If you’ve ever worked on a high-stakes product team, or in military operations,
you’ve probably heard of the Known-Unknowns Matrix: a risk assessment model
splitting knowledge into four phases. This matrix is appropriated from a
psychological theory known as the Johari
Window: a mental model addressing
interpersonal relationships.
The DTC realm overlaps nicely with both of these constructs: brands are trying
to build sustainable relationships based on knowledge of their consumer market,
while internal and external influences create challenges along that path to
sustainable success. Within Fairing, this Ecommerce Known-Unknowns Matrix
is an idea we reference often when we discuss the importance of
direct-from-consumer data.
Explaining The Ecommerce Known-Unknowns Matrix
The four phases of the matrix can be explained like so, using a few examples
pulled directly from our conversations with DTC brands:
Known-Knowns: Knowledge – I know what I have
- We generated $2MM in revenues last month.
- 26% of purchases came from TikTok according to our post-purchase survey.
- GA reported a 31% increase in visits to this PDP week-over-week.
Known-Unknowns: Awareness – I know what I need
- Where can I get a second opinion on my Facebook-reported ROAS?
- Which of my customers are buyers and which are users?
- What generation does our customer identify with?
Unknown-Knowns: Bias – I don’t know what I have
- There’s no way to find the source of Direct traffic.
- Facebook’s reporting works well for us; we don’t need an additional tool.
- We’ve basically maxed out our conversion rate on this landing page.
Unknown-Unknowns: Ignorance – I don’t know what I need
You might notice some interesting distinctions here, which we’ll get to in a
moment. For now, it’s also worth noting that a brand could think of this
structure as “who should I ask?” when pursuing knowledge:
- Known-Knowns (KK): you don’t need to ask anyone, because you hold the
knowledge. A good example here would be dashboards from last month you’ve
already reviewed, like 1PD analytics or store revenue metrics.
- Known-Unknowns (KU): you ask others — your team, your customers, your
tools — for the knowledge you’re aware that they have. 1PD and ZPD both live
here, whether it’s asking a team member to pull data from a platform they
manage, or surveying consumers for a simple data point only they would know.
- Unknown-Knowns (UK): you don’t know to ask others, because you assume to
have knowledge. The bias here can only be avoided by asking people to
challenge your own assumptions. Think: analytics experts who can reveal your
misinterpretations of data, or consumers who can tell you something you
wrongly believed about your market.
- Unknown-Unknowns (UU): you can’t ask anyone, because nobody knows. In
these situations, you’d have to dismantle the need for knowledge into more
accessible pieces, attempting to gather enough information to mitigate or
reframe the unknown.
Of course, we’d all love to sit squarely in the Known-Knowns corner all day
long, running our businesses with total omniscience. But that urge is precisely
what lures us into Unknown-Known blind spots, or stops us from critical
thinking through Unknown-Unknowns, or causes us to deprioritize investigating
Known-Unknowns.
Direct traffic
serves up a perfect illustration for the Ecommerce Known-Unknowns Matrix. If
we’re being honest as marketers, many of us simply assume Direct traffic is an
unsolvable mystery, and operate under that assumption. 10%, 20%, 30%... it
doesn’t really matter what % of your sales are attributed to “direct”, right?
Because there’s nothing you could do with that information anyway. “What do I
do with this Direct traffic?” is an unknown-unknown.
Now, what’s really going to bake your noodle is the relativity of this whole
matrix. To you, Direct traffic may be an unknown-unknown; an ever-present
anomaly on your books. But to a more experienced marketer who has awareness of
the tools and processes that can address this gap in the data, Direct traffic
is a known-unknown that can, at least in part, become a known-known just by
implementing a few resourceful solutions.
And of course, the stinger here is that the more experienced marketer looks at
what you’re doing and realizes something you don’t: that you’re actually stuck
in the unknown-knowns quadrant. In other words, your assumption about Direct
traffic is the blind spot, not the data.
Said another way, the same question can fall into different corners of the
matrix depending on whom you ask. If the question is “how much of my revenue
comes from podcast ads?”, a novice marketer might say the answer is an
unknown-unknown and revert to measuring incremental sales lift whenever the ads
run. But an Fairing partner like Right Side Up would tell you it’s a
known-unknown — you just need to apply a measurement
methodology
using some key tools. Once you’ve done that, a brand like
Füm
would say it’s a known-known, sitting right there in the marketing analytics
dashboard.
That’s a world of difference, and one that extrapolates to the entire business.
A brand run by an overconfident self-proclaimed “expert”, who hires a bunch of
yes-men and doesn’t talk to customers, is going to be dragged down by the
gravity of their own Unknown-Knowns bias. These ecommerce brands think they’re
winning, and it’s hard to convince them otherwise until something catastrophic
shakes their thinking loose.
This “z-pattern” of progress towards knowledge is fairly natural, but it’s not
at all optimal. Instead, you want to avoid Unknown-Knowns as much as possible…
though that doesn’t mean your learning path will be linear.
So how do you know you’re moving in the right direction at any given moment? In
large part, this comes from maintaining a team mindset of continuous learning,
curiosity, humility, and experimentation. The Unknown-Knowns corner is where
you get stuck when those traits atrophy — and whether novice or expert, we all
have our blind spots which reside in that quadrant. Squashing them should be
both a personal and professional goal.
We like to say data doesn’t lie until you start asking it questions... that’s
when your team’s curiosity and resourcefulness will influence where you end up
on the matrix. Knowing when, where, why and
how to ask questions
is your key, and perhaps that’s a bit of a brain-twister at first pass: the
Known-Knowns quadrant expands when you obsess over every other quadrant.
More Real-World Matrix Examples
To get a more actionable feel for the Ecommerce Known-Unknowns Matrix, let’s
touch on a few real-world paths to knowledge using direct-from-consumer data:
Personalization
- UK: “Our core audience is college kids, so we’ll focus creative
on that demo.”
- KU: “But, maybe we should ask customers what generation or lifestyle they
identify with?”
- KK: “Turns out 71% of our customers identify as millennials,
regardless of age.”
Channel Diversification
- UU: “What media channels should we invest in next?”
- KU: “Well, what ‘Other’ responses do customers write into our ’How did you
hear about us’ survey?”
- KK: “We’ve found customers are increasingly hearing about us from
influencers on this new video platform that’s invite only.”
Revenue Analysis
- KK: “Our AOV is reported as $55.”
- UK: “So, our typical customer spends around $55 per order.”
- KU: “I wonder, are gifters and event buyers skewing that AOV figure?”
- KK: “We asked customers who the purchase was for, and apparently buyers &
gifters are a totally different revenue tier, generating 2x the AOV of
users.”
Consumer Insights
- UU: “What will happen if we drop our fabric supplier who’s overcharging us?”
- KU: “Where does ‘quality of fabric’ sit on our customers’ reasons for buying?”
- KK: “The customers we surveyed had fabric quality ranked last, behind 4
other reasons for buying.”
Competitor Research
- UK: “I’ve never heard of someone cross-shopping us against Competitor X.”
- KU: “Hmm… how many of our customers replaced Competitor X with us?”
- KK: “Actually, 20% of our buyers reported replacing that competitor
with our product.”
- UU: “So how many of our potential customers are buying Competitor X instead?”
In truth, what the Ecommerce Known-Unknowns Matrix reveals is that you’re never
done learning… but each question broadens your perspective, and consequently,
your potential for predictable and sustainable growth.
Taking Action On The Known-Unknowns Matrix
- Invest in always-on solutions for asking questions
and testing assumptions at speed & scale.
- Keep your opinions loosely held, and give teammates a clear onramp to
challenging status quo ideas.
- Build your data advantage on ground truth sources, instead of renting
customer relationships and letting third parties grade their own homework.
- Challenge your team to break Unknown-Unknowns down into problems that can be
tackled productively.
- Avoid stacking assumptions — if you have a metric that’s been daisy chained
from a string of 3 other metrics, you might be looking at an Unknown-Known.
- Be critical of the questions you ask of your data. Too often, the question is
where the limitation lies.
- Listen to the people and tools that have seen a world outside of your own.
- Don’t force data into models just because the models are rigid.
The models exist to serve the data,
not the other way around.
Now that you’ve seen the Ecommerce Known-Unknowns Matrix up close, what
examples come to mind in your own experience? Drop us a
line; we’re always interested in hearing what
brands learn from challenging their assumptions.