Continuous Discovery Habits: Discover Products that Create Customer Value and Business Value

Chapter 4 introduces techniques for organizing and understanding customer experiences before beginning product development. Torres emphasizes the importance of creating visual representations to build shared understanding among team members.

The chapter opens with a case study about a product development team tasked with increasing application submission rates. This team consisted of three members: a new product manager with two years of company experience, a recently hired vice president of design, and a veteran engineer who lacked discovery experience. Their diverse backgrounds and perspectives proved valuable in understanding the complete customer experience.

Torres advocates for a structured approach to mapping customer experiences. The first step involves defining the scope of the investigation based on desired outcomes. For example, a streaming service seeking to increase viewing time might examine how customers engage with video entertainment broadly rather than focusing solely on their own platform’s usage patterns.

To prevent groupthink, Torres recommends that team members initially work independently to create their visual representations of customer experiences. This approach, while seemingly inefficient, allows each team member to fully develop their perspective before sharing it with the group. The author emphasizes that drawing skills are less important than the act of visualization itself; simple boxes, arrows, and stick figures suffice for representing complex customer journeys.

The process then moves to collaborative synthesis. Team members share their individual experience maps, asking questions and exploring differences rather than immediately advocating for their viewpoints. The final step involves creating a unified map that incorporates all team members’ insights. This collaborative map should include nodes (representing distinct moments or actions), links (showing relationships between nodes), and contextual information about customer thoughts and feelings at each stage.

Torres identifies several potential pitfalls in this process. Teams should avoid excessive debate over minor details, resist the urge to use words instead of visuals, and remember that their initial map represents hypotheses rather than confirmed facts. Additionally, the experience map should evolve as the team gains new insights from customer interviews and solution testing.

In this chapter, Torres challenges the conventional wisdom that product teams should simply give customers what they request. She begins by examining Steve Jobs’s approach at Apple, noting that Jobs rejected market research in favor of anticipating customer needs. The author uses Apple’s development of visual voicemail as an example: While no customers specifically requested this feature before the iPhone’s release, Apple identified and solved an underlying problem with traditional voicemail systems.

Torres explains that direct questioning often yields unreliable results, because people struggle to accurately report their behavior. She illustrates this through an example of a woman who claimed fit was her primary consideration when buying jeans, yet made her most recent purchase based on brand familiarity and price. This disconnect stems from what neuropsychologist Michael Gazzaniga terms the “left-brain interpreter”—the brain’s tendency to create coherent but potentially inaccurate explanations for one’s actions.

The chapter outlines a more effective approach to customer interviews. Instead of asking direct questions about preferences or habits, Torres advocates for story-based interviewing. This method involves asking customers to recount specific, recent experiences. For example, rather than asking about general shopping habits, an interviewer might request details about a customer’s most recent purchase. This approach yields more accurate insights because it focuses on actual behavior rather than perceived behavior.

Torres emphasizes the importance of “excavating the story”—guiding interviewees to share complete narratives with temporal prompts such as “What happened first?” and “What happened next?” (81). She cautions that customers often drift into generalizations, and interviewers must consistently redirect them back to specific instances. The author draws from her professional experience, describing how her team developed an unsuccessful recruiting product because they relied on what customers said they wanted (passive candidate sourcing) rather than observing what customers did (sourcing active candidates).

The author introduces the concept of an “interview snapshot”—a one-page document that captures key insights from each customer conversation. These snapshots should include a photo of the participant when possible, memorable quotes, relevant context, and identified opportunities. Torres stresses that opportunities should be framed as needs rather than solutions.

Torres concludes by advocating for weekly customer interviews, emphasizing that continuous interviewing helps teams maintain awareness of evolving customer needs and market changes. She recommends automating the recruitment process to sustain this practice and suggests multiple recruitment strategies, including in-product surveys and partnerships with customer-facing teams. The author advises against common mistakes such as relying on a single team member for interviews or only conducting interviews when they seem immediately necessary.

Chapter 6 explores the strategic process of organizing and evaluating customer opportunities in product development. Torres opens with a case study about Ahmed Guijou, a product director at Seera Group, who faced a business crisis during the COVID-19 pandemic in March 2020. When Saudi Arabia closed its borders, Guijou’s hotel booking business collapsed overnight. However, his team noticed customers pivoting to booking local properties called istrahas—resting places used for social gatherings. Using continuous discovery methods, the team interviewed hosts and guests, mapped opportunities, and developed a new market strategy within weeks.

This case study introduces the chapter’s central concept: Opportunity mapping helps product teams organize and prioritize customer needs systematically. Torres explains that customer interviews generate numerous opportunities—gaps between customer expectations and reality. Rather than addressing opportunities randomly, teams should map them systematically to identify those most likely to achieve desired outcomes.

Torres critiques the common practice of maintaining opportunity backlogs—prioritized lists of customer needs and pain points. She argues that flat lists make prioritization difficult because opportunities vary in scope and complexity, and many opportunities interrelate or overlap. Instead, she advocates for using opportunity solution trees, which organize opportunities hierarchically.

The tree structure, Torres explains, establishes two key relationships: parent-child and sibling relationships. Parent-child relationships represent subsets—smaller opportunities that contribute to larger ones. Sibling relationships represent distinct but related opportunities that fall under the same parent category. This structure helps teams break down large, complex problems into smaller, more manageable components that can be addressed incrementally.

Torres provides specific guidance for creating effective opportunity maps. Teams should first identify distinct branches based on key moments in the customer experience. These moments can come from experience maps or interview drawings. Each branch should represent a discrete phase with no overlap. Teams then inventory opportunities from customer interviews, adding only those that meet three criteria: They must be framed as customer needs rather than solutions, appear in multiple interviews, and potentially drive desired outcomes.

The chapter concludes with common pitfalls to avoid when mapping opportunities. These include viewing opportunities from the company’s perspective rather than the customer’s, creating vertical stacks of single opportunities without siblings, allowing opportunities to have multiple parents, keeping opportunities too vague, disguising solutions as opportunities, and mistaking emotional responses for opportunities themselves.

Torres emphasizes that opportunity mapping requires significant critical thinking but should not become an endless pursuit of perfection. The process should provide enough structure to guide decision-making while remaining flexible enough to evolve as teams learn more about their customers’ needs. This balance allows teams to maintain systematic inquiry while adapting to new information and changing circumstances.

Chapter 7 opens with Torres addressing product developers at a conference in San Francisco. During her presentation, she emphasizes a crucial message: Organizations cannot achieve success through individual feature releases. This anecdote establishes the chapter’s central argument against what product expert Melissa Perri terms “the build trap”—a pattern in which organizations evaluate their achievements based on outputs rather than outcomes.

The chapter examines how product teams often become trapped in solution-focused thinking, measuring progress by feature implementation rather than customer value. Torres argues that effective product strategy emerges from decisions about desired outcomes, target customers, and key opportunities rather than from feature development. This perspective shift, she says, moves teams from the solution space to the opportunity space.

To guide teams in opportunity assessment, Torres presents a structured approach that begins with examining top-level opportunities in an opportunity solution tree. Teams should evaluate one opportunity at a time rather than spreading resources across multiple initiatives. This focused approach allows for iterative value delivery and aligns with Kanban principles—a method that researchers at Finland’s University of Oulu found increases quality and consistency while reducing customer complaints.

Torres outlines four key criteria for opportunity assessment: opportunity sizing (evaluating the number of affected customers and frequency of impact), market factors (considering competitive positioning and external influences), company factors (aligning with organizational vision and capabilities), and customer factors (gauging the importance of opportunities to users and their satisfaction with existing solutions). Rather than using numerical scoring systems, Torres advocates for maintaining the subjective nature of these assessments, encouraging teams to engage in substantive discussions about each factor.

The chapter introduces Jeff Bezos’s concept of Level 1 and Level 2 decisions, which she frames as “one-way door decisions” and “two-way door decisions.” Two-way door decisions, or reversible decisions, allow teams to change course if new information emerges. Torres references research by social psychologists at the University of Amsterdam indicating that viewing decisions as reversible enables ongoing critical evaluation, while irreversible decisions can lead to confirmation bias.

Torres concludes by identifying common pitfalls in opportunity assessment: excessive data gathering that delays action, overemphasis on single assessment factors at the expense of others, and predetermined conclusions that prevent genuine evaluation. The chapter emphasizes that while these strategic decisions require careful consideration, teams should avoid analysis paralysis and maintain the ability to adjust course based on new insights.

In this chapter, Torres examines the complexities of ideation in product development, challenging common assumptions about group brainstorming while offering evidence-based alternatives for generating innovative solutions. The chapter begins by addressing a fundamental tension in product development: While individuals often generate immediate solutions upon hearing about problems, research indicates that initial ideas rarely represent optimal solutions.

Torres presents three primary criteria researchers use to measure creativity: the quantity of ideas generated (fluency), the variety of ideas (flexibility), and the uniqueness of ideas (originality). Research demonstrates that generating more ideas correlates with increased diversity and novelty in the solutions proposed. Notably, the most original ideas typically emerge toward the end of ideation sessions, suggesting the value of sustained ideation efforts.

The author then critically examines the history and effectiveness of traditional brainstorming. Alex Osborn introduced brainstorming in his 1953 book Applied Imagination, establishing four fundamental rules: prioritize quantity, defer judgment, welcome unusual ideas, and combine ideas to create better ones. However, subsequent academic research revealed that individuals working alone consistently outperformed brainstorming groups in generating ideas. Researchers identified four key factors limiting group brainstorming effectiveness: social loafing (reduced individual effort in groups), group conformity (tendency to generate similar ideas), production blocking (losing ideas while waiting to speak), and downward norm-setting (group performance declining to match lowest-performing members).

Torres cites a significant finding from Bernard Nijstad and colleagues at the University of Groningen, who identified the “illusion of group productivity.” Groups typically overestimate their performance and report high satisfaction despite generating fewer and less diverse ideas than individuals working alone. This illusion stems from reduced cognitive failures in groups; when one person becomes stuck, others’ ideas help maintain momentum.

Torres presents a hybrid approach based on research by Runa Korde and Paul Paulus at the University of Texas Arlington. Their studies demonstrated that alternating between individual ideation and group sharing improved idea quality. This method captures the benefits of exposure to others’ ideas while maintaining the advantages of individual creative work.

The chapter provides practical strategies for effective ideation, including taking frequent breaks, varying ideation times and locations, and utilizing incubation periods. Torres emphasizes the value of seeking inspiration from analogous products across different industries, citing how Velcro’s invention stemmed from examining a cocklebur’s attachment mechanism. The author encourages consideration of extreme users’ needs and unconventional solutions to spark innovation.

Torres concludes with a structured approach to idea evaluation, introducing the “dot voting” method for group assessment. This technique involves team members allocating votes across proposed solutions, focusing specifically on how well each idea addresses the target opportunity. The process aims to identify three promising solutions for further development through prototyping and assumption testing.

The chapter emphasizes several critical pitfalls to avoid: excluding diverse perspectives during ideation, generating multiple variations of the same idea rather than truly different solutions, limiting ideation to single sessions, and selecting ideas that fail to address the target opportunity. Torres maintains that successful ideation requires sustained effort, diverse input, and careful alignment with strategic objectives.

Chapter 9 explores the critical importance of identifying and testing assumptions in product development. Torres opens with a case study about a failed affordable housing project in Portland, Oregon. The city invested millions in a condominium building designed to help displaced families return to their historically Black neighborhoods. Despite offering the units at below-market rates exclusively to displaced residents, the project failed because the developers built primarily one- and two-bedroom units when most potential residents had families of four or more people. This miscalculation exemplifies how untested assumptions can lead to costly mistakes.

Torres explains that product teams often make similar errors due to two cognitive biases identified by psychologist David Kahneman: confirmation bias and escalation of commitment. Confirmation bias leads teams to seek evidence that supports their existing beliefs while ignoring contradictory information. Escalation of commitment describes how increasing investment in an idea makes teams more resistant to abandoning it, even when faced with evidence of its flaws.

The chapter identifies five key categories of assumptions that product teams must examine. Desirability assumptions concern whether customers want the product and will use it as intended. Viability assumptions address whether the product can generate sufficient business value to justify its creation and maintenance. Feasibility assumptions encompass technical possibilities and organizational constraints. Usability assumptions focus on whether customers can effectively interact with the product. Ethical assumptions consider potential negative impacts, including data privacy concerns and unintended consequences.

Torres presents several methodologies for uncovering hidden assumptions. Story mapping requires teams to outline each step users must take to derive value from a product, helping reveal unstated expectations about user behavior and technical requirements. Pre-mortems ask teams to imagine their product has failed and work backward to identify potential causes. Teams can also trace connections between solutions and desired outcomes using opportunity solution trees, examining each logical link as a testable assumption.

The chapter emphasizes the importance of generating numerous assumptions before prioritizing them for testing. Torres introduces David J. Bland’s assumption mapping technique, which plots assumptions based on their importance to project success and the current level of supporting evidence. This method helps teams identify their most critical “leap of faith” assumptions that require immediate testing.

Torres concludes by warning against common mistakes in assumption identification. These include generating too few assumptions, phrasing assumptions negatively, lacking specificity, and overlooking certain categories of assumptions. The author stresses that while product teams may eventually develop an intuitive ability to spot assumptions, they should continue using structured methods to address their specific blind spots and weaknesses in assumption identification.

In Chapter 10, Torres addresses the critical process of testing assumptions in product development, emphasizing the importance of systematic and measured approaches over hasty experimentation.

The chapter opens by acknowledging a common pitfall: Product teams often rush into testing their assumptions without proper preparation. Torres describes how teams in her coaching program frequently initiated data collection prematurely, leading to various problems such as misaligned test objectives, unclear success metrics, and improper audience selection.

Torres emphasizes the value of testing multiple ideas simultaneously rather than focusing on a single concept. This approach helps teams avoid confirmation bias and the escalation of commitment—two cognitive biases that can impair judgment. By evaluating several ideas concurrently, teams can better compare and contrast different approaches, ultimately identifying the most promising direction.

The author introduces a key methodology: simulating experiences to evaluate behavior. Rather than asking participants about hypothetical scenarios, Torres advocates creating specific situations that allow people to demonstrate their actual behaviors. For example, when testing assumptions about sports viewing preferences, teams should create mock-ups of viewing interfaces rather than simply asking users about their preferences.

Success criteria emerge as a crucial element in Torres’s framework. She stresses the necessity of establishing specific, numerical targets before conducting tests. Instead of using vague terms like “some” or “many,” teams should specify exact numbers—such as “7 out of 10 participants must exhibit the desired behavior” (180). This precision helps prevent disagreements about result interpretation and guards against confirmation bias.

Torres distinguishes between early signals and large-scale experiments, advocating for starting with smaller tests. While acknowledging that small-scale testing may produce false positives or negatives, she argues that the benefits of rapid iteration outweigh these risks. The author explains that product teams differ from scientists: While scientists seek absolute truth through rigorous experimentation, product teams aim to mitigate risk through iterative testing and validation.

The chapter concludes with practical guidance on testing tools and methods. Torres recommends unmoderated user testing and single-question surveys as efficient ways to gather data quickly. She cautions against common mistakes such as creating overly complex simulations, using percentages instead of specific numbers, establishing insufficient evaluation criteria, and testing with inappropriate audiences.

Chapter 11 examines the crucial relationship between product testing and outcome measurement, demonstrating how teams can effectively evaluate their solutions while maintaining focus on broader business goals. The chapter opens with Torres’s experience as vice president of product and design at AfterCollege, a company helping recent graduates secure their first jobs.

The narrative focuses on a significant product redesign at AfterCollege. Upon interviewing college seniors, Torres discovered that the standard job board approach—asking users about desired job types and locations—failed to serve most students effectively. These young job seekers lacked sufficient work experience to know what positions suited them and often remained flexible about location. This insight prompted Torres’s team to reimagine their approach, creating a system that instead asked students about their academic background and used this information to suggest suitable positions.

Before implementing a sophisticated matching algorithm, the team developed a basic prototype that linked areas of study to relevant job categories. This strategic decision allowed them to test their concept quickly and economically. The prototype produced striking results: While the traditional interface prompted only 36% of visitors to initiate searches, the new study-based system achieved an 83% engagement rate. However, Torres emphasizes that these preliminary metrics, while promising, did not automatically validate the solution’s success.

The chapter then transitions into broader lessons about product measurement and evaluation. Torres advocates for a focused approach to data collection, cautioning against attempting to measure every possible metric from the outset. She recommends beginning with specific measurements needed to evaluate current assumptions and then gradually expanding data collection as understanding grows. In the AfterCollege example, this meant tracking not only search initiations but also subsequent user actions and, crucially, actual job placement outcomes.

Torres introduces an important distinction between counting individual users versus counting actions, explaining that teams must choose their measurement approach based on their specific goals. The AfterCollege team needed to track both metrics: Individual user engagement helped evaluate interface accessibility, while action-counting revealed how efficiently users found relevant positions.

The chapter concludes by identifying common pitfalls in product measurement. These include becoming overly focused on comprehensive data collection before launch, fixating on positive early results while neglecting broader business impacts, and failing to verify connections between product outcomes and business objectives. Torres emphasizes that while immediate user engagement metrics provide valuable feedback, teams must maintain sight of their ultimate business goals—in AfterCollege’s case, successfully placing students in jobs.

In this chapter, Torres examines how product teams navigate the iterative nature of the discovery process. The author emphasizes that following a linear path rarely leads to success; instead, teams must adapt to new information by revisiting earlier steps in their process.

To illustrate this point, Torres presents four case studies from different companies. The first involves Simply Business, a UK insurance provider. A product team at this company identified late client payments as a significant challenge for their small business customers. Despite market research supporting this finding, assumption testing revealed that customers did not want third-party intervention in payment collection, as they valued maintaining direct relationships with their clients. This discovery prompted the team to select a different target opportunity.

The second case study focuses on CarMax, a car retailer. Their team aimed to address customer concerns about vehicle condition but faced a unique challenge: Unlike competitors who highlighted cosmetic issues, CarMax’s reconditioning process meant these issues were typically already fixed. Initial tests showed customers would pay more for reconditioned vehicles, but general messaging about the reconditioning process proved insufficient. The team realized that vehicle-specific information would be necessary, requiring a larger investment and longer timeline.

In the third example, Farm Credit Services of America balanced digital transformation with maintaining valued customer relationships. Their team discovered that while customers appreciated personal interaction with financial officers, they preferred independent online research during the initial stages of determining property affordability. This insight led to the development of an online calculator that complemented rather than replaced human interaction.

The final case study examines Snagajob’s efforts to improve communication between hiring managers and job candidates. The team identified that traditional phone calls were ineffective, and they gradually developed text-based solutions. By addressing smaller, related challenges incrementally, they achieved significant improvements in their overall goal of connecting employers with potential employees.

Torres concludes by identifying four common pitfalls in the discovery process: becoming overly committed to unsuitable opportunities, avoiding complex challenges, making decisions based on superficial information, and abandoning incremental improvements before they can demonstrate cumulative impact. The author emphasizes that successful product teams must remain flexible, willing to tackle difficult problems, thorough in their research, and patient in evaluating results.