Preboot for supply chain planning
People ask for things you don't offer; every ask dies as a polite no. Preboot counts them, reads the season in time to buy, and turns dying hours into decisions you approve. Demand and capacity planning for selling through chat — never freight.
Sound familiar?
I find out we should have offered it when the season's already over.
The asks were there all along — a thousand threads, six channels, none of them ever counted.
Half the chairs sit empty on a Tuesday and I still can't tell you why.
Staffing, price, a competitor, or just the day — everyone has a theory, nobody has evidence.
Every late cancellation is money I never get back.
Somebody has to notice the freed hour, find a taker, and call — mid-rush.
None of that is a spreadsheet problem. It is a signal problem — the demand is already sitting in your conversations, and nothing has ever counted it.
Demand you can hear
Sales tell you what sold from what you happened to have. Conversations tell you what people wanted — that should decide next season.
Every “do you do X?” counted, themed, priced against the market — the next treatment or package, asked for by name.
Competitor menus and prices researched on the open web, joined to your demand data.
Designed to lift consults and bookings 20–40% — the audience existed before the campaign.
The unanswered “do you have…”, the price pushback, the size that sells out — counted across the whole inbox, never sampled.
Rising themes flagged weeks before they dent revenue — a courier souring, an objection returning.
When only your buyer knows, Loop asks them — restock, drop, launch settled on evidence.
“Do you deliver here?” — a location signal. Buyers’ budgets and sizes — a unit-mix signal. Loop makes them a bankable case.
Years of orders, queryable by district — your own data, not a forty-person survey.
Weighed against rents, rival density, and neighbourhood growth — including areas that cannot support you.
Loop, on the weeks ahead
Nothing waits to be asked. Loop watches your calendar and arrives with the call already made — reasoning attached, risk priced, approval yours.
Peak-week questions surge before bookings do; prep times slip at one branch. Loop flags both unprompted — in time to buy.
Asked-for versus booked, by destination and month — every channel, not the loudest customers.
Every alert: recommendation, expected outcome, risk. Approve, and Loop executes — audited, reversible.
Twelve unsold seats. Empty Tuesday rooms. Chairs idle till noon. All arrive as decisions, not heatmaps — approve or hold.
Season, price, or one person’s schedule — settled causally, before any panic discount moves money.
Offers target flexible regulars mined from behaviour — weekend full-payers never see the discount.
Revenue per room-hour, utilization by chair — numbers most owners have never seen, all drillable.
A month out, the prep arrives unasked: last season’s numbers, this year’s dates, staffing, campaign drafts. Every year, forever.
Dates, demand trends, competitors’ offers — researched fresh, not copied from last year’s file.
Occasion segments built before the rush; early-bird campaigns designed to cut walk-away demand 20–30%.
Seasonal journeys — pre-orders, iftar timing — switch on for the season, roll back cleanly after.
Capacity that expires
A slot, a chair, a seat — none of it keeps. Lost to no-shows and cancellations too late to resell. Both fixable.
Confirm-or-release reminders in the customer’s thread: confirmers show, the rest bow out in time, the chair goes back on sale.
“Confirm at 48 hours, again at 3” — one sentence, and Loop builds the journey.
Deposits only where causal tracing says they pay — designed to cut no-shows there 40–60%.
New timings preview first, roll back in one click; one-doctor clustering flagged in days.
A cancellation lands; Loop offers the hour to the waitlist. A restock syncs; the askers hear first. No receptionist sprint.
A durable journey, not a sticky note — designed to add 5–12 utilization points.
Calendar and stock sync via custom integrations — the freed slot is offered in minutes.
“Do you have X?” becomes the waitlist — designed to recapture 20–40% of stockout-lost demand.
Running the supply side
The reviews a busy month skips are the ones that would have caught the drift. So they stop depending on memory.
No-shows, utilization, rebooking, the drift you keep ignoring — waiting when you pick up your phone. Even in peak weeks.
Leads with what changed, names what you keep scrolling past — no wall of numbers.
Every number is drillable — ask in plain language, the chart comes back in-thread.
Some gaps aren’t in the data. Loop asks the branch manager — trail hours old, answer in the day’s brief.
Anomaly caught first — the question carries context, not a month-end “any idea why?”
Context and data on one page: recommendation, expected outcome, risk — yours to approve.
Traffic by hour and branch, one chart — the staffing case makes itself. Rota changes arrive as briefs to approve.
Targets and bonuses set from what the team actually did — incentives staff call fair.
Reply times, resolutions, upsells per person — measured from real conversations, not a manager’s impression.
Before & after
Six more moments from the map — run by hand, and run on Preboot.
A slow week you can see coming
A blanket discount that teaches regulars to wait
Loop spots the soft night, drafts the offer, targets lapsed regulars, launches on approval — killed if it flops
Comparing branches, chairs, and hours
A day of spreadsheets, then an argument about numbers
“Utilization by chair, stylist, hour” asked in chat and charted — why branch B trails A, ranked with evidence
What stays on the menu
An annual redesign and a feeling about what sells
Margin per dish, per daypart — kill, reprice, or promote briefs, plus dishes diners ask for and never find
Couriers and returned orders
The usual courier, and a weekly argument about fault
Lateness and refusals scored per courier per city, lanes re-routed on approval — designed to cut worst-lane RTO 20–35%
Orders that pay a delivery-app commission
A 15–30% cut on every order, forever
Orders taken end to end in chat, QR-in-the-bag journeys converting app customers — commission-free share 10–20% → 35–50%
The systems you retype between
Copy-paste from portal to calendar to CRM, every week
Portal leads ingested, viewings booked, status pushed back — any API through custom integrations, approval-gated, reversible. Re-entry → zero
"Published averages" are industry benchmarks from public studies, not Preboot results. Your own numbers will live on your dashboard — that's rather the point.
In your industry
Every line above is industry-neutral on purpose. Here is what it turns into where you work.
We will count it on your own inbox — how often, what it was worth, which hours it cost. Message us on WhatsApp; our number runs on Preboot.