Data impact on Strategy
Chapter 1: Introduction to Data-Driven Disruption
Data-driven disruption is a significant paradigm shift that can transform the strategic landscape of businesses. It explores the potential threats for companies that fail to adapt to a data-driven approach and how these companies can themselves become disruptors by formulating a disruptive vision of the future. This vision is rooted in leveraging data to drive decision-making, operations, and strategy. It involves understanding the potential disruptions from data-driven companies and comprehending the strategic levels of these disruptions, which can affect various aspects of a business, from its value chain to its business strategy and operating model.
Chapter 2: Origins of Data-Driven Disruption
2.1 Understanding the Sources
Data-driven disruption can originate from three primary sources: within a company's own value chain, from adjacent markets, and from other geographical locations. These disruptions can occur due to vertical, horizontal, or geographic differentiation in corporate strategy. Vertical differentiation refers to changes within a company's value chain, horizontal differentiation refers to changes across different markets, and geographic differentiation refers to changes across different locations.
2.2 Disruptions in the Value Chain
For instance, a supplier in a company's value chain could potentially disrupt the company by directly serving the company's clients. This is possible because data makes the end-customer more accessible, and automation reduces transaction costs. This disruption can lead to a shift in the power dynamics within the value chain, potentially affecting the company's market position and profitability.
2.3 Disruptions from Adjacent Markets
Similarly, a distributor with access to customer data could strengthen their bargaining power, thereby disrupting the company's business. For example, Nike decided to move away from one of its most important distributors, Foot Locker, because Foot Locker would not share customer data with them. This example illustrates how data can be leveraged to gain a competitive advantage in the market.
Chapter 3: Impact of Data-Driven Disruption on Business Strategy
Data-driven disruption can significantly affect a company's business strategy. This disruption can impact a company's value proposition, value architecture, and value retention.
3.1 Impact on Value Proposition
For instance, a company like Uber leverages data to identify overlooked customer segments or to orchestrate different channels effectively. The value proposition refers to the unique value a company offers to its customers. Data can enhance this value proposition by providing insights into customer behavior and preferences, enabling companies to tailor their offerings more effectively.
3.2 Impact on Value Architecture
Similarly, a company like AVIS uses AI to predict customer lifetime value, allowing it to focus its marketing budget on long-term profitable customer segments. The value architecture refers to the way a company creates and delivers its value proposition. Data and AI can enhance this architecture by providing insights that inform strategic decisions, such as where to allocate resources or how to target marketing efforts.
3.3 Impact on Value Retention
Data-driven strategies can also innovate the value retention aspect of a business. For example, an Israeli startup, Weissberger, put sensors on beer taps and combined it with an app for clients. This allowed them to charge customers for the exact quantity of beer they consumed, leading to increased consumption and variety in beer types. Value retention refers to the ability of a company to retain its customers by continuously providing value. Data can enhance this aspect by enabling companies to understand customer behavior better and adapt their offerings accordingly.
Chapter 4: The Importance of a Disruptive Theory of Value
To survive the threat of data-driven disruption, companies need to build a unique Theory of Value. This theory should challenge the status quo and provide a new perspective on reality.
4.1 Developing a Theory of Value
For instance, Steve Jobs had the disruptive theory that personal computers would become mass-market products, a theory that contradicted the common belief at the time. Having a disruptive theory of value can help companies look at reality from a different perspective and find new evidence to support their theory.
4.2 Implementing the Theory of Value
Implementing this theory of value involves identifying the established assumptions in the industry, proposing alternative assumptions, identifying the problems that need to be solved to make the alternative assumptions true, and imagining the potential results if these problems were solved.
Chapter 5: The Impact of Data-Driven Disruption on Operating Models
Data-driven disruption can result in economies of scale, economies of scope, and economies of learning and decision-making.
5.1 Economies of Scale
For example, a company like Uber can easily replicate its data-driven best practices in other countries, leading to economies of scale. Economies of scale refer to the cost advantages that companies obtain due to their size, output, or scale of operation.
5.2 Economies of Scope
Similarly, a company like Hagleitner, which produces soap dispensers, can leverage data to enter two adjacent markets, leading to economies of scope. Economies of scope refer to the efficiencies gained by producing a variety of products, allowing companies to leverage shared resources across different products or markets.
5.3 Economies of Learning and Decision-Making
Lastly, companies like Consulting firms and Union Pacific Railroad can leverage data to make knowledge widely available and establish transparency, leading to economies of learning and decision-making. Economies of learning refer to the cost advantages that companies gain from learning and improving their processes over time.
Key Learnings:
- Data-driven disruption can originate from various sources and significantly impact a company's business strategy and operating model.
- To survive the threat of data-driven disruption, companies need to build a unique Theory of Value that challenges the status quo.
- Data-driven disruption can result in economies of scale, economies of scope, and economies of learning and decision-making.
- Companies need to leverage data to innovate their value proposition, create synergies between their different business areas, and establish transparency in their collaborations.