Page purposeIs our media investment working, and is marketing delivering the patient volume the business is counting on? One page, readable in sixty seconds.Corporate
DOFV — from YTD spend · corporate KPI
6,820
of 7,600 plan · 90% pacing
▲ 1.6% vs prior month
NPV — from YTD spend · clinic KPI
6,418
94.1% of DOFV qualified for treatment
▲ 1.5% vs prior month
Blended CAC (cost / DOFV)
$616
$4.2M spend ÷ 6,820 DOFV credited to it
▲ 0.9% vs prior month
Cost per NPV
$654
Spend ÷ NPV credited to it
▲ 0.9% vs prior month
Marketing Spend — YTD
$4.20M
of $4.40M budget · 95.5% pacing
$200K under budget
Each tile carries a 13-month trend line. The purple line under a tile shows how much of the figure has landed and the range it is likely to finish in.
DOFV and NPV by Month of Spend — vs. Plan
What did each month's spend produce, against plan and the month before?
Bars are the first visits and treatment starts credited to each month's spend: solid has landed, pale is still to land. Dashed lines are the plan, which is set by completion month, so that comparison is approximate until the plan is restated by month of spend. Charts end at October, the last complete month.
DOFV Growth vs. CAC Growth — Indexed
Is the cost of a first visit growing faster than the number of first visits?
When the two lines diverge, incremental volume is costing progressively more. The lines only cross in September and October, which are still landing, so the crossing is not confirmed until those months close.
NPV Growth vs. Cost per NPV Growth — Indexed
Is the cost of a treatment start growing faster than the number of treatment starts?
Same construction and axis as the DOFV chart. The NPV gap is roughly three times wider — 6 index points against 2.
Page purposeCan we still reach the annual DOFV plan, what would it take, and by when must we decide?Corporate
DOFV Gap to Plan
780
6,820 credited to spend vs 7,600 plan
▼ 10.3% below plan
Spend to Close Gap
$481K
at marginal CAC of $617, if every market could absorb it
capacity allows about $356K of that · closes 577 of 780
Closeable by Spend
324
DOFV from the $200K headroom at marginal CAC of $617
42% of the gap · not limited by capacity
Decision Lockout
Nov 15
Last date spend can land as 2026 DOFV
5 days remaining
Cumulative DOFV by Month of Spend — Landed, Expected and Plan Pace
Are we on the trajectory needed to reach the annual plan?
The solid line is what has landed. The dashed line is what we expect in total from spend to date, part of which is still landing. The chart ends at October, the last complete month; the 6,820 in the tiles also includes November to date. Plan pace is set by completion month, so the gap is approximate.
Closing the Gap — Levers and Limits
What combination of levers actually closes the gap, and does any single lever suffice?
Each bar is DOFV added by year-end against the 780 gap. The headroom is limited by budget, not capacity: about $356K could be put to work in markets where clinics have room, and the headroom is $200K. The pale bar shows that capacity limit, which needs budget beyond the headroom. No lever closes the gap alone; the headroom and recovery together reach about 70% of it.
Budget, Spend and Return
What we committed, whether we are on budget, and what it returned.
Cost per Appointment
$513
$4.2M spend ÷ 8,190 appointments booked
▲ 1.8% vs prior month
Attributed Revenue
$84.1M
of $95M annual goal · 88% pacing
▲ 2.5% vs prior month
Blended ROI
19.0x
$84.1M revenue ÷ $4.20M spend ·
▲ 0.3x vs prior month
DOFV per $10K Spend
16.2
Media productivity ·
▼ 1.9% vs prior month
Spend vs. Budget — Monthly
Are we spending to budget each month?
Bars are actual spend; the dashed line is the monthly budget. Spend is final for every month shown.
Cumulative Spend vs. Budget
Cumulatively, are we on budget, and how much headroom is left?
Cumulative spend against cumulative budget. The gap at October is the $200K headroom. Volume against plan is on the cumulative DOFV chart above and on the summary page.
Position by Clinic — vs. Prior Month
Clinic goals do not exist yet, so each clinic is read against its own prior month and the network rate, not an even split of the network goal.
DOFV Change vs. Prior Month by Clinic
Which clinics are growing faster or slower than the network, and do they have room to grow?
Dashed line is the network change of 1.6%. Bar colour is capacity status: green open, amber tight, red constrained. A slow-growing clinic with an open schedule (Arizona, Virginia Beach) has a demand or conversion problem. A fast-growing clinic that is constrained (Colorado) is near its ceiling.
Budget and Growth Position by Clinic
For each clinic, are we under or over budget, and is volume growing?
Capacity:OpenTightConstrained
Bars are DOFV growth vs prior month; the line is spend variance to budget. Under budget and growing slowly is the case for deploying headroom, provided the clinic has room: a green bar does, a red bar does not. Over budget and not growing points at efficiency or operations.
Page purposeGiven a fixed budget, where does the next dollar buy the most patients, and can that market actually see them?Corporate
Marginal Return vs. Capacity Headroom
Where does strong return coincide with room to see patients, and where does it not?
Upper left is where to invest: high return, short wait. Upper right is the trap: Colorado returns the most per dollar but sits past the constrained line. Bubble size is current spend.
Marginal DOFV per Incremental $10K
If we added ten thousand dollars to each market, how many extra patients would each return?
Capacity:OpenTightConstrained
Marginal return, not average — the relevant number for allocation decisions. Estimated from observed spend/volume response by market. Bar colour is capacity status: a high bar only helps if the clinic can see the patients, so a long red bar is return that cannot be collected yet.
Capacity Check — Can the Market See More Patients?
The chart above shows return against capacity. This table turns that into a call for each market, and the trend shows which markets are running out of room.
Allocation Readiness — Return × Capacity
Which markets combine strong marginal return with room to see more patients?
The call is a rule, so it can be built as a Power BI measure. Invest: marginal return of 12 or more DOFV per $10K and capacity Open. Shift mix: the same return but capacity Constrained. Reduce: marginal return under 6. Everything else holds. Booking lead time is computable today from appointment and first-visit dates. Slot use is illustrative until slot-availability data exists (open question C2). Thresholds are placeholders to agree with clinic operations.
Booking Lead Time by Market — 13-Month Trend
Which markets are running out of room, and how fast?
Colorado crossed the constrained line in July and has kept climbing. Boston has hovered at it since. A rising line is the earliest signal available that a market is approaching its ceiling.
Market Composition
Before moving budget, understand what each market's demand is made of. A market that is mostly unattributed has a smaller addressable base for paid spend than its size suggests.
Channel Mix Breakdown by Clinic · 2026 YTD, with month-over-month shift
Which markets have the digital headroom to absorb more paid spend, and which channels are gaining or losing share month over month?
Digital
Physician
Patient
Other
| Archetype | Market | Channel mix breakdown, 2026 YTD | Channel mix, MoM | ||
|---|---|---|---|---|---|
| Digital | Physician | Patient | |||
| High digital contribution |
San Francisco | ▲ 8% | ▲ 4% | ▲ 12% | |
| Orange County | ▲ 18% | ▼ 5% | ▲ 33% | ||
| Austin | ▲ 9% | ▼ 3% | ▲ 14% | ||
| Dallas | ▼ 10% | ▲ 17% | ▲ 22% | ||
| Avg digital contribution |
Chicago | ▲ 4% | ▼ 8% | ▲ 19% | |
| Northern VA | ▼ 16% | ▲ 11% | ▲ 9% | ||
| Miami | ▼ 9% | ▲ 42% | ▲ 64% | ||
| Houston | ▼ 12% | ▲ 15% | ▲ 26% | ||
| Boston | ▼ 6% | ▲ 22% | ▲ 31% | ||
| Greenville | ▲ 21% | ▲ 9% | ▲ 38% | ||
| New York | ▲ 7% | ▲ 48% | ▼ 22% | ||
| Below avg digital contribution |
Colorado | ▲ 11% | ▲ 6% | ▼ 14% | |
| Delaware | ▲ 14% | ▼ 19% | ▼ 8% | ||
| Virginia Beach | ▲ 5% | ▼ 21% | ▲ 47% | ||
| Arizona | ▼ 13% | ▲ 9% | ▼ 31% | ||
Note the counter-intuitive finding: Colorado sits in the lowest digital-contribution group at 22% — yet it is our best market on CAC and ROI. Its volume is referral- and organic-led, which is precisely why it acquires patients cheaply. Read low digital share as a sign of healthy earned demand, not weakness.
Attribution & ROI by Clinic · 2026 YTD · Digital vs. Physician split
How does each market's revenue attribution, spend, ROI, and CAC break down between digital and physician referral?
| Market | Total rev | Attribution (% of DOFV) | Spend | Revenue | ROI | CAC | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Digital | Physician | Patient | Insur. | Other | Digital | Physician | Digital | Physician | Digital | Physician | Digital | Physician | ||
| Colorado | $18.70M | 22% | 16% | 8% | 1% | 53% | $402K | $246K | $4.11M | $2.99M | 10.2x | 12.2x | $1,364 | $1,147 |
| Houston | $12.60M | 28% | 26% | 8% | 4% | 34% | $388K | $200K | $3.53M | $3.28M | 9.1x | 16.4x | $1,322 | $734 |
| Northern Virginia | $9.51M | 32% | 16% | 8% | 3% | 41% | $319K | $137K | $3.04M | $1.52M | 9.5x | 11.1x | $1,308 | $1,124 |
| Chicago | $6.87M | 32% | 22% | 11% | 4% | 31% | $264K | $108K | $2.20M | $1.51M | 8.3x | 14.0x | $1,422 | $846 |
| Orange County | $6.42M | 40% | 14% | 11% | 12% | 23% | $186K | $66K | $2.57M | $0.90M | 13.8x | 13.6x | $957 | $970 |
| Boston | $5.73M | 26% | 14% | 9% | 6% | 45% | $223K | $95K | $1.49M | $0.80M | 6.7x | 8.4x | $1,813 | $1,435 |
| New York | $5.28M | 26% | 4% | 15% | 4% | 51% | $223K | $63K | $1.37M | $0.21M | 6.2x | 3.4x | $2,052 | $3,768 |
| Arizona | $4.56M | 18% | 2% | 3% | 3% | 74% | $322K | $80K | $0.82M | $0.09M | 2.5x | 1.1x | $4,020 | $8,989 |
| Miami | $4.25M | 30% | 6% | 5% | 4% | 55% | $188K | $60K | $1.27M | $0.26M | 6.8x | 4.2x | $1,685 | $2,688 |
| Virginia Beach | $3.68M | 19% | 6% | 1% | 0% | 74% | $226K | $60K | $0.70M | $0.22M | 3.1x | 3.7x | $3,519 | $2,959 |
| All other markets | $6.50M | 27% | 14% | 9% | 5% | 45% | $248K | $96K | $1.75M | $0.91M | 7.1x | 9.5x | $1,640 | $1,224 |
| Network total | $84.1M | 27% | 14% | 9% | 4% | 46% | $2,989K | $1,211K | $22.9M | $12.7M | 7.6x | 10.5x | $1,623 | $1,268 |
Rebuilt on the current reconciled period so it ties to the $4.20M spend and $84.1M revenue shown throughout this dashboard. The "Other" column averaging 46% is unattributed revenue — reducing it would tighten every efficiency metric downstream.
Market Drill-Down — Did Spend Actually Produce Volume?
Audit the reallocation recommendation one market at a time. If spend rose and volume did not follow, that market is saturating.
Colorado
How does digital spend track against digital DOFV volume in this market?
Colorado
How is total DOFV volume trending here, and what share of it is digital?
Colorado — Spend to ROI Funnel
What is the full conversion path from spend to return in this market?
Which Channel, Not Just Which Market
Market-level guidance alone does not answer where the money goes. This section adds the channel dimension so the answer is a market and a channel.
Channel Efficiency by Market — Digital vs. Physician Referral
Within a market we want to invest in, which channel returns more per dollar?
CAC by channel within each market. Where physician referral undercuts digital, incremental dollars are better spent on referral development than on media in that market.
Channel ROI by Market
Which market-and-channel combinations deliver the strongest return?
Read together with the chart to its left: a market with high physician ROI and rising digital CAC is a referral investment case, not a media one.
Channel Allocation — Efficiency vs. Share of Spend
Is our spend split across channels consistent with where each channel performs?
Restricted to the two channels that carry spend, since share-of-spend is undefined for the rest. Physician referral takes 28% of budget and returns 34% of DOFV; digital takes 72% and returns 66%. That gap is the case for shifting budget toward referral development — bounded by how much referral capacity exists.
Page purposeBetween first contact and treatment start, where do we lose the most people, and what is each leak worth?Corporate Clinic
The Full Funnel — Volume, Conversion, and Value of Each Leak
Where is the biggest drop-off in the patient journey, and what would fixing each stage be worth in DOFV?
Stage-to-stage conversion shown on each bar. Widths are proportional to volume. Corporate's metric (DOFV) and the clinics' metric (NPV) are the final two stages — note they are separated by an eligibility gate, not a marketing step.
Stage Conversion Rates — 13-Month Trend
Is each leak getting better or worse?
The funnel above is a snapshot; these are the same three stage rates over time. Attendance is the only one moving the wrong way, down about 1.3 points over the year.
Appointment → DOFV (Show Rate) by Clinic
Of appointments booked, what share actually attend — and which clinics lose the most before the patient ever arrives?
The step immediately before qualification, and the one worth the most: every recovered appointment is a DOFV at zero incremental media cost. Dashed line is the 83.3% network average.
Unattended Appointments — Share of Bookings by Month
Of appointments booked, what share attend, no-show or cancel, and is that shifting?
Reschedules present as no-shows in the source data and cannot be separated (confirmed 31 July), so the no-show share is an upper bound on truly lost patients. breaks this down by booking path, wait and clinic.
Show Rate by Clinic — 13-Month Trend
Which clinics are losing attendance over time, not just today?
One panel per clinic, all on the same 74–90% scale so they can be compared directly. Dashed grey line is the 83.3% network average. Green ends above it, red below. The last three months of each line are dashed: results from that spend are still landing.
By Booking Path — Where People Come In
Lodus (online self-serve), PSC (call center), and web form are three different doors into the same funnel, and they leak at different rates. This is the diagnostic layer under the Lead → Appointment step above.
Source performance vs. target · last month (Oct 2026)
· Inline ▲▼ = month-over-month · "vs Target" = gap to monthly target
Targets illustrative — no agreed source targets
Which booking path converts leads to attended visits best, and where is the handoff between marketing and operations breaking down?
| Source | Volume | Efficiency | Conversion | Value | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Leads | Appointments | DOFV | NPV | Spend | CAC | Cost/NPV | Sched→DOFV | DOFV→NPV | Rev/DOFV | ROI | |||||
| Current | vs Target | Current | vs Target | Current | vs Target | Current | vs Target | ||||||||
| Lodus (online booking) | 784 ▲ 4% | ▲ +3% | 517 ▲ 3% | ▼ -10% | 411 | ▼ -16% | 384 | ▼ -16% | $259K | $633 ▼ 2% | $676 | 79.4% ▼ 1.2pp | 93.6% ▲ 0.4pp | $11,700 | 18.5x ▲ 0.5x |
| Call Center | 434 ▲ 3% | ▼ -2% | 267 ▲ 5% | ▼ -1% | 243 | ▼ -1% | 231 | ▼ -1% | $134K | $550 ▼ 3% | $576 | 91.1% ▲ 0.9pp | 95.5% ▲ 0.8pp | $13,800 | 25.1x ▲ 0.9x |
| Office (catch-all) | 182 ▲ 6% | ▲ +3% | 70 ▲ 7% | ▲ +1% | 58 | ▲ +0% | 53 | ▲ +0% | $75K | $1,300 ▲ 3% | $1,410 | 82.6% ▼ 0.6pp | 92.2% ▼ 0.2pp | $11,000 | 8.5x ▲ 0.3x |
| Total | 1,399 ▲ 4% | ▲ +2% | 853 ▲ 4% | ▼ -6% | 711 | ▼ -10% | 669 | ▼ -10% | $468K | $659 ▲ 2% | $700 | 83.3% ▼ 0.5pp | 94.1% ▲ 0.5pp | $12,360 | 18.8x ▲ 0.6x |
The finding that matters: Lodus generates the most leads but only 79.4% of its booked appointments are attended, against 91.1% for the call center — an 11.7 point gap. Self-serve booking wins on volume and loses on commitment, which is consistent with a phone conversation creating more follow-through. Applied to Lodus’s 517 monthly appointments that gap is worth roughly 60 DOFV per month at no incremental media cost — the single cheapest source of additional first visits in the funnel. Note also that Lodus is above target on leads but well below on DOFV, which locates the problem at booking and attendance rather than at demand generation.
DOFV Source Mix Breakdown by Clinic · 2026 YTD, with month-over-month shift
What is the source-level breakdown of DOFV volume across clinics, and how is it shifting month over month?
Lodus
Call Center
Office (catch-all)
| Archetype | Market | YTD DOFV — source mix breakdown | Source mix, MoM | ||
|---|---|---|---|---|---|
| Lodus | Call Center | Office | |||
| High Lodus contribution |
San Francisco | ▲ 8% | ▲ 3% | ▲ 15% | |
| New York | ▲ 5% | ▲ 38% | ▼ 18% | ||
| Miami | ▼ 7% | ▲ 35% | ▲ 50% | ||
| Orange County | ▲ 14% | ▼ 4% | ▲ 22% | ||
| Average Lodus contribution |
Colorado | ▲ 9% | ▲ 5% | ▼ 11% | |
| Northern VA | ▼ 12% | ▲ 9% | ▲ 7% | ||
| Austin | ▲ 6% | ▼ 2% | ▲ 11% | ||
| Houston | ▼ 9% | ▲ 12% | ▲ 21% | ||
| Chicago | ▲ 3% | ▼ 6% | ▲ 16% | ||
| Dallas | ▼ 8% | ▲ 13% | ▲ 18% | ||
| Boston | ▼ 5% | ▲ 18% | ▲ 26% | ||
| Greenville | ▲ 17% | ▲ 7% | ▲ 30% | ||
| Below average Lodus contribution |
Delaware | ▲ 11% | ▼ 15% | ▼ 6% | |
| Virginia Beach | ▲ 4% | ▼ 17% | ▲ 38% | ||
| Arizona | ▼ 10% | ▲ 7% | ▼ 25% | ||
Markets skewing heavily to the call center (Arizona 49%, Virginia Beach 47%) are the ones where self-serve booking is least adopted — worth checking whether that is patient demographics or a local Lodus experience problem, since the call center path is also the more expensive one to staff.
DOFV → NPV Qualification Rate by Clinic
Of patients who attend a first visit, what share qualify for treatment — and does this vary more than clinical variation alone would explain?
Network average 94.1% (dashed line). Wide dispersion here is worth investigating — but the cause may be case mix or patient demographics rather than performance. Present to clinical leadership as a question, not a scorecard.
Qualification Rate by Acquisition Channel
Do some channels deliver patients who are systematically less likely to qualify for treatment?
This is the test that separates marketing quality from clinical eligibility. If a channel's qualification rate sits well below network average, its true cost per treated patient is higher than its CAC suggests — see Tab 5.
Page purposeWhich booked appointments are we losing before the first visit, where are we losing them, and which upcoming ones are most at risk?Internal — owner: Kelly
Unattended Appointments — YTD
1,370
16.7% of 8,190 booked
▲ 0.1 pp vs prior month
No-Shows (upper bound)
932
11.4% of booked · includes reschedules
▲ 0.1 pp vs prior month
Cancellations
438
5.3% of booked
▲ 0.1 pp vs prior month
Value of Recovering 20%
274DOFV
about $3.4M attributed revenue · no media cost
35% of the 780 gap to plan
Lost at Constrained Clinics
325
Colorado 214 · Boston 111
slots that cannot be re-sold quickly
Attendance Rate by Booking Path — 13-Month Trend
Is the gap between self-serve and assisted booking widening or closing?
Lodus has drifted down about three points over the year while the call center has held. The gap is now 11.7 points.
No-Show Rate by Wait Between Booking and Visit
Shape illustrative — confirm with data
Does a longer wait make a patient less likely to turn up?
This is where attendance and capacity meet. If the relationship holds in CCRM's data, shortening the wait at a constrained clinic recovers visits as well as improving access. Computable today: both dates exist.
Unattended Appointments — Share of Bookings by Month
Is the unattended share growing, and is it no-shows or cancellations?
Reschedules present as no-shows in the source data and cannot be separated, so the no-show share is an upper bound on truly lost patients.
Unattended Appointments by Clinic
Which clinics lose the most booked appointments, in volume and in rate?
Bars are unattended appointments year to date, split by outcome. The line is attendance rate. Volume and rate rank differently: Colorado loses the most appointments while holding the best rate.
Work List — Next 14 Days
Where to point confirmation calls and reminders this fortnight. Ranked by expected no-shows, weighted up where the clinic has no spare capacity.
At-Risk Appointments by Clinic
Confirmation status pending data
Which clinics have the most upcoming appointments at risk, and why?
Expected no-shows apply each clinic's own attendance rate to appointments already on the schedule. "Booked 28+ days ago" and Lodus share are the two risk drivers shown above. A column for unconfirmed appointments belongs here once we know whether reminder and confirmation status is captured.
Page purposeWhich channels are earning their budget, and how exposed are we to platforms we do not control?Corporate
Paid Share of DOFV
70%
digital + physician referral · 4,776 of 6,820
▲ 0.5 pp vs prior month
Paid-Channel CAC
$879
$4.20M ÷ 4,776 paid DOFV · blended is $616
▲ 2.3% vs prior month
Brand Share of Paid Search
52%
of paid-search DOFV · demand already earned
▲ 0.3 pp vs prior month
Unattributed Revenue
46%
no channel recorded ·
▼ 0.8 pp vs prior month
Channel Scorecard — Volume, Efficiency, and Quality
· Top-level channels · paid search / social detail sits in Digital Media
How does each channel perform on volume, cost, patient quality, and return?
| Channel | Volume | Efficiency | Quality | Return | DOFV 13 mo |
||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Leads | DOFV | NPV | Spend | CAC (/DOFV) | Cost /NPV |
Qual. rate | Rev / DOFV | Revenue | ROI | ||
| Digital (Google + Meta) | 8,230 | 3,178 | 2,867 | $3.02M | $950 | $1,053 | 90.2% | $11,207 | $35.6M | 11.8x | |
| ↳ Paid search — brand | 2,640 | 1,140 | 1,072 | $0.53M | $465 | $494 | 94.0% | $11,930 | $13.6M | 25.7x | |
| ↳ Paid search — non-brand | 3,010 | 1,060 | 946 | $1.42M | $1,340 | $1,501 | 89.2% | $11,698 | $12.4M | 8.7x | |
| ↳ Paid social | 2,580 | 978 | 849 | $1.07M | $1,094 | $1,260 | 86.8% | $9,816 | $9.6M | 9.0x | |
| Physician referral | 1,880 | 1,598 | 1,558 | $1.18M | $738 | $757 | 97.5% | $18,650 | $29.8M | 25.3x | |
| CRM (nurture, lead-gen, abandoned) | 1,140 | 512 | 489 | $0 | — | — | 95.5% | $12,240 | $6.3M | — | |
| Patient referral | 720 | 612 | 592 | $0 | — | — | 96.7% | $14,820 | $9.1M | — | |
| Insurance referral | 380 | 298 | 282 | $0 | — | — | 94.6% | $10,940 | $3.3M | — | |
| Organic / direct | 190 | 622 | 630 | $0 | — | — | 99.1% | $3,590 | $2.2M | — | |
| Network | 12,540 | 6,820 | 6,418 | $4.20M | $616 | $654 | 94.1% | $12,330 | $84.1M | 19.0x | |
Brand search is split out because it mostly captures demand already earned; judge incremental media on the non-brand and paid social rows. Select a row to drill through to that channel's detail page. Only digital and physician referral carry spend. The client invests media dollars in Google and Meta, and liaison and collateral cost against physician referral. CRM, patient referral, insurance referral and organic have no acquisition cost, so CAC, cost per NPV and ROI are shown as not applicable rather than as a fabricated figure — those channels are effectively infinite-return and comparing them on cost would be meaningless. Bold column is the one to watch: cost per NPV reflects what the business actually monetizes.
Cost per DOFV vs. Cost per NPV
Does any channel look meaningfully worse once we account for how many patients actually qualify for treatment?
Only the two channels that carry acquisition cost are shown — CRM, referral and organic have no spend, so a cost comparison would be meaningless. The gap between the bars is the qualification penalty: digital loses proportionally more patients between first visit and treatment, so its cost per treated patient is $103 higher than its CAC suggests.
CAC Trend by Channel — Actual Dollars, with Network DOFV
Which channels are becoming more expensive over time, and what happened to DOFV volume as they did?
Only channels with acquisition cost appear. Actual CAC in dollars (lines, left axis) against network DOFV (bars, right axis). Reading them together answers both "is CAC rising" and "did the volume we bought justify it" — a rising CAC line over flat bars is the saturation signal.
Paid vs. Unpaid Dependency — Share of DOFV by Month
How much of our patient volume depends on advertising platforms we do not control?
Concentration risk view, shown over 13 months so the shift is visible. The two paid platforms have gained about five points of share over the year.
Channel Growth — Paid vs. Unpaid
Is our unpaid demand growing as fast as our paid demand?
Referral channels carry a CAC, so they count as paid here; only organic and CRM are treated as unpaid. If paid grows faster, platform dependency increases even when referral volume is up.
Page purposeAre we attracting the patients the business wants, and would optimizing for value rather than volume change where we spend?Corporate
Revenue per DOFV
$12,330
Network blended
▲ 0.8% vs prior month
Revenue per NPV
$13,104
Per patient starting treatment
▲ 0.9% vs prior month
Commercial + Benefits Share
65%
vs 35% self-pay
▲ 0.5 pp vs prior month
IVF Share of Services
54%
Highest-value service line
▼ 0.3 pp vs prior month
Revenue per DOFV by Channel
Which channels deliver the highest-value patients, and does that reorder our channel ranking?
Compare with the CAC ranking on Tab 5. A channel can be expensive per patient and still be the best investment if the patients are worth substantially more.
Value-Adjusted Return by Channel
After accounting for both acquisition cost and patient value, which channel actually contributes most per dollar?
Revenue per NPV ÷ cost per NPV, shown only for the two channels we can actually invest in. Physician referral returns more than twice what digital does per dollar, which is the core allocation argument — subject to how much referral volume can realistically be grown.
Payor Mix by Channel
Do certain channels bring in better-reimbursing patients?
By Treatment Type
An egg-freezing DOFV and an IVF DOFV count identically but are worth very different amounts. This section separates volume from value, and explains ROI gaps between clinics with otherwise similar numbers.
Volume and Revenue by Treatment Type
Which treatments drive our volume, and which drive our revenue?
Bars are DOFV count, line is revenue per DOFV. The divergence is the point: egg freezing is a meaningful share of volume at a fraction of the revenue per patient.
CAC and Show Rate by Treatment Type
What does it cost to acquire each treatment type, and are some more likely to attend than others?
Answers two of the pressure-test questions directly: what an egg-freezing patient costs to acquire, and whether egg-freezing patients show up more reliably than IVF patients.
Treatment Mix by Clinic
Why do two clinics with similar CAC and DOFV counts produce very different ROI?
Usually treatment mix. A clinic weighted to egg freezing will show weaker ROI at identical volume and cost — which points to an action (build the IVF referral base, or add IVF capability) rather than a marketing fix.
Treatment Mix by Channel
Which channels bring in IVF patients rather than lower-value treatments?
Physician and patient referral over-index heavily on IVF, which is the practical answer to a clinic asking how to shift its own mix.
Page purposeWhich markets are strong, which are struggling, and is the difference market conditions, execution, or room to see patients?Corporate
Market Performance Quadrant
Which markets combine strong volume with efficient acquisition, and which do neither?
Horizontal = CAC (lower is better, so left is better). Vertical = DOFV volume. Bubble size = revenue. Upper-left is the strong quadrant. Colour is capacity status: a strong market that is constrained has little room to grow.
DOFV by Market
Which markets contribute the most patient volume to the network?
DOFV, NPV, CAC and Cost per NPV by Market
How many first visits and treatment starts is each clinic delivering, and what does each cost?
Bars are DOFV and NPV volume (left axis); lines are CAC and cost per NPV (right axis), following the convention of volume as bars and cost as lines. Where the two bars diverge most — Boston and Virginia Beach — the cost-per-NPV line pulls sharply away from CAC.
Market Scorecard
How does each market perform across volume, efficiency, conversion, and value?
| Market | Volume | Efficiency | Conversion | Value | Capacity | Trend · 13 mo | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Leads | Appointments | DOFV | NPV | Spend | CAC | Cost/NPV | Sched→DOFV | DOFV→NPV | Rev/DOFV | ROI | Lead days | Status | DOFV | CAC | |||||
| Current | vs prior mo. | Current | vs prior mo. | Current | vs prior mo. | Current | vs prior mo. | ||||||||||||
| Colorado | 2,410 | ▲ +3.5% | 1,555 | ▲ +3.3% | 1,340 | ▲ +3.1% | 1,288 | ▲ +3.0% | $648K | $484 | $503 | 86.2% | 96.1% | $13,955 | 28.9x | 31 | Constrained | ||
| Houston | 1,920 | ▲ +2.5% | 1,246 | ▲ +2.3% | 1,048 | ▲ +2.1% | 994 | ▲ +2.0% | $588K | $561 | $591 | 84.1% | 94.8% | $12,020 | 21.4x | 16 | Open | ||
| Northern Virginia | 1,460 | ▲ +1.8% | 914 | ▲ +1.6% | 762 | ▲ +1.4% | 718 | ▲ +1.3% | $456K | $598 | $635 | 83.4% | 94.2% | $12,480 | 20.9x | 24 | Tight | ||
| Orange County | 980 | ▲ +2.9% | 572 | ▲ +2.7% | 486 | ▲ +2.5% | 462 | ▲ +2.4% | $252K | $519 | $545 | 85.0% | 95.1% | $13,210 | 25.5x | 12 | Open | ||
| Chicago | 1,120 | ▲ +1.4% | 706 | ▲ +1.2% | 580 | ▲ +1.0% | 542 | ▲ +0.9% | $372K | $641 | $686 | 82.1% | 93.4% | $11,840 | 18.5x | 15 | Open | ||
| Boston | 920 | ▲ +1.0% | 584 | ▲ +0.8% | 473 | ▲ +0.6% | 385 | ▲ +0.5% | $318K | $672 | $826 | 81.0% | 81.4% | $12,110 | 18.0x | 29 | Constrained | ||
| New York | 840 | ▲ +1.2% | 520 | ▲ +1.0% | 418 | ▲ +0.8% | 392 | ▲ +0.7% | $286K | $684 | $730 | 80.4% | 93.8% | $12,640 | 18.5x | 19 | Open | ||
| Miami | 760 | ▲ +1.5% | 455 | ▲ +1.3% | 372 | ▲ +1.1% | 348 | ▲ +1.0% | $248K | $667 | $713 | 81.8% | 93.5% | $11,420 | 17.1x | 14 | Open | ||
| Virginia Beach | 620 | ▲ +0.0% | 428 | ▼ -0.2% | 338 | ▼ -0.4% | 274 | ▼ -0.5% | $286K | $847 | $1,043 | 78.9% | 81.1% | $10,880 | 12.8x | 9 | Open | ||
| Arizona | 740 | ▲ +0.6% | 575 | ▲ +0.4% | 445 | ▲ +0.2% | 396 | ▲ +0.1% | $402K | $903 | $1,015 | 77.4% | 89.0% | $10,240 | 11.3x | 8 | Open | ||
| Network (all markets) | 12,540 | ▲ +1.9% | 8,190 | ▲ +1.7% | 6,820 | ▲ +1.6% | 6,418 | ▲ +1.5% | $4.20M | $616 | $654 | 83.3% | 94.1% | $12,330 | 19.0x | 20 | — | ||
Read against prior month, not target. Clinic goals do not exist yet, so the even-split placeholder targets have been replaced with the change on the prior month. Capacity is booking lead time in days, with the status it implies. The two trend columns are 13 months of DOFV and CAC. Boston and Virginia Beach both convert DOFV → NPV at around 81% against a 94% network average, which is what drives their cost per NPV well above their CAC. Select a row, then drill through to Clinic Detail.
Capacity & Access
Whether each market has room to see more patients. A market can be short of volume because of demand, conversion, or access, and each has a different fix.
Booking Lead Time by Market
Which markets are booking furthest out, and is that getting worse?
Bars are today; the dash is 12 months ago. Colorado has gone from 19 to 31 days and Boston from 22 to 29. Arizona and Virginia Beach are booking inside ten days, so their shortfall is not an access problem.
New-Patient Slot Utilisation by Market
Illustrative — pending slot data
How full is each clinic's new-patient schedule?
Placeholder. We do not yet have slot-availability data (open question C2). Booking lead time, on the left, is the proxy in use until then. This chart shows where the direct measure would sit.
Market Mix — One-Stop View
Channel, payor and treatment mix by market, duplicated here deliberately so a market can be evaluated end to end without leaving the page.
Channel Mix by Market
Where is each market’s demand coming from?
Payor Mix by Market
Which markets bring in better-reimbursing patients?
Treatment Mix by Market
Which markets skew to high-value IVF and which to lower-value treatments?
These three also appear on Patient & Revenue Mix, where they are cut by channel and treatment rather than by market. Duplicated here by design: Market Comparison is where a market gets evaluated end to end, and hunting across tabs to answer one question about one clinic was the specific complaint. Read together, they explain most ROI variance — Arizona and Virginia Beach skew to egg freezing and self-pay, which is why their revenue per DOFV trails Colorado’s despite comparable volume.
CAC by Market — DMA Cluster View
Which markets acquire patients most and least efficiently?
Scheduled → DOFV Attendance Rate by Market
Which markets are losing the most already-booked appointments?
Dashed line = network average 83.3%. Markets below it are leaving paid-for demand on the table and are the fastest place to recover DOFV without new spend.
Page purposeIs our referring-physician base growing or eroding, and who has stopped sending patients?Corporate Clinic
Active Referring Practices
312
Sent ≥1 referral in last 90 days
▼ 14 vs prior period
New Referrers Added
23
First referral in last 90 days
▲ 4 vs prior period
Lapsed Referrers
37
Active before, silent 90+ days
▲ 11 — recoverable pipeline
Referrals per Active Practice
6.0
1,880 referrals ÷ 312 practices
▲ 0.7 vs prior period
Referrer Base Health — Active, New, and Lapsed
Is our referring-physician base expanding or contracting over time?
The line to watch is active practices, not referral volume. Volume can rise while the base shrinks — which is exactly what is happening now.
Referral Volume vs. Active Base
Is referral growth coming from more relationships or from fewer relationships doing more?
Diverging lines indicate concentration risk: growth dependent on a narrowing set of practices.
Referring Practice Detail
Who is actually sending patients, whether they are Unify network practices or external, and what happens to those patients downstream.
Unify Network Referrals
742
39.5% of all physician referrals · no acquisition cost
▲ 12.4% vs prior period
External Practice Referrals
1,138
60.5% · carries liaison and outreach cost
▲ 4.1% vs prior period
Unify Coverage
61%
of Unify practices in CCRM markets have referred
39% have never referred — the opportunity
Unify Referral CAC
$0
internal network — no media or liaison cost
vs $738 physician channel blended
Unify vs. External Referrals — Trend
Are Unify network referrals growing as a share of our physician volume?
Unify referrals come from parent-company practices and carry no acquisition cost, so growth here improves blended CAC directly. Stated as a strategic priority by the client.
Unify Referral Coverage by Market
Are Unify doctors referring to the CCRM location in their own market?
Capacity:OpenTightConstrained
Answers Anne’s question directly. A market with many Unify practices but low referral coverage is uncaptured internal demand — the cheapest volume available to the network. The upper segment is coloured by the clinic’s capacity: uncaptured referrals are only worth chasing where the clinic can take the patients.
Referring Practice Scorecard
Pending data confirmation — see OI-C
Which practices are referring, which have lapsed, and what treatments do their patients go on to start?
| Referring practice | Market | Type | Referral volume | Downstream funnel | Treatments started | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Last 90d | Prior 90d | 12 mo | Status | Appts | DOFV | NPV | IVF | IUI | Egg frz | Other | |||
| Cherry Creek Women’s Health | Colorado | Unify | 18 | 15 | Growing | 17 | 15 | 14 | 9 | 3 | 1 | 1 | |
| Front Range OB/GYN | Colorado | External | 0 | 14 | Lapsed | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Memorial Women’s Health | Houston | Unify | 0 | 11 | Lapsed | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Bayou City Fertility Partners | Houston | External | 14 | 12 | Growing | 13 | 11 | 10 | 6 | 2 | 1 | 1 | |
| Potomac Women’s Care | N. Virginia | Unify | 1 | 9 | Slipping | 1 | 1 | 1 | 1 | 0 | 0 | 0 | |
| Arlington OB/GYN Associates | N. Virginia | External | 12 | 10 | Growing | 11 | 10 | 9 | 6 | 2 | 1 | 0 | |
| Bay State Reproductive | Boston | External | 0 | 8 | Lapsed | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Newton Women’s Health | Boston | Unify | 9 | 7 | Growing | 8 | 7 | 6 | 3 | 2 | 1 | 0 | |
| Desert Valley OB/GYN | Arizona | External | 0 | 7 | Lapsed | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Scottsdale Women’s Care | Arizona | Unify | 6 | 4 | Growing | 5 | 4 | 4 | 2 | 1 | 1 | 0 | |
| Aurora Women’s Clinic | Colorado | Unify | 6 | 0 | New | 6 | 5 | 5 | 3 | 1 | 1 | 0 | |
| Total — top 11 practices | — | 40 of 66 from Unify | 66 | 97 | 61 | 53 | 49 | 30 | 11 | 6 | 2 | ||
This answers the “Dr. James sent us three egg freezings and two IUIs” question — each practice traced from referral through to the treatments its patients started. Lapsed practices are a named, finite, recoverable list, which is the practical advantage referral has over paid media where lost demand is anonymous. Named-practice attribution is not yet confirmed in the source data — see Open Items OI-C.
Referred Patients — Downstream Quality Advantage
Do referred patients qualify and convert better than patients from other channels?
Metrics on this page are structural placeholders. We have not confirmed whether referral volume is attributable to a named referring practice, whether liaison and outreach costs are loaded into physician-channel CAC (which would change its apparent ROI), or whether referrer churn is tracked today. See Tab 10, open question C6.
Page purposeEverything about one clinic in one place, so a clinic's questions can be answered without leaving the page. Opened by drilling through from a market row.Internal Clinic-facing view
How to use this page
Two views share this page. Internal carries the full picture, including spend, CAC, ROI and cost per NPV. Clinic-facing hides the commercial section and keeps volume, conversion, capacity and mix, and is the version to take into a per-clinic meeting.
Volume and cost are corporate's to answer for; attendance and qualification are the clinic's. Capacity decides which conversation to have: a full clinic needs better mix and fewer no-shows, not more demand.
What's Coming — Your Booked Pipeline
Appointments already on the schedule. These are confirmed bookings, not forecasts.
Scheduled Appointments — Next 6 Weeks
How many patients are already booked to arrive, so I can plan staffing and capacity?
Expected attendance applies this clinic's recent 86.2% attendance rate. Later weeks will continue to fill as new bookings arrive, so near-term weeks are more complete than distant ones.
Capacity & Access
Whether this clinic has room to see more patients. This decides whether the answer to "I need more patients" is more demand, better mix, or better attendance.
What Arrived — Your Volume
Leads, appointments, first visits, and patients who started treatment.
Commercial Picture Internal only
Spend, efficiency and return for this clinic. Use this to answer "what will it cost to get me more patients" and "is digital working here".
“I Need More Patients” — What It Would Cost
If this clinic asks for N more patients, how much media spend does that require here?
Based on this clinic's own cost per NPV of $503 and cost per DOFV of $484 — not the network average.
Spend, CAC and ROI by Channel — This Clinic
Is digital working here, or is physician referral the better place for this clinic's next dollar?
Answers the "digital isn't working" call directly, with this clinic's own numbers rather than the network's.
Spend vs. DOFV Delivered — Monthly
Has the spend we put into this market actually produced visits?
Bars are DOFV, line is spend — the same convention used throughout. Spend rising while visits stall is the saturation signal for this market.
Your Monthly Volume Trend
How is my patient volume trending month to month?
First visits and treatment starts credited to each month's spend: solid has landed, pale is still to land. Charts end at October, the last complete month.
Your Funnel — Lead to Treatment
Where in my patient journey are people dropping off?
Your Conversion Path — Lead To Treatment
The same funnel view corporate uses for markets, rebuilt around volume and conversion rather than spend and return.
Appointments Booked vs. Attended
Are the appointments I book actually turning into visits, and is that improving?
Bars are volume; the line is your attendance rate. A rising bar with a falling line means you are booking more but keeping less.
First Visits vs. Treatment Starts
Of the patients who come in, how many go on to start treatment?
The line is your qualification rate. Remember this is partly a clinical eligibility outcome, not purely a conversion metric.
Your Funnel — Lead to Treatment Start
What is my full conversion path, stage by stage?
Deliberately ends on treatment start rate rather than ROI — the operational outcome you control, not the marketing return you do not.
What's Not Working — Appointments That Fall Through
The clearest lever a clinic controls: appointments already booked that don't become visits.
Unattended Appointments — Share of Bookings by Month
Of this clinic's booked appointments, what share attend, no-show or cancel, and is that shifting?
Reschedules present as no-shows in the source data and cannot be separated, so the no-show share is an upper bound on truly lost patients.
Attendance Rate vs. Peer Clinics
Is my attendance rate better or worse than comparable clinics?
Peer comparison is shown only on measures clinics genuinely influence — attendance, qualification, and referral activity. Demand volume is not compared, since that is driven by centrally-controlled marketing allocation.
Whose Problem Is It? — Conversion vs. Network
The two rates the clinic owns, benchmarked. This is what separates "we aren't sending you enough" from "you aren't converting what we send".
This Clinic's Conversion Rates vs. Network
Is this clinic's shortfall a demand problem or a conversion problem?
Appointment → DOFV and DOFV → NPV for this clinic against network average and network best. A clinic below network on both is converting poorly, not under-supplied — and the DOFV → NPV gap is partly clinical eligibility, so raise it with clinical input rather than as a marketing failure.
Who Is Arriving — Patient Mix
Payor and service mix of patients coming to your clinic.
Your Payor Mix
What insurance and payment profile do my incoming patients have, and is it shifting?
Relevant to scheduling decisions if your capacity is constrained — where slots are scarce, mix matters more than volume.
Your Service Mix
What treatments are my patients pursuing, and does that match my clinical capacity?
Your Referring Physicians
Which practices are referring to me, and has anyone stopped?
| Practice | Last 90d | Prior 90d | Status |
|---|
This is the area where clinic staff have the most direct influence, since referral relationships are local and often attach to a specific physician.
Time to Convert — Lead to Appointment to First Visit
How long does this clinic take to convert, and how far out is it booking?
Capacity proxy. We have no direct slot-availability or utilisation data, so appointment-booked → first-visit elapsed days stands in for it: a lengthening line means this clinic is booking further out, which is the closest signal we have to a capacity ceiling. It is the same booking lead time shown in the Capacity & Access tiles above. Direct slot data remains an open question (C2).
Page purposeWhat each metric means and how data maturity is handled, so a challenge on a number can be answered in the room.
Metric Definitions
The DOFV / NPV distinction is the most important thing on this page.
| Metric | Definition | Notes |
|---|---|---|
| Basis | Month of spend. Every volume, cost and return figure is credited to the month the money was spent, not the month the visit happened. | Completed-in-month actuals are in the MOR and MSR. Results are linked to spend through the month the appointment was booked. |
| Change vs. prior month | The latest complete spend month against the month before it. | Both months are usually still landing, so the change compares two projections. Prior year is available in the Compare-to filter but is not the default. |
| Lead | A prospective patient who has submitted contact information via web form, Lodus, or the call center. | Captured in Salesforce as Lead. |
| Appointment booked | A first-visit appointment scheduled, via Lodus (online) or the call center. | Booking typically occurs days after first contact. |
| DOFV | Date of First Visit — the patient physically attends their first appointment, receiving a preliminary consultation and bloodwork. | Corporate's primary KPI. Source assumed Salesforce — pending confirmation (C1). |
| NPV | New Patient Visit — the patient qualifies for treatment following consultation and bloodwork, and treatment starts. | Clinics' primary KPI. Source assumed Athena EHR — pending confirmation (C1). Triggered at treatment start regardless of treatment length. |
| NPV ⊆ DOFV | Every NPV is necessarily a DOFV. NPV can never exceed DOFV. | The gap between them is partly a clinical eligibility outcome, not purely a marketing conversion. |
| CAC | Marketing spend ÷ DOFV credited to that spend. Cost / DOFV | The efficiency metric aligned to corporate's KPI. |
| Cost per NPV | Marketing spend ÷ NPV credited to that spend. | Shown alongside CAC because the business monetizes NPV. Channels with low qualification rates look worse here. |
| Qualification rate | NPV ÷ DOFV. | Varies by case mix and demographics as well as performance — interpret with clinical input. |
| Attendance rate | DOFV ÷ appointments booked. | Currently may conflate no-show, cancellation, and reschedule — see C4. |
| Unattended appointment | Appointments booked minus DOFV. Split into no-show and cancellation. | A reschedule presents as a no-show and cannot be separated, so no-show is an upper bound. |
| Booking lead time | Median days from appointment booked to first visit, for appointments booked in the period. | The capacity proxy. Computable today from appointment and DOFV dates. |
| Capacity status | Open: lead time under 21 days. Tight: 21–27. Constrained: 28 or more. | Thresholds are placeholders to agree with clinic operations. |
| Slot utilisation | New-patient slots booked ÷ new-patient slots available. | Not yet available. Shown as illustrative wherever it appears — pending C2. |
| Allocation call | Invest: marginal return ≥ 12 DOFV per $10K and capacity Open. Shift mix: same return, capacity Constrained. Reduce: marginal return under 6. Otherwise hold. | A rule, so it is a measure rather than a model. |
| ROI | Attributed revenue ÷ marketing spend. | Uses the client's existing attribution model. |
Reporting Basis and Data Maturity
This dashboard reports by month of spend.
There is roughly a 90-day lag between a patient booking and being seen, so same-month spend and same-month visits do not match up. This dashboard therefore credits every result to the month the money was spent, so a month’s cost is always set against the visits it produced. Completed-in-month actuals are already reported in the MOR and MSR and are not repeated here. The consequence is that recent months are still landing: a spend month closes at day 90 for DOFV and day 120 for NPV. Until then its figures are projections, drawn pale or dashed, and hovering gives their likely range.
Open Questions With the Client
Items that would materially change parts of this dashboard. Blocking items marked.
C1 · BLOCKING
What is the system of record for DOFV and NPV, and which date field stamps each?
Assumed Salesforce and Athena respectively. If they differ, the DOFV→NPV comparison carries a reconciliation and latency problem on top of a genuine conversion question. Also determines whether a DOFV is dated on the visit or on record creation — which changes month-end attribution.
C2 · BLOCKING
Is capacity a binding constraint at any clinics? Is there slot-availability, physician-utilization, or time-to-next-available data?
Working assumption is that it varies by clinic. This inverts the recommendation per clinic: demand-constrained clinics need more spend, capacity-constrained clinics need better patient mix. Without it, the dashboard risks telling a clinic booked out six weeks to generate more demand. Update (v4): a proxy is now in the dashboard — booking lead time, computable from existing date fields. Slot-availability data is still needed to replace the illustrative utilisation figures and to confirm the thresholds.
C4 · BLOCKING
Does Salesforce distinguish no-show from cancellation from reschedule?
Three different problems with three different owners. If reschedules are counted as no-shows, DOFV is understated now and overstated later, and clinics get diagnosed with a conversion problem when they have a calendar problem. Given weeks-long booking lead times in an emotionally weighty decision, reschedule rates are likely material.
C5
Are reasons captured when a patient cancels or reschedules?
Cost, chose another provider, conceived naturally, scheduling conflict, lost interest — each implies a different fix and at least one is not a failure. Recommend adding a short structured reason field if absent: likely cheap, with disproportionate payoff.
C3
How will clinic-level DOFV/NPV targets be set once they exist?
Capacity-based, market size, prior-year growth, or worked back from revenue? Determines whether the clinic view ever becomes an accountability tool. Note that if goals are revenue-derived but measured in DOFV volume, a clinic can hit volume and miss revenue.
C6
How are physician referrals generated and tracked?
Four parts: (a) is volume attributable to a named practice, or only to a bucket? (b) are liaison and outreach costs loaded into physician-channel CAC — if not, its ROI is overstated? (c) is referrer churn monitored? (d) do referred patients qualify at higher rates? Tab 8 is structurally built but unpopulated without these.
C7
Who owns reducing no-shows today?
Call center confirmation, clinic staff, automated reminders, or nobody explicitly. Determines whether the largest recoverable leak in the funnel has an actionable owner or is merely described.
C8
How should clinic CAC be reported given DMA-attributed spend and patient-selected clinics?
Several DMAs contain multiple CCRM clinics and clinics draw from neighbouring DMAs, so clinic CAC can never reconcile exactly to spend. This dashboard reports at DMA-cluster level; needs client agreement on clustering.
Open Items
Updated after the 31 July v3 review. Items closed in that session are listed at the foot of the page.
OI-A · BLOCKING BUILD
Which two elements were flagged for removal during the v3 walkthrough?
Two “I forgot to take this out” moments were recorded — one on Goal Attainment & Forecast, one on Patient & Revenue Mix. Neither is identifiable from the transcript alone. Most likely leftover placeholder or pending callouts. Identify them and they can be stripped in minutes.
OI-B
Confirm removal of the LTV & ROI tactical page.
The client calculates LTV themselves and will supply it as a table, so there is nothing to report against. Agreed in principle pending David checking with them. The page is still present pending that confirmation.
OI-C · BLOCKS PHYSICIAN DETAIL
Is named referring-practice attribution available in the source data?
The Referring Practice Scorecard is built and populated with illustrative data. It cannot go live until we confirm that referrals carry a practice or physician identifier rather than only a channel tag.
OI-D · BLOCKS UNIFY VIEWS
Can Unify network practices be identified and filtered?
Unify referrals come from parent-company practices, carry no acquisition cost, and are a stated growth priority. The Unify vs. external split and the coverage-by-market view both depend on a flag distinguishing internal from external referrers.
OI-E
What does “PL markets” mean in the client requirements deck?
Appears twice. Best guess is “Physician Led”, but unconfirmed. Affects how physician-channel performance should be segmented.
OI-F · DECISION NEEDED
What should the default comparison basis be? Decided 11 Nov: prior month
The deck asks for Google-Analytics-style period comparison (seven days of November vs. seven days of October). Our position is that comparisons should default to the same period last year, because month-over-month in a seasonal business misleads — but prior-year data quality is known to be weak. A Compare to selector now sits in the top bar with both options; the open question is which is the default and whether the client accepts a single global basis rather than mixing them.
OI-G
Does treatment-type data join cleanly to campaign?
The campaign tables now carry treatments started, which assumes a patient can be traced from the campaign that acquired them through to the treatment they began. Needs the same data check as the physician work.
OI-H
How should clinic-level goals be set?
Still the network goal split evenly across clinics. David’s guidance is to start there and re-base on last year’s actuals once available. Every “vs target” figure below network level remains a placeholder until then.
OI-I
Is media spend genuinely allocable to a clinic?
Carried forward and still unresolved. If spend is apportioned rather than directly attributable, clinic-level CAC and ROI need labelling as estimates.
OI-J · DECISION NEEDED
Can the DOFV plan be restated by month of spend?
The dashboard now credits results to the month the money was spent, but the plan is set by completion month, which is the MOR and MSR basis. Comparing the two mixes bases. Either restate the plan by month of spend, or keep that comparison labelled as approximate.
OI-K
Is booking date an acceptable stand-in for spend date?
Results are linked to spend through the month the appointment was booked. Booking follows spend by days rather than weeks, so the month-level error should be small. Strict spend-date attribution would need the date of each lead’s first paid touch.
Closed in the 31 July Review
Which previously open questions have now been answered?
| Question | Resolution |
|---|---|
| Who owns reducing no-shows? | Kelly. Confirmed on the call. |
| Can reschedules be distinguished from no-shows? | No. A reschedule presents as a no-show — original cancelled, new record created, no link retained. The separate slice has been removed and the no-show figure is now labelled as an upper bound. |
| How is physician referral spend incurred? | Sales representatives visiting physician offices plus printed collateral, analogous to pharmaceutical rep activity. |
| Can insurance or patient referral be invested in? | No. Only digital and physician referral carry spend. Patient referral has been removed from the clinic channel view. |
| Is time-to-convert data available? | Yes. Appointment date, DOFV date and NPV date all exist, so elapsed time is a date subtraction. |
Parked for Later
What did we consciously defer, so it doesn't get mistaken for an oversight?
| Item | Why parked |
|---|---|
| CRM incrementality | Measuring genuine incremental effect rather than crediting demand that would have converted anyway. Requires holdout design. |
| Service-line P&L depth | IVF, IUI, egg freezing, PGT, donor and surrogacy have very different economics. Needed for full value-weighted allocation. |
| Competitive / market benchmarking | Share of voice and market growth by DMA — needed to separate "we underperformed" from "the market contracted". |
| Lodus vs. call center comparison | Whether self-serve and assisted booking differ in booking rate, attendance, and qualification. Relevant to where booking friction sits. |
| Charts removed as not buildable | Recommended Reallocation, Current vs. Proposed Allocation, What Changed Most This Period, and the leak-value what-if have been removed — each required AI-style scenario generation rather than a Power BI measure. The marginal-DOFV analysis is retained because it is computable. The saturation quadrant was later replaced by the return-versus-capacity chart. |
| Operating rhythm | Audience confirmed as Board / CEO / C-suite plus separate per-clinic meetings. Frequency and presenter still to be agreed. |
CombinedPaid Media Totals — responds to the platform filter above
Total Paid Media Spend
$2.84M
▲ 1.6% vs prior month
Spend Needed to Achieve Goal
$3.08M
▲ $240K vs current spend · about $178K deployable where clinics have room
Goal: 7,000 DOFV · CAC $440
Spend Pacing vs Budget
96.2%
On track
Total Clicks
3.09M
▲ 8.4%
Blended CPL
$226
▼ $18
Paid → Lodus Booking
3.8%
▲ 0.4 pp
Media Campaign Funnel KPIs — Combined
Impressions
142M
▲ 8.7%
Clicks
3.09M
▲ 8.4%
CTR
2.18%
▲ 0.12 pp
Media Cost
$2.84M
▲ 1.6% vs prior month
Revenue
$33.85M
▲ 9.2%
ROI
11.9x
▲ 0.6x
Leads
12,630
▲ 7.3%
Scheduled
8,530
▲ 5.9%
No Shows
2,070
▲ 3.1%
DOFV
6,460
▲ 6.1%
CAC [Cost/DOFV]
$440
▼ $22
NPV
6,080
▲ 5.7%
Volume KPI by Location — Meta vs Google (Stacked)
How does volume split between Meta and Google in each market?
Select a volume-based KPI to break it down by clinic location and channel
Bars are stacked Meta + Google since these are volume counts.
Rate KPI by Location — Meta vs Google (Side-by-Side)
Which platform performs better on efficiency and rate metrics, market by market?
Select a rate-based KPI to compare Meta vs Google by clinic location
Bars are shown side-by-side since rates can't be summed across channels.
Spend by Platform & Month
How is media spend split between Meta and Google over time?
Performance by Platform
Which platform delivers more volume, and at what cost and return?
| Platform | Spend | Clicks | CTR | CPC | CPL | Conv % |
|---|---|---|---|---|---|---|
| Meta | $1.18M | 1.42M | 1.84% | $0.83 | $254 | 3.2% |
| Google Search | $1.66M | 1.67M | 8.42% | $0.99 | $208 | 4.4% |
| Combined | $2.84M | 3.09M | 3.18% | $0.92 | $226 | 3.8% |
Campaign Performance — Through to Treatment
Meta — Campaign Performance
· full funnel, cost ladder, and treatments started
Which Meta campaigns are actually producing treating patients, what does each stage cost, and which treatments do they deliver?
| Campaign | Reach & Cost | Funnel | Cost per… | Treatments Started (NPV by type) | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Spend | Impr. | Clicks | Leads | Appts | DOFV | NPV | Click | Lead | Appt | DOFV | NPV | IVF | Egg frz | IUI | Other | |
| Always-On Prospecting — IVF | $312K | 18.4M | 41,200 | 1,180 | 742 | 601 | 566 | $7.57 | $264 | $420 | $519 | $551 | 372 | 84 | 74 | 36 |
| Egg Freezing — Under 35 | $268K | 22.1M | 58,400 | 1,420 | 826 | 644 | 587 | $4.59 | $189 | $324 | $416 | $457 | 106 | 362 | 76 | 43 |
| Retargeting — Site Visitors | $184K | 9.6M | 33,800 | 968 | 704 | 592 | 561 | $5.44 | $190 | $261 | $311 | $328 | 285 | 148 | 82 | 46 |
| Donor & Surrogacy Awareness | $96K | 6.2M | 12,100 | 284 | 176 | 141 | 132 | $7.93 | $338 | $545 | $681 | $727 | 38 | 14 | 22 | 58 |
| Brand — Success Rates | $160K | 11.8M | 19,600 | 412 | 262 | 214 | 202 | $8.16 | $388 | $611 | $748 | $792 | 96 | 48 | 34 | 24 |
| Meta total | $1020K | 68.1M | 165,100 | 4,264 | 2,710 | 2,192 | 2,048 | $6.18 | $239 | $376 | $465 | $498 | 897 | 656 | 288 | 207 |
The right-hand block is the addition that closes the loop: a campaign can now be traced to the treatments it produced, so “how many IVF cycles did this ad give me” is answerable per campaign. Note Egg Freezing — Under 35 is the largest lead generator but produces the fewest IVF starts, so it looks strong on cost per lead and weak on revenue contribution.
Google — Campaign Performance
· full funnel, cost ladder, and treatments started
Which Google campaigns are producing treating patients, and how does branded demand compare with non-branded?
| Campaign | Reach & Cost | Funnel | Cost per… | Treatments Started (NPV by type) | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Spend | Impr. | Clicks | Leads | Appts | DOFV | NPV | Click | Lead | Appt | DOFV | NPV | IVF | Egg frz | IUI | Other | |
| Brand — CCRM Exact | $248K | 4.1M | 62,800 | 1,640 | 1,128 | 946 | 902 | $3.95 | $151 | $220 | $262 | $275 | 561 | 158 | 132 | 95 |
| Non-Brand — IVF Clinic Near Me | $486K | 12.7M | 74,200 | 1,512 | 942 | 762 | 706 | $6.55 | $321 | $516 | $638 | $688 | 468 | 108 | 118 | 68 |
| Non-Brand — Egg Freezing Cost | $322K | 9.4M | 51,600 | 982 | 568 | 441 | 402 | $6.24 | $328 | $567 | $730 | $801 | 74 | 246 | 54 | 28 |
| Non-Brand — Fertility Testing | $214K | 7.8M | 38,400 | 684 | 372 | 288 | 262 | $5.57 | $313 | $575 | $743 | $817 | 92 | 56 | 86 | 28 |
| Competitor Conquesting | $148K | 3.2M | 16,800 | 298 | 164 | 126 | 114 | $8.81 | $497 | $902 | $1175 | $1298 | 62 | 22 | 26 | 14 |
| Google total | $1418K | 37.2M | 243,800 | 5,116 | 3,174 | 2,563 | 2,386 | $5.82 | $277 | $447 | $553 | $594 | 1,257 | 590 | 416 | 233 |
Brand — CCRM Exact carries the lowest cost per NPV by a wide margin, which is expected: it captures demand we largely already earned. The non-brand campaigns are the true incremental spend and should be judged against each other rather than against brand.
Cost Ladder by Campaign
Cost per Appointment by Campaign
What does each campaign cost us per appointment booked?
First rung of the ladder. Dashed line is the $513 network cost per appointment. All three charts share the same axis range so the cost escalation across stages is directly comparable.
CAC by Campaign (cost / DOFV)
What does each campaign cost us per patient who actually attends a first visit?
Second rung. Dashed line is the $616 network CAC. The step up from cost per appointment is the no-show penalty — campaigns with weak attendance widen most here.
Cost per NPV by Campaign
What does each campaign cost us per patient who actually starts treatment?
End of the ladder, and the number that matters commercially. Dashed line is the $654 network cost per NPV. The step from CAC is the qualification penalty.
Treatments Started by Campaign
Which campaigns drive high-value IVF starts rather than lower-value treatments?
Same data as the tables above, read as mix. Campaigns skewing to egg freezing convert at a lower revenue per patient even where their cost per NPV looks attractive — Meta Egg Frz U35 is the clearest example: second-cheapest per NPV, but the fewest IVF starts of any Meta campaign.
Digital Performance by Clinic
Digital Media Funnel by Clinic — Through to Treatment
How does paid media volume, cost and conversion break down by clinic?
| Clinic | Funnel volume | Cost | Stage conversion | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Leads | Appointments | DOFV | NPV | Spend | /Appt | /DOFV | /NPV | Lead→Appt | Appt→DOFV | DOFV→NPV | |
| Colorado | 1,617 | 749 | 624 | 563 | $593K | $792 | $950 | $1,053 | 46.3% | 83.3% | 90.2% |
| Houston | 1,264 | 586 | 488 | 440 | $464K | $792 | $951 | $1,055 | 46.4% | 83.3% | 90.2% |
| Northern Virginia | 919 | 426 | 355 | 320 | $337K | $791 | $949 | $1,053 | 46.4% | 83.3% | 90.1% |
| Chicago | 700 | 324 | 270 | 244 | $257K | $793 | $952 | $1,053 | 46.3% | 83.3% | 90.4% |
| Orange County | 586 | 271 | 226 | 204 | $215K | $793 | $951 | $1,054 | 46.2% | 83.4% | 90.3% |
| Boston | 571 | 264 | 220 | 199 | $209K | $792 | $950 | $1,050 | 46.2% | 83.3% | 90.5% |
| Arizona | 537 | 248 | 207 | 187 | $197K | $794 | $952 | $1,053 | 46.2% | 83.5% | 90.3% |
| New York | 504 | 234 | 195 | 176 | $185K | $791 | $949 | $1,051 | 46.4% | 83.3% | 90.3% |
| Miami | 449 | 208 | 173 | 156 | $165K | $793 | $954 | $1,058 | 46.3% | 83.2% | 90.2% |
| Virginia Beach | 408 | 188 | 157 | 142 | $150K | $798 | $955 | $1,056 | 46.1% | 83.5% | 90.4% |
| All other markets | 676 | 313 | 261 | 235 | $248K | $792 | $950 | $1,055 | 46.3% | 83.4% | 90.0% |
| Digital total | 8,231 | 3,811 | 3,176 | 2,866 | $3,020K | $792 | $951 | $1,054 | 46.3% | 83.3% | 90.2% |
Reconciles to the digital row on Channel Performance: 8,230 leads, 3,178 DOFV, 2,867 NPV, $3.02M spend. Media spend is DMA-attributed while patients select their own clinic, so clinic-level cost here is an apportionment rather than directly traceable spend — see Open Items OI‑I.
Digital DOFV and CAC by Clinic
Which clinics does paid media serve most efficiently?
Bars are digital DOFV, line is digital CAC. Dashed line marks the $950 digital-channel average.
Platform Split by Clinic
Does the Meta / Google balance differ by market?
GoogleGoogle — Keyword, Match and Device Detail
Google — Branded vs Non-Branded
How much of our Google performance is branded demand we may already own?
Google — Device Performance Split
Does performance differ enough by device to justify separate bidding?
Google — Top Converting Keyword Terms
Which search queries actually convert into patients?
| Search Query | Type | Impressions | Clicks | CTR | CPC | Conv |
|---|---|---|---|---|---|---|
| ccrm fertility | Branded | 142,000 | 38,400 | 27.0% | $0.62 | 1,840 |
| ivf clinic near me | Non-Brand | 88,200 | 9,420 | 10.7% | $3.20 | 412 |
| egg freezing colorado | Non-Brand | 54,800 | 7,640 | 13.9% | $2.84 | 318 |
| fertility doctor houston | Non-Brand | 42,140 | 5,820 | 13.8% | $2.94 | 241 |
| ccrm reviews | Branded | 38,400 | 12,820 | 33.4% | $0.58 | 488 |
| donor egg program | Non-Brand | 28,140 | 3,420 | 12.2% | $3.42 | 142 |
About this page Corporate
Media buys attention; the website decides whether that attention becomes a booked appointment. This page combines site-wide traffic behaviour with landing-page performance so both live in one place: is our web experience converting the traffic we pay for, and which pages carry patients all the way to treatment?
The recurring pattern is that traffic volume and conversion quality rarely coincide. Our highest-traffic pages are educational and convert modestly; the direct scheduling page converts several times harder on a fraction of the sessions. That argues for routing more paid traffic to intent-led destinations rather than content.
Site Engagement
Sessions
198.8K
▲ 8.4%
Users
154.2K
▲ 7.1%
Pages / Session
3.4
▲ 0.2
Avg. Session Duration
2:48
▲ 14s
Bounce Rate
41.2%
▼ 1.8 pp
Mobile Share
68%
▲ 3 pp
Conversion & Efficiency
Session → Lead
4.6%
▲ 0.3 pp
Leads
9,119
▲ 9.2%
DOFV
4,910
▲ 7.8%
NPV
4,594
▲ 7.4%
Cost per DOFV
$287
▼ 2.1%
Cost per NPV
$306
▼ 1.9%
Landing Page Performance — Through to Treatment
Landing Page Funnel — Session Through to Treatment Start
Which landing pages turn paid traffic into treating patients, and where does each one leak?
| Landing page | Traffic | Funnel volume | Stage conversion | Cost | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sessions | Session→Lead | Leads | Appointments | DOFV | NPV | Lead→Appt | Appt→DOFV | DOFV→NPV | Spend | /Appt | /DOFV | /NPV | |
/egg-freezing |
61,400 | 4.6% | 2,824 | 1,642 | 1,188 | 1,082 | 58.1% | 72.4% | 91.1% | $340,000 | $207 | $286 | $314 |
/ivf-treatment |
48,200 | 3.9% | 1,842 | 1,204 | 986 | 924 | 65.4% | 81.9% | 93.7% | $406,000 | $337 | $412 | $439 |
/fertility-testing |
31,600 | 2.8% | 885 | 542 | 428 | 392 | 61.2% | 79.0% | 91.6% | $213,000 | $393 | $498 | $543 |
/success-rates |
26,400 | 1.9% | 502 | 312 | 264 | 248 | 62.2% | 84.6% | 93.9% | $162,000 | $519 | $614 | $653 |
/schedule-consultation |
22,800 | 11.2% | 2,554 | 1,988 | 1,702 | 1,618 | 77.8% | 85.6% | 95.1% | $286,000 | $144 | $168 | $177 |
/for-physicians |
8,400 | 6.1% | 512 | 388 | 342 | 330 | 75.8% | 88.1% | 96.5% | — | — | — | — |
| All landing pages | 198,800 | 4.6% | 9,119 | 6,076 | 4,910 | 4,594 | 66.6% | 80.8% | 93.6% | $1,407,000 | $232 | $287 | $306 |
/schedule-consultation converts sessions to leads at 11.2% against a 4.6% site average and carries the lowest cost at every rung of the ladder — unsurprising, since visitors arrive with intent. /for-physicians shows no cost because it is an organic and referral surface rather than a paid destination. /success-rates is the weakest paid destination on every measure and is the clearest candidate for either redesign or removal from paid routing.Landing Page Performance by Clinic
How does web-driven volume and efficiency break down by clinic?
| Clinic | Traffic | Funnel volume | Conversion | Cost | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Sessions | Session→Lead | Leads | Appointments | DOFV | NPV | Appt→DOFV | DOFV→NPV | Spend | /DOFV | /NPV | |
| Colorado | 39,049 | 4.6% | 1,791 | 1,193 | 964 | 902 | 80.8% | 93.6% | $276K | $286 | $306 |
| Houston | 30,540 | 4.6% | 1,401 | 933 | 754 | 706 | 80.8% | 93.6% | $216K | $286 | $306 |
| Northern Virginia | 22,205 | 4.6% | 1,019 | 679 | 548 | 513 | 80.7% | 93.6% | $157K | $286 | $306 |
| Chicago | 16,902 | 4.6% | 775 | 517 | 417 | 391 | 80.7% | 93.8% | $120K | $288 | $307 |
| Orange County | 14,163 | 4.6% | 650 | 433 | 350 | 327 | 80.8% | 93.4% | $100K | $286 | $306 |
| Boston | 13,784 | 4.6% | 632 | 421 | 340 | 319 | 80.8% | 93.8% | $98K | $288 | $307 |
| Arizona | 12,968 | 4.6% | 595 | 396 | 320 | 300 | 80.8% | 93.8% | $92K | $288 | $307 |
| New York | 12,181 | 4.6% | 559 | 372 | 301 | 281 | 80.9% | 93.4% | $86K | $286 | $306 |
| Miami | 10,840 | 4.6% | 497 | 331 | 268 | 251 | 81.0% | 93.7% | $77K | $287 | $307 |
| Virginia Beach | 9,850 | 4.6% | 452 | 301 | 243 | 228 | 80.7% | 93.8% | $70K | $288 | $307 |
| All other markets | 16,319 | 4.6% | 749 | 499 | 403 | 377 | 80.8% | 93.5% | $115K | $285 | $305 |
| Network | 198,801 | 4.6% | 9,120 | 6,075 | 4,908 | 4,595 | 80.8% | 93.6% | $1,407K | $287 | $306 |
Web volume is apportioned to clinics on each clinic’s share of network DOFV, since a landing page is national but the patient selects a location. Treat clinic-level cost figures here as apportionments rather than directly attributable spend — see Open Items OI‑I.
Sessions vs. Conversion Rate by Landing Page
Are we sending our paid traffic to the pages that actually convert?
Bars are sessions, line is session-to-lead conversion. A tall bar under a low line means we are paying to send traffic somewhere that does not convert.
Cost Ladder by Landing Page
What does each page cost per appointment, per first visit, and per treatment start?
Three rungs on a shared axis so the escalation is comparable. Widening gaps indicate loss at the corresponding stage.
Landing Page Mix by Clinic
Which pages drive traffic for each clinic?
Where a clinic over-indexes on the egg-freezing page, expect a lower-value treatment mix downstream — consistent with the treatment-mix findings on Patient & Revenue Mix.
Sessions by Traffic Source — 12-Month Trend
Where does our website traffic come from?
Site Behaviour Detail
Sessions Trend — Mobile vs Desktop
How is traffic split between mobile and desktop, and is that shifting?
Service Page Engagement by Treatment
Which treatment pages attract the most interest?
Physician Form Submissions
Is the physician referral page generating referrals?
Patient Portal & Pay-My-Bill Clicks
How much site activity is existing patients rather than prospects?
Top Site Search Queries
What are visitors looking for that they cannot find?
| Query | Searches | Exit rate |
|---|---|---|
| cost of ivf | 1,842 | 18.2% |
| insurance coverage | 1,204 | 24.6% |
| egg freezing price | 986 | 21.4% |
| success rates by age | 742 | 12.8% |
| financing options | 618 | 28.4% |
Cost and coverage dominate site search and carry the highest exit rates — a content gap with a direct conversion consequence.
Spend
Digital Ad Spend
$2.84M
▲ 1.6% vs prior month
Budget: $2.95M · 96% pacing
Physician Referral Spend
$1.36M
▲ 3.0% vs prior month
Budget: $1.40M · 97% pacing
Spend vs. Budget
96.4%
$155K under budget
All channels combined
Spend Needed to Hit NPV Target
$4.6M
▲ $400K gap to target · about $296K deployable where clinics have room
7,200 NPV goal · $639/NPV historical
Spend by Channel — last 12 months
How has spend been allocated across channels over the last year?
Spend vs. Budget by Clinic
Which clinics are over or under their media budget?
Reach
Impressions
142M
▲ 9.1% vs prior
Click-Through Rate (CTR)
2.18%
▲ 0.21 pp vs prior
Cost per Click (CPC)
$0.92
▲ $0.07 vs prior
Impressions & CTR Trend
Are we buying more reach, and is it engaging at the same rate?
CPC by Channel & Clinic
Where are we paying the most per click, and is that justified?
| Clinic | Meta CPC | Google CPC | Blended | Δ vs prior |
|---|---|---|---|---|
| Colorado | $0.84 | $1.12 | $0.96 | ▲ $0.04 |
| Northern Virginia | $1.06 | $1.38 | $1.18 | ▲ $0.14 |
| Houston | $0.71 | $0.94 | $0.81 | ▼ $0.05 |
| Boston | $0.92 | $1.21 | $1.05 | ▲ $0.02 |
| Atlanta | $0.78 | $0.99 | $0.87 | ▼ $0.03 |
| Minneapolis | $0.82 | $1.04 | $0.91 | ▼ $0.01 |
Visit Volume
Total Visits
28,940
▲ 7.2%
Digital Visits
11,576
▲ 12.4%
Physician Referral Visits
8,973
▲ 5.1%
Patient + Insurance Referrals
6,225
▲ 3.8%
Unattributed / Other
2,166
▲ 1.4% (investigate)
Channel Attribution Share — 12-Month Trend
How is visit volume attributed across channels?
Visits by Channel — last 12 months
How is each channel trending in visit contribution?
Cost Efficiency
Cost per Visit — Digital
$245
▼ $18 vs prior
Cost per Visit — Physician
$152
▼ $9 vs prior
Cost per Visit — Blended
$145
▼ $11 vs prior
Attribution by Clinic (% of visits)
How does channel attribution differ by clinic?
Campaign Funnel Detail
Campaign-level funnel · last 12 months
· Stage rates coloured against network benchmark — green at or above, red below
How does each campaign convert from lead through to treatment start?
| Campaign | Funnel volume | Stage conversion | End to end | |||||
|---|---|---|---|---|---|---|---|---|
| Leads | Appointments | DOFV | NPV | Lead→Appt | Appt→DOFV | DOFV→NPV | Lead→NPV | |
| Google — Branded | 1,640 | 1,128 | 946 | 902 | 68.8% | 83.9% | 95.3% | 55.0% |
| Google — Non-Brand IVF | 1,512 | 942 | 762 | 706 | 62.3% | 80.9% | 92.7% | 46.7% |
| Google — Non-Brand Egg Freezing | 982 | 568 | 441 | 402 | 57.8% | 77.6% | 91.2% | 40.9% |
| Google — Fertility Testing | 684 | 372 | 288 | 262 | 54.4% | 77.4% | 91.0% | 38.3% |
| Meta — IVF Prospecting | 1,180 | 742 | 601 | 566 | 62.9% | 81.0% | 94.2% | 48.0% |
| Meta — Egg Freezing U35 | 1,420 | 826 | 644 | 587 | 58.2% | 78.0% | 91.1% | 41.3% |
| Meta — Retargeting | 968 | 704 | 592 | 561 | 72.7% | 84.1% | 94.8% | 58.0% |
| Meta — Brand Awareness | 412 | 262 | 214 | 202 | 63.6% | 81.7% | 94.4% | 49.0% |
| All campaigns | 8,798 | 5,544 | 4,488 | 4,188 | 63.0% | 81.0% | 93.3% | 47.6% |
Network benchmarks: 65.3% lead to appointment, 83.3% appointment to DOFV, 94.1% DOFV to NPV. Meta Retargeting converts best end to end at 58.0% — unsurprising for an audience that has already visited the site. Google Non-Brand Egg Freezing is weakest at 40.9%, losing ground at every stage.
Funnel Volume by Campaign — Leads to NPV
Which campaigns lose the most volume between stages?
End-to-End Conversion Rate — Lead to NPV
What share of leads ultimately become treating patients?
Where the patients are leaking
- Meta — Egg Freezing Prospecting is the weakest performer: drop-off at three consecutive stages (1.1% inquiry rate, 52% scheduling, 71% attendance). Highest priority to fix.
- Non-brand Egg Freezing (Google) loses patients at the scheduling step: 55% scheduled vs ~70% benchmark. Likely a call-center / lead-quality issue specific to egg-freezing inquiries.
- Meta IVF Prospecting under-converts at the form step: 1.5% session→inquiry vs 2.5% network average. Landing-page or creative-relevance issue.
- Retargeting campaigns dominate on conversion: Meta IVF and Egg Freezing retargeting both hit ~80% attended-NPV rates. Strong case for increasing retargeting budget allocation.
- Branded Google remains the gold standard: 27% CTR, 5% inquiry rate, 80% attended rate — patients searching for "CCRM" already have intent.
Funnel Volume
Leads
12,540
▲ 11.4%
All sources
Appointments Booked
8,190
▲ 9.4%
Lodus + Call Center + Office
DOFV
6,820
▲ 6.6%
Attended first visits
NPV
6,418
▲ 6.1%
Started treatment
DOFV vs. Plan
90%
Plan: 7,600
Stage Conversion
Lead → Appointment
65.3%
▲ 2.1 pp
Appointment → DOFV
83.3%
▲ 1.3 pp
DOFV → NPV
94.1%
▲ 0.5 pp
No-Show Rate
11.4%
▲ 0.4 pp
Includes reschedules
Lead → NPV
51.2%
▲ 1.8 pp
End to end
Leads → Appointments → DOFV → NPV (trend)
How is the full funnel trending month to month?
Stage Conversion by Channel
Which channels convert leads into treating patients most reliably?
Referral channels convert hardest at every stage — patients arriving on a clinician’s recommendation are further along in their decision than someone who clicked an ad.
Clinic Scorecard — Funnel & Attendance
How does each clinic perform across the funnel and on attendance?
| Clinic | Leads | Appointments | DOFV | NPV | Lead→Appt | Appt→DOFV | DOFV→NPV | No-show | Cancelled |
|---|---|---|---|---|---|---|---|---|---|
| Colorado | 2,410 | 1,554 | 1,340 | 1,288 | 64.5% | 86.2% | 96.1% | 9.4% | 4.4% |
| Houston | 1,920 | 1,246 | 1,048 | 994 | 64.9% | 84.1% | 94.8% | 10.8% | 5.1% |
| Northern Virginia | 1,460 | 914 | 762 | 718 | 62.6% | 83.4% | 94.2% | 11.3% | 5.3% |
| Chicago | 1,120 | 707 | 580 | 542 | 63.1% | 82.0% | 93.4% | 12.2% | 5.7% |
| Orange County | 980 | 572 | 486 | 462 | 58.4% | 85.0% | 95.1% | 10.2% | 4.8% |
| Boston | 920 | 584 | 473 | 385 | 63.5% | 81.0% | 81.4% | 12.9% | 6.1% |
| Arizona | 740 | 575 | 445 | 396 | 77.7% | 77.4% | 89.0% | 15.4% | 7.2% |
| New York | 840 | 520 | 418 | 392 | 61.9% | 80.4% | 93.8% | 13.3% | 6.3% |
| Miami | 760 | 455 | 372 | 348 | 59.9% | 81.8% | 93.5% | 12.4% | 5.8% |
| Virginia Beach | 620 | 428 | 338 | 274 | 69.0% | 79.0% | 81.1% | 14.3% | 6.7% |
| Network (named markets) | 11,770 | 7,555 | 6,262 | 5,799 | 64.2% | 82.9% | 92.6% | 11.6% | 5.5% |
Reconciles to the network figures used throughout: 12,540 leads, 8,190 appointments, 6,820 DOFV, 6,418 NPV across all markets. No-show includes reschedules, which cannot be separated in the source data. Boston and Virginia Beach stand out on DOFV → NPV at around 81% against a 94% network average.
Appointment → DOFV by Clinic
Which clinics lose the most already-booked appointments?
Dashed line is the 83.3% network average. Every recovered appointment is a DOFV at zero incremental media cost.
Unattended Appointments — Share of Bookings by Month
Of appointments booked, how many attend, no-show, or cancel?
Reschedules present as no-shows in the source data and cannot be separated, so the no-show figure is an upper bound on truly lost patients.
Return on Investment
Attributed Revenue
$84.1M
▲ 9.8%
Marketing Spend
$4.20M
▲ 8.2%
Blended ROI
19.0x
▲ 1.2x
Revenue per DOFV
$12,330
▲ 3.0%
Revenue per NPV
$13,104
▲ 3.5%
Return by Channel
Which channels generate the most revenue, and which return the most per dollar invested?
| Channel | Volume | Cost | Return | |||||
|---|---|---|---|---|---|---|---|---|
| DOFV | NPV | Spend | CAC | Cost/NPV | Revenue | Rev/DOFV | ROI | |
| Digital (Google + Meta) | 3,178 | 2,867 | $3.02M | $950 | $1,053 | $35.6M | $11,202 | 11.8x |
| Physician referral | 1,598 | 1,558 | $1.18M | $738 | $757 | $29.8M | $18,648 | 25.3x |
| CRM | 512 | 489 | $0 | — | — | $6.3M | $12,305 | — |
| Patient referral | 612 | 592 | $0 | — | — | $9.1M | $14,869 | — |
| Insurance referral | 298 | 282 | $0 | — | — | $3.3M | $11,074 | — |
| Organic / direct | 622 | 630 | $0 | — | — | $2.2M | $3,537 | — |
| Network | 6,820 | 6,418 | $4.20M | $616 | $654 | $84.1M | $12,330 | 19.0x |
Only digital and physician referral carry spend. CRM, patient referral, insurance referral and organic have no acquisition cost, so CAC, cost per NPV and ROI are shown as not applicable rather than as a fabricated figure. Their revenue contribution is still shown, since that is the meaningful measure for a zero-cost channel. Lifetime value is calculated by the client and supplied separately, so it is not reported here.
Revenue Attribution by Channel
Which channels generate the most attributed revenue?
ROI by Channel — Investable Channels Only
Where does each dollar of marketing spend return the most revenue?
Shown only for digital and physician referral, the two channels we can actually invest in. Dashed line is the 19.0x network blended ROI.
Payor Mix by Channel
Do certain channels bring in better-reimbursing patients?
Physician referral over-indexes on commercial coverage; paid social skews self-pay, consistent with a younger, egg-freezing-weighted audience.
Revenue per DOFV by Channel
Which channels deliver the highest-value patients?
Read alongside the cost columns above: physician referral costs more per patient than CRM or organic but delivers roughly five times the revenue of organic per first visit.
About this page Corporate
CRM is the cheapest channel we have and the least visible. This page asks: which email programmes actually recover patients who would otherwise have been lost, and which are simply sending volume?
Engagement and outcome diverge sharply here. The programmes closest to a decision — abandoned booking recovery and consultation reminders — convert several times harder than the educational sends, despite far smaller audiences. Judge programmes on the right-hand columns of the table below, not on open rate.
Measurement caveat: attribution credits CRM with patients who may well have converted anyway. Nothing here proves incrementality; establishing that needs a holdout test, which sits on the parked list.
Delivery & List Health
Emails Sent
65.0K
▲ 6.2%
Delivered
64.0K
98.4% delivery rate
Bounce Rate
1.5%
▼ 0.3 pp
Unsubscribe Rate
0.43%
▲ 0.06 pp
Active List Size
48.6K
▲ 4.1% net growth
Spam Complaint Rate
0.02%
Well below 0.1% threshold
Engagement
Open Rate
41.7%
▲ 1.8 pp
Click-Through Rate
7.0%
▲ 0.4 pp
Click-to-Open Rate
16.9%
▲ 0.6 pp
Unique Clicks
4,512
▲ 8.4%
Click → Lead
40.8%
▲ 2.1 pp
Reply / Reply-to-Book
312
▲ 11.4%
Outcome
Leads
1,840
▲ 7.8%
Appointments
1,404
▲ 8.2%
DOFV
1,296
▲ 9.1%
NPV
1,072
▲ 8.6%
Attributed Revenue
$15.9M
▲ 9.4%
Media Cost
$0
Platform cost only — excluded from CAC
Programme Performance — Through to Treatment
CRM Programme Funnel — Send Through to Treatment Start
Which email programmes produce treating patients rather than just opens and clicks?
| Programme | Delivery & list health | Engagement | Funnel volume | Stage conversion | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Sent | Delivered | Bounce | Unsub | Open | CTR | CTOR | Leads | Appointments | DOFV | NPV | Click→Lead | Lead→Appt | Appt→DOFV | DOFV→NPV | |
| Abandoned Booking Recovery | 6,200 | 99.0% | 1.0% | 0.20% | 51.4% | 11.2% | 21.8% | 412 | 336 | 318 | 274 | 60.0% | 81.6% | 94.6% | 86.2% |
| Consultation Reminder | 4,800 | 99.5% | 0.5% | 0.08% | 68.2% | 22.4% | 32.9% | 312 | 302 | 298 | 276 | 29.2% | 96.8% | 98.7% | 92.6% |
| New Lead Nurture (7-touch) | 18,400 | 98.6% | 1.4% | 0.32% | 42.1% | 6.9% | 16.4% | 486 | 342 | 312 | 254 | 38.8% | 70.4% | 91.2% | 81.4% |
| Financing & Benefits | 9,800 | 98.6% | 1.4% | 0.32% | 44.3% | 6.0% | 13.5% | 214 | 156 | 138 | 104 | 37.0% | 72.9% | 88.5% | 75.4% |
| Egg Freezing Education | 14,600 | 98.3% | 1.7% | 0.50% | 38.6% | 4.3% | 11.1% | 268 | 174 | 148 | 106 | 43.7% | 64.9% | 85.1% | 71.6% |
| Re-engagement — 90 day | 11,200 | 98.0% | 2.0% | 0.87% | 26.4% | 2.9% | 10.8% | 148 | 94 | 82 | 58 | 47.3% | 63.5% | 87.2% | 70.7% |
| All CRM programmes | 65,000 | 98.5% | 1.5% | 0.43% | 41.8% | 7.0% | 16.9% | 1,840 | 1,404 | 1,296 | 1,072 | 40.8% | 76.3% | 92.3% | 82.7% |
Ordered by conversion strength rather than send volume, which inverts the usual reading. Consultation Reminder is the smallest programme and the strongest at every stage; Re-engagement is the third-largest send and the weakest converter with the highest unsubscribe rate — a candidate for retirement or a lower send frequency.
CRM Performance by Clinic
How does CRM-driven volume break down by clinic?
| Clinic | Reach | Funnel volume | Stage conversion | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Sent | Send→Lead | Leads | Appointments | DOFV | NPV | Lead→Appt | Appt→DOFV | DOFV→NPV | |
| Colorado | 12,768 | 2.83% | 361 | 276 | 255 | 211 | 76.5% | 92.4% | 82.7% |
| Houston | 9,985 | 2.83% | 283 | 216 | 199 | 165 | 76.3% | 92.1% | 82.9% |
| Northern Virginia | 7,260 | 2.84% | 206 | 157 | 145 | 120 | 76.2% | 92.4% | 82.8% |
| Chicago | 5,526 | 2.82% | 156 | 119 | 110 | 91 | 76.3% | 92.4% | 82.7% |
| Orange County | 4,631 | 2.83% | 131 | 100 | 92 | 76 | 76.3% | 92.0% | 82.6% |
| Boston | 4,507 | 2.84% | 128 | 97 | 90 | 74 | 75.8% | 92.8% | 82.2% |
| Arizona | 4,240 | 2.83% | 120 | 92 | 85 | 70 | 76.7% | 92.4% | 82.4% |
| New York | 3,983 | 2.84% | 113 | 86 | 79 | 66 | 76.1% | 91.9% | 83.5% |
| Miami | 3,544 | 2.82% | 100 | 77 | 71 | 58 | 77.0% | 92.2% | 81.7% |
| Virginia Beach | 3,220 | 2.83% | 91 | 70 | 64 | 53 | 76.9% | 91.4% | 82.8% |
| All other markets | 5,336 | 2.83% | 151 | 115 | 106 | 88 | 76.2% | 92.2% | 83.0% |
| Network | 65,000 | 2.83% | 1,840 | 1,405 | 1,296 | 1,072 | 76.4% | 92.2% | 82.7% |
CRM sends are national, so volume is apportioned to clinics on each clinic’s share of network DOFV. Conversion rates are held constant across clinics here because programme performance is centrally controlled; once real data lands, variance in these columns would indicate local follow-up differences worth investigating.
Engagement vs. Outcome by Programme
Do the programmes with the best open rates actually produce the most patients?
Bars are DOFV produced, line is open rate. Where they diverge, engagement is measuring interest rather than intent.
Funnel Conversion by Programme
Which programmes move people closest to booking?
Recovery and reminder programmes sit closest to an existing decision, which is why they convert hardest. Educational sends work earlier in the journey and should be judged on assisted contribution rather than last touch.
List Health Trend
Is our email list growing, and is engagement holding as it grows?
Bars are active list size, line is open rate. A growing list with a falling open rate usually means acquisition quality is slipping.
DOFV Contribution by Clinic
Which clinics benefit most from CRM activity?