# Jobs to Be Done and Demographic Segmentation Milkshake Case Study

The development and launch of new products represent one of the most capital-intensive and strategically perilous endeavors within the modern corporate ecosystem. Historical market data and academic research consistently indicate that between 75 and 85 percent of all new products introduced to the market fail to succeed financially [cite: 1]. For decades, the prevailing approach to product development, market sizing, and consumer research relied almost exclusively on demographic segmentation and product-centric attribute analysis. Corporations categorized their prospective consumer bases by age, income bracket, geographic location, and marital status, assuming that individuals possessing similar statistical characteristics would inherently exhibit similar purchasing behaviors [cite: 2, 3]. 

However, this traditional paradigm frequently resulted in innovations that failed to resonate with actual market behavior, largely because demographic profiles do not capture the causal mechanisms driving consumer choices [cite: 2, 4]. The Jobs to Be Done (JTBD) methodology emerged as a profound theoretical corrective to this high failure rate. Rather than assuming that consumers purchase products simply because they fit a specific demographic profile, the JTBD framework posits that individuals effectively "hire" products or services to accomplish specific "jobs" in their lives [cite: 4, 5, 6]. 

This theoretical concept traces its philosophical origins to the renowned American economist and Harvard Business School professor Theodore Levitt, who famously noted in his teachings that consumers do not inherently desire to purchase a quarter-inch drill; rather, they desire a quarter-inch hole [cite: 5, 7]. The JTBD framework operationalizes Levitt's axiom by shifting the primary unit of analysis away from the product itself, and away from the demographic profile of the customer, focusing entirely on the underlying process, situational struggle, or specific progress the customer is attempting to execute [cite: 8, 9]. 

## The Foundational Case Study of the Milkshake

The most pedagogically significant illustration of the Jobs to Be Done methodology is the fast-food milkshake case study, extensively popularized by Harvard Business School professor Clayton Christensen and his consulting colleagues, including Bob Moesta and Gerald Berstell [cite: 4, 10, 11]. The empirical findings of this case study encapsulate the severe limitations of traditional marketing research and demonstrate the profound strategic pivot required to view consumer goods through the lens of situational customer jobs.

### The Failure of Traditional Market Segmentation

Prior to the application of the JTBD framework, the fast-food chain in question attempted to increase the aggregate sales volume of its milkshakes using conventional market research methodologies. The corporation identified its target demographic of historical milkshake consumers and subsequently assembled focus groups consisting exclusively of individuals who fit this predetermined profile [cite: 4, 12]. Researchers asked these panels highly targeted, attribute-based questions regarding what specific modifications could be made to the product to incentivize further purchasing. The inquiries focused on product features: whether the thickness should be altered, whether the flavor profile should be sweeter or fruitier, or whether the portion sizing should be expanded [cite: 7, 11, 13].

The focus group panelists provided clear, logical feedback based on their stated preferences. The corporation subsequently reformulated the product, introducing new flavors and adjusting the recipes according to the exact specifications requested by the demographic panels [cite: 10, 11]. Despite implementing these evidence-backed changes and investing significant capital into the product rollout, milkshake sales remained completely stagnant [cite: 4, 11, 12]. The failure highlighted a core epistemological flaw in traditional research: directly asking customers what they theoretically want out of a product often fails to uncover the contextual, behavioral reality of why they are purchasing it in the first place [cite: 10, 11].

### The Shift to Observational Ethnography

Recognizing the stark limitations of the focus groups, researchers Gerald Berstell and Clayton Christensen’s team abandoned the conference room environment and instead spent 18 hours conducting observational ethnography inside one of the fast-food restaurants [cite: 10, 11]. The researchers discarded predetermined questionnaires and instead meticulously documented the contextual variables surrounding every milkshake purchase. They recorded the exact time of day the milkshake was bought, whether the customer was alone or accompanied by others, what the customer was wearing, whether other menu items were purchased alongside the beverage, and whether the product was consumed on the premises or immediately taken out to a vehicle [cite: 10, 11, 14].

The observational data revealed a highly surprising behavioral pattern that traditional demographic profiles had completely obscured. The data indicated that nearly 50 percent of all milkshakes were sold before 8:30 in the morning [cite: 4, 12, 14]. Furthermore, these early-morning buyers shared a distinct, uniform set of behavioral characteristics that transcended age or income: they were almost exclusively alone, they purchased the milkshake and absolutely nothing else, and they immediately returned to their vehicles to drive away [cite: 4, 11, 15]. 

To deduce the causal mechanism behind this uniform behavior, researchers returned the following morning to intercept these early-morning customers as they exited the restaurant. Instead of asking about flavor preferences, the researchers asked the customers what specific "job" they were attempting to get done that caused them to come to the restaurant and hire a milkshake at that hour [cite: 4, 15]. 

## Contextual Dimensions of the Commuter and Caregiver Jobs

The ensuing interviews revealed that these morning commuters were not purchasing a milkshake because of a demographic affinity for dairy or a specific flavor profile. Instead, they were hiring the milkshake to fulfill a highly specific set of functional, emotional, and social criteria during a long, tedious commute to their workplaces [cite: 12, 16]. The research ultimately uncovered that the identical product was being hired for two completely distinct jobs depending on the time of day and the situational context of the buyer.

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### The Morning Commuter Job

The morning commuters faced a specific set of environmental and biological constraints. Functionally, they required physical sustenance; they were not yet hungry at 8:00 AM, but they recognized from experience that by 10:00 AM they would suffer a mid-morning hunger attack that would distract them from their professional duties [cite: 10]. They were constrained by their physical environment: they were operating a vehicle, meaning they possessed only one free hand, and they were wearing professional work attire that could not be soiled by spills or crumbs [cite: 10, 16]. Emotionally, they faced a long, monotonous drive and required an activity to stave off the boredom of the commute [cite: 10, 11, 15]. 

When researchers asked what alternative products the commuters occasionally hired for this same job, the true competitive landscape of the market emerged. The milkshake was not competing against Burger King or Wendy's milkshakes; rather, it was competing across traditional product categories against bananas, donuts, bagels, and Snickers bars [cite: 4, 12, 15]. However, these traditional breakfast alternatives repeatedly failed to satisfy the holistic requirements of the job. Bananas were consumed too rapidly, failing to occupy the duration of the commute and leaving the driver hungry again before mid-morning [cite: 10, 12, 15]. Bagels proved too dry, and applying cream cheese or jam required two hands, inevitably resulting in sticky fingers and messy steering wheels [cite: 10, 12]. Donuts generated excessive crumbs on professional attire and left the consumer with sticky hands [cite: 10, 15]. Finally, while Snickers bars successfully accomplished the functional job of providing energy, they generated immense emotional guilt for the consumer regarding the consumption of candy for breakfast [cite: 12].

The milkshake, by contrast, executed the required job perfectly. Its high viscosity meant that it required significant effort to consume, taking nearly 20 minutes to suck up through a thin plastic straw, thereby effectively entertaining the driver for the entire duration of the boring commute [cite: 4, 10]. It required only one hand to hold, fit perfectly into a standard automotive cup holder, carried virtually no risk of generating crumbs, and provided sufficient caloric density to keep the consumer satiated until noon [cite: 10, 11, 14]. The fact that the milkshake was not a healthful food was entirely irrelevant to the consumer, because "becoming healthy" was simply not the job it was being hired to perform in that specific circumstance [cite: 14]. 



### The Afternoon Parent Job

The observational research also uncovered a secondary, entirely distinct cohort of milkshake buyers: parents purchasing the product alongside meals for their children in the late afternoon [cite: 14, 15]. While the morning job was heavily driven by functional constraints, the afternoon job was deeply rooted in emotional and social dimensions. 

Parents returning from work or picking up their children from school were frequently exhausted from constantly saying "no" to their children's various requests throughout the day [cite: 14]. These parents "hired" the milkshake as an innocuous mechanism to placate their children, facilitate bonding time, and fulfill the internal emotional desire to feel like loving, accommodating parents [cite: 14, 15, 17]. In this context, the competition for the job was not alternative fast food, but rather alternative methods of entertaining or placating a child, such as stopping at a toy store or visiting a local park [cite: 15].

Crucially, the exact product features that made the milkshake perfect for the morning commuter caused it to fail the afternoon job. Because the milkshake was highly viscous, young children struggled immensely to suck it up through the standard thin straw. Researchers observed frustrated parents waiting impatiently after finishing their own meals while their children labored over the beverage, entirely defeating the goal of a quick, harmonious bonding experience [cite: 14]. If the fast-food chain had only optimized the product for the morning commute by making the shake even thicker, they would have inadvertently ruined the product's utility for the afternoon cohort [cite: 17]. 

This revelation definitively proved that product innovation requires tailoring the solution to the specific job context rather than averaging the desires of a broad demographic. To capitalize on these findings, the chain could optimize for the morning commuters by making the shakes thicker, moving the dispensing machines to the front of the store, and implementing prepaid cards to accelerate the transaction process [cite: 1, 12]. Conversely, serving the afternoon parents would require offering thinner milkshakes in smaller portion sizes to ensure the child could consume it efficiently without delaying the parent [cite: 17].

## Contrast with Demographic Segmentation

The Jobs to Be Done framework operates as a direct counter-model to demographic determinism. Traditional demographic segmentation relies on the underlying assumption that individuals possessing shared statistical characteristics—such as age, income, educational background, or marital status—will naturally exhibit similar purchasing behaviors and product preferences [cite: 2, 3, 4]. 

### The Limits of Demographic Determinism

Demographic segmentation is inherently limited in product development because it relies on broad correlations rather than establishing causation. Demographics fundamentally fail to predict consumer intent [cite: 4]. Knowing that a customer is a 35-year-old male earning a specific salary does not explain why he chose a milkshake over a bagel on a Tuesday morning. The situational circumstances of his life—the long commute, the lack of a free hand, the need for sustained energy—are the actual causal drivers of the purchase [cite: 2, 10, 12]. 

When product engineering is anchored strictly to demographic profiles, companies frequently build products that offer average utility to a broad group rather than executing a specific job perfectly for any particular individual. The fast-food chain's initial approach of surveying a demographic panel to find the "perfect" average flavor profile yielded zero sales growth because there is no single "perfect" milkshake; there are only perfect solutions for specific contextual circumstances [cite: 17, 18]. As modern business strategists note, customers who share similar jobs often have vastly different demographics, while customers with identical demographics often have entirely different jobs to be done [cite: 19].

### Synergistic Applications in Media Buying and Market Sizing

It is critical to note that the academic and strategic literature does not recommend abandoning demographic segmentation entirely. Rather, demographic analysis and the JTBD methodology serve different functional purposes within a comprehensive corporate business model. 

While JTBD is widely considered the superior framework for product innovation, feature prioritization, competitive strategy, and identifying unmet needs, demographic segmentation remains highly effective and economically necessary for market sizing and media buying [cite: 19, 20]. Once an enterprise understands the precise "job" its product performs, it must still determine where to place digital advertisements and how to allocate a finite marketing budget. Demographic variables—such as targeting high-income earners for luxury goods that fulfill a status-oriented job, or segmenting by geographic location—ensure that marketing capital is deployed efficiently on platforms like Facebook or Google without sacrificing relevance [cite: 3, 21, 22].

In a sophisticated, unified approach, the Jobs to Be Done framework identifies the core value proposition and guides the product architecture, while demographic segmentation helps operationalize the distribution and audience targeting of that solution [cite: 2, 19].

| Analytical Framework | Demographic Segmentation | Jobs to Be Done (JTBD) |
| :--- | :--- | :--- |
| **Core Question Investigated** | *Who* is the customer? | *Why* does the customer buy? |
| **Primary Unit of Analysis** | The consumer (age, income, location, gender). | The situational context and the required progress. |
| **Strategic Utility** | Media buying, ad targeting, market sizing, localized distribution strategy. | Product innovation, feature prioritization, disruptive strategy, core messaging. |
| **Predictive Power** | Low. Assumes statistical correlation equates to future behavior. | High. Identifies the causal mechanism driving the purchase decision. |
| **View of Competition** | Products within the exact same category (e.g., Milkshake vs. Milkshake). | Anything competing for the same task (e.g., Milkshake vs. Banana vs. Silence). |

## Methodological Schisms: Progress Versus Activities

As the efficacy and popularity of the Jobs to Be Done framework expanded globally, a theoretical schism developed among its foundational thinkers. Today, the academic and practitioner literature generally recognizes two distinct, and often philosophically incompatible, interpretations of the theory: the "Jobs-As-Progress" model and the "Jobs-As-Activities" model, the latter of which is formally known as Outcome-Driven Innovation [cite: 23].

### Jobs-As-Progress: Psychological Emphases and "Be" Goals

Championed by Clayton Christensen, Bob Moesta, and Alan Klement, the Jobs-As-Progress model asserts that a job is the process a consumer undergoes when aiming to transform their existing life situation into a preferred one, overcoming constraints that previously stopped them [cite: 23, 24]. 

In this interpretation, tasks and physical activities are merely a means to an end; they do not represent what the consumer ultimately desires. Utilizing William Powers' hierarchy of goals, the Jobs-As-Progress model focuses heavily on "Be goals" (the aspirational desire to be a certain way, such as "be a thoughtful parent" or "be perceived as successful") rather than lower-level "Do goals" (the physical act of executing a task, such as "drill a hole") [cite: 23, 24, 25]. Consequently, this model suggests that consumers inherently do not want to "do work" at all. True innovation often involves eliminating tasks and activities entirely rather than optimizing them [cite: 23]. For example, the creation of automated lawnmowers, professional lawn-care services, or artificial field turf succeeded because consumers did not want a faster way to engage in the activity of mowing the lawn; they wanted the progress of having a manicured yard without any labor [cite: 23]. The framework emphasizes the emotional and social components of decision-making, arguing that the desire for self-betterment is the true driver of creative destruction in the market [cite: 24].

### Outcome-Driven Innovation: The Universal Job Map

In stark contrast, business consultant Tony Ulwick developed Outcome-Driven Innovation (ODI), a highly quantitative framework that views jobs primarily as functional processes or activities that customers are actively attempting to execute [cite: 5, 6, 23]. Drawing on his background applying Six Sigma thinking to innovation in the early 1990s, Ulwick positioned ODI as the practical, mathematical application of JTBD theory, explicitly aimed at removing subjectivity and making innovation predictable [cite: 5, 6, 8].

The ODI framework mandates breaking down every customer job into a "Universal Job Map," which consists of eight distinct, sequential steps. By systematically mapping the job to these specific steps, a corporation can isolate exactly where customers face friction and innovate accordingly [cite: 23, 26].

| Universal Job Map Step | Definition of Customer Action | Example of Step-Specific Innovation |
| :--- | :--- | :--- |
| **1. Define** | Determine goals and plan the necessary resources. | Weight Watchers streamlines planning by offering a point system rather than counting calories [cite: 26]. |
| **2. Locate** | Gather the items and information needed to do the job. | U-Haul provides prepackaged moving kits with the exact number of required boxes [cite: 26]. |
| **3. Prepare** | Set up the physical or digital environment to do the job. | Bosch added adjustable levers to circular saws to easily accommodate common roofing bevel angles [cite: 26]. |
| **4. Confirm** | Verify that readiness conditions are met before execution. | Oracle software confirms the optimal timing and level for store markdowns before execution [cite: 26]. |
| **5. Execute** | Carry out the primary functional job. | Kimberly-Clark created a system that automatically circulates heated water to prevent surgical hypothermia [cite: 26]. |
| **6. Monitor** | Assess whether the job is progressing as intended. | Nike incorporates real-time pacing and distance monitoring into running applications [cite: 1]. |
| **7. Modify** | Make adjustments to improve the ongoing execution. | Navigation software automatically reroutes drivers when real-time traffic conditions change. |
| **8. Conclude** | Finish the job and prepare for future cycles. | Automated billing systems instantly generate and distribute invoices upon project completion. |

A critical differentiator of the ODI approach is its reliance on quantitative metrics called "desired outcomes." According to Ulwick, customers evaluate product performance using up to 100 or more specific metrics related to speed, predictability, efficiency, and waste reduction [cite: 27]. ODI utilizes a specific mathematical formula, the "opportunity algorithm," defined as `Importance + Maximum(Importance - Satisfaction, 0)`. Using statistically valid surveys, customers rate the importance of an outcome and their current satisfaction with existing solutions on a 1-to-10 scale. This formula mathematically ranks which outcomes are most important to the customer but least satisfied by current market offerings, dictating exactly where R&D budgets should be allocated [cite: 28].

This theoretical divergence has led to public debate within the product management community. Proponents of Jobs-As-Progress argue that ODI is merely traditional cognitive task analysis repackaged, warning that an over-indexing on functional activities and numeric metrics blinds developers to the deeper emotional drivers of consumer behavior [cite: 23, 24, 29]. Conversely, ODI practitioners and Strategyn consultants argue that the Jobs-As-Progress model lacks the rigorous quantitative mechanisms necessary to reliably direct corporate investments, maintaining that ODI is the only framework capable of statistically ensuring a reduction in product failure rates [cite: 6, 29, 30].

## Quantitative Scaling of Qualitative Research

Despite its theoretical superiority to demographic determinism, the practical execution of the Jobs to Be Done framework poses significant operational and methodological challenges for data researchers, largely due to the difficulties of scaling qualitative insights into quantitative data [cite: 31, 32].

### Limitations of Traditional Qualitative Interviews

The discovery of a true "job" relies heavily on deep, qualitative observational research and open-ended interviewing. Researchers must probe beyond superficial answers to uncover hidden anxieties, contextual constraints, and unarticulated emotional drivers [cite: 33, 34]. However, traditional qualitative research is inherently susceptible to multiple forms of cognitive and operational bias.

First, human researchers frequently suffer from confirmation bias, wherein they subconsciously steer interview subjects toward validating preexisting product concepts rather than neutrally investigating the user's struggle [cite: 35, 36]. Second, the Hawthorne effect (or observation bias) can heavily taint observational studies; consumers often alter their natural behavior when they are aware they are being monitored by corporate researchers [cite: 36]. Furthermore, humans are notoriously poor at recalling their own motivations accurately. Retrospective JTBD interviews that ask customers why they switched products frequently suffer from memory bias, as consumers naturally post-rationalize decisions that were originally made on instinct or emotion [cite: 37, 38].

When processing this qualitative data, unstructured transcripts spanning hundreds of hours must be manually coded to identify recurring themes [cite: 32, 35]. This manual process introduces severe subjectivity, and researchers often struggle to maintain inter-coder reliability. As a result, it is exceedingly difficult to translate rich, narrative insights into the hard, quantifiable variables that executive boards and engineering teams require to justify multimillion-dollar investments [cite: 31, 36].

### Scaling via Artificial Intelligence and Mixed Methods

Scaling JTBD insights across global enterprises requires transitioning from deep qualitative methods to broad quantitative validation. Relying solely on a small sample of interviews can lead to dangerous overgeneralization, while relying solely on highly structured quantitative surveys fails to capture nuanced emotional context [cite: 31, 39].

To bridge this epistemological gap, modern researchers increasingly deploy mixed-method approaches. Once qualitative interviews yield a hypothesized list of potential jobs, organizations frequently use MaxDiff (Best/Worst Scaling) surveys distributed to thousands of representative respondents. MaxDiff forces respondents into a rank-order evaluation of the prevalence and importance of each job [cite: 40]. Data scientists then apply K-means clustering or Latent Class Analysis to these data points to generate robust, statistically valid needs-based market segments [cite: 40]. Quadrant analysis can then be utilized to plot the importance of a job on one axis against the market's current satisfaction level on the other, immediately visualizing the most lucrative areas for innovation [cite: 41].

Additionally, the integration of Artificial Intelligence is resolving the historical bottleneck of moderator fatigue and scaling limitations. In traditional qualitative research, a single human moderator begins to experience "drift" after conducting dozens of interviews, naturally altering their probing depth and succumbing to pattern-matching habits [cite: 42]. Modern platforms utilizing AI-moderated voice agents can now conduct hundreds of JTBD interviews simultaneously. These systems maintain absolute consistency in probing discipline, eliminate social desirability bias through anonymity, and achieve segment-level saturation at a fraction of traditional logistical costs [cite: 38, 42, 43]. Furthermore, supervised machine learning algorithms are being deployed to code massive volumes of open-ended interview transcripts in minutes, preserving the bottom-up inductive logic of qualitative reading while unlocking the statistical power and standard error reduction required by modern economists and enterprise executives [cite: 44].

## Modern Applications in Complex Business Markets

While the milkshake case study remains the foundational Consumer Packaged Goods (CPG) example, the JTBD framework has seen extensive, highly lucrative modern application in software, technology, and complex Business-to-Business (B2B) markets [cite: 45]. In B2B environments, the application of JTBD becomes exponentially more complex due to the presence of multiple stakeholders in the purchasing decision, lengthy procurement cycles, and the critical distinction between internal jobs (e.g., streamlining corporate operations or reducing costs) and external jobs (serving the final end-user) [cite: 45].

The pandemic-era explosion of Zoom provides a clear illustration of technology fulfilling a specific job. The demographic profile of a Zoom user was entirely irrelevant; the software was hired universally by enterprises and individuals alike to fulfill the job of managing and engaging with colleagues in an environment devoid of in-person interaction [cite: 1]. By successfully fulfilling this specific functional and social job, Zoom experienced a 354 percent increase in customer growth during the initial stages of the COVID-19 pandemic [cite: 1].

In the realm of enterprise software, the European recruitment platform Stepstone utilized the JTBD framework to restructure its entire product organization, deliberately shifting from an inside-out (technology-driven) perspective focused on scaling existing capabilities to an outside-in (customer-need-driven) mindset [cite: 46, 47]. Stepstone trained internal champions to conduct large-scale JTBD research across more than 50 product teams [cite: 47]. The resulting insights led to the rapid development of specific, high-value AI solutions, such as a cover-letter generator and a virtual interviewer tool that achieved an exceptional Net Promoter Score (NPS) of 70 [cite: 46].

Similarly, in B2B SaaS, companies like Thought Industries transitioned away from building product roadmaps based merely on addressing the "squeakiest wheels" among their clients. By deploying JTBD research, the company systematically aligned its product teams around the underlying problems its enterprise clients were attempting to solve, resulting in immediate operational shifts within weeks [cite: 48]. In the competitive Southeast Asian tech sector, Carro, a used-car marketplace, utilized JTBD to refine its Go-To-Market messaging, shifting from promoting "AI-driven analytics features" to addressing the specific job of "predicting inventory shortages 30 days in advance to avoid stockouts" [cite: 49]. AutoQuotes implemented JTBD methodologies and subsequently generated 30 percent of its new bookings in 2020 entirely from new products developed through the framework [cite: 48].

Furthermore, as the market for vertical AI agents expands, startups are leveraging JTBD to disrupt horizontal incumbents by altering fundamental business models. Rather than charging flat SaaS seat licenses, companies are beginning to price their software directly based on outcomes—such as Intercom’s Fin AI agent, which prices its service at $0.99 per successful ticket resolution [cite: 50]. This pricing model perfectly aligns corporate revenue with the successful execution of the customer's job.

## Strategic Implications for Corporate Innovation

The Jobs to Be Done methodology represents a fundamental paradigm shift in how organizations conceptualize value creation and competitive strategy. As the McDonald's milkshake case study definitively illustrates, consumers do not structure their lives or their purchasing decisions around corporate product categories or arbitrary demographic cohorts. Instead, individuals find themselves operating within specific, context-bound circumstances, grappling with functional constraints and emotional aspirations, and they subsequently "hire" the tool best suited to facilitate their desired progress. 

While demographic segmentation remains a vital, economically efficient operational tool for media targeting and high-level market sizing, it cannot serve as the foundational architecture for product innovation or feature prioritization. Whether an organization utilizes the transformative, emotional lens of the Jobs-As-Progress model, or the rigorous, metric-driven approach of Outcome-Driven Innovation, the strategic mandate for modern businesses remains the same: sustained financial success and market disruption require an obsessive focus not on the product being sold, nor on the statistical attributes of the person buying it, but entirely on the precise nature of the job being done.

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49. [Thesis: Jobs to Be Done Logic](https://www.theseus.fi/bitstream/handle/10024/334389/Thesis_TaisDiSciascio.pdf?sequence=2&isAllowed=y)
50. [Master's Thesis on Innovation](https://www.theseus.fi/bitstream/handle/10024/704030/masters-thesis-petri-partanen.pdf?sequence=2)
51. [Journal of Strategic Economic Research](https://strategic-economic.com.ua/web/uploads/journals_pdf/Journal%20of%20Strategic%20Economic%20Research_Vol.25_No.4_2024.pdf)
52. [Customer Value Propositions](https://www.diva-portal.org/smash/get/diva2:1657006/FULLTEXT01.pdf)
53. [Rethinking Student Motivation](https://tdlc.ucsd.edu/tdlc2/emails/images/RethinkingStudentMotivation.pdf)
54. [Essential JTBD Strategies](https://www.zigpoll.com/content/8-essential-jobstobedone-framework-strategies-midlevel-long-term-strategy)
55. [Jobs-to-be-done at scale](https://www.researchgate.net/publication/386432477_Jobs-to-be-done_at_scale_Disrupting_the_status_quo_with_customer_focus)
56. [Scaling Qualitative Research](https://enumerate.ai/blog/research-ops/scaling-qualitative-research/)
57. [Jobs to be Done Methods](https://www.viima.com/blog/jobs-to-be-done)
58. [Qualitative Interviews at Scale](https://voxdev.org/topic/methods-measurement/qualitative-interviews-scale-new-method-application-aspirations)
59. [Live Jobs to be Done Case Studies](https://businessofsoftware.org/talks/live-jobs-to-be-done-case-studies/)
60. [Guide to JTBD](https://greatquestion.co/blog/jobs-to-be-done)
61. [JTBD Case Study - Vendbridge](https://www.vendbridge.com/jobs-to-be-done-case-study)
62. [Cascade Insights B2B Research](https://www.cascadeinsights.com/service/b2b-market-research/b2b-customer-experience-research/b2b-jobs-to-be-done-research/)
63. [Applying JTBD to Tech Discovery](https://agileseekers.com/blog/applying-jobs-to-be-done-jtbd-framework-to-tech-product-discovery)
64. [Outcome-Driven Innovation Strategyn](https://strategyn.com/jobs-to-be-done/)
65. [Outcome Driven Innovation (ODI) & JTBD](https://medium.com/@amydunn92/outcome-driven-innovation-odi-jobs-to-be-done-jtbd-46fa8fba7561)
66. [JTBD: The theory and the frameworks](https://gopractice.io/product/jobs-to-be-done-the-theory-and-the-frameworks/)
67. [ODI is Jobs-to-be-Done Theory in Practice](https://jobs-to-be-done.com/outcome-driven-innovation-odi-is-jobs-to-be-done-theory-in-practice-2944c6ebc40e)
68. [Outcome-Driven Innovation Wikipedia](https://en.wikipedia.org/wiki/Outcome-Driven_Innovation)
69. [When Coffee and Kale Compete Summary](https://howtoes.blog/2024/04/14/when-coffee-and-kale-compete-book-summary/)
70. [Jobs to be Done Definition](https://www.edelias.com/jobs-to-be-done)
71. [Two Interpretations of Jobs to be Done](https://jtbd.info/know-the-two-very-different-interpretations-of-jobs-to-be-done-5a18b748bd89)
72. [Alan Klement's War on JTBD](https://jobs-to-be-done.com/alan-klements-war-on-jobs-to-be-done-dad8eaed567c)
73. [Thesis on Innovation Focus](https://www.theseus.fi/bitstream/handle/10024/704030/masters-thesis-petri-partanen.pdf?sequence=2)
74. [Building a Winning GTM Strategy](https://jdi.group/building-a-winning-go-to-market-strategy-in-sea/)
75. [Journal of Digital & Social Media Marketing](https://henrystewartpublications.com/journal/journal-of-digital-social-media-marketing/volume-12-2024-25/)
76. [Business of Software Talks](https://businessofsoftware.org/talks/)
77. [Innovators Summit Camille Ricketts](https://carta.com/blog/innovators-summit-camille-ricketts/)
78. [How AI Products Can Nail Positioning](https://userpilot.com/blog/pitt/how-ai-products-can-nail-their-positioning/)
79. [Challenges of Integrating Qualitative Insights](https://www.zigpoll.com/content/what-are-the-key-challenges-data-researchers-face-when-integrating-qualitative-insights-with-quantitative-data-in-their-workflow)
80. [5 Flaws with Qualitative Data](https://alpha-diver.com/5-flaws-with-qualitative-data/)
81. [Qualitative Data Analysis Challenges](https://contentsquare.com/guides/qualitative-data-analysis/challenges/)
82. [The Qualitative Report](https://nsuworks.nova.edu/cgi/viewcontent.cgi?article=7560&context=tqr)
83. [Qualitative Results Challenges](https://www.statisticssolutions.com/qualitative-results-challenges/)
84. [ODI & JTBD Frameworks](https://medium.com/@amydunn92/outcome-driven-innovation-odi-jobs-to-be-done-jtbd-46fa8fba7561)
85. [Outcome-Driven Innovation Strategy](https://digitalleadership.com/blog/outcome-driven-innovation/)
86. [JTBD Theory and Frameworks](https://gopractice.io/product/jobs-to-be-done-the-theory-and-the-frameworks/)
87. [Outcome-Driven Innovation Process](https://strategyn.com/outcome-driven-innovation-process/)
88. [New Views On Innovation](https://uploads-ssl.webflow.com/619cee99ae033de927f370eb/62bc7605dc1635eec0441d6f_NewViewsOnInnovation.pdf)
89. [Understanding Customer Needs Through JTBD](https://uxdesign.cc/understanding-customer-needs-though-jobs-to-be-done-part1-5415555e230a)
90. [Sci-Hub Case Study](https://scholarworks.indianapolis.iu.edu/bitstreams/c645fdbb-f88f-4dab-b8ae-e8f349917bcf/download)
91. [Jobs to Be Done Framework - HBS](https://online.hbs.edu/blog/post/jobs-to-be-done-examples)
92. [Jobs to be Done Explained](https://www.suebehaviouraldesign.com/en/blog/jobs-to-be-done-explained/)
93. [Jobs to Be Done - Christensen Institute](https://www.christenseninstitute.org/theory/jobs-to-be-done/)
94. [JTBD vs Personas](https://www.thrv.com/blog/jobs-to-be-done-vs-personas-the-ultimate-guide-to-unified-customer-understanding-in-product-development)
95. [Jobs to be Done Segmentation](https://aytm.com/post/jobs-to-be-done-segmentation)
96. [Power of JTBD Studies in Market Research](https://www.lab42.com/blog/unlocking-consumer-decisions-the-power-of-jobs-to-be-done-studies-in-market-research)
97. [Demographic vs Behavioral Segmentation](https://www.circana.com/post/demographic-vs-behavioral-segmentation-which-offers-greater-marketing-precision)
98. [Quantitative Methods Behind Jobs Research](https://medium.com/new-markets-insights/the-quantitative-methods-behind-jobs-consumer-based-research-f14d215ddb2f)
99. [Scaling JTBD Research](https://www.useresonant.com/blog)
100. [Types of User Interviews](https://www.lyssna.com/blog/types-of-user-interviews/)
101. [UX Design Reddit Discussion](https://www.reddit.com/r/UXDesign/comments/1qwwwif/personas_are_mostly_for_stakeholders_not_designers/)
102. [JTBD Interviews - Qualz AI](https://qualz.ai/blog/)
103. [Product Strategy Example](https://www.aakashg.com/product-strategy-example/)

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31. [zigpoll.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFNwE6oK0f5tWCZ5k_um_Lp-Ueanw-iE_9iPH2RusojFoMTb_zmZa1EOb6tPUlpaGdhH-akvBsPt0a0fqVXtMSTi_zED3UBDsMrlIUdD-cHQGLFYfgPBU0yqwCr2qmuDeCU_nWFGTCpZY6C7_kzDbD8t9vwr2f6uYtD9FBj7YewVNHo0K1u4bdbB33kOg_undpzQYA17XH5kmA-6mY_uTtSS5l7RYWKhvkr41POom-ppWdudCsFQRTz_zEM2HfDeZWcToHGAEhh0eGJJAPRL6ADh06Pcs4lxw==)
32. [statisticssolutions.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFMoLIu3dUYHcom4hALRVFyN2BjtkKSgUn1wQFE9mcqhTLYpBSTvqqDZybOatAYTa36aHe3oqWj2lCJpXauCABq8QYXIEnKSdtLRidWdp4oQpHyhsCwcWENczY8eHtGA_sF9MwUv5LgLoHjbvaDKj3kTmvhuR_daojs)
33. [digitalleadership.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFP9h-JjgfSsauRiGyRjvmI6Lhd152H34K0_-gxoKmaCUh4Vh7e2787SD6QCoZfgRvnhI8nVlF1wrnLEMzzYoWD6MZs0J1qJgjbC_ZOEfdNDn3mN9HGSjCLnbKuDm43-LOPv7lzq3KvNVotHTaiNAZ1w3Su9HSkgBp6tn-lStoh_xW6Lb4U1oqjMretgMs=)
34. [viima.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEzMx4NZEKDFSfQw7ZZ9bgjreLZjjKdDVP-MEuJDLYQZBC5nysRWz8SSxFLfT78gQhHk71SeoxYejhh7Vjp-pt4gRQPH5LA_EmrC9eX6F2kR8CjI_Jyf-ESuLlhv-BY89E=)
35. [alpha-diver.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHCIMkD-qCLekNQgfra5KOEkyVboQVv7Xv8Y9aeddMYjz9b3yp0IxnHG-WeAjEg2OeIYIObdHgYYrr8h4ZflmWraCz1AjxCcaU7gfn10LMGLKKMQro15E6bqBpFejQNTQZMv87KjTzJWvL9fmY=)
36. [contentsquare.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHwhjGbH4CPELSdv4QS98AWABhQQ0bNjWIXlNZvMM6uFG0CENkRlRLPNKEhD_jd-NClxJ869k57IlJe9xhz9sJKT1eWjWrk18uv7s46yr6hGL0Kzxl4tU8S5jjDJH5vgVhXLCMidsCfh0Vq35G5TTizYc59wlyiFk3N5iW-)
37. [brianrhea.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGUWmL7YW8PeAScwXXGhO-FsvP3IDDwswdyQ66_IdrOi5zLemkCY9ON2DVbp9rMkEUz88pQF4J0Hlhd_M3wOigs4miz8N8DEgE5QysfwCzcqR2VPKLUhUAo6EBbs0Ie_wvGH6U3JjrlanQIOPEO-Hgt)
38. [qualz.ai](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFKakzvLvueRfOYJPu_BOznduosX4mcYKGuy-mRN_t0D5eZ4kp7NTCTHSqXRrwQqv08m-CS9F6wmUqQkYzVQvOr7Kl1IZkQdgzD8VVD)
39. [zigpoll.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFHKai6JnTmqhJFoQkmydh-qcTha0OeOxhnNMQB-RyHmHXt4n_FPRIZTrkvKK8kz30ejIXGsTO7fPdCsG90Q3Xz2l5iMbyjctRDe2lo5Roy5jDTBUFoaNxmTsnpF5nnVqA0BpbHryclrgS3z-6qTYjnQ8Q3nfdJ4z1CI0HXT7p9VoM6wgdsV6T1Ieq9OTrYm78dR_c2twH9q-9uGTD9qS0=)
40. [aytm.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEEOI9PVOWUkV8rzZ38D7PTWzOwuQFjPu62ds_C1yU1NgOyzWAjJMMxpQF9hvECsJuf_ACcnpNO36jFqvQifebGPie_UtJUUd9s3HvFqJDBMgbJaV59L5riyRQRU7_FJgSF0O0Uwx-wHQ==)
41. [medium.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEevVq5rE-K5nR08Mi6Ql7wraw6h3r4QGsZ1g_J3s5qnERKaxtlP0YtkYDRMb0njTiZ82GF8f4u2PBxEFEnuUf52UfFNZTKrcjQifKUCFpsPYhCjnHJkIHBgvqggiNa9TRBaxLX8u6qj0I9A2v6vIjMUScFbXs3qKcLNxPKyCf0fRvWmgiwH36o-84D79s7_1b-4dxfqzZDwM8wyLMPnGx8e1mLNeTAXA==)
42. [enumerate.ai](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQF6xSv7sfzx_Q_T34txgSIEKXuBgaPJfkWAXsMiFG-SuyEdk5YDXLjh-hy8lTu7mg6tRAncHseO-_x7WqH1tyQHP7Dfc7uCxU6ZLt1NP1Da16TK3aWy1LlLqfFGV2EXaWBbMDUf34oOL6qXSAZbfIyJVYcOUb-7m0JBFg==)
43. [useresonant.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHXSwkqJAWCipdrqJwu7iVkpy7vQ0VwWhMpkzFdYK_912gio5FbRh9ym4mhcema2SXUbprHhou3R4H777pHKHfPUTr64cS1gAj2m7w7TmWREUpHmXwjGA==)
44. [voxdev.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGU4jL7Vrm-FfvRjAmnffiPVlVZDJ_Pq_tLnu7MGkcQZnWdTfpf3tDdTekC6ccz67zNEyzP-iGqYkJ9dU0oVQKawGcaMTmjKJumtmi99REevAVxQ1oj_rAq_0xxNRbQ47i0uXdEZekVDuRYTZw4ARmsm3MYYp03UgVt_TM0pgHxqMMepvnMB91bS_YlNr-YgUKQFDhoQtZFma47kRluXr4Gfs0=)
45. [pdma.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHK9Onhvgc2beo_MRUEiap495SqeJwSrzfWHco2YlBqYQia5WE2hy8yQsrK5WxLv1_6EV67c0X0EROYvnn4jRyM5OuVm5n7YggeZHc7mVgDu49Dsc73-Zf68svft_WDys6H4smI9SwuZRAErCaafUNyUa60-PelL_A2gcc43Ng-jDoo7IIY5DsW5PCLMzX5xnH4Bqnky127g3m7UnYIz-jWRYm8BCsbaQAG3-26qEeYEIp-wQ==)
46. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFRnZWmGh7WLeQn05r0e4QAJD462vYxzS54Q8z1F4hqox1pPp0-I6E00q4B7DOY4VOOzeGQKjRF8JCnqgZUCe5V0vTf5EvxGkOKSKXibCc7HJctMps_eO4LXiay73XbEQQho56V_ibFjcele6g7uYv1L_Dvhu_nU4vCuXrKruf-FyvbMJIgphIjEa5P2pMzFEUXahk89o2tlofZIoRt6a_i7mWHdLPrLh5rEJiGrfvc)
47. [vendbridge.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHDVQSB0OWfhWYTVz-Q_WWkUhyWxc5Qqmohr8EjNXY7ugd6e_Ah1q7SQ42lGoibdkxUdaHbBSy_jF2NyJy7UXzjVULS9h6DA6UJkOJEdbEQ4Y4B0kQh4qS57wB1dr94e_V-wpVe9jc-tAvyAg==)
48. [thrv.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEq9YJbh7Y9O3EKjG1_OpY7E0Vq6JZj0TJ-T9m0TXaxml6G2io6aO1Pcfa2uPfaB61SS2mxFLD6kRgN0jLYy6BFNCnom0jgzkXCo2vhptOIji0c7HJK9r-OKJqY4A==)
49. [jdi.group](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGZkbWOKdyuNCbwoXJpf0GC9vcBrl7Z-Y6y7e6m5qJL6RWmOypavTI3Al8ODXci6Sv22v4m-i5W_solWbVasKQ1zeaXuNWRDUgSKrs8Bw3ROH9IK00hD-t3siY730GoSgb6Z9Qd4vnRpVQJ-k5oMuHVfWIONoERIUI=)
50. [userpilot.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHAzHsJul2BTFEZU5WUrX2zzzqIwj-_8p70394B6EYbARkEcl6xpYRT4zISsTjNpH7WluLapMxz_9P3n6-IVQVRvLrBlZ08JjJJiD2y4aVk1UyKcNko8kc_auzYqesD229QFEP7b6QG6R_69xHiIYD9Yv8Kh3J1s5kqVZwZYJl8zR4=)