytics, artificial intelligence, and machine learning to process, connect, and transform large amounts of data into actionable information. Companies are buying these technologies and integrating them into their existing systems.
- Capacity Building
The success of a data-driven strategy also depends on the skills a company possesses. Therefore, it is often necessary for companies to invest in training and education for existing employees and to hire new talent with specific data expertise.
- Safeguarding through compliance
Compliance with data privacy policies and regulations is critical. Robust data protection policies and their consistent implementation not only minimize legal risks, but also strengthen customer trust. This is essential to build and maintain long-term customer relationships.
- Strategy development based on data intelligence
By analyzing the collected data, companies gain valuable insights. They use these insights to make strategic decisions, whether for marketing initiatives, supply chain optimization or new product development. Data intelligence enables informed decisions to be made and resources to be deployed more effectively.
- Operational implementation and monitoring
Once a data-driven strategy has been developed, implementation is critical. Companies set clear key performance indicators (KPIs) for this purpose and monitor them continuously. This ensures that data-driven initiatives deliver the expected return on investment (ROI) and makes it possible to react quickly to unexpected developments.
- Iterative optimization
The business market is dynamic and subject to constant change. Therefore, a data-driven business model requires continuous adaptation and improvement. To this end, companies use feedback loops and continuous data analysis to evaluate the success of their data-based activities. They then improve identified weaknesses, whether in customer experience, process efficiency, or product innovation.
Data-based Business Models - Use Case
To better understand the steps involved in developing and implementing a data-driven business model, let's take a look at a detailed use case:
Initial situation for a data-based business model
A medium-sized retail company that manufactures and sells high-quality handmade products faces the challenge of remaining competitive in an increasingly digital market. The company decides to develop a data-driven business model to optimize its sales strategies and expand its customer base. How does it go about doing this?
Development and implementation of the data-based business model
The company is taking the following actions to integrate a data-driven business model:
Establishment of a data infrastructure
The company is starting to develop a robust data infrastructure. It integrates point-of-sale systems like Square, online sales platforms like Amazon, and customer databases like the CRM platform Salesforce. It collects all transaction data, customer preferences and feedback and consolidates it into a central data store.
Data analysis and customer profiling
The company analyzes this data to create comprehensive customer profiles. With the help of data analysis tools such as Power BI it identifies purchasing patterns, popular products and seasonal trends. This makes it possible to create new, personalized offers for different customer groups.
Implementation of personalized marketing strategies
Based on the customer profiles created, the company launches personalized marketing campaigns. Customers receive tailored emails with product recommendations based on their previous purchases. In addition, the company uses social media to target ads to specific customer segments.
The implementation of the data-based business model leads to significant improvements. Data evaluation enables the development of new products, while personalized marketing campaigns increase customer loyalty. At the same time, sales increase in this way, as the company offers precisely the products that customers demand.
Data-based Business Models - Examples
Numerous practical examples show that companies with data-based business models have been able to consolidate their position on the market and win new customers with innovations:
Nike and the digitization of the fitness experience
Nike, traditionally a manufacturer of sports apparel and footwear, has successfully ventured into the digital space in recent years. With apps such as "Nike Training Club" and "Nike Run Club," the company offers personalized training programs and running analyses. By using wearables and sensors in its products (e.g. Nike Adapt, self-lacing shoes), the company collects data and offers personalized experiences. This is not only a new sales channel, but also a way to strengthen customer loyalty and tap into new data sources.
Rolls-Royce and "Power by the Hour
Traditionally a manufacturer of aircraft engines, Rolls-Royce has introduced a digital business model called "Power by the Hour." Under this model, the company does not simply sell engines, but a guaranteed uptime. Using sensors on the engines, Rolls-Royce collects data on their performance and condition. This enables the manufacturer to anticipate maintenance needs and act proactively. Customers pay for the guaranteed availability of their aircraft - not just the engine itself. This approach has transformed the business model from product sales to a service-oriented approach.
Schneider Electric and EcoStruxure
Schneider Electric, a traditional electrical engineering and energy management provider, has developed EcoStruxure. This is an IoT-enabled, open and interoperable architecture and platform solution. This platform collects data from connected devices, analyzes it, and provides customers with energy management and automation optimization solutions based on it. Through this offering, Schneider Electric has made the transition from hardware provider to digital solutions provider.
Coca-Cola and Freestyle vending machines
Coca-Cola has created an innovative way to better understand customer needs with its interactive Freestyle vending machines. These vending machines allow customers to create their own beverage mixes by combining different flavors. In the process, the vending machines collect valuable data about customer preferences. This collected data helps Coca-Cola identify the most popular combinations, develop new flavors and optimize inventory accordingly. Through the personalized experience, Coca-Cola not only increases customer satisfaction, it also generates new revenue opportunities through innovative product creations.
LEGO and Digital Gaming
LEGO has successfully transferred its physical toys to the digital world. Through the development of digital games such as "LEGO Tower" and "LEGO Legacy: Heroes Unboxed," children and adults are expanding their LEGO experience in a virtual way. In addition, LEGO provides an online platform where users design and share their own models. This not only encourages user creativity, but also allows LEGO to collect data about player preferences. This data is incorporated into the development of new products and deepens interaction with the brand.
Disney and Disney+
Disney has launched Disney+, a streaming service that not only offers a wide range of entertainment content, but also enables the company to engage directly with customers. This is because by using Disney+, the company collects data on users' viewing behavior. This data enables personalized recommendations and content that match individual preferences. Disney is thus able to optimize the customer experience, strengthen customer loyalty and carry out targeted marketing campaigns.
Harley-Davidson and networked motorcycles
Harley-Davidson has created "HD Connect," a networked platform for its motorcycles. This platform allows owners to access important information about their vehicles via an app. For example, they can get vehicle diagnostics, monitor the location of their motorcycle and activate anti-theft features. These connected features not only offer customers greater convenience, but also allow Harley-Davidson to collect data on vehicle usage and condition. The company uses this data for product improvement. It also serves as the basis for personalized customer service.
L'Oréal and AR technology
L'Oréal is integrating augmented reality (AR) into its digital strategy to offer customers a unique experience. Through mobile apps, users perform virtual makeup tests by digitally trying different products on their face. This AR technology allows customers to test products in a real environment without having to physically apply them. In the process, L'Oréal collects data on which products customers try and which they like best. This information helps the company optimize its offering and provide personalized recommendations for customers.
Konfuzio as a powerful Partner for building data-driven Business Models
In a time of rapid digitalization, a data-driven strategy is crucial for long-term business success. With Konfuzio at your side, you secure a partner that not only has advanced software for data extraction and analysis, but also the expertise to efficiently leverage the complexity of Machine Learning and Deep Learning. Konfuzio thus supports you in recognizing the true value of your data and using it to develop new data-based business models.
As a leader, seize the opportunity now to gain an advantage in the data-driven competitive landscape. To do so, contact Konfuzio and explore the possibilities of your future-proof, data-driven strategy.