Clayton Christensen’s Jobs to Be Done framework begins with a
deceptively simple observation: people don’t buy products. They hire
them to make progress on something they are trying to accomplish.
The classic example is the milkshake. A fast-food chain wanted to
improve milkshake sales. They surveyed customers, tested flavors,
adjusted sweetness. Nothing moved. Then someone asked a different
question: what job are people hiring a milkshake to do? The answer
turned out to be: make the morning commute less boring and more filling.
The milkshake wasn’t competing with other milkshakes. It was competing
with bananas, bagels, and podcasts.
The product was irrelevant. The job was everything.
I have been doing information architecture and search strategy work
since 1995. The Christensen framework is one I have returned to
repeatedly, but it took working with search data at scale to understand
what JTBD was missing, and what Market Semantics Analysis was missing,
and how the two complete each other. And more recently, it took a
specific case study to understand something I had not previously
articulated clearly: the two frameworks are not symmetrically useful.
They each do different things, and MSA does one of them better than most
practitioners realize.
JTBD tells you what job the buyer is trying to do. MSA tells you
what vocabulary the buyer uses when they go looking for someone to hire.
But MSA also does something else: it maps most of the barrier chain
before you ever conduct a single interview.
The gap between the job and the search
Here is the problem with JTBD used alone. It is brilliant at
articulating the job. But it does not tell you how the buyer names that
job in search. And the buyer’s search vocabulary is often completely
different from the job vocabulary.
I worked recently on a premium retractable awning system for
commercial venues. The JTBD analysis was clear: the commercial buyer’s
job was to maximize the number of revenue-generating days in their
outdoor space, regardless of weather. The ladder-up analysis went like
this: protect guests from rain and sun, so they will stay longer, so
they will spend more, so the outdoor space becomes profitable
year-round, so the business can serve more guests across more days of
the year.
That is a precise and powerful understanding of the buyer’s
motivation. It is also completely invisible in the search data.
The 12-month search corpus for this business showed buyers arriving
with ‘restaurant retractable roof,’ ‘hotel awning,’ ‘outdoor dining
cover,’ ‘retractable awning miami.’ Product and geography terms. Not a
single query in 378,000 impressions used the word ‘revenue’ or
‘profitable’ or ‘year-round.’ The buyer knows their job. They do not use
job language to search for a solution. They use product language.
JTBD gives you the destination. MSA gives you the vocabulary
buyers use to start the journey. Without both, your content either
speaks to the job in language no one searches for, or ranks for searches
that never convert.
The asymmetry: where each framework actually
performs
Here is what I did not say clearly enough when I first wrote about
this connection. MSA and JTBD are not symmetrically useful across the
two components of the JTBD framework.
On the ladder-up, the motivation hierarchy, MSA reliably evidences
only the bottom rung: the functional job. The intermediate rungs, the
revenue and capacity arguments above the functional need, are inferred
from patterns but not stated in queries. The social and emotional rungs
at the top are entirely invisible in search language. For the ladder-up,
MSA gives you the starting rung and a set of plausible hypotheses.
Interviews confirm the rest.
On the ladder-down, the barrier chain, the situation is almost the
reverse. Barriers manifest in search behavior directly and consistently.
A high-impression zero-conversion query is a precise behavioral
signature of a barrier.
In the retractable awning case: price qualifier queries at near-zero
conversion surfaced the financial barrier. Question and informational
queries identified the knowledge barrier. Commercial action queries like
‘retractable awning system supplier’ at near-zero volume identified the
supplier discovery barrier. Geographic queries at strong purchase intent
but zero conversion identified the proximity and trust barrier. The
barrier chain was almost fully populated before a single interview was
conducted.
The practical payoff is interview efficiency. When the barrier chain
is already 70% populated with Direct and Partial evidence from MSA, the
interview program can be scoped to the specific gaps: the upper
motivation rungs, the authorization and organizational barriers, the
emotional fear of making the wrong investment. You enter the interviews
knowing what you already know, and you spend the conversation on what
the corpus cannot confirm.
MSA does not replace JTBD interviews. It scopes them. The result
is a fully populated Web of Abstraction produced in a fraction of the
time a purely qualitative approach requires, with quantitative grounding
at the layers where search data is reliable.
Where this sits in the sequence
The full sequence remains: MSA first, then JTBD, then Dunford’s
positioning framework. MSA surfaces the buyer vocabulary and the gap
register. JTBD interprets the motivation hierarchy and barrier chain,
with MSA evidence pre-populating most of the barrier chain. Dunford then
structures both into a defensible positioning statement.
What has changed in my understanding of this sequence is the weight
of what MSA contributes to the JTBD step. It is not just the starting
vocabulary. It is a partially completed barrier analysis that the
interview program then completes, rather than builds from scratch.
For anyone building a content or positioning strategy: start with
what the market actually says, use that to pre-populate the barrier
chain, then design a targeted interview program for what the corpus
cannot confirm. The sequence is not optional. Each step depends on the
one before it. But the steps are now more clearly divided in what they
contribute.
Paul Fervoy
Director, Siftia · Information architect and digital strategist since
1995
siftia.tech · linkedin.com/in/paulfervoy
