The Jobs To Be Done Playbook · Jim Kalbach · 2022
Rather than viewing customers through the lens of demographics or market segments, there's a more powerful way to understand what drives human behaviour: focusing on what people are trying to accomplish in their lives. This perspective shift reveals that people don't buy products because of who they are, but because they have specific goals they're trying to achieve in particular circumstances.
At its heart, this approach recognises that people "hire" products and services to get specific jobs done. These jobs are the processes through which individuals reach their objectives in given circumstances. Unlike traditional market research that might focus on age, income, or lifestyle, this method examines the fundamental goals that motivate behaviour.
What makes this perspective particularly valuable is its stability over time. Whilst technology constantly evolves, the underlying jobs people need to accomplish remain remarkably consistent. People have always needed to communicate with distant friends, manage their finances, or prepare meals – the tools change, but the jobs endure.
The power of the ‘Jobs To Be Done (JTBD)’ approach lies in its systematic structure, which examines five key elements:
The job performer is the individual who executes the main job – the actual end user.
The jobs themselves represent what the performer aims to accomplish.
The process describes how the job gets done
The needs explain why performers act in certain ways during job execution.
The circumstances provide the contextual factors of when and where job execution occurs.
This structure helps distinguish between different types of jobs. The main job is the overall functional aim: broad, straightforward activities like "prepare a meal" or "plan long-term financial wellbeing". These should be purely functional, without adjectives like "quick" or "easy".
Related jobs are adjacent but significantly different activities that help us understand the main job better. Meanwhile, emotional and social jobs reflect how people want to feel whilst performing the job – such as feeling confident about home security or avoiding public embarrassment.
Consistency in articulating jobs is crucial. The formula is simple:
Verb + Object + Clarifier.
For example, "Listen to music on a run".
Imagine a silent "I want to..." preceding each statement to ensure you're capturing the individual's perspective.
Good job statements avoid references to specific technologies or solutions, start with action verbs, and have clear end states. They shouldn't include compound concepts with "and" or "or", nor should they reflect mere observations or preferences.
Job maps illustrate the chronological sequence of stages in accomplishing a job. These typically follow eight universal stages: Define, Locate, Prepare, Confirm, Execute, Monitor, Modify, and Conclude. Each stage might involve activities like planning, gathering resources, setting up, validating decisions, performing the core task, tracking progress, making adjustments, and wrapping up.
These maps provide a window into people's behaviours in their daily lives – which may or may not include your solution. They're about understanding goals, not just mapping activities.
Express needs as desired outcome statements following a specific format:
Direction of change + Unit of measure + Object + Clarifier.
Minimise the time it takes to gather documents whilst doing annual tax forms
These statements explain why job performers act as they do whilst getting the job done. A single main job might have 50-150 intended needs, each representing a success criterion for job completion.
Jobs are discovered, not invented. The primary method for uncovering them is through in-depth interviews with job performers. These conversations should explore:
Background about the participant and when they last performed the job
The main job and related jobs they're trying to accomplish
The process of executing the job from start to finish
Specific needs and pain points
Circumstances that influence job execution
Effective interviewing requires creating rapport, asking open-ended questions, and following interesting threads. The critical incident technique – asking participants to recall specific situations – often yields rich insights.
Finding opportunities through unmet needs: By surveying job performers about the importance and current satisfaction level of each desired outcome, organisations can identify opportunities. The opportunity score (Importance + Satisfaction Gap) reveals which needs are both important and poorly satisfied by current solutions.
This quantitative approach helps prioritise where to focus innovation efforts, targeting the areas where customers experience the greatest frustration with existing solutions.
How JTBD can help product managers:
Goal-based personas focus on what users are trying to achieve rather than their demographics. By mapping interviews to behavioural variables and identifying patterns in goals, teams can create more meaningful user representations.
Competitive analysis shifts from feature comparisons to evaluating how well different solutions help customers complete their jobs. This reveals gaps in the market that represent innovation opportunities.
Value propositions become clearer when framed around the jobs customers are trying to accomplish, the pains they experience, and the gains they seek.
Development roadmaps can align features and capabilities with customer jobs, ensuring that product evolution serves real customer needs rather than technological possibilities.
Solution architecture should mirror users' work patterns rather than technical constraints, increasing the likelihood of adoption and success.
Rich context (detail about time, manner, and place) greatly enhances solution design. Understanding not just what job needs doing, but the circumstances surrounding its execution, enables more nuanced and effective solutions.
This approach also recognises that people often juggle multiple goals that can collide, intersect, or compete. By understanding the full context of job execution, organisations can design solutions that accommodate these complexities.
The strength of this approach lies not in its complexity but in its clarity. By focusing on what people are trying to accomplish rather than who they are, organisations can create more relevant, enduring solutions. The framework provides a common language that breaks down organisational silos and aligns teams around customer needs.
Whether applied to product development, service design, or strategic planning, this jobs-focused perspective offers a more stable foundation for innovation than traditional demographic analysis. In a world of rapid technological change, understanding the timeless jobs people need to accomplish provides a north star for meaningful innovation.
Quick Links
40 Product Leaders asked what is discovery? · Article
The genius of SVPG by Cutler · Article
The ultimate guide to fine-tuning LLMs · Paper
Consistency in design · Article
Levels of AGI: Operationalising Progress on the Path to AGI
Meredith Ringel Morris et al. 2023. (View Paper → )
We propose a framework for classifying the capabilities and behavior of Artificial General Intelligence(AGI) models and their precursors. This framework introduces levels of AGI performance, generality, and autonomy. It is our hope that this framework will be useful in an analogous way to the levels of autonomous driving, by providing a common language to compare models, assess risks, and measure progress along the path to AGI.
The authors present a structured approach to understanding and categorizing progress towards Artificial General Intelligence (AGI), which has proven to be a challenging area due to its broad and often ambiguous definitions.
Defining AGI is a real challenge and is a subject of much debate in the field. This paper begins by summarizing nine popular views on how to define AGI, including the Turing Test, and discusses their pros and cons. It then extracts six principles from these definitions to move forward.
The paper introduces five levels of AGI (No AI, Emerging, Competent, Expert, Virtuoso, Superhuman) based on percentile performance compared to skilled adults in a set of tasks that are not defined. It provides a clear distinction between Narrow and General AI at each level, with examples.
Additionally, the paper introduces levels of autonomy, which offers an interesting perspective that I had not considered before. These levels include No AI, AI as a tool, AI as a consultant, AI as a collaborator, AI as an expert, and AI as an agent.
If you are interested in AGI, this paper is highly recommended.
Book Highlights
Features are built because teams believe they are useful, yet in many domains most ideas fail to improve key metrics. Only one third of the ideas tested at Microsoft improved the metric(s) they were designed to improve. Success is even harder to find in well-optimized domains like Bing and Google, whereby some measures’ success rate is about 10–20%. Ron Kohavi, Diane Tang, and Ya Xu · Trustworthy Online Controlled Experiments
Try to be humble and grateful for other people’s perspectives, even if you disagree with them or regard them as inappropriate.
Roman Pichler · How to Lead in Product Management
There are three categories of viral expansion loops: viral loops, viral networks, and double viral loops, the last a hybrid of the first two.
Adam L.Penenberg · Viral Loop
Quotes & Tweets
Design has bugs too.
Unknown
Take a human desire, preferably one that has been around for a really long time… identify that desire and use modern technology to take out steps.
Evan Williams