And in theory this is the way to go, right? But there are two issues with this line of thinking. The first is that it does not work for start-ups and new products were it is not clear yet who the customer is - the Lean Startup Methodology from Eric Ries is based around this fact. And the second issue is something Clayton Christensen has discovered.
Clayton Christensen, the famed Harvard Business School Professor known for coining the term “disruptive innovation”, believed that one of his most enduring legacies will be an idea he first put forward in his 2003 book "The Innovator’s Solution": don’t sell products and services to customers, but rather try to help people address their jobs to be done. This seemingly simple idea has profound implications for re-framing industries and products.
Never have companies known more about their customers. Thanks to the big data revolution, companies now can collect an enormous variety and volume of customer information, at unprecedented speed, and perform sophisticated analyses of it. Many firms have established structured, disciplined innovation processes and brought in highly skilled talent to run them. Direct access to customers is not a problem, and customers are more then willing to voice there opinion on social media or interviews.
So why is it still so difficult to get products right, and actually have a market for them?
The fundamental problem is, most of the masses of customer data companies create is structured to show correlations: This customer looks like that one, or 68% of customers say they prefer version A to version B. While it’s exciting to find patterns in the numbers, they don’t mean that one thing actually caused another. And though it’s no surprise that correlation isn’t causality, Clayton Christensen and his team suspect that most managers have grown comfortable basing decisions on correlations.Why is this misguided? Consider the case of myself. I am 36 years old. I am 1.74m tall. My shoe size is 43. Me and my wife have one child. I drive a Volvo CX60, and go to work by train. I have a lot of characteristics, but none of them has caused me to go out and buy my new Salomon trail running shoes. My reasons for buying these shoes are much more specific. I buy it because I need shoes to run in the mountains and my previous pair were not giving enough support anymore after running too many kilometers on them. Marketers who collect demographic or psychographic information about me—and look for correlations with other buyer segments—are not going to capture those reasons.
After decades of watching great companies fail, Christensen and his team have come to the conclusion that the focus on correlation—and on knowing more and more about customers—is taking companies in the wrong direction. What they really need to home in on is the progress that the customer is trying to make in a given circumstance—what the customer hopes to accomplish. This is what he has come to call the job to be done.
We all have many jobs to be done in our lives. Some are little (pass the time while waiting in line); some are big (find a more fulfilling career). Some surface unpredictably (dress for an out-of-town business meeting after the airline lost my suitcase); some regularly (pack a healthful lunch for my son to take to school). When we buy a product, we essentially “hire” it to help us do a job. If it does the job well, the next time we’re confronted with the same job, we tend to hire that product again. And if it does a crummy job, we “fire” it and look for an alternative. (I am using the word “product” here as shorthand for any solution that companies can sell; of course, the full set of “candidates” we consider hiring can often go well beyond just offerings from companies.)
Jobs-to-be-done theory transforms our understanding of customer choice in a way that no amount of data ever could because it gets at the causal driver behind a purchase.
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If you didn't have the opportunity to listen Prof. Clayton Christensen presenting the jobs to be done concept, or to read his latest book "Competing Against Luck", please invest a few minutes in listening to his milkshake example story.
So what has this to do with user stories?
In the Agile world we make the same mistake! We focus on persona and/or roles. Instead of the job that needs to be done. Another problem with user stories is that it contains too many assumptions and doesn’t acknowledge causality. When a task is put in the format of a user story ( As a [persona/role], I want to [action], so that [outcome/benefit] ) there’s no room to ask ‘why’ — you’re essentially locked into a particular sequence with no context. Alan Klement from https://jtbd.info/ puts it very nicely in an Image.
Job stories slightly revise the format to be less prescriptive of a user action, and thereby give more meaningful information for the designer and developer to build for the user’s expected outcome.
Here are a few examples of the same feature story in user story and job story format:
User Story: As a subject matter expert, I want a badge on my profile that when I am a top poster, so people know I am a top poster.
Job Story: When I am one of the top posters for a topic I want it to show on my profile so that people know that I am an expert in a subject.
User Story: As a visitor, I want to see the number of posts in a topic for another user, so I can see where their experience lies.
Job Story: When I visit someones profile page I want to see how many posts that they have in each topic so that I have an understanding of where they have the most knowledge.
User Story: As a tax specialist that has used the application multiple times, I should get an alert to contribute
Job Story: When I have used the application multiple times I get nudged to contribute so that I am encouraged to contribute.
There is a very slight but meaningful difference between the two. By removing “As a __ ” from the user story, we remove any sort of biases that the team might have for that persona. Personas create assumptions about the users that might not be validated.
In my experience, user stories have a tendency to be easily manipulated to proposing a solution rather than explaining an expected outcome for that particular user. In particular, I’ve found people leave off the “so that __” in a user story with the feeling that it is optional. This leaves off the benefit that the user would get from adding new functionality.
In a job story we replace “As a __ ” with “When __ ”. This gives the team more context for the user’s situation and allows us to share his or her viewpoint. Next, the “I want to __” is transformed into situational motivation in the job story, as opposed to a prescriptive solution for a persona in the user story.
Because the differences in wording are negligible, it is an easy transition to shift from writing user stories to job stories. By placing the user’s situation upfront, you have a better understanding how it feels to be in the user’s shoes, as opposed to thinking about a particular persona. This allowed for more discussion of the expected outcome and how to best go about achieving said outcome for the user. Which in the end will result in a better product.
In a nutshell: Jobs-to-be-done theory transforms our understanding of customer choice in a way that no amount of data ever could because it gets at the causal driver behind a purchase. Posted on Thursday, July 13, 2017 by Henrico Dolfing