Before the time slot and the number, there's the list

When a calling session produces nothing, three explanations come up: the number displayed, the time you called, and the list. The first two get discussed the most, because they’re the easiest to change.

The list is what weighs the most. A poorly targeted or outdated list will produce nothing whatever the time slot and whatever the caller ID, while a good list can survive a mediocre time slot.

We’ve covered the other two factors elsewhere: the time slots that work in France and what local numbers are really worth. The subject here is the list, and what you can know about one before handing it to a team.

Where your list comes from

Three origins, each with different flaws.

Bulk purchase. You pay per contact, the file arrives complete, and its quality depends entirely on how fresh the vendor’s database is. Quality can therefore vary a lot from one segment to another, even with the same provider.

Enrichment from a list of companies you picked yourself. Targeting is better since it comes from you; reachability depends on the tool that fills in the numbers. That’s the subject of lead enrichment, so we won’t redo it here.

Building by hand. Slow and expensive in SDR time. It’s often the best option on narrow segments or high-stakes accounts.

There’s a fourth one teams forget: their own CRM. The inbound leads nobody called back. The opportunities lost a year and a half ago. These are rarely clean lists.

What you can judge before the first call

A few checks in a spreadsheet are enough to weed out some of the bad lists before handing them to the team.

Look at the share of direct numbers versus switchboards. A file made up mostly of switchboards isn’t bad in itself, but it doesn’t get called the same way. Better to know that before setting a target for the team.

Every list has a build date, and if the vendor won’t give it, that’s already information. On roles where people change jobs often, a list ages faster.

Existing customers and people who asked not to be called again have to come out before the list goes in, which means cross-checking the file against your CRM. Without that cross-check, an SDR discovers the existing customer during the call.

Consistency with what you sell is the most basic check and the most often skipped, because it requires opening fifty random records and honestly asking whether these people have the problem you solve.

None of these checks replaces a sample actually called. Run that test on the time slot and with the SDR profile that will call the list afterwards. If your best rep does the calling on a Tuesday morning, you’re mostly measuring their skill.

What wears a list out over time

A list doesn’t expire all at once, and it wears out for several reasons at the same time.

Part of it doesn’t depend on you: people change jobs, and direct numbers get reassigned once freed up. The only useful thing is knowing how fast things move on your segment.

Another part comes from how you call. Each pass consumes contacts, at a rate that depends on how many calls you launch at once. A conversation obtained while calling 3 prospects at a time interrupts 2 calls mid-ring; at 5, it interrupts 4. None of those contacts ever spoke to an SDR, and they all go back into the queue. That’s the trade-off we detail in why we recommend calling only 3 prospects at the same time.

There are also the contacts called back too often. We’d rather space out retries than hit the same contact again within the week: it weighs on the reputation of your outbound number. At Ubic, when progressive pause is enabled on a list, a contact who doesn’t pick up is frozen and called back after a delay you set, which gets longer with each new freeze.

List or team: how to settle it

The question comes up in just about every activity review, and it gets settled with two numbers kept separate.

Pick-up rate on one side. Conversion rate once someone is on the phone on the other. If pick-up drops while conversion stays stable, the problem comes from the list or your numbers. If it’s the other way around, it comes from the pitch.

That still requires call statuses to be reliable and readable list by list. If the team enters them by hand at the end of the day, you’ll mostly be comparing data-entry habits. That’s why our reports read list by list.

There’s a faster way: give the same list to two SDRs on the same time slot. Then two different lists to the same SDR. A few sessions are usually enough to see which side the gap is on.

What a burned list costs

The first cost is the contact. You paid for it, it’s spent, and teams do run that math.

The other cost can’t be counted to the cent: a list crossed too fast is a whole segment that has already heard from you at the wrong moment and will be harder to reopen in six months, on a French market where target segments are often narrow.

That’s also why we don’t push speed as a selling point: a parallel dialer gets through a list faster, that’s all.

Where to start

Take the list your team is working on right now. Write down two things: where it came from, and how old it is.

Frequently asked questions

How do you judge a file before buying it in volume?

By calling a sample of fifty to a hundred contacts on your actual target, not on the vendor’s demo segment. Count the reachable ones, the wrong numbers and the off-target ones. The full method is in the lead enrichment article.

How long before a list is out of date?

It depends on the turnover in your segment. Date your batches and compare their results against each other: that’s what will give you your target’s actual pace.

Does a parallel dialer make up for a bad list?

No. Automatic filtering of voicemails, faxes and invalid numbers keeps the SDR from wasting time on them, but it doesn’t make anyone reachable. On a weak file, a dialer mostly produces more useless attempts per hour.

Should you stop calling a list that gets poor results?

Not necessarily. First check whether the problem comes from a sub-segment rather than the whole list: very often, part of the file holds up and the rest drags the average down. Split the file by sub-segment and compare results before shelving the list.