At the beginning of each week, Store Managers can complete their Pulse submission for the previous week.
The process is intentionally simple:
Rate the week
Select the rating that best reflects the overall week:
Great week
Steady week
Tough week
Very tough week
What affected the week?
Add the main context that helps explain what happened.
Identify the biggest challenge
Select the most relevant challenge tags and briefly explain the most important difficulty the store faced.
Identify the biggest win
Select the appropriate tags and describe something that worked particularly well.
Pulse is not intended to duplicate the dashboard. The numbers are already available in Flow. The purpose of the submission is to preserve the store-level context behind those numbers. Flow defines Pulse as structured qualitative reporting that connects store context with KPI movement.
A strong Pulse report is specific, concise and operational.
Instead of writing:
UPT was weak, so we focused on add-on selling.
Write something closer to:
Motion Joggers sold well, but many purchases were single-item. From Thursday, the team started showing the AirFlex Tee in Gray during the fitting-room conversation. Tanaka-san's approach worked particularly well, so we shared it in the Saturday briefing.
The second example gives the Area Manager much more useful information. It identifies:
the product involved;
what the team observed;
what action was taken;
who was involved;
when the change was made; and
whether the action appeared to help.
This is the same principle used for Flow memos: important operational notes should record what happened, what action was taken and whether the action appeared to help.
Useful Pulse context may include:
strong or weak products;
stock or size shortages;
VMD or entrance-display changes;
fitting-room congestion;
cashier queues;
staffing shortages or changes;
successful staff behaviors;
training or coaching issues;
campaigns and promotions;
unusual customer behavior;
local events;
weather conditions;
repeated product questions;
actions the team tested;
actions that did not work as expected.
These are exactly the kinds of store realities Flow is designed to preserve alongside performance data.
Details such as these make a report much more useful:
“Motion Joggers M was down to two units Saturday evening.”
“Three groups were waiting for fitting rooms at around 15:00.”
“We moved one staff member from replenishment to fitting-room support.”
“The new entrance display increased customer interest, but we did not see a clear improvement in entry.”
“Customers repeatedly asked about the Navy color.”
You do not need to include this level of detail in every sentence. Include it when it helps another manager understand what actually happened.
Avoid writing the report as an analysis of the dashboard.
For example:
Conversion was lower than normal.
does not add much because Flow already knows the conversion result.
More useful:
Saturday afternoon fitting-room waiting reached three groups. We moved one staff member from replenishment, but cashier coverage then became thin.
Pulse should contribute information the data alone cannot tell us.
This distinction matters because Flow AI can later combine the human observation with the quantitative evidence. Flow AI is expected to use Pulse and memos as evidence while distinguishing known facts from reasonable inference rather than automatically treating correlation as causation.
Try not to repeat the same event in every Pulse field.
A useful structure is:
What affected the week?
Describe the overall conditions.
Heavy weekend traffic and unusually high interest in the new outerwear range.
Biggest challenge
Describe the most important operational difficulty.
Fitting rooms backed up between 15:00 and 17:00, and we were short one staff member on Saturday.
Biggest win
Describe something the store did particularly well.
We paused replenishment during the peak and assigned Sato-san to fitting-room support. Waiting reduced and customer service remained stable.
This creates a clearer operational picture than repeating “busy weekend” three times.
Pulse is not a success report.
If an experiment had little effect, record that.
For example:
We moved the campaign display closer to the entrance on Friday. More customers appeared to look at it, but we did not see a clear improvement in store entry. We will try changing the mannequin next week.
That information is valuable. It helps prevent unsuccessful actions from being repeatedly rediscovered and gives Flow AI more context when comparing actions and outcomes over time.
The objective is an accurate operational record, not a perfect story.
Memos and Pulse serve related but different purposes.
Memos capture important operational events as they happen during the week. Examples include stockouts, fitting-room congestion, customer complaints, weather effects, staffing shortages, strong product reactions and VMD changes. NAMI's operating practice, for example, requires significant store events to be documented so they can provide context to performance later.
Pulse provides the weekly reflection.
When the weekly submission is created, the memos from that period are associated with the store's report. This means the Store Manager does not need to reconstruct the entire week from memory.
A useful habit is:
During the week: record meaningful events in Memos.
At the beginning of the next week: use Pulse to identify the most important context, challenge and win.
Once a Store Manager submits the weekly Pulse report, it becomes available to the appropriate Area Manager.
The Area Manager can review the individual store submissions or use the AI Summary function to analyze the area as a whole.
The summary brings together:
Pulse submissions from the Area Manager's stores;
weekly store performance data;
sales and commercial KPIs;
traffic and conversion information;
relevant store memos;
staffing and operational context where available;
observations and actions reported by Store Managers; and
relevant comparisons between stores.
Flow is specifically designed to let Area Managers combine KPI movement with field context, identify priority stores and prepare more effectively for store visits.
The AI Summary is more than a condensed list of Pulse comments.
Flow AI can analyze the information across the Area Manager's stores to identify meaningful patterns, exceptions and areas requiring attention. Its analytical model can combine performance data, store context, operational notes, Pulse inputs and relevant comparisons rather than relying on a single source.
For example, the summary may identify:
several stores experiencing the same stock issue;
one store maintaining strong conversion despite weaker traffic;
repeated fitting-room congestion at a particular location;
a successful selling approach appearing in a higher-performing store;
repeated low UPT together with weak add-on execution;
a VMD adjustment that appears worth investigating elsewhere;
staffing pressure coinciding with weaker service periods; or
a store that does not require intervention because its current execution appears healthy.
For Area Managers, Flow AI should prioritize store comparisons, patterns, coaching opportunities and where intervention matters most.
The generated Area Manager summary is saved in Flow, allowing it to be revisited later rather than disappearing after the initial review.
The AI Summary should help the Area Manager determine:
What needs attention?
Which stores or issues deserve follow-up?
What appears to be working?
Are there successful behaviors or operating practices worth preserving or sharing?
What needs verification?
Does a Store Manager's explanation appear consistent with the available data and memos?
What should happen next?
Is coaching, a store visit, a stock escalation, a staffing adjustment, a VMD review or simply continued observation appropriate?
Flow AI is decision support. It is intended to reduce the analytical burden on managers, not replace managerial judgment.
Before submitting, ask:
Did I explain something the KPI data alone cannot tell the Area Manager?
Did I mention the specific product, time, staff situation or operational condition when relevant?
Is the biggest challenge actually the most important challenge?
Does the biggest win describe something useful that the team did?
If we changed something, did I say whether it appeared to help?
Did I avoid presenting an assumption as proven fact?
Did I record unresolved issues that may continue into next week?
Could someone who was not in the store understand what happened?
A good Pulse submission does not need to be long.
It needs to be useful.
When Store Managers consistently preserve the reality behind their weekly performance, Area Managers gain a much stronger view of the business, and Flow AI has the quantitative and qualitative evidence required to produce better analysis, identify repeatable execution and support more informed action.
