The Abductive Advantage - Part 2: From Whiteboard to Wallet

The failure of Blockbuster illustrated how traditional, deductive strategy—focused on analyzing backward-looking historical data—cannot withstand rapid environmental change. The modern imperative is to adopt Design Thinking for Strategy (DTS), which relies on abductive reasoning, starting with deep customer observation (as discussed in Post 01) and iteratively refining solutions.

Once the crucial insights and knowledge have been gathered in the initial phases (Observing and Learning), the strategy challenge shifts from understanding the present to designing the future. This shift demands moving concepts off the abstract whiteboard and transforming them into tangible, testable models.

This is the purpose of the Designing (D) and Validating (V) phases of DTS. Designing is the creative, divergent, forward-looking phase where ideas are generated and transformed into blueprints. Validation is the convergent, confirmatory phase where these blueprints are ruthlessly tested against reality, reducing strategic uncertainty down to an acceptable level.

We explore how firms use rapid prototyping and rigorous experimentation to forge strategies that are simultaneously Desirable, Feasible, and Viable. These concepts are illustrated through cases ranging from medical device pivots to digital banking applications, demonstrating that success hinges not just on having a great idea, but on testing and refining its underlying assumptions swiftly and cheaply.

I. The Crucible of Creativity: Defining and Designing the Strategy Blueprint

Designing is the point in the strategy design process where novel ideas are generated, and existing knowledge is combined in new ways to define how the firm will conduct its business. This systematic creativity is essential because strategy itself is a wicked problem, meaning it is complex, open, dynamic, and lacks a single, definitive solution.

The result of the Designing phase is not a final strategy document, but rather multiple testable prototypes of the firm’s target Detailed Business Model.

The Business Model as the Common Language

To structure this complex creative task, DTS uses the Detailed Business Model (DBM) framework, an extended version of the Business Model Canvas, as the common language for all stakeholders involved.

The DBM ensures a holistic characterization of the firm, focusing on four components: Customers, Offerings, Capabilities, and Financials. By defining the 15 underlying elements of the DBM (e.g., customer segments, value proposition, key resources, revenue streams) and the relationships between them, abstract ideas are converted into concrete blueprints.

Designing should be an absolute, firm-focused approach, rather than a relative one focused on competitors; adjustments for competition are reserved for the final Competition Layer. Designing begins by focusing exclusively on the firm’s chosen strategic focus (Customers, Offerings, Capabilities, or Financials).

Ideation and Transformation

Successful innovation often arises not from completely new ideas, but from the novel combination of existing knowledge and concepts.

Designers refine initial ideas by applying transformation techniques like:

• Magnifying: Thinking bigger or smaller.
• Inverting: Trying the opposite of the original idea.
• Stretching: Extending one specific property of the original idea.
• Compacting: Reducing the impact of a specific property.

The initial prototype focuses only on the strategic focus, the Value Proposition (OVP), and the Products and Services (OPS) elements of the DBM. A crucial consistency check is performed to ensure that every value proposition characteristic is supported by at least one offering characteristic, and that these align with the chosen strategic focus (e.g., Customer focus implies desirability, Capability focus implies feasibility).

Application: The Gas Station ATM Concept

A powerful example of transforming existing knowledge comes from retail banking. The idea to offer cash retrieval through human-operated gas station cash registers serves as an innovative blueprint for the Customer Delivery (CD) element of a digital bank’s business model.

• Existing knowledge: Cash registers, ATMs, and 24/7 gas station attendants.
• Novel combination: Using the gas station cash register as a human-operated ATM.

This design offers value to both the customer and the firm. For the customer, it provides round-the-clock access to cash, potentially increasing perceived trust due to human operation and reducing the fear of robbery associated with traditional ATMs. For the gas station owner, it reduces the cost of transporting cash to the bank and lowers the risk of robbery by reducing the amount of cash held on site. This idea must then be integrated and validated across the DBM, ensuring it is Desirable (customers want it), Feasible (gas stations can implement it), and Viable (the fees charged cover the costs).

II. Prototyping Strategic Viability: Case Studies in Iteration

Strategy prototypes, unlike product prototypes, are often mental activities rather than physical mock-ups, though they may involve user interfaces (UIs) or storyboards. Their primary function is to transform abstract ideas into actionable concepts that can be shared, reviewed, and, crucially, validated.

The purpose of prototyping is to build and test elements designed to achieve four critical objectives simultaneously: Desirability (customer interest), Feasibility (delivery capability), Viability (sustainable profit), and Competitiveness (differentiation).

Case Study: The Owletcare Pivot—From Failure to Fit

The story of the Owletcare baby monitor exemplifies how rapid iteration, pivoting, and focusing on desirability in a different market segment can convert a non-viable idea into a success.

A team of students observed a difficulty in hospitals related to pulse oximetry equipment: the wiring was cumbersome for staff and hindered patient mobility.

1. Initial design & prototype: They designed a wireless pulse oximetry prototype to address this problem.
2. Initial validation failure (viability): The nurses loved the concept (Desirability met for the end-user). However, when the team approached the hospital administrators (the decision takers or check writers), they received “no interest in spending money.” The initial business model failed the Viability test because the person paying the bill (the administrator) did not perceive the value of the solution.
3. Iteration and pivot: Confronted with this flaw, the team iterated and searched for other applications of their wireless technology. They identified the serious problem of infants dying from respiratory failure.
4. Redesign and success: This insight led to a new prototype: a sock solution to comfortably fit the pulse oximetry equipment on infants and newborns. This pivot successfully launched the Owletcare baby monitor in the US market.

This case demonstrates the fundamental principle of DTS: designers foster creativity and iteratively improve solutions by combining existing knowledge (wireless pulse oximetry) with new knowledge (unmet needs of infants and their parents). It highlights that strategy development, especially for wicked problems, relies on iteratively trying out options and accepting that the first solution is almost never the final one.

III. Managing Uncertainty: The Validating Phase (V)

Strategy requires a degree of uncertainty, but successful execution depends on reducing major risks to an acceptable level. The Validating phase uses experiment-based testing to determine whether assumptions underpinning the designed DBM prototype are likely to hold true in the real world.

Validation is a forward-looking, confirmatory process that uses judgmental insights—gathering evidence until the marginal added knowledge is nearly zero—rather than relying on backward-looking statistical hypothesis testing derived from historical data.

The Central Role of Assumptions

Every element and relationship within the DBM prototype rests on explicit or implicit assumptions. To avoid costly mistakes, assumptions must be formulated and tested early.

Assumptions can be classified into three types:

1. Element-based assumptions: Are the descriptions of specific DBM elements valid? (e.g., Are there enough home owner families requiring mortgage financing in the suburbs covered by the bank? - CS element)
2. Relationship-based assumptions: Are the relationships between elements valid? (e.g., Are targeted customers willing to do all their payments via their mobile phone? - linking CS to OPS/OVP)
3. Externality-based assumptions: Are the causalities between external players/environment and DBM elements valid? (e.g., Are gas stations willing to function as human serviced ATMs for a fee? - linking Financials to Customer Delivery and external suppliers)

Only those assumptions that have a material impact on the validity of the business model and for which confidence is insufficient should be validated.

Prioritization and Decision-Maker Involvement

To ensure validation is cost-efficient, assumptions are classified and prioritized. Priority is given to assumptions that are highly relevant to the strategic focus and whose failure would be critical for the DBM’s success (e.g., a Customer strategic focus requires high relevance in the ‘Desirability’ category).

Furthermore, assumptions are prioritized based on the required validation effort (time/money) versus the impact of failure. High-impact, low-effort validation experiments should always be conducted first.

Crucially, decision takers must be actively involved in performing the validation. If executives “have heard first hand from a customer” that an idea is valid or invalid, they gain significantly higher confidence in the associated strategic decision.

IV. Experimentation in the Wild: Case Studies in Validation

Designing experiments requires creativity and adheres to specific constraints to maintain agility. A key principle is attempting hard to invalidate the assumption, rather than merely confirming what is already believed to be true.

A well-designed strategy experiment typically adheres to the 5-5-5 Rule: it requires no more than 5 weeks to perform, costs no more than $5,000, and involves no more than 5 strategy design team members (including decision takers). For lower-impact assumptions, the timeline may be reduced further.

Experiment Types

Common validation experiments include:

• Mock-up/prototype feedback: Presenting an informant with a physical or digital prototype (e.g., a screen sequence or app interface) to test features, packaging, or distribution channels.
• Confirmatory interviews: Close-ended questions designed to confirm or reject an assumption, followed by questions that explore the why behind the answer (“What would make you change your mind?”).
• Split testing (A/B testing): Used to test different alternatives, such as pricing models, offering features, or bundling.
• Surveys: Used to reach large populations, though they require meticulous question quality to avoid bias and misunderstanding.

Case Study: Testing Feasibility—The Hardware Store AI Kiosk

A hardware store aiming for a Financials strategic focus decides to compete on cost superiority. A core component of their strategy is reducing the high cost of human pre-sales support.

Design idea: Replace human staff with artificial intelligence (AI) driven kiosks or autonomous robots to deliver pre-sales support to customers.

This design choice generates several critical assumptions that must be validated to confirm the strategy’s Feasibility and Viability:

1. Assumption 1 (Desirability/Acceptance): Customers accept pre-sales support mechanisms/robots as an alternative to human professionals.
2. Assumption 2 (Feasibility/Quality): AI-powered kiosks can provide support quality accepted by customers as equivalent to humans.
3. Assumption 3 (Viability/Cost): Robots can be built and trained cheaply enough to support the discounter’s strategic focus.

Experiment design: To test Assumption 1, a mock-up kiosk is built in a real store environment. Customers interact with the kiosk, but unbeknownst to them, the answers are provided remotely by a human, allowing the validation team to test customer acceptance of the interface (the kiosk style) without risking quality concerns. The ultimate outcome of the strategy depends on game theoretical analysis of competitor reaction; for instance, if competitors offer superior human-based support that customers prefer, the AI strategy might fail regardless of cost savings.

Case Study: Testing Desirability—The Digital Bank Mortgage App

A suburban retail bank decides on an Offerings strategic focus, aiming to become a pure digital bank offering services solely via technology (e.g., mobile apps). A unique offering designed is a 100% online mortgage application process.

Assumption: Customers are willing to contract their first-time mortgage via a mobile phone app without human interaction or support.

This assumption must be validated to confirm the Desirability of the core offering.

Experiment design (mock-up and confirmatory interview):

• Experiment: Informants who recently contracted a mortgage are presented with a user interface mock-up illustrating the end-to-end mortgage application process on a mobile app.
• Goal: Determine if they would be willing to use such an app.
• Measurement criterion: Count all informants who answer “yes” or “maybe.”
• Decision threshold: Accept the assumption if 75%–80% of informants respond positively.

By interacting with the app mock-up, the informant can provide feedback on specific pain points and needs. For example, a customer might disagree with the “cash availability at any time” assumption, stating that what they actually need is cash access early morning for coffee or late evening for home-delivered pizza. This insight allows the designers to rephrase the assumption (e.g., change “at any time” to “6 AM to midnight”) or adjust the prototype, thereby refining the Customer Delivery element of the DBM based on validated customer needs.

This disciplined process of defining, testing, and refining allows the strategic decision-makers to gain sufficient evidence to move forward with confidence, transforming what might otherwise be a costly gamble into a calculated competitive advantage.

V. Validation’s Strategic Imperative: Desirability, Viability, Feasibility, Holistically

The final step of the Validating process involves top-down tests to ensure the overall consistency of the entire designed Detailed Business Model prototype. A strategy must meet all three criteria: Desirable, Viable, and Feasible.

Desirability Check (Customer and Offerings)

The strategy must validate that enough customers exist in the target segments, that the firm can build a relationship such that its offering enters the customer’s consideration set, and that the value proposition covers enough customer jobs-to-be-done to trigger a buying decision.

Viability Check (Financials)

This validates whether the firm can generate a sustainable profit. The key assumption here is that customers are willing to pay a price that exceeds the costs of production and delivery. Viability is achieved when a sufficient number of customers buy the offering to cover fixed expenses and generate profit.

Feasibility Check (Capabilities)

Feasibility ensures the firm can actually deliver the promises made at the quality level expected by customers. This involves checking that the necessary activities can be performed, that resources (labor, capital, perishable assets) are available at reasonable costs, and that key assets are used effectively. Unless the firm is disrupting or highly inexperienced, feasibility is often the easiest trait to confirm.

By executing Designing and Validating iteratively, DTS ensures that the resulting strategic blueprint is not merely an abstract plan but a robust, tested model prepared for the realities of the market and the inevitable reaction of competitors—the focus of the next phase.

Summary