A dispatcher sits down on a Thursday night with 14 quotes for Saturday, June 28th. Every single one is priced at the same hourly rate he charges on a slow Tuesday in February. Three of those leads already landed with competitors who simply said yes faster. The other eleven? They’ll book because they have to — it’s peak season and everyone’s full. But he left money on the table for every one of them, and he’ll run his trucks half-empty the following Tuesday to make up for it.
This is how most moving companies price. One rate, every day, all year.
The Problem with Flat-Rate Moving
Movers price like it’s 1999. One hourly rate. Maybe a “summer surcharge” that’s really just a flat $25 added to every job. The result is predictable: peak days are underpriced and overbooked, slow days are empty, and the operator spends June firefighting dispatch problems while leaving revenue on the table.
The hospitality industry figured this out in the 2010s. Airlines figured it out decades earlier. Ride-hail made it visible to every consumer with a phone. The research backs it up: Cornell’s analysis of AI-driven revenue management in hotels found an average revenue lift of 7.2% over traditional static pricing methods (Hotel Dive, 2024). That’s not a rounding error — that’s the difference between a 4% net margin and an 11% net margin for a moving company.
Moving demand is some of the most predictable demand in any service industry. The patterns repeat every single year.
What Demand Looks Like in Moving Data
Per moveBuddha’s 2025 peak season analysis, more than 60% of all U.S. moves happen between May and September. June 30, July 31, and August 1 are the three highest-demand days of the year. Saturdays consistently run 2–3x weekday volume. End-of-month dates cluster with lease cycles. Canadian movers see an additional Quebec-specific spike around July 1 (Moving Day).
This isn’t a mystery. It’s a curve. And you can price against it.
How MoveRight’s Demand Tiers Work
MoveRight assigns one of five demand tiers to every calendar day, per location. The tiers are generated by a forecasting model (built on Facebook/Meta’s Prophet — more on that in our forecasting deep-dive) that learns from each location’s own historical job data.
- Tier 1 — Slow. Midweek in February. Discount aggressively to fill trucks.
- Tier 2 — Below average. A Wednesday in March.
- Tier 3 — Average. A normal week in April.
- Tier 4 — Above average. A Friday in late May.
- Tier 5 — Peak. The last Saturday in June. July 31. August 1.
Rates flex per tier. The customer sees one quote — the price already reflects the day they picked. No “surge” label. No haggling. The rate is the rate, and it’s calibrated to what the market will bear on that specific day in that specific market.
The “Ask for Discount” Button
Here’s the part that changes behavior: when a customer picks a Tier 4 or Tier 5 day, the quote shows the price. But slow-day availability exists. So MoveRight offers a discount ladder:
0 / 3 / 6 / 9 / 12% — five rungs, each tied to a willingness to flex the move date by 1–3 days.
The sales agent never has to invent a number. The customer self-selects into flexibility. And on Tier 5 days, the button doesn’t appear at all — there’s no need to discount when every truck is booked. Margin is protected by design.
Hotels learned years ago that opaque discounts — corporate rates, AAA rates, AARP rates — outperform a public sale. Same psychology here. The customer feels like they got a deal. The operator keeps margin on peak days and fills trucks on slow ones.
What This Looks Like to Your Customer
The customer picks June 28th. They see a quote for $895. There’s a small note: “Flexible on your date? Save up to 12% by moving a few days earlier.” They click, they see the options, they pick June 24th (a Tuesday) for $787. They saved $108. You filled a Tuesday truck that would have run empty.
Done right, the customer never sees a “surge price.” They see a quote for their date, and a discount option for flexibility. That’s not haggling. That’s honest pricing.
What We Learned in the First Two Weeks
We launched demand tiers on April 10–15. The early signal from operators using the system: jobs moved to lower-tier days. Customers who were flexible took the discount and picked midweek dates. Off-peak occupancy ticked up. Peak-day rates held firm because the model was already pricing them appropriately — no manual overrides needed.
This is exactly what should happen. The model is doing the work that a human dispatcher can’t: adjusting prices on 365 days simultaneously, based on each location’s own history, without emotion or favoritism.
Where This Is Going
Tier overrides — so an operator can manually shift a day up or down a tier based on local intel the model doesn’t see. Dispatcher visibility — so the dispatch board shows tier-aware occupancy projections, not just raw job count. Integration with the OBE workflow — so online bookings automatically reflect tier pricing.
This is the first quarter of a pricing system that will compound every year. The more data each location feeds it, the sharper the tiers get. The sharper the tiers, the more margin the operator captures without lifting a finger.
Want to see how your week breaks down by demand tier?
References:
- moveBuddha. (2025). When is Peak Moving Season? — https://www.movebuddha.com/blog/peak-moving-season/
- Allied Van Lines. (2017). Why Summer is the Peak Moving Season — https://www.allied.com/blog/view/all-blogs/2017/06/20/why-summer-is-the-peak-moving-season
- Mews. (2024). Hotel Dynamic Pricing — https://www.mews.com/en/blog/dynamic-pricing-hotels
- Garg, N. & Nazerzadeh, H. (2021). Driver Surge Pricing, Management Science — https://pubsonline.informs.org/doi/10.1287/mnsc.2021.4058
- Taylor, S.J. & Letham, B. (2018). Forecasting at Scale, The American Statistician 72(1):37–45 — https://doi.org/10.1080/00031305.2017.1380080