Setting Conversion Values for Treatments With a 6-Month Sales Cycle

This post is for hospital marketing and revenue teams advertising elective surgery and other considered treatments, where the gap between the first click and the surgery can run for months. Value-based bidding asks you to tell Google what each conversion is worth. For a patient who may take six months to decide, a form fill is a weak proxy for revenue, and the real revenue arrives after the point at which Google can still link it to the click.

The article explains how to build a stage-weighted value scheme, how to work with Google's conversion windows, and how to measure the lag in your own data. It uses hypothetical numbers where numbers are needed and labels them. It assumes you already track enquiries in a CRM and use or plan to use Smart Bidding.

The constraint that shapes everything: 90 days

Two documented limits matter.

First, Google's help on conversion delay says conversions can be reported up to 90 days after a click, depending on the conversion window you choose. Second, Google's page on offline conversion imports using GCLID describes a click-to-conversion cycle of 14 or 90 days depending on the data source, and says to create conversion actions first and wait several hours before uploading to them.

So a surgery that takes place five months after the click cannot be attributed to the click through a standard import. Do not try to force it. Design the scheme around events that happen inside the window, and assign them values that reflect their likely contribution to the eventual surgery.

Step 1: Map the funnel into stages

Write the journey as observable stages recorded in your CRM. A typical elective surgery pathway might be:

1. Enquiry received (form, call or WhatsApp).
2. Contact made and enquiry qualified by the care coordinator.
3. Consultation booked.
4. Consultation completed.
5. Diagnostics or second opinion completed.
6. Treatment plan accepted or surgery scheduled.
7. Surgery performed.

Stages 1 to 4 often fall within 90 days for most patients. Stages 6 and 7 may not. The CRM must record a date for each stage. A system such as Pioneer CRM keeps enquiries, lead ownership and follow-ups in one place, which is what makes stage dates available to analyse.

Step 2: Measure your own lag distribution

Do not guess the lag. Export, for a past cohort of leads that have since completed, the click date, the enquiry date and the date of each later stage. Then compute, for each stage, what share of eventual completions had reached that stage within 30, 60 and 90 days. The method:

1. Take all enquiries from a set of months old enough for most to have resolved.
2. For each, record whether it reached each stage and when.
3. For each stage, produce a cumulative distribution of days since the click.
4. Read off the median and the 90th percentile.

The result tells you which stages fall inside Google's window for most patients, and which do not. Do not borrow a distribution from another hospital; elective cataract and bariatric surgery will not behave alike, and your patient mix is your own.

Step 3: Weight the stages

Stage weighting assigns each trackable stage a value that reflects its probability of becoming revenue, multiplied by the revenue. A clear formula:

value of stage = probability of eventual surgery from that stage × average revenue per surgery × a discount for uncertainty

A hypothetical example for illustration only. Suppose that, in a hospital's own past data, one in twenty enquiries became a surgery, one in six completed consultations did, and one in two accepted treatment plans did. Suppose average billing per surgery is a round figure the finance team confirms. The values for stages would then run from a small fraction of the surgery value at enquiry, to a larger fraction at consultation, to a substantial fraction at plan acceptance. These ratios are invented to show the structure. Your numbers come from your own cohort.

Two cautions. Use net revenue or margin, as the finance team defines it, and keep the definition fixed. And apply some conservatism: the most advanced stage values should not equal the surgery value if the plan can still fall through.

Step 4: Decide which events you send

You do not need to send every stage. Choose by volume and timing.

For Smart Bidding, an event with too few conversions per month is hard to learn from. A common design is to optimise on the qualified enquiry and consultation-completed events with different values, and to use the later stage mainly for analysis in the CRM. If you send the later stage, use the date of the stage as the conversion time, and expect it to be attributed only where it falls inside the window.

Step 5: Send the values properly

You can import stage events through offline conversion import. Google's help describes the GCLID workflow: auto-tagging on, a hidden GCLID field on forms, the GCLID stored against the lead and later uploaded with the conversion name, time and value. Our companion article on offline conversion tracking for clinics gives the wider setup, and the privacy handling is covered in a separate method on matching without patient data.

Each stage should be a separate conversion action so that the values and counts stay distinct. Set the primary or secondary status of each deliberately: primary actions are used for bidding, secondary actions are reported but not optimised on. Keep the early, high-volume action as primary and consider the later stage as secondary until its volume supports bidding.

Check whether a different value for the same action is needed per treatment. If a joint replacement is worth much more than a physiotherapy course, give them different values, either by using separate conversion actions or by passing a value per upload from the CRM.

Step 6: Use conversion value rules sparingly

Google's API documentation on conversion value rules describes rules that adjust a conversion's value based on audience, location or device, using ADD, MULTIPLY or SET actions at account or campaign level. For a hospital they might reflect that enquiries from a distant city convert less often. Use them only after your base values are stable, and document each rule, because the platform applies at most one rule per conversion. A layer of rules on top of unreliable base values only adds confusion.

Step 7: Allow for the lag when reading performance

Because stage events arrive late, recent days will always look weaker. Google's help explains that recent performance can look weak because some clicks have not converted yet while spend is fully reported. Do not change targets in response to the last two weeks. Use the days-to-conversion segment to see what has not yet arrived, and compare periods that have had time to mature.

Smart Bidding bid strategy reports also show an estimate of conversions not yet reported. Use that as a guide, not as a promise.

What to do for the part you cannot send

For the stages that happen after 90 days, close the loop in your own reporting. In the CRM, join surgeries to the original source campaign using the stored click ID, enquiry source or campaign tag, and report revenue by campaign on your own cohort basis. This does not feed Google's bidding, but it tells you whether the stage values you set are directionally right. If campaigns with a high stage-weighted value produce few eventual surgeries, lower the weights.

Review the weights every quarter against fresh cohorts. If the care team changes the follow-up process, the probabilities change with it.

Failure modes

• Setting values by feel. A round number with no cohort behind it will quietly bias bidding.
• Sending the same value for every treatment. Smart Bidding will chase the cheapest enquiry, which may be the least valuable service.
• Counting duplicates. A patient who re-enquires can be recorded twice. Deduplicate with a lead ID.
• Sending patient details. Send the click ID, event name, time and value, not diagnosis or treatment text. That detail belongs to the clinical record.
• Changing weights too often, which restarts learning.

Where to start

1. Define the stages and confirm the CRM records a date for each.
2. Compute the lag distribution and stage conversion ratios from a past cohort.
3. Agree the revenue definition with finance.
4. Compute stage values and set them as separate conversion actions.
5. Optimise on the volume stage first and use later stages for analysis.
6. Review after a full lag window and every quarter after.

Our conversion tracking service and lead quality optimisation work cover the CRM and account setup.