Start at the End

Start at the End Plot Summary

Introduction

We live in a world where countless products and services are created, yet surprisingly few achieve their intended impact on human behavior. Why is this happening? Traditional design approaches often prioritize features, aesthetics, or what executives find appealing rather than focusing on the actual behaviors we want to encourage. This misalignment creates a fundamental disconnect between what we build and why we build it. The Intervention Design Process (IDP) presented in this book offers a revolutionary framework that inverts this approach by starting with the end in mind. Rather than beginning with a product idea and hoping it changes behavior, we first define the specific behavior we want to create, identify the pressures that influence it, and then design interventions to modify those pressures. This process places behavior change at the center of creation, providing a systematic, evidence-based methodology that can be applied across industries, from technology platforms to public health initiatives, and even personal habit formation.

Chapter 1: The Intervention Design Process: A Framework for Change

The Intervention Design Process represents a structured approach to creating meaningful behavior change. At its core, the IDP is a systematic method that works backward from desired outcomes rather than forward from proposed solutions. This reversal fundamentally changes how we approach design by ensuring that everything we create serves a clear behavioral purpose. The process begins with identifying a potential insight about the distance between our current world and a counterfactual one where the desired behavior already exists. This insight highlights a gap we seek to bridge through careful intervention design. Unlike traditional approaches that jump straight to solutions, the IDP requires validation of this insight through both quantitative and qualitative methods, establishing convergent validity from multiple sources before proceeding. What makes the IDP particularly powerful is its recognition that behavior results from competing pressures - those that promote a behavior and those that inhibit it. By mapping these pressures explicitly and validating them, designers gain a comprehensive understanding of why a behavior does or doesn't occur. This understanding forms the foundation for intervention design, ensuring that solutions address the actual factors influencing behavior rather than assumed ones. The IDP follows a clear sequence: potential insight → insight validation → behavioral statement → pressure mapping → pressure validation → intervention design → ethical check → pilot → test → scale. Each step builds upon the previous one, creating a coherent path from observation to implementation. This structured progression helps organizations avoid the common pitfall of falling in love with solutions before understanding problems. Consider how Uber transformed transportation. Rather than simply creating a better taxi app, they identified key inhibiting pressures (payment friction, uncertain wait times, geographic limitations) and systematically addressed them. By applying a behavior-first approach similar to the IDP, they fundamentally changed how people move from point A to point B.

Chapter 2: Potential Insights and Validation

Potential insights are observations that reveal a gap between our current reality and a counterfactual world where behavior occurs differently. These insights serve as the seeds of behavior change, highlighting opportunities to move closer to an optimal state. Recognizing these gaps requires developing a sensitivity to inconsistencies, unfulfilled needs, and behavioral patterns that don't align with people's intentions. These insights can emerge from four primary sources. Quantitative insights arise from data patterns or statistical anomalies that suggest underlying behavioral mechanisms. Qualitative insights develop through direct observation and interaction with people, capturing nuances that numbers might miss. Apocryphal insights represent organizational knowledge that "everyone just knows" but may not be formally documented. External insights come from outside your organization, including academic research, cross-industry observations, or consultations with subject matter experts. The validation process is crucial for determining which insights deserve further exploration. Without rigorous validation, we risk building interventions based on assumptions rather than reality. Effective validation employs multiple methods to achieve convergent validity - confirmation from diverse sources supporting the same conclusion. This might include analyzing data patterns, conducting user interviews, consulting organizational wisdom, and reviewing external research. By triangulating from different perspectives, we strengthen our confidence in an insight's validity. Validation helps overcome confirmation bias, our natural tendency to notice evidence supporting what we already believe. When teams assign different validation methods to different researchers who work independently before comparing findings, they create structural safeguards against premature consensus. Organizations that excel at behavior change often institutionalize this diversity of perspective through cross-functional teams and regular knowledge exchange sessions. The story of Flamin' Hot Cheetos illustrates this process beautifully. Richard Montañez, a janitor at Frito-Lay, noticed the absence of snacks appealing to Latino tastes - a potential insight. He validated this by sharing his homemade spicy Cheetos with friends and colleagues, whose enthusiasm confirmed his observation. This validated insight eventually led to one of Frito-Lay's most successful products, demonstrating how recognizing and validating a behavioral gap can create tremendous value.

Chapter 3: Behavioral Statements: Defining Clear Outcomes

A behavioral statement articulates the specific outcome you aim to create, serving as the North Star for your intervention design efforts. Unlike vague vision statements, behavioral statements are concrete, measurable, and focused on observable actions. They define not just what you want to build, but the behavior change you intend to produce, bringing clarity and alignment to everyone involved in the design process. The structure of an effective behavioral statement follows a precise formula: "When [population] wants to [motivation], and they [limitations], they will [behavior] (as measured by [data])." Each element serves a crucial purpose. The population defines who will perform the behavior, creating boundaries for your intervention. The motivation articulates why people would engage in the behavior, ensuring alignment with their existing desires. Limitations establish preconditions necessary for the behavior that fall outside your control. The behavior itself must be specific and observable, while the data component ensures measurable outcomes. Consider Uber's behavioral statement: "When people want to get from Point A to Point B, and they have a smartphone with connectivity and an electronic form of payment and live in San Francisco, they will take an Uber (as measured by rides)." This statement clearly defines who (people in San Francisco with smartphones and electronic payment), why (transportation), what limitations exist (technology requirements), what behavior is desired (taking Uber), and how success will be measured (ride count). Common mistakes in crafting behavioral statements include choosing the wrong behavior (focusing on product presence rather than usage), selecting no behavior at all (like "making customers love our product"), creating overly timid statements that limit potential, and clinging to initial statements when circumstances change. Microsoft once focused on putting "a computer on every desk and in every home running Microsoft software," but this vision didn't address whether people would actually use those computers. This led to optimizing for sales rather than usage, creating vulnerability that competitors like Google exploited. Beyond guiding intervention design, behavioral statements serve broader organizational functions. They create transparency around goals, enable cascading accountability throughout an organization, and provide clear metrics for measuring success. When each person in an organization can connect their work to a specific behavioral outcome, they understand not just what they're doing but why it matters, creating alignment that traditional vision statements often fail to achieve.

Chapter 4: Pressure Mapping: Promoting vs. Inhibiting Forces

Pressure mapping reveals the forces that create the gap between current and desired behavior, providing the blueprint for effective interventions. The core concept is elegantly simple: behavior results from the balance between promoting pressures (forces making a behavior more likely) and inhibiting pressures (forces making it less likely). By understanding these competing pressures, we can design interventions that systematically modify them to achieve desired behavioral outcomes. The process begins by drawing two arrows - one pointing up (promoting pressures) and one pointing down (inhibiting pressures). Teams then populate both sides with all the factors that influence the target behavior. For example, when examining why people eat M&M's, promoting pressures might include taste, color appeal, hunger satisfaction, and cultural associations. Inhibiting pressures could include cost, health concerns, availability, and contextual appropriateness. This comprehensive mapping reveals the complex interplay of forces affecting behavior. A critical insight from pressure mapping is our predictable bias toward certain types of pressures. When trying to increase a behavior, we naturally focus on strengthening promoting pressures (making M&M's tastier). When trying to decrease a behavior, we concentrate on enhancing inhibiting pressures (adding warning labels to cigarettes). This bias creates significant untapped opportunity in the neglected pressures - reducing inhibiting factors often yields greater behavior change than further enhancing promoting ones. This explains much of Uber's success. While competitors focused on making transportation more appealing (promoting pressures), Uber systematically eliminated barriers to using car services (inhibiting pressures). By addressing payment friction, availability limitations, and uncertainty about arrival times, they created behavior change that transformed an industry. The automatic payment system alone removed a significant inhibiting pressure that few would have articulated as important before experiencing its absence. Effective pressure mapping requires diverse perspectives to avoid blind spots, flipping the behavioral statement to identify additional pressures, and separating validation by researcher type to prevent groupthink. The goal isn't perfect mapping but sufficient understanding to design interventions that change behavior. The pressures themselves may be rational or irrational, conscious or unconscious - what matters is their influence on the target behavior. By systematically identifying and validating these pressures, we create the foundation for intervention design that addresses the actual forces governing behavior.

Chapter 5: Intervention Design and Selection

Intervention design transforms understanding into action by creating specific modifications to the pressures that govern behavior. While the concept is straightforward - strengthen promoting pressures and weaken inhibiting ones to increase a behavior - effective execution requires disciplined focus on behavioral outcomes rather than creative novelty. The goal isn't designing interventions that sound good but designing ones that actually change behavior. The process begins with divergent thinking, generating as many potential interventions as possible based on the validated pressure map. Teams should explore interventions targeting different pressures, combinations of pressures, and varying implementation approaches. This divergent phase emphasizes quantity and diversity over practicality or perfection. By suspending judgment about feasibility, teams create space for innovative approaches that might otherwise be prematurely discarded. When designing flu shot interventions at Clover Health, researchers discovered that distrust of medical establishments was a key inhibiting pressure for Black members. Rather than focusing solely on education about flu shot benefits (a promoting pressure), they explored multiple ways to reduce this trust barrier, including partnering with trusted community institutions like churches. This approach addressed the actual pressure preventing the behavior rather than assuming more information would be sufficient. The selection phase narrows options by evaluating interventions against criteria like optimum distinctiveness (covering the broadest range of pressure points with minimal overlap), implementation feasibility, population coverage, and resource requirements. Teams should select multiple interventions to pilot rather than betting everything on a single approach, as this creates more learning opportunities and reduces confirmation bias. Selection isn't about picking the "best" intervention but creating a portfolio that maximizes chances of behavior change. Effective selection requires maintaining skepticism about whether interventions will work. This counter-intuitive stance - selecting interventions while expecting them to fail - helps teams avoid the sunk cost fallacy and remain objective about results. By treating intervention design as a hypothesis-testing process rather than a solution-implementation one, organizations create space for the systematic learning that powers lasting behavior change. A powerful example comes from Meetup, where executives debated adding a checkbox stating "I pledge to create real, face-to-face community" to their registration flow. While conventional wisdom suggested adding steps would reduce sign-ups, the checkbox actually increased successful meet-up creation by 16% by strengthening the promoting pressure of organizer passion. This counterintuitive result emerged because the team remained open to evidence rather than assumptions, focusing on actual behavior change rather than conventional product wisdom.

Chapter 6: Pilots, Tests and Scaling: Evidence-Based Implementation

The pilot-test-scale sequence transforms promising interventions into validated behavior change solutions through progressive validation and refinement. Each stage increases confidence in an intervention's effectiveness while gathering critical information about its operational requirements and real-world impact. This methodical approach minimizes risk while maximizing learning, ensuring resources are invested only in interventions that demonstrably change behavior. Pilots are small-scale, operationally "dirty" implementations designed to quickly evaluate whether an intervention changes behavior at all. Their primary purpose is to provide an initial signal about effectiveness with minimal resource investment. Pilots should be implemented rapidly (ideally within two weeks) and with just enough fidelity to test the core behavioral mechanism. This lightweight approach reduces employee abrasion when interventions fail and allows organizations to test more interventions simultaneously. Pilot validation combines qualitative and quantitative measures to determine whether an intervention shows promise, not statistical significance. Tests expand successful pilots to larger populations with greater operational diligence. They answer the crucial question: "Is this intervention worth doing at scale?" Tests evaluate not just whether an intervention changes behavior but whether its impact justifies the resources required for full implementation. They also reveal operational challenges that might not appear in smaller pilots. Test validation typically requires stronger statistical evidence (though not necessarily the p<0.05 standard from academia) and more comprehensive qualitative feedback before proceeding to scaling decisions. The scaling decision represents a formal evaluation of whether an intervention merits full implementation. This assessment typically follows a structured format: "We are [confidence] that [intervention] will [direction] [behavior] (as measured by [data]). Scaling this requires [effort] and will result in [change]." This format forces explicit consideration of the evidence, required resources, and expected impact, creating accountability for scaling decisions while documenting findings for future reference - even when interventions fail. Once scaled, interventions require continuous monitoring to detect changes in effectiveness over time. All interventions eventually experience diminishing returns due to the "piranha effect" (interventions competing for limited cognitive resources) or shifting pressures in the environment. Continuous monitoring allows organizations to modify or replace interventions when their effectiveness declines, maintaining behavior change over time. The power of this approach lies in its emphasis on evidence over intuition. When Microsoft's Bing in the Classroom initiative followed this process, it produced a 40% increase in school searches - far greater impact than would have resulted from the marketing department's initial instinct to launch a curiosity-focused advertising campaign. By systematically testing and validating interventions rather than relying on executive preferences or conventional wisdom, organizations dramatically increase their ability to create meaningful behavior change.

Chapter 7: Advanced Applications and Ethics of Behavior Change

The ethical application of behavior change requires both principled frameworks and ongoing vigilance. While behavior change itself is ethically neutral - teachers, doctors, and parents change behavior constantly without ethical concern - the methods used and outcomes sought demand careful consideration. The key ethical question isn't whether to change behavior but how to do so responsibly. The ethical foundation begins with ensuring alignment between interventions and people's existing motivations. Behavior change should help people act on intentions they already have rather than manipulating them into behaviors that don't serve their interests. This distinction manifests in two fundamental gaps: the intention-action gap (wanting to do something but not doing it) and the intention-goal gap (wanting an outcome but not intending to perform the behavior that leads to it). Ethical interventions bridge these gaps while respecting autonomy and transparent about their methods and objectives. A comprehensive ethical framework can be distilled into three principles: If your outcome behavior is not the result of any of the population's motivations, or if the benefit of the behavior doesn't outweigh the cost to alternative motivations, or if you're unwilling to publicly describe your intervention, it's unethical. This framework helps designers navigate complex trade-offs while maintaining accountability for the consequences of their work. Advanced applications extend beyond basic behavior change to address complex scenarios. Identity pressures - how we see ourselves and wish to be seen by others - represent powerful levers for behavior change when properly understood. Cognitive resource management recognizes that attention is limited and interventions should help people spend mental energy on what matters most to them. Understanding the balance between uniqueness and belonging helps designers create interventions that honor people's need to both stand out and fit in. The ethical stakes rise with sophisticated techniques like priming (activating existing associations), mediation (creating new associations), and designing for competing behaviors. These advanced approaches require greater care to avoid manipulation while still creating meaningful change. Even techniques for eliminating behaviors demand ethical consideration - removing a behavior without providing an alternative pathway to meet the underlying motivation often leads to unintended consequences. Real-world applications demonstrate these principles in action. Countdown clocks in subway stations don't make trains arrive faster but reduce perceived waiting time by eliminating the inhibiting pressure of uncertainty. GetRaised.com helps women secure higher salaries not by telling them to "lean in" but by reducing the inhibiting pressures of ambiguity and effort through structured guidance. These interventions succeed by systematically addressing the actual pressures governing behavior while respecting people's autonomy and existing motivations.

Summary

The Intervention Design Process fundamentally transforms how we create products, services, and policies by placing behavior change at the center of design. Rather than beginning with features or functions, we start at the end - defining the specific behavior we want to create, mapping the pressures that influence it, and designing interventions that systematically modify those pressures. This approach replaces intuition and assumption with evidence and validation, dramatically increasing our ability to create meaningful change. The implications of this framework extend far beyond individual products or companies. When applied systematically, behavior-centered design has the potential to address our most pressing challenges - from public health crises to environmental sustainability to social inequities. By understanding the actual mechanisms that drive human behavior and designing interventions that work with rather than against these mechanisms, we gain unprecedented ability to create a better world. The future belongs not to those who create the most features or generate the most buzz, but to those who most effectively change the behaviors that matter.

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Matt Wallaert

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