Link Leads Blog · September 30, 2026
A 10,000-lead CSV lands in your account as one file, sorted however it happened to export. Turning that into a week's worth of dials that actually get made takes a few minutes of setup before anyone picks up a phone.
The download you get after an order is a flat list — first name, last name, state, ZIP, cell number, age — in whatever row order the export produced. Nothing about that file tells you which 500 records to call Monday and which to call Friday. Left as one file, two things tend to happen: either the list gets worked top to bottom until the day runs out, which means the last few thousand rows sit untouched while the first few thousand get called two or three times, or the whole file gets queued into a dialer at once with no plan for pacing, which just moves the same problem into the dialer's own delivery order. Either way, a chunk of what you paid for is aging on a hard drive instead of getting dialed. The file keeps getting older the longer it sits — see our post on why the 24–48 hours after delivery matter — and a batch plan is what actually keeps that clock from running out on part of the list.
Do the order in this sequence, not the reverse. If you split a raw file into daily batches and then scrub each batch the morning you're about to work it, a landline or a DNC-registered number just rides along in whichever day's file it happened to land in — and if that day gets skipped or compressed, the bad numbers never get caught at all. Running one scrub pass across the full delivered file before you divide it means every batch you build afterward is already clean, and the count you're dividing is the real number of callable records, not the raw row count on the invoice. We recommend Landline Remover for this (disclosure: that's an affiliate link and we may earn a commission if you sign up through it) — upload the CSV once, get back a file flagged for landlines and DNC matches, and batch off that instead of the raw delivery.
Calling hours run on the recipient's local time, not yours, and the field that tells you the correct zone is state and ZIP, not the phone number's area code — a mobile number keeps its original area code for years after someone moves. Our calling-hours post covers the full logic; the piece that matters for batching is to compute a time-zone column on the whole file before you split it, not after. If you divide the file into daily batches first and add time zone later, you end up manually re-sorting finished batches to fix scheduling. Tag it once at the top of the workflow and every batch you cut afterward already carries the field a dialer or SMS platform needs to gate its send window correctly.
The easy mistake is dividing total leads by calendar days remaining — a 10,000-lead file over two weeks becomes "714 a day" on a spreadsheet, with no relationship to how many dials one agent or one seat can realistically put in. A single pass through a batch, one attempt per record, undercounts what a cold list takes to convert — most of the connects on any aged file come from a second or third attempt at a different time of day, which is exactly why spreading attempts across day-parts matters more than the size of the batch itself. Size each day's file to what your dial capacity can cover two or three times over, not to what clears the file fastest on paper. A smaller batch worked properly beats a larger one skimmed once.
A daily batch file needs one more column before it's ready: a place to tag what happened on each record. Our disposition-code post lays out a nine-code set that routes a record back into rotation, into a callback queue, or out of the pool entirely. Build that column into the batch template from day one — a "worked" record with no disposition is indistinguishable from one nobody's touched yet, and that's how leads quietly get re-called or dropped between one day's file and the next. Unresolved dispositions (no answer, voicemail, callback requested) are what should carry forward into tomorrow's batch; closed and DNC-tagged records are what should not.
Once the file is scrubbed, time-zone tagged, and sorted by that zone, cutting it into daily
batches is mechanical. In a spreadsheet: sort by time zone, then state, so each batch stays
geographically coherent instead of jumping across the country row to row. Add a batch column with
a formula like =ROUNDUP(ROW()/500,0) (adjust 500 to your daily size) to number every
row into its batch without manually slicing ranges. Filter or pivot on that batch number to export
each day's file. Keep the workbook as the single source of truth — daily exports are throwaway
views of it, not separate lists — so a record's disposition history stays attached to it as it
rolls from one day's batch into the next.
If you're buying the same volume every billing cycle, this stops being a one-time setup task and becomes a recurring one — the file that lands each month is the same size, from the same states and age range you set once, so the scrub-tag-split routine above just runs on a schedule instead of getting rebuilt from scratch per order. See monthly plan vs. one-time order for when the subscription pricing is worth switching to on top of the workflow benefit.
Link Leads sells SMS and email lead lists at a flat $0.012 per lead, minimum order 5,000 leads ($60), filterable by state and age band (18–64), deduplicated, and delivered as an instant CSV. Build an order in the order builder, or pull a free 100-row sample first to see the exact columns you'll be scrubbing, tagging, and splitting.