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For Denticon

An acquired practice brings three thousand patients. None of them are marketing's.

Export collections from Denticon, upload the leads behind them, and rank sources on the patients the group actually won — office by office.

No API key Nothing to install in DenticonNothing to install No card requiredNo card

CloseRev reads a Denticon export of collected payments — the patient's phone or email, the amount, the date and the office — and matches it against the campaigns, calls and bookings that produced the first appointment. Dental groups on a shared database grow two ways, by winning new patients and by acquiring practices that bring patients with them, and an honest report has to tell those apart before it credits any marketing. Collections with no traceable lead are reported as Direct / Unknown.

Last checked against Denticon's own documentation on September 25, 2026.

The gap

The group's new-patient number jumps every time it buys a practice, and the marketing report takes the credit.

Denticon sees

What was sold, to whom, and for how much.

no shared row
Your ad account sees

The click, the keyword, the call, and what each one cost.

The file

What the export needs in it.

Three things carry the match: who, how much, and when. Anything else is optional and only changes how the report can be sliced.

  1. Needed

    Patient phone or email

    On the patient record in the shared database, which is what lets one patient be recognised across offices.

  2. Needed

    Amount collected

    Payments received from patients and insurers together.

  3. Needed

    First visit date and office

    When and where the patient was first seen. Both are needed to tell a new patient from an inherited one.

  4. Optional

    Office joined date

    When each office joined the group. Patients first seen before that date came with the practice.

Step by step

Getting the file out of Denticon.

Written for somebody with Denticon open in the next tab. Report names vary by edition, so each step says what to look for.

  1. Export collections with patient, office and date

    One row per payment with a contact detail, the amount, the date and the office.

  2. List when each office joined the group

    It is how an acquired practice's existing patients are kept out of the new-patient count.

  3. Export central and local lead sources

    Brand campaigns run for the whole group, local campaigns per office, and the central call centre's booking records.

  4. Take a year or more

    Treatment plans complete over months, and recall is where the second year's revenue sits.

  5. Upload both

    The join runs on the phone and the email, normalised, with results per office and for the group.

What comes back

The page Denticon cannot show you.

Revenue by channel, the count of sales behind each figure, and an honest bucket for the ones nobody could trace. Sample figures, from the worked example on the Dentists page — not from a Denticon account.

Traced to a channel$207,00068% of $304,400
Sales matched83 of 129high confidence only
Average sale$2,340per paid sale
ChannelShareSalesRevenue
Google Ads38$124,800
Meta Ads31$54,800
Email marketing14$27,400
Direct / Unknown46$97,400

Unmatched sales stay in Direct / Unknown. They are never spread across the paid channels to make the total look better.

The argument

What changes when the patient and the ad share a row.

An acquired practice's patients are not new patients

When a group buys a practice, the practice's existing patients move into the group's database. In a naive report they appear as new patients in the month the practice joined.

Every campaign running that month then looks extraordinary, and the group's marketing is credited with growth it bought rather than won.

The fix is to record when each office joined and treat patients first seen before that date as inherited, whatever their record's created date says.

Reported apart, the group sees two honest numbers: how many patients marketing won, and how many came with acquisitions.

For a group growing by acquisition, the first number is the one that shows whether the marketing works at all.

Brand campaigns and local campaigns are paid for differently

A group typically runs some marketing centrally for all its offices and lets offices, or regions, run their own.

Credited together, a brand campaign's patients get attributed to whichever office they happened to book at, and a local campaign's results get blended into the group's.

Keeping the campaign's level on the lead file lets the report show central spend against the patients it produced across every office, and local spend against its own office.

That is the split a group's budget is actually set on, so it is the one the report produces.

It also settles a recurring argument inside groups: whether the central marketing team is producing patients for the offices or taking credit for what the offices would have won locally. With both levels on the lead file, the answer is a number rather than a debate.

The central call centre is the lead file

Many groups book appointments through a central team rather than each office's front desk, and that team's records hold the caller, the date and often how they heard about the group.

Those records are the most complete lead file a group has, and they are frequently never joined to the collections that followed.

Exporting them alongside collections gives revenue per booking by source, across every office the call centre serves.

It also shows which offices convert the bookings the centre sends, which is an operations finding rather than a marketing one.

One patient, one database, however many offices

A patient who moves house or switches to an office nearer work is still one patient, and a shared database recognises them as one.

A report that counts patients per office without that recognition would count the move as a new patient at the second office.

Matching on the patient, not the office, keeps them attached to the source that first produced them, with the offices they visited recorded beside it.

It is one of the practical advantages of a group on a single database, and it makes the group's attribution cleaner than a collection of separate practices could produce.

Fair questions

“Denticon already does that.” Not quite.

They say

“Denticon already reports new patients by office.”

We say

It reports patients first seen at each office. It cannot tell an acquired patient from a won one, or say which campaign produced either.

They say

“Our growth is mostly acquisitions.”

We say

Then separating inherited patients is what makes the marketing figure honest, and it may be much smaller than it looks.

They say

“Our call centre already tracks sources.”

We say

It tracks bookings. Joining them to collections is what says which sources produced revenue.

Questions

Denticon, specifically.

Something else? Ask us and a person answers.

No. It reads a file you exported, so your charts, schedules and database stay where they are.

Denticon and the other product names and logos on this page belong to their owners and are shown to identify the software a file comes from. CloseRev is not affiliated with or endorsed by them, and connects to none of them: it reads a file you export.

Start today

Two exports, and you will know.

Nothing to install in Denticon, no API key, and no need to have been tracking anything until now. Last year works as well as this month.