Hello and welcome back to the Deep Dive. Today
we are opening up the dashboard. And I don't
mean the one in your car. I mean the one you
stare at every, well, every Monday morning. If
you're listening to this, you know exactly the
ritual I am talking about. the Monday morning
trade meeting, the heartbeat of the business,
or sometimes the heart attack. Depending on the
weekend, right? You've got your coffee, you've
got your oversized Excel sheets, and you are
just dissecting the holy trinity of retail metrics.
You're looking at comp sales, you're looking
at UPT units per transaction, and you're obsessing
over conversion. And if you're really getting
into the weeds, you're probably pulling apart
GMROI and labor leverage. That's how we keep
score. I mean, it's how every merchant, every
planner, every VP of stores justifies their existence
for the week. But here is the provocation for
today's deep dive. And frankly, it's a bit of
a challenge to that entire Monday morning ritual.
We've been reading through a stack of research
on retail operations and, well, next -gen training
data. And the core argument is kind of unsettling.
It suggests that all those numbers, comp, margin,
conversion, are effectively an autoxy. That is
a grim way to start a Monday, but it's accurate.
It feels harsh, but think about it. Those metrics
tell you what already happened. They tell you
the patient, the margin is already dead. Or alive
if you got lucky. Right. But today we're exploring
a shift. We're looking at how to move from that
reactive reporting. Why did we misplan? to proactive
prediction. And we're doing it using what might
be the most undervalued asset in your P &L. Real
-time product training data. No, hold on. I have
to stop you there. Because when I hear training
data, and I think most of our listeners would
agree, my eyes just glaze over. I think of an
HR report. I think of a compliance checklist.
Did Store 504 complete the safety module? Yes
or no. How is that predictive? I mean, how is
that business intelligence? That's the skepticism
we need to address right up front. Because you're
right, the old model, the LMS or learning management
system model, is purely compliance. Right. It's
binary. It just tells you if a body occupied
a seat and clicked a button, that is useless
for a VP of sales. Right. It's a CYA metric.
It doesn't put dollars in the register. Exactly.
But the shift we are seeing in the source material,
specifically looking at data from platforms like
INCITE, is moving from completion to readiness.
OK. It's shifting from did they do it to Do they
get it? And more importantly, can they sell it?
So we are not talking about a generic training
score. No, not at all. We are talking about granular
question level analytics that map the capability
of your workforce against your inventory investor.
It's about knowing, OK, we have $5 million of
inventory landing on Thursday, but only 40 %
of the fleet understands the key selling feature.
OK, that is a completely different conversation.
That links the human variable directly to the
inventory risk. It does. But let's play devil's
advocate here. Retail executives aren't flying
blind. We have sophisticated forecasting tools.
We plan inventory down to the SECCU level. We
have open to buy. We know what should happen.
You are flying blind on product. That's true.
You know exactly how many units of that new technical
parka are landing in the Chicago flagship on
Tuesday. You know the AUR average unit retail
you need to hit. Right. But here's the blind
spot. Do you know if the human beings standing
in that store on Tuesday actually know how to
sell it? Well, we assume they do because we sent
down a directive We send a lookbook and that
assumption is the most expensive mistake in retail
Because just because the store manager signed
off on the directive doesn't mean the part -time
associate who is actually facing the customer
knows the difference between water resistant
and waterproof and that's That's where the sale
is won or lost. Like the whole game. So the traditional
KPIs, they measure the transaction outcome, but
they completely miss the interaction capability.
Precisely. And this is critical because of a
concept we found called the narrow window. The
product drop. Right. In modern retail, especially
fashion and lifestyle, the product lifecycle
is just accelerating faster than ever. You have
a honeymoon phase, maybe two weeks, maybe three,
where a new item commands full price. It's fresh,
it's exciting, it's front of store. That's the
hype cycle. That's when you get your AUR lift.
If you miss that cycle because your team spends
the first 10 days figuring it out on the floor,
stumbling through customer questions, you never
get that margin back. The sales report two weeks
later will tell you low sell -through. but it
won't tell you why. It'll probably just say traffic
was soft or price resistance. Exactly. It masks
the real issue. The issue wasn't the price. The
issue is that the team was scared to talk about
the product because they didn't understand the
tech specs. They avoided the item. So the premise
here is capturing this readiness data before
the inventory even hits the dock. But let's get
into the weeds of what this data actually looks
like. You mentioned question level analytics.
Give me a specific example. What should a VT
of stores be looking for on a dashboard? OK,
perfect. So imagine you have a new denim launch,
high stakes. You've got a new fit. Let's call
it a high rise wide leg. You send out the training
bite. A sophisticated platform won't just tell
you who watched the video. It will tell you.
85 % of the fleet in the Northeast region failed
the specific question about how to size this
fit for a curvy customer. Okay, now that is a
diagnostic. That is a red alert. It's a leading
indicator. You haven't sold a single pair of
jeans yet, but you now know... mathematically,
that the Northeast is going to struggle to convert
on that fit. You can predict the failure before
it happens. Exactly. And there's another layer
here that I found really interesting in the research,
the difference between competence and confidence.
Because I can guess on a multiple choice quiz
and get it right. That doesn't mean I can sell
it. This is huge. The unconsciously incompetent
associate is the most dangerous person on your
sales floor. The one who doesn't know but thinks
they know. Exactly. The associate who confidently
tells a customer, yes, you can machine wash this
raw silk blouse. Oh, disaster. Returns, bad reviews,
brand damage. Exactly. So modern platforms are
measuring confidence, asking the associate, how
sure are you? Or even measuring the speed of
response. If they take 45 seconds to answer a
simple feature question, they don't know it.
They're guessing. They're hunting for the answer.
And you can't hunt for an answer when a customer
is standing in front of you waiting. Correct.
So when we treat training data as business intelligence,
we're looking for those gaps. We are mapping
the knowledge health of the organization. And
that allows you to intervene before the damage
is done. Okay, let's talk about that intervention.
Because data is great, but let's be real, retail
executives are drowning in data. Absolutely.
If I'm a district manager or store manager, I
don't need another report telling me I have a
problem. I need to know what to do. Right. What's
the action? The source material breaks us down
into actionable levels, and I want to start at
the store level. This is where the rubber meets
the road. There was a concept here about scheduling
that honestly disrupts the traditional labor
model. Because usually, how does a manager write
a schedule? Availability and seniority. Or maybe
sales per man hour, if they're being tactical.
Sarah can work Saturdays, and she's been here
three years, so she gets the peak shift. Right.
It's logistical. But the sources suggest scheduling
based on readiness. Walk through the scenario.
You have that major denim launch this weekend.
You have two associates available. Associate
A has a 98 % retention score on the training
for this specific fit. They know the fabric,
the sizing, the story. OK. Associate B, who might
be a senior key holder. Watch the video, but
failed the quiz twice. Usually the key holder
gets the hours. And that's the friction point.
Yeah. But the data argues that if you put the
unprepared person on the floor during peak traffic,
you are statistically guaranteeing lost sales.
You are burning traffic. So it gives the store
manager the ammunition to make a hard decision.
I'm putting the rookie on the floor because she
nailed the specs and I'm putting the veteran
in the back of house until they get up to speed.
Or you use the data for peer pairing. This is
a smart low conflict tactical takeaway. You don't
just bench the unprepared person, you pair them.
Hey, Shadow Sarah today. She nailed the training
on the new fit. So it turns the floor into a
live training environment. It's micro coaching
based on facts, not just a manager nagging their
team. Exactly. It removes the emotion from the
coaching. It's not, I think you don't know this.
It's the data shows we missed this question.
So let's review it. Now let's zoom out to the
district manager level. The DM ride. I've been
on those. You have 12 stores. You have eight
hours in a day. You usually go to the store that
is burning down the one missing plan by 20 percent.
The firefighter approach. You go where the heat
is. Or you go to the flagship because the RVP
is coming next week and the visuals need to be
perfect. The source suggests a pivot. Prioritize
visits based on readiness gaps. It's pre -firefighting.
So I'm looking at my iPad before I start the
car. And you see that Store 104 is hitting its
sales plan today. They look fine on the sales
report, but their readiness score for next week's
promo is sitting at 30 percent. That's a ticking
time bomb. That store is about to drive off a
cliff. If the DM visits now, they can fix the
knowledge gap before the inventory lands. They
shift from being an auditor of past failures
to a strategist ensuring future success. It effectively
changes the conversation from why did you misplan
last week? to how are we going to crush plan
next week? Which is a much more empowering conversation
for everyone involved. It builds culture instead
of destroying it. And then at the HQ level, the
merchant and planning level, this feels like
it solves a massive disconnect. Oh, a huge one.
I've sat in those Monday trade meetings where
the buyers are frustrated because the stores
just didn't get the product and the stores are
frustrated because the buyers bought stuff nobody
wants. The eternal war. Bad product versus bad
execution. How does this data broker a peace
treaty? It removes the anecdote. It removes the
opinion. If a product fails, you can look at
the data. If the readiness score was 95%, meaning
the team knew exactly how to sell it, they were
confident, they were ready, and it still didn't
sell. Then it's a product problem. The buyer's
got it wrong. The fit was off. The price was
too high. The trend is dead. But if the readiness
score was 40%, Then it's an execution problem.
The product might have been a winner, but we
failed to equip the team to sell it. This clarity
allows HQ to actually fix the right problem.
You aren't adjusting the buy if the problem is
training, and you aren't retraining the staff
if the problem is the fit. That brings us to
the bottom line impact. We teased the 15 % markdown
reduction earlier. I want to really drill into
this because in retail, where net margins can
be single digits, saving 15 % on markdowns is
not just a nice -to -have, it's transformative.
It's the difference between a bonus and a layoff
in some years. It's that significant. But I have
to be the skeptic again. Retail is noisy. You
have weather. You have competitor promos. You
have macroeconomic factors. How can we confidently
attribute that saving to training? How do we
know it wasn't just a cold winter that sold the
coats? That's a fair push. Correlation is not
always causation. But the mechanism here comes
down to Time on floor and weeks of supply. We
know that the longer an item sits, the less valuable
it becomes. Inventory is like a banana. It goes
bad quickly. Perfect analogy. The goal is to
sell the banana while it's yellow. When an associate
is fully prepared, when they have high readiness,
they can position the value immediately. They
can handle the objection to the price point on
day one. Why is this jacket $400? Because it
uses a three -layer Gore -Tex membrane that breathes
while keeping you dry, and the seams are fully
taped. Boom, sale made, full price. And if they
don't know that answer, if they hesitate. The
customer walks. The jacket stays on the hanger.
It sits there for four weeks. The weeks of supply
creeps up. The allocation algorithm notices the
stagnation. The planner panics because they need
to free up open to buy for next month. And the
red sticker goes on the tag. Exactly. So the
markdown. is essentially the penalty we pay for
the associate's silence. That is a powerful way
to frame it. Markdowns are the cost of ignorance.
By increasing readiness, you are increasing the
velocity of full -price sell -through. You are
clearing the inventory while the margin is intact.
You're keeping your weeks of supply healthy.
And that changes the conversation in the boardroom.
You aren't asking for a learning and development
budget anymore. That's usually the first thing
cut. Right. You are asking for a margin protection
strategy. I want to circle back to something
you mentioned earlier about the confidence metric
because I feel like this applies to more than
just the product features, it applies to the
brand experience. Oh, an absolute. We talk a
lot about experiential retail. We are seeing
a massive shift where the transaction is moving
online. If I just want to buy the thing, I can
do it for my couch. If a customer drives to a
store, parks, and walks in, they are looking
for something the website can't give them. They
want expertise. They want validation. They want
a human to look them in the eye and say, that
fits you perfectly, or this is the right tool
for the job. You cannot automate that confidence,
but you can train for it. and you can measure
it. And if your data shows that your team lacks
that confidence, you are effectively paying rent
on a warehouse, not running a store. Exactly.
You're just a fulfillment center with better
lighting. So let's recap the journey here. We've
moved from the rear view mirror of comp sales
and UPT to the forward facing windshield of readiness
and capability. We've looked at how this granular
data changes scheduling, moving away from seniority
to readiness. We discussed how it changes the
district manager's route, moving from firefighting
to fire prevention. And we landed on the financial
reality that prepared teams protect margin by
increasing inventory velocity. It's a comprehensive
shift in how we view the people metric. It really
is. Which brings me to the final thought for
our listeners, the VPs, the directors, the managers
finishing that second cup of coffee and getting
ready to walk into that trade meeting. What's
the challenge for them? Next time you open that
dashboard, look at your wins and your losses
from last week and ask yourself a hard question.
Did we win because we were lucky? Because the
weather brode and traffic spiked? Or did we win
because we were ready? And conversely, do we
lose because the market was tough? Or did we
lose because we sent our team onto the field
without a playbook? Because if you aren't measuring
readiness, you're just guessing. And in this
economy, with these margins, guessing is a luxury
nobody can afford. Amen to that. That's it for
this deep dive into the world of retail intelligence.
We'll catch on the next one. Go check those readiness
scores. See you next time.