Jobs To Be Done · Anthony W. Ulwick 2016
Traditional innovation processes have a poor success rate at 17%, while Outcome Driven Innovation (ODI) can reach 86%. ODI focuses on the metrics customers use to measure success and the value they get from getting a job done. If you understand those metrics, you can create solutions that address unmet needs and dramatically improve your odds of success.
Many companies start with an “ideas-first” approach, generating lots of concepts and quickly filtering them out. But this is like prescribing medicine without diagnosing symptoms—if you don’t know what customers really need, you’re bound to miss the best solution. Customers often can’t reliably and accurately articulate exactly what they want. The “needs-first” approach sounds better, but many organisations fail to uncover most of those needs in the first place, don’t define what a “need” is, or rely only on customers’ own words, resulting in incomplete or misleading insights.
Jobs-to-be-done theory provides a framework for categorising, defining, capturing and organising all of your customer’s needs. Tying customer-defined performance metrics (in the form of desired outcome statements) to the Job-to-be-done.
Knowing the customers needs allows you to unlock better strategy by revealing hidden segments of opportunity and determining which needs are underserved or over served.Remember: People are more loyal to getting a job done than they are to any brand.
Identifying these “jobs” can help reveal underserved or overserved needs in the market. You look at core functional jobs, related jobs, emotional jobs, consumption chain jobs, and financial outcomes.
Jobs-to-be-done Needs Framework
We must consider different types of customer needs: The core functional job, related jobs, emotional jobs, consumption chain jobs and financial outcome jobs.
Job: the overall task the customer is trying to execute
Outcome: the metric the customer uses to measure success and value while executing a job
Customer need statements should be mutually exclusive and defined independently of each other. A complete set of customer needs should be collectively exhaustive.
The goal of innovation is to devise solutions that address unmet customer needs. So we need to… know the needs, know which are unmet and understand segments of customers with different unmet needs.
You’re looking for a segment of customers with unmet needs you can serve - that make up a big enough % of the market to make it attractive
The core functional job is stable, solution-agnostic, and unbounded by geography, and the real key is to pinpoint the desired outcome statements that reflect how customers define success and value each step of the way.
Armed with that understanding, you can help customers get the job done either better or more cheaply. Incremental improvements won’t cut it; you typically need something significantly better or cheaper… small changes won’t have the same effect.
Uber example:
Uber Lux → charging significantly more for significantly better
UberX → charging less for better
UberPool → charging significantly less for worse
Imagine trying to launch a differentiated strategy in a market that was over-served… there would be nobody looking to spend more as they’re already satisfied. You’d fail. This is the power of JTBD to select macro strategy.
Outcome Driven-Innovation
ODI is a comprehensive, data-driven innovation process built on JTBD. It links a company’s value-creation efforts to the performance metrics customers actually care about. It breaks down into these 10 steps:
Define the customer
Define the Jobs-to-be-Done
Define the job statement in the correct format
Job statement = verb + object of the verb (noun) + context clarifier
E.g. Listen to music whilst on the go
Uncover customer needs
Outcome Statement = Direction of Improvement + performance metric + object of control + contextual clarifier
Find segments of opportunity
Differences in needs don’t come from demographics or psychographics
To discover segments of customers with unmet needs, you need to segment the market around unmet needs
Define the value proposition
know where customers are underserved
secure the value proposition that communicates to customers that their needs can be satisfied
do everything to satisfy the unmeet needs better that competition
Conduct the Competitive Analysis
Knowing how customers measure value, and how the competition stack up enables an organisation to create products and services that get the job done better or more cheaply
Formulate the innovation strategy
Target hidden growth opportunities
Opportunity Score = outcome importance + (outcome importance - outcome satisfaction)
Formulate the market strategy
Formulate the product strategy
Implementing the complete ODI is complex, it has 6 phases and 84 steps 😖
Initiate an ODI project (15 steps)
Uncover the customer’s needs (18 steps)
Gather quantitative data (18 steps)
Discover hidden opportunities for growth (10 steps)
Formulate the market strategy (12 steps)
Formulate the product strategy (10 steps)
It’s usually best managed by a small, well-trained team, leaving everyone else to build the solutions once unmet needs and strategic directions are clear. You can roll it out in three phases:
Understand the jobs to be done
Discover hidden opportunities in your market
Use those insights to drive growth.
By staying focused on what customers actually need, you’ll unlock more successful innovations and outpace traditional approaches.
ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)
Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton. 2012. (View Paper → )
We trained a large, deep convolutional neural network to classify the 1.3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 39.7\% and 18.9\% which is considerably better than the previous state-of-the-art results. The neural network, which has 60 million parameters and 500,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and two globally connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of convolutional nets. To reduce overfitting in the globally connected layers we employed a new regularization method that proved to be very effective.
This paper marked a breakthrough in image recognition: dramatically improving the accuracy of image classification, marking a turning point in computer vision. It demonstrated the power of deep learning and convolutional neural networks (CNNs) for complex tasks, sparking widespread adoption of these techniques. AlexNet's success led to rapid advancement in image recognition technologies, affecting industries from autonomous vehicles to medical imaging. It opened up new possibilities for products incorporating visual recognition capabilities.
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