Tab 1 said we are behind. This page answers the obvious follow-up: can we still get there, what would it take, and by when must we decide?

The uncomfortable answer is that the window is closing. Because spend takes two to six weeks to produce a first visit, money committed late in the year cannot land inside the year. There is a date after which the annual DOFV number is mathematically fixed regardless of budget — and that date is the most decision-relevant fact on this page.

The honest conclusion: closing the full gap with spend alone is no longer realistic. A blended approach — modest incremental spend in the two markets with genuine headroom, plus recovering booked-but-not-attended appointments — is the only path that arithmetic supports.

Marketing budget is held centrally and is fungible across markets. That single fact makes this the most actionable page in the dashboard, because the answer changes where real money goes: given a fixed envelope, where does the next dollar buy the most patients?

The story is one of misallocation rather than underinvestment. Two markets are absorbing spend at deteriorating efficiency — rising cost per patient with flat volume, the signature of saturation. Three markets are converting spend efficiently and are not yet at their ceiling. Moving roughly $310K between them produces an estimated 168 additional DOFV at no incremental total budget.

The table below is deliberately framed as a recommendation, not an observation. It is the page a CEO should be able to approve or reject in one sitting.

This is the one page both audiences should care about equally, and the only place their two metrics visibly join up. It asks: between a person first raising their hand and a patient starting treatment, where do we lose the most, and what is each leak worth?

The finding that should reframe the conversation: the largest recoverable loss is not at the top of the funnel where marketing spends money — it is at Scheduled → DOFV, where roughly 4,100 appointments this year were booked and paid for through media spend but never attended. Recovering even a fifth of those would deliver more DOFV than the entire remaining budget headroom could buy.

The second finding is a caution. The DOFV → NPV step is partly a clinical eligibility outcome, not a marketing failure. Some patients legitimately do not qualify after consultation and bloodwork. Treating that rate as a marketing conversion metric would be wrong — but if certain campaigns systematically attract people unlikely to qualify, that is marketing's problem. Separating those two causes is the highest-value analysis on our roadmap.

Tab 3 said where to move money geographically. This page asks the same question by channel: which channels are genuinely earning their budget, and how exposed are we if one of them turns?

Two things stand out. First, judged on cost per DOFV, paid search looks acceptable — but judged on cost per NPV, it looks materially worse, because the patients it delivers qualify for treatment at a lower rate. Corporate is measured on DOFV, but the business monetizes NPV, so both numbers belong side by side; only showing the first flatters paid media.

Second, a concentration risk worth board attention: roughly two-thirds of DOFV traces to two advertising platforms we do not control. Referral and organic demand are cheaper and qualify better, but they are growing more slowly than paid — meaning our dependency is increasing, not decreasing.

Volume is not the same as value. This page asks: are we attracting the patients the business actually wants — and would optimizing for value rather than volume change where we spend?

It would. Revenue per patient varies by roughly five times across channels, driven by service mix and payor mix. Physician referrals over-index heavily on IVF and commercial insurance and are worth about $18,650 per DOFV. Organic and direct traffic skew toward lower-intensity services and are worth about $3,590. A channel comparison based on volume alone conceals a difference that large.

This matters more if capacity is constrained anywhere in the network. Where a clinic's schedule is full, the right goal is not more patients but better-matched patients — which turns payor and service mix from a reporting dimension into an allocation lever.

This is the network view of clinic performance — built for corporate to compare markets, not for a clinic to review itself. (Clinic leaders get Tab 9, which deliberately excludes spend and efficiency metrics they do not control.) It asks: which markets are strong, which are struggling, and is the difference explained by market conditions or by execution?

The pattern: performance clusters into three groups rather than sitting on a continuum. A small set of markets combines high volume, low CAC, and strong qualification rates. A large middle performs adequately. A small tail shows rising CAC and weak conversion — and those are the same markets flagged for budget reduction on Tab 3.

One caution before drawing conclusions about any individual market: because spend is attributed by DMA and patients choose their own clinic, market-level cost metrics are reported at DMA-cluster level where clinics share a media market.

Physician referral is our highest-value channel and the only one that is not bought. It behaves like a relationship portfolio, not a media channel, so it needs different metrics entirely: is our referring-physician base growing or quietly eroding, and who has stopped sending patients?

The story here is a warning that volume metrics conceal. Total referral volume is up 8% year over year — which looks healthy — but the number of active referring practices has fallen by 14. Growth is coming from a smaller set of relationships working harder. That is a fragile position, and it is invisible on any channel-level chart.

This is also the natural bridge between corporate and clinics. Corporate funds the outreach, but the relationship belongs to the clinic and often to a named physician — so the recovery actions on this page have local owners.

This page is built for the person who fields the clinic phone calls. It is not clinic-facing and is not exported to clinics, so it deliberately carries the full picture — volume, conversion, spend, CAC, ROI and cost per NPV — in one place: who is coming to this clinic, what are they worth, what are we spending to get them, and where is this clinic's own performance the constraint?

The design principle here is the opposite of the executive tabs. Of all the places in this dashboard to repeat a metric, this is the one — the aim is that a clinic call can be answered without navigating away. Everything a clinic director is likely to challenge should be reachable from this single page.

Note the split of accountability. Volume and cost are ours; attendance and qualification rates are the clinic's. The peer comparisons below are what let you separate a demand problem from an operations problem in the moment.

Every dashboard eventually gets challenged on a number. This page exists so that challenge can be answered in the room rather than taken away as an action item.

It documents three things: what each metric actually means, why recent periods are labelled directional rather than final, and which questions remain open with the client. The open questions are listed deliberately — a dashboard that is honest about what it does not yet know is more credible than one that implies completeness.

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.

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.