A promising lead and an awkward spreadsheet
A growing brand wants to move from their current warehouse. They send a year of order history exported from their shop platform and a product list. Your operations manager is asked to look at it between everything else. It has one row per order line, dates in two formats, and product sizes that are missing for half the range.
The analysis takes days to fit in. By the time the quote goes out, the brand has had two others. And the quote itself leans on assumptions: an average number of items per order, a guess at how many pallets they will need at peak, a returns rate someone mentioned on the call.
Why quoting is slow and unreliable
The right price depends on the shape of the work, not the volume. Two clients shipping the same number of orders can cost very different amounts to serve. Single-item orders of small products are cheap to pick; multi-line orders of bulky products with high returns are not. That shape is in the prospect's data, but getting it out takes analysis skills and time that most 3PL sales teams do not have on hand.
| Question the quote depends on | Where the answer is |
|---|---|
| Lines per order and units per line | Order line export, once grouped properly |
| Peak week versus average week | Order dates, by week, over a full year |
| Storage needed | Stock holding, product dimensions, pallet quantities |
| Parcel size mix | Product dimensions and typical order contents |
| Returns workload | Returns or refunds export, if they will share it |
| B2B share | Customer types or channels in the order data |
When the analysis is rushed, quotes default to the averages, and averages are exactly where pricing goes wrong.
The cost of guessing
Quote too high and you lose a client who would have been profitable. Quote too low and you win a client who loses you money for the length of the contract, which is worse. Slow quotes lose deals regardless of price, because the prospect is usually under time pressure to move.
There is also the internal cost: your operations lead doing sales analysis instead of running the warehouse.
And there is the onboarding cost of a wrong profile. If the quote assumed forty pallets at peak and the client arrives with ninety, the space has to be found by moving other clients' stock around, which nobody priced.
The quoting tool we build
- An upload for the prospect's order and product exports, with mapping for common platform formats such as Shopify, WooCommerce and Amazon reports.
- Automatic profiling: orders per week across the year, peak weeks, lines and units per order, B2B share, product size bands, and gaps in the data called out plainly.
- A storage estimate from stock holding and product or carton sizes, with the assumptions shown.
- Your cost model applied: labour per activity, storage per location type, packaging and carrier costs by parcel band. Your finance team owns these numbers and can change them.
- A draft rate card and a monthly cost estimate at average and peak, with margin shown internally and not on the client's version.
- A proposal document generated from your template, which sales can edit before sending.
Every assumption is visible, so when the prospect questions a figure, sales can point to the data behind it.
What sales gets
Quotes based on the prospect's own numbers, produced while the conversation is still warm. Operations reviews a profile rather than building one. Over time, comparing quoted profiles with what clients actually did tells you where your cost model needs adjusting.
Is your quoting like this?
- Quotes wait for an operations manager to analyse order data.
- Prices are based on average order size and a guess at peak.
- Some clients turned out much less profitable than the quote assumed.
- Prospects' data exports need a lot of cleaning before anyone can use them.
- Your cost model lives in one person's spreadsheet.