Some thoughts on ‘Competing Against Luck’ by Clayton Christensen

Competing Against Luck is a book in 3 sections written by Clayton Christensen, a Harvard Business School professor who is known for coining the theory of ‘Disruptive Innovation.’ He was inspired to write this particular book when looking at large, successful companies that “lost it,” and found themselves on the wrong side of innovation. How does this happen?

Christensen doesn’t dive into digital products right away in his book; instead, he begins with real world examples of simple consumer products like milkshakes. (Christensen often uses analogies to explain his points.)

To begin, here is an overview of Jobs Theory…How do we define ‘job?’

• The key question in jobs theory is — What causes a customer to purchase and use a particular product or service?
• How do we define a job? A “job” is defined as “the progress that a person is trying to make in a particular circumstance…” We usually think of a product as having to solve a problem, but here Christensen is clear that making progress against a circumstance qualifies. Progress matters to users, not necessarily discrete products.
• To fully understand the job, you have to understand the customer and the progress he/she is trying to make. For customers — which functional, social, emotional dimensions influence them?
• The choice of a product solution that is hired for a certain job depends on the circumstances (environment, time of day, mental state, etc.)
• “We don’t create jobs — we discover them.” So approaching this challenge like a scientist discovering a natural phenomenon is key.
• Bad jobs are too general, abstract and unspecific like, “I need to eat” / “I need to feel healthy” / “I want to be a good Dad …”

What are the limits of Jobs Theory?

I thought this was a really interesting to think about because every theory has its limits and can be incorrectly applied. Tools have specific purposes and ‘best fit’ curves of when they are most effective.

• Do NOT use JTBD if there is no real struggle for a consumer.
• Do NOT use JTBD if the existing solutions are good enough.
• This makes me wonder, should optimizations or smaller projects NOT use the JTBD theory because the existing solution is “good enough?”

So if we continue with the Milkshake Analogy, what does it look like if we apply Jobs Theory to it?

• Morning commuters “hire” a milkshake to sustain them for a commute but also to prevent hunger later. Specific users hire a specific product for a specific reason, under specific circumstances. In the morning, on the way to work, milkshakes compete with bagels, donuts. Breakfast foods that you grab to keep you from getting hungry on a morning commute.
• But in the afternoon, that same milkshake is hired as a treat on the way home. (Treat yo self) They compete with toy stores or rushing home early to shoot a few hoops — (usually Dads trying to entertain / treat their kids with food)

Examples of digital products and the JTBD as described by Christensen:

• Facebook: Taking a break from work, relaxing for a few minutes (real world corollary: smoke break)
• Airbnb: Stay somewhere cheap and central — like a local who lives there
• Snapchat: Sending a message that disappears (real world corollary: passing notes in class to friends)

A note of caution about data, as it can be misleading….What are the fallacies of data that we fall into?

• Fallacy of active data — treating the symptom rather than the root cause. Standardized tests — Teachers teach to standardized test outcomes. Just because scores are high, it doesn’t mean ppl are learning.
• Fallacy of conforming data — We are coming to data with our biases. There is no objective truth. So what are our principles and goals to frame how we should interpret this mission?
• Data is not objective. We look at data to confirm our hypotheses and biases.
• Fallacy of Surface Growth — Product is good, it meets needs. SO we try to expand it! You become a “Master of None” of the jobs bc you’re trying to master. Too many!

Discussion Questions

After reading this book, I had a few questions that have stayed with me…

• For mature companies with a very diverse set of customers that may have competing needs / jobs — how best to solve and prioritize for these competing jobs?
• I appreciate Christensen’s analogies, but where do the analogies to ‘real world’ products break down? e.g.) You may only have enough room in your stomach for 1 milkshake per morning, but the ‘appetite’ for digital products has limits that differ from our stomachs :)
• For a company that has a product with great Product-Market fit OR if their solution is ‘good enough’ to be hired frequently by a customer, what could drive innovation? (What should it be versus what does it end up being?)
• What is the balance to strike in resourcing between optimizations and step level innovations?
• What are key differences between product-market fit and product-job fit? (Other than scale)
• It seems like much of the ‘power’ of disruptors is that they are thinking of a very specific user and use case. eg: College / art school kids needing a cheap place / air mattress to crash on for a conference. As a result…should we only make JTBD statements with a specific user and their circumstances in mind? (How specific? Like “American teen, 13–19 on the way to class?”) Why do we have a tendency to separate the user and her circumstances from JTBD statements?
• Early adopters of a new consumer behavior often hire products (adopting behaviors) that the rest of the population will adopt down the road. “Out of specificity comes the universal…“ To what extent does successfully innovating on new consumer behaviors mean that you are actually designing for early adopters?