Competing Against Luck
Progress not problems
Customers don’t have problems, they struggle to make progress
theory helps understand the how and why instead of trial and error
Data
quantitative data doesn’t tell you about the situation customers are in
nor does it tell you why they chose one product over the other
Situation and circumstance
situation and circumstance have more of an effect on whether your product or service can help a customer make progress
don’t use a bicycle in a motor race
something that’s well designed for one situation can be a total failure in others
the context of the situation matters
Progress, not products
A Job is defined by the progress people are trying to make in particular circumstances
Jobs have inherent complexity
Jobs include the functional, social, and emotional forces that cause people to make tradeoffs when making choices
great innovation insights come through depth, not breadth
JTBD
helps you understand the cause and effect of the choices customers make during their struggle
deconstructing complex experiences into binary data destroys meaning that allows us to understand the causal mechanism
jobs is about clustering similar stories instead of segmenting details
there’s always more than one solution to a Job, including taking no action
circumstance actually changes the competitive field
blue ocean strategy (don’t compete on features, compete for different situations)
build the right set of experiences in how customers find, purchase, and use your product
non-consumption is often the biggest competitor
Negative Jobs
this is when someone wants to make progress by not doing something
fruitful situation to explore
Job Hunting
enabling something new can often be more valuable than producing something new
uncovering jobs adds narrative – language and causality
tells you which pieces of information are needed, how they relate, and how they can be used create value
in order to hire a new solution, by definition customers must fire the suboptimal solution or compensating behaviour
this includes doing nothing
Loss aversion
the forces compelling change must outweigh the forces opposing change for a customer to switch
people’s tendency to want to avoid loss is twice as powerful as the allure of gains
it’s rare for people to be able to articulate what they want, but they can tell you about their struggles
Big and little hires
big hire: The moment you buy a product
little hire: The moments you actually use the product
failing in Little Hire moments pushes people towards a new solution
Product versus experience
businesses succeed because of the experiences they enable
not the features and functionality the business offers
rare that the product itself is the source of long-term competitive advantage
Inverse
consider who shouldn’t use your product to avoid a mismatch in expectations
you can lose an understanding of the job when trying to add new benefits and features
Process
if you can’t describe what you are doing as a process, then you don’t know what you are doing
processes can’t be seen on a balance sheet
processes are much harder for competitors to reverse-engineer
Evolve how you solve the job
make it work
make it good
make it cheap
make it good and cheap
Active vs passive data
passive data is soft or in the context of the struggle (qualitative)
active data is easy to track, feels real (quantitative)
mistaking the model created by active data for the real world of passive data is poison for innovation
Surface growth
inclined to sell more products to existing customers, leading them to lose focus on the job that brought them success in the first place
several competitors who focus only on one job, as you expand you find you’re not the best at solving any job
Confirming data
healthiest mindset for innovation is that nearly all data is built upon human bias and judgement
models compared to real world
Job focused organization
what get measured, gets done
this is only useful if what’s being measured is helping the customer make progress
jobs provide a compass for how to shape solutions but is also a filter for what not to do
Theory of jobs
a job is a construct
an abstraction that’s rarely directly observable
it’s intentionally precise and there are boundaries to the theory
not everything that motivates us is a Job to Be Done
well-defined jobs are expressed in verbs and nouns, not adjectives and adverbs
not at the right level of abstraction and you’re not uncovering a job if only products in the same class can solve the problem