431 touches, 288 calls, and about 43 people nobody had tried
19 accounts had absorbed 431 logged touches and 288 calls between them, and about 43 named decision makers sitting on those same accounts had never been contacted on any channel.
Each of those people had a job title, an email address and a mobile number already on the record, and we had paid for that data. 17 of the 19 accounts had been worked on more than one channel, so this was not a team that only knows how to pick up the phone.
I only found them because I turned my filter around.
The filter I started with hid the finding
The question handed to me was whether a batch of our target accounts was being worked properly, so I filtered for accounts with almost no activity on them, which is what everybody does.
Every example that came back was weak in the same way. One call, a shared inbox as the only contact on the record, nobody named. There was nothing our reps could have done differently on any of them, which told me the filter was wrong rather than the accounts.
So I asked the opposite question. Which of the accounts that had been worked hardest are carrying fully enriched decision makers that nobody ever contacted?
The four conditions I searched on
An account had to meet all four at once.
| Condition | Why it is in there |
|---|---|
| More than four logged touches on the account | Four or more means somebody already worked this account |
| A named person with a job title | Shared inboxes were what polluted my first search |
| An email address and a mobile number on file | Somebody already paid for that data, so this is a contact waiting rather than a research task |
| Zero contact attempts, across every channel | The point is that nobody had tried, not that nobody had replied |
19 accounts came back, and 47 named decision makers sat on them.
Then I had to clean the list before anybody could use it. Three of the 47 people were on file twice, and one had a do-not-call marker typed into the mobile number field, where no filter would ever look for it. The honest figure is about 43.
About 43 people. Named, with a job title, an email address and a mobile number already on the record, and not one contact attempt against any of them.
They were sitting on 19 accounts that had already absorbed 431 logged touches and 288 calls.
From six months inside a B2B GTM organization with 36,000 accounts in its CRM.
Two accounts with 23 and 39 touches and nobody senior contacted
Two of the 19 show the pattern at full size, because the effort on them was not small.
| The account | Logged touches | Untouched decision makers on it |
|---|---|---|
| The first | 23 | Five, all C-level |
| The second, marked bad timing | 39 | Nine, all directors |
The second one is the harder read. A bad timing status describes a conversation with somebody, and on that account the somebody had never been anyone on the list.
Why the reporting cannot show this
Activity is logged against the account, and every report counts it that way, so an account with 39 touches looks like one of the best covered accounts in the book.
No report asks which named people on that account were tried. Our reps worked the account, which is what our system asked of them and what our reporting rewarded. Whether the buying committee was ever addressed is a different question, and nobody was asking it.
The data was there, bought and complete, and both channels were in use. What nobody checked was who on the account should be addressed.
Why this is not an argument against calling
The reading I nearly went with was that the calling had been wasted, and that reading is wrong.
What was wrong on those 19 accounts is who got called. I wrote the recommendation onto selection quality on purpose and kept it away from volume. Calling is a large part of what our team actually does, and a finding like this gets read as an argument against activity unless somebody says plainly that it is about targeting.
What to do instead
- Turn the filter around once. Ask which of your most worked accounts carry named, fully enriched contacts with no contact attempt on them. The weak cases live where the activity is low, and the expensive ones live where it is high.
- Clean the list before it goes to anybody. Duplicates and markers typed into the wrong field are both invisible to the filter and obvious to a rep, and one bad row costs you the credibility of the whole list.
- Count touches per named person, not only per account. An account total tells you effort was spent. It cannot tell you whether the person who signs was ever asked.
- Read a bad timing status against the people it covers. If nobody in the buying committee was ever contacted, the status describes one conversation and not the account.
The mirror image of this is worth checking in the same afternoon: the accounts that score as a good fit and have nobody on the record at all. And where a whole cohort only ever gets called, the cause is usually one of five, which is worth ranking so each account counts once.