Cold calling data: the list is usually the problem, not the call
A cold call list that is thirty percent wrong costs a BD consultant real hours every week, and the fix starts with the list, not with the script. Cold calling data is the raw input to a BD session, and most of what goes wrong with it happens before anyone dials. When a block of calls goes nowhere, most consultants rewrite the opener. Check the list first. There are five ways a list breaks, and the one that does the most damage barely gets discussed.
What cold calling data means and why the script usually isn't the problem
Cold calling data is three things: a name, a number, and a reason to call now. Get any one of those wrong and the call fails before you say a word. Most BD training focuses on the opener, because it's the part you can rehearse. The list is the part nobody audits.
The five ways a list goes wrong, roughly in order of how much they cost you: wrong timing, wrong person, stale records, wrong company, and duplicates. The rest of this piece works through each one, because the fix for most call lists is knowing which of these five is costing you the most this week.
Warm lead versus cold list: why the distinction matters more than the name suggests
Recruiters treat "warm" and "cold" as a measure of familiarity. It's actually a measure of timing. A contact you've never spoken to, at a company that posted a job ad this morning, behaves like a warm lead: they have a problem, you have a solution, and the call lands. A contact you placed three roles with two years ago, at a business with nothing open right now, behaves like a cold list entry, no matter how friendly the last conversation was.
This reframes the whole quality question. A list isn't good because the names are familiar or the titles look senior. It's good because it's tied to something happening right now. That's the test to apply before anything else on this list.
Wrong timing: the biggest problem with cold calling data
The correct name, at the correct company, with no live vacancy, is a wasted call. The opener doesn't matter. The pitch doesn't matter. There is nothing for that person to say yes to, so the best you get is "nothing right now, call back later", and later rarely comes.
Timing failures don't announce themselves. A bad number bounces. A wrong title gets you redirected. A timing miss just produces a polite, empty conversation that looks like a normal call in your activity log, so it never gets flagged as the problem it is. A consultant can run forty calls like that and read the day as "quiet", not "broken".
The fix is to stop building call lists from static title lists, like department heads at target companies, and start building them from what's open today. A list tied to live job ads stays accurate until the vacancy closes, which is a different maintenance problem to a title list that decays from the day you pull it. This is also where buying a list from a tool like Lusha or ZoomInfo runs into trouble: those tools are good at finding a person who matches a title, not at telling you whether that company has a live need this week.
Wrong person, stale records, wrong company and duplicates
Four more ways a list fails, each with its own tell.
- Wrong person. The title on the record is accurate but the hiring authority sits elsewhere. An "HR Manager" record looks like the right call for a site manager vacancy, but the site manager role is owned by the construction director, not HR, and HR will tell you so after you've burned the call.
- Stale records. The named contact left the business eighteen months ago. The switchboard forwards you to someone who has never heard of them, and you spend the first thirty seconds re-explaining who you're trying to reach instead of pitching.
- Wrong company. The business doesn't hire the roles you place. A registered nurse recruiter calling a civil engineering firm because it matched a generic "operations manager" title search wastes the call before it starts.
- Duplicates. The same person appears twice under slightly different spellings, "Mike Chen" and "Michael Chen" at the same employer, and you call them twice in the same week. The second call doesn't land as thorough. It lands as sloppy.
Each of these is checkable before you dial, which is the point. None of them require a better script to fix.
What a bad list actually costs: a worked example
Here's a worked example, built from stated assumptions, not a measured figure. A BD consultant makes forty calls a day at a loaded cost of roughly $45 an hour, call it $0.75 a minute including time between dials. If thirty percent of a two-hundred-name weekly list is wrong across the five failure types above, that's sixty calls a week spent on people who were never going to convert regardless of pitch quality.
At an average of three minutes a call, including dialling, the wrong-number check, and the polite exit, that's three hours a week gone. Three hours is roughly nine more genuine conversations at the same strike rate the good names are producing. That's not a measured outcome. It's arithmetic from the assumptions above, and the shape of it holds at most list sizes: a thirty percent error rate doesn't cost you thirty percent of your time, it costs you the conversations that time would have produced.
What a realistic connect rate looks like
There's no safe industry number to quote here, and anyone who gives you one without naming their assumptions is guessing. What actually moves a connect rate is mechanical, not magical: timing accuracy (is there a live need right now), whether you're dialling a direct mobile or a switchboard, title accuracy (right person, not right-sounding title), and time of day.
A smaller list that's right on all four beats a bigger list that's wrong on one. Twenty names tied to job ads posted this week, called on mobiles, between roughly 8am and 10am before the day fills up, will out-convert eighty names pulled from a generic title search. This is the practical argument for spending time on list quality over list size, and it's also why a list that's three weeks old is worth less each day it sits unused.
Cleaning a list before a campaign: de-duplication and bad number handling
Before any list goes into a calling session, run three checks.
- De-duplicate on mobile number and company domain, not name spelling. A name-only check lets "Mike Chen" and "Michael Chen" both through if they're typed differently. Matching on the mobile number and the company's email domain catches the duplicate regardless of how the name was entered.
- Flag and date dead numbers, don't delete them. A number that disconnects or redirects to someone else gets a "dead as of [date]" flag, so it doesn't quietly resurface on next month's list pulled from the same source.
- Set a minimum field standard before a record is call-ready. At minimum: current company, direct number or mobile, title, and the reason it's on the list today, which job ad or event triggered the call. A record missing the last field is a guess dressed up as a lead.
Do not call lists are a legal requirement in Australia, not a courtesy
The Do Not Call Register Act 2006 makes this a legal obligation, not a best practice. Residential and personal numbers registered on the Do Not Call Register are protected from unsolicited telemarketing calls, and a recruiter's BD call to a personal mobile can fall under that depending on how the number is used and whether there's an existing business relationship. Business numbers used for business purposes are largely exempt, but a personal mobile given to you in a different context doesn't automatically carry that exemption across.
The practical obligations: check numbers against the register before a calling session, not after a complaint, and keep a record of when that check was run. If you're handed a purchased list with no evidence it's ever been checked against the register, that's a reason to clean it before you dial, or walk away from it entirely. The time it takes to check is smaller than the time it takes to respond to a complaint.
The one number worth tracking: conversations per hour
Calls per day rewards volume. Conversations per hour rewards list quality, because it's the one number that drops the moment timing, person, or number accuracy goes wrong, regardless of how many dials you make. A consultant doing twenty calls and getting six genuine conversations is doing better BD than one doing fifty calls for the same six.
Track it weekly. If conversations per hour drops for two weeks running and the script hasn't changed, the list has decayed, and it's time to refresh it against what's actually open now rather than push harder on the same names.
Common questions
Is cold calling businesses legal in Australia?
Business numbers are mostly outside the Do Not Call Register, but residential and personal mobile numbers registered on it are protected by law, and a recruiter calling those without consent risks a breach, so check the register before a session, not after a complaint.
What's a good connect rate for BD calls in recruitment?
Benchmarks for this get quoted a lot, and most are guesses without stated assumptions behind them. Connect rate moves almost entirely on timing and number accuracy, so track your own rate over a few weeks and treat a drop as a list problem before a script problem.
Should I buy a cold call list or build one from live job ads?
A purchased list is only as good as the day it was pulled, while one built from what's currently open stays accurate until the vacancy closes, so the choice comes down to how much stale data you're willing to dial through.
How do I stop calling the same person twice from one list?
Deduplicate on mobile number and company domain before you dial, not on name spelling, since two records for "Mike Chen" and "Michael Chen" at the same company will slip past a name-only check.
A list built from job ads that are actually open this week, with contacts ranked by who owns the hire, fixes the timing problem before it costs you a single call. If you want to see the mechanism before changing anything about how you build lists, there's a $49 trial with 300 credits on your own markets.
